References

[1]
N. J. Schork, Personalized medicine: Time for one-person trials,” Nature News, vol. 520, no. 7549, pp. 609–611, 2015, doi: 10.1038/520609a.
[2]
C. Shivade et al., A review of approaches to identifying patient phenotype cohorts using electronic health records,” Journal of the American Medical Informatics Association, vol. 21, no. 2, pp. 221–230, 2014, doi: 10.1136/amiajnl-2013-001935.
[3]
W. Guo, M. Li, Y. Dong, and others, Diabetes is a risk factor for the progression and prognosis of COVID-19,” Diabetes/Metabolism Research and Reviews, vol. 36, no. 7, pp. 1–9, 2020, doi: 10.1002/dmrr.3319.
[4]
A. K. Boehme, C. Esenwa, and M. S. V. Elkind, Stroke Risk Factors, Genetics, and Prevention,” Circulation Research, vol. 120, no. 3, pp. 472–495, 2017, doi: 10.1161/CIRCRESAHA.116.308398.
[5]
D. Oliver et al., What Causes the Onset of Psychosis in Individuals at Clinical High Risk? A Meta-analysis of Risk and Protective Factors,” Schizophrenia Bulletin, vol. 46, no. 1, pp. 110–120, 2020, doi: 10.1093/schbul/sbz039.
[6]
J. Sánchez-Valle, H. Tejero, J. M. Fernández, and others, Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships,” Nature Communications, vol. 11, no. 1, pp. 1–13, 2020, doi: 10.1038/s41467-020-16540-x.
[7]
F. A. Cimini, I. Barchetta, G. Ciccarelli, and others, Adipose tissue remodelling in obese subjects is a determinant of presence and severity of fatty liver disease,” Diabetes/Metabolism Research and Reviews, vol. 37, no. 1, pp. 1–13, 2021, doi: 10.1038/s41467-020-16540-x.
[8]
J. B. Cohen, S. J. Schrauben, L. Zhao, and others, Clinical Phenogroups in Heart Failure With Preserved Ejection Fraction: Detailed Phenotypes, Prognosis, and Response to Spironolactone,” Heart Failure, vol. 8, no. 3, pp. 172–184, 2020, doi: 10.1016/j.jchf.2019.09.009.
[9]
A. A. Tsiatis, Dynamic Treatment Regimes: Statistical Methods for Precision Medicine. CRC Press, 2019.
[10]
H. G. Hong, D. C. Christiani, and Y. Li, Quantile regression for survival data in modern cancer research: expanding statistical tools for precision medicine,” Precision Clinical Medicine, vol. 2, no. 2, pp. 90–99, 2019, doi: 10.1093/pcmedi/pbz007.
[11]
A. Guglielmi, G. Guidoboni, A. Harris, I. Sartori, and L. Torriani, Statistical Methods in Medicine: Application to the Study of Glaucoma Progression,” in Ocular fluid dynamics, 2019, pp. 599–612.
[12]
H. Völzke, D. Alte, C. O. Schmidt, and others, Cohort Profile: The Study of Health in Pomerania,” International Journal of Epidemiology, vol. 40, no. 2, pp. 294–307, 2011, doi: 10.1093/ije/dyp394.
[13]
W. M. P. P. Investigators and others, The World Health Organization MONICA Project (monitoring trends and determinants in cardiovascular disease): a major international collaboration,” Journal of Clinical Epidemiology, vol. 41, no. 2, pp. 105–114, 1988, doi: 10.1016/0895-4356(88)90084-4.
[14]
R. Holle, M. Happich, H. Löwel, H.-E. Wichmann, null for the MONICA/KORA Study Group, and others, KORA–a research platform for population based health research,” Das Gesundheitswesen, vol. 67, no. S1, pp. 19–25, 2005, doi: 10.1055/s-2005-858235.
[15]
A. Hofman, M. M. B. Breteler, C. M. van Duijn, and others, The Rotterdam Study: 2010 objectives and design update,” European Journal of Epidemiology, vol. 24, no. 9, pp. 553–572, 2009, doi: 10.1007/s10654-009-9386-z.
[16]
P. Klemm, S. Oeltze-Jafra, K. Lawonn, K. Hegenscheid, H. Völzke, and B. Preim, Interactive Visual Analysis of Image-Centric Cohort Study Data,” Transactions on Visualization and Computer Graphics (TVCG), vol. 20, no. 12, pp. 1673–1682, 2014, doi: 10.1109/TVCG.2014.2346591.
[17]
S. Shilo, H. Rossman, and E. Segal, Axes of a revolution: challenges and promises of big data in healthcare,” Nature Medicine, vol. 26, pp. 29–38, 2020, doi: 10.1038/s41591-019-0727-5.
[18]
M. Viceconti, P. Hunter, and R. Hose, Big Data, Big Knowledge: Big Data for Personalized Healthcare,” Biomedical and Health Informatics, vol. 19, no. 4, pp. 1209–1215, 2015, doi: 10.1109/JBHI.2015.2406883.
[19]
P. Friederich, M. Krenn, I. Tamblyn, and A. Aspuru-Guzik, Scientific intuition inspired by machine learning generated hypotheses,” Machine Learning: Science and Technology, 2021, doi: 10.1088/2632-2153/abda08.
[20]
W. W. Stead, Clinical Implications and Challenges of Artificial Intelligence and Deep Learning,” Journal of the American Medical Association, vol. 320, no. 11, pp. 1107–1108, 2018, doi: 10.1001/jama.2018.11029.
[21]
J. Car, A. Sheikh, P. Wicks, and M. S. Williams, “Beyond the hype of big data and artificial intelligence: Building foundations for knowledge and wisdom,” BMC Medicine, vol. 17, no. 143, 2019, doi: 10.1186/s12916-019-1382-x.
[22]
J. H. Chen and S. M. Asch, Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations,” The New England Journal of Medicine, vol. 376, no. 26, pp. 2507–2509, 2017, doi: 10.1056/NEJMp1702071.
[23]
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
[24]
J. H. Friedman, Greedy function approximation: A gradient boosting machine,” The Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001, doi: 10.1214/aos/1013203451.
[25]
A. Adadi and M. Berrada, Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI),” IEEE Access, vol. 6, pp. 52138–52160, 2018, doi: 10.1109/ACCESS.2018.2870052.
[26]
D. V. Carvalho, E. M. Pereira, and J. S. Cardoso, Machine Learning Interpretability: A Survey on Methods and Metrics,” Electronics, vol. 8, no. 8, pp. 1–34, 2019, doi: 10.3390/electronics8080832.
[27]
B. Preim and K. Lawonn, A Survey of Visual Analytics for Public Health,” in Computer graphics forum, 2020, vol. 39, pp. 543–580, doi: 10.1111/cgf.13891.
[28]
A. Vellido, The importance of interpretability and visualization in Machine Learning for applications in medicine and health care,” Neural Computing and Applications, vol. 32, pp. 18069–18083, 2019, doi: 10.1007/s00521-019-04051-w.
[29]
A. Corvo, H. S. G. Caballero, M. A. Westenberg, M. A. van Driel, and J. van Wijk, Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology,” Transactions on Visualization and Computer Graphics (TVCG), pp. 1–18, 2020, doi: 10.1109/TVCG.2020.2990336.
[30]
U. Niemann, B. Boecking, P. Brueggemann, W. Mebus, B. Mazurek, and M. Spiliopoulou, “Tinnitus-related distress after multimodal treatment can be characterized using a key subset of baseline variables,” PLOS ONE, vol. 15, no. 1, pp. 1–18, 2020, doi: 10.1371/journal.pone.0228037.
[31]
U. Niemann et al., Plantar temperatures in stance position: A comparative study with healthy volunteers and diabetes patients diagnosed with sensoric neuropathy,” EBioMedicine, vol. 54, no. 102712, pp. 1–11, 2020, doi: 10.1016/j.ebiom.2020.102712.
[32]
U. Niemann et al., Rupture Status Classification of Intracranial Aneurysms Using Morphological Parameters,” in Computer-based medical systems (CBMS), 2018, pp. 48–53, doi: 10.1109/CBMS.2018.00016.
[33]
A. Oussous, F.-Z. Benjelloun, A. A. Lahcen, and S. Belfkih, Big Data technologies: A survey,” Journal of King Saud University-Computer and Information Sciences, vol. 30, no. 4, pp. 431–448, 2018, doi: 10.1016/j.jksuci.2017.06.001.
[34]
B. Röhrig, J.-B. du Prel, D. Wachtlin, and M. Blettner, Types of Study in Medical Research,” Deutsches Ärzteblatt International, vol. 106, no. 15, pp. 262–268, 2009, doi: 10.3238/arztebl.2009.0262.
[35]
M. S. Thiese, Observational and interventional study design types; an overview,” Biochemia Medica, vol. 24, no. 2, pp. 199–210, 2014, doi: 10.11613/BM.2014.022.
[36]
E. Hariton and J. J. Locascio, Randomised controlled trials – the gold standard for effectiveness research,” BJOG: An International Journal of Obstetrics & Gynaecology, vol. 125, no. 13, p. 1716, 2018, doi: 10.1111/1471-0528.15199.
[37]
N. D. Glenn, Cohort analysis, Second edition. Sage, 2005.
[38]
H. Völzke et al., Prevalence Trends in Lifestyle-Related Risk Factors: Two Cross-Sectional Analyses With a Total of 8728 Participants From the Study of Health in Pomerania From 1997 to 2001 and 2008 to 2012,” Deutsches Ärzteblatt International, vol. 112, no. 11, pp. 185–192, 2015, doi: 10.3238/arztebl.2015.0185.
[39]
G. Wiesner and E. K. Bittner, Life expectancy, potential years of life lost (PYLL), and avoidable mortality in an East/West comparison,” Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, vol. 47, no. 3, pp. 266–278, 2004, doi: 10.1007/s00103-003-0793-0.
[40]
H. Völzke et al., Hepatic steatosis is associated with an increased risk of carotid atherosclerosis,” World Journal of Gastroenterology, vol. 11, no. 12, pp. 1848–1853, 2005, doi: 10.3748/wjg.v11.i12.1848.
[41]
H. Völzke, “Multicausality in fatty liver disease: Is there a rationale to distinguish between alcoholic and non-alcoholic origin?” World Journal of Gastroenterology, vol. 18, no. 27, pp. 3492–3501, 2012, doi: 10.3748/wjg.v18.i27.3492.
[42]
C. Antunes, M. Azadfard, and M. Gupta, Fatty Liver.” StatPearls Publishing, 2021, [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK441992/.
[43]
D. Baguley, D. McFerran, and D. Hall, “Tinnitus,” The Lancet, vol. 382, no. 9904, pp. 1600–1607, 2013, doi: 10.1016/S0140-6736(13)60142-7.
[44]
J. M. Bhatt, N. Bhattacharyya, and H. W. Lin, Relationships Between Tinnitus and the Prevalence of Anxiety and Depression,” The Laryngoscope, vol. 127, no. 2, pp. 466–469, 2017, doi: 10.1002/lary.26107.
[45]
I. H. L. Maes, R. F. F. Cima, J. W. Vlaeyen, L. J. C. Anteunis, and M. A. Joore, Tinnitus: A Cost Study,” Ear and Hearing, vol. 34, no. 4, pp. 508–514, 2013, doi: 10.1097/aud.0b013e31827d113a.
[46]
C. R. Cederroth, S. Gallus, D. A. Hall, and others, Towards an Understanding of Tinnitus Heterogeneity,” Frontiers in Aging Neuroscience, vol. 11, no. 53, pp. 1–7, 2019, doi: 10.3389/fnagi.2019.00053.
[47]
J. Hobson, E. Chisholm, and A. El Refaie, Sound therapy (masking) in the management of tinnitus in adults,” Cochrane Database of Systematic Reviews, no. 11, pp. 1–22, 2012, doi: 10.1002/14651858.CD006371.pub3.
[48]
B. Kröner-Herwig, A. Frenzel, G. Fritsche, G. Schilkowsky, and G. Esser, “The management of chronic tinnitus: Comparison of an outpatient cognitive–behavioral group training to minimal-contact interventions,” Journal of Psychosomatic Research, vol. 54, no. 4, pp. 381–389, 2003, doi: 10.1016/S0022-3999(02)00400-2.
[49]
J. L. Henry and P. H. Wilson, The Psychological Management of Tinnitus: Comparison of a Combined Cognitive Educational Program, Education Alone and a Waiting-List Control. The International Tinnitus Journal, vol. 2, pp. 9–20, 1996.
[50]
P. Martinez‐Devesa, R. Perera, M. Theodoulou, and A. Waddell, Cognitive behavioural therapy for tinnitus,” Cochrane Database of Systematic Reviews, no. 9, 2010, doi: 10.1002/14651858.CD005233.pub3.
[51]
J. S. Phillips and D. McFerran, Tinnitus retraining therapy (TRT) for tinnitus,” Cochrane Database of Systematic Reviews, no. 3, pp. 1–16, 2010, doi: 10.1002/14651858.CD007330.pub2.
[52]
B. Langguth et al., Different Patterns of Hearing Loss among Tinnitus Patients: A Latent Class Analysis of a Large Sample,” Frontiers in Neurology, vol. 8, pp. 1–46, 2017, doi: 10.3389/fneur.2017.00046.
[53]
R. Tyler et al., Identifying Tinnitus Subgroups With Cluster Analysis,” American Journal of Audiology, vol. 17, no. 2, 2, pp. 176–184, 2008, doi: 10.1044/1059-0889(2008/07-0044).
[54]
M. Landgrebe et al., The Tinnitus Research Initiative (TRI) database: a new approach for delineation of tinnitus subtypes and generation of predictors for treatment outcome,” BMC Medical Informatics and Decision Making, vol. 10, no. 1, pp. 1–7, 2010, doi: 10.1186/1472-6947-10-42.
[55]
J. L. Bernheim and M. Buyse, The Anamnestic Comparative Self-Assessment for Measuring the Subjective Quality of Life of Cancer Patients,” Journal of Psychosocial Oncology, vol. 1, no. 4, pp. 25–38, 1993, doi: 10.1300/J077v01n04_03.
[56]
L. S. Radloff, The CES-D Scale: A Self-Report Depression Scale for Research in the General Population ,” Applied Psychological Measurement, vol. 1, no. 3, pp. 385–401, 1977, doi: 10.1177/014662167700100306.
[57]
M. Hautzinger and M. Bailer, ADS-Allgemeine Depressionsskala. Beltz, 2003.
[58]
M. Hörhold, D. Bolduan, C. Klapp, H. Volger, G. Scholler, and B. Klapp, “Testing a screening strategy for identifying psychosomatic patients in gynecologic practice,” Psychotherapie, Psychosomatik, medizinische Psychologie, vol. 47, no. 5, pp. 156–162, 1997.
[59]
M. Hörhold, B. F. Klapp, and U. Schimmack, Testungen der Invarianz und der Hierarchie eines mehrdimensionalen Stimmungsmodells auf der Basis von Zweipunkterhebungen an Patienten-und Studentenstichproben,” Zeitschrift für Medizinische Psychologie, vol. 2, no. 1, pp. 27–35, 1993.
[60]
K. Tritt, F. von Heymann, M. Zaudig, I. Zacharias, W. Söllner, and T. Loew, Entwicklung des Fragebogens ICD-10-Symptom-Rating (ISR),” Zeitschrift für psychosomatische Medizin und Psychotherapie, vol. 54, no. 4, pp. 409–418, 2008, doi: 10.13109/zptm.2008.54.4.409.
[61]
R. L. Spitzer, K. Kroenke, J. B. W. Williams, and others, Validation and Utility of a Self-report Version of PRIME-MD: The PHQ Primary Care Study,” Journal of the American Medical Association, vol. 282, no. 18, pp. 1737–1744, 1999, doi: 10.1001/jama.282.18.1737.
[62]
H. Fliege et al., The Perceived Stress Questionnaire (PSQ) Reconsidered: Validation and Reference Values From Different Clinical and Healthy Adult Samples,” Psychosomatic Medicine, vol. 67, no. 1, pp. 78–88, 2005, doi: 10.1097/01.psy.0000151491.80178.78.
[63]
E. Geissner, The Pain Perception Scale–a differentiated and change-sensitive scale for assessing chronic and acute pain,” Die Rehabilitation, vol. 34, no. 4, pp. 35–43, 1995.
[64]
M. Bullinger and M. Morfeld, Der SF-36 Health Survey,” in Gesundheitsökonomische evaluationen, Springer, 2008, pp. 387–402.
[65]
P. Brueggemann, A. J. Szczepek, M. Rose, L. McKenna, H. Olze, and B. Mazurek, Impact of Multiple Factors on the Degree of Tinnitus Distress,” Frontiers in Human Neuroscience, vol. 10, no. 341, pp. 1–11, 2016, doi: 10.3389/fnhum.2016.00341.
[66]
G. Scholler, H. Fliege, and B. F. Klapp, Fragebogen zu Selbstwirksamkeit, Optimismus und Pessimismus,” Leibniz-Zentrum für Psychologische Information und Dokumentation (ZPID), vol. 49, no. 8, pp. 275–283, 1999, doi: 10.23668/psycharchives.337.
[67]
G. Goebel and W. Hiller, Psychische Beschwerden bei chronischem Tinnitus: Erprobung und Evaluation des Tinnitus-Fragebogens (TF),” Verhaltenstherapie, vol. 2, no. 1, pp. 13–22, 1992, doi: 10.1159/000258202.
[68]
G. Goebel and W. Hiller, Tinnitus-Fragebogen (TF). Ein Instrument zur Erfassung von Belastung und Schweregrad bei Tinnitus. Hogrefe, 1998.
[69]
A. J. Boulton, L. Vileikyte, G. Ragnarson-Tennvall, and J. Apelqvist, “The global burden of diabetic foot disease,” The Lancet, vol. 366, no. 9498, pp. 1719–1724, 2005, doi: 10.1016/S0140-6736(05)67698-2.
[70]
M. J. Kumari, J. Subash, and S. Jagdish, How to Prevent Amputation in Diabetic Patients,” International Journal of Nursing Education, vol. 6, pp. 40–44, 2014, doi: 10.5958/0974-9357.2014.00602.3.
[71]
E. W. Gregg et al., Changes in Diabetes-Related Complications in the United States, 1990–2010,” The New England Journal of Medicine, vol. 370, no. 16, pp. 1514–1523, 2014, doi: 10.1056/NEJMoa1310799.
[72]
M. Volmer-Thole and R. Lobmann, Neuropathy and Diabetic Foot Syndrome,” International Journal of Molecular Sciences, vol. 17, no. 6, pp. 1–11, 2016, doi: 10.3390/ijms17060917.
[73]
A. S. Fard, M. Esmaelzadeh, and B. Larijani, “Assessment and treatment of diabetic foot ulcer,” International Journal of Clinical Practice, vol. 61, no. 11, pp. 1931–1938, 2007, doi: 10.1111/j.1742-1241.2007.01534.x.
[74]
N. Singh, D. G. Armstrong, and B. A. Lipsky, Preventing foot ulcers in patients with diabetes,” Journal of the American Medical Association, vol. 293, no. 2, pp. 217–228, 2005, doi: 10.1001/jama.293.2.217.
[75]
J. Grützner, T. Szczepanski, J. Kellersmann, J. Malanowski, S. Klose, and P. R. Mertens, Smart Diabetic Insole - Towards home feet health monitoring in order to prevent diabetic foot ulcer,” in Biomedical engineering/ biomedizinische technik, 2015, vol. 60, pp. 252–253, [Online]. Available: https://www.degruyter.com/downloadpdf/journals/bmte/60/s1/article-p1.pdf.
[76]
M. J. H. Wermer, I. C. van der Schaaf, A. Algra, and G. J. E. Rinkel, Risk of Rupture of Unruptured Intracranial Aneurysms in Relation to Patient and Aneurysm Characteristics,” Stroke, vol. 38, no. 4, pp. 1404–1410, 2007, doi: 10.1161/01.STR.0000260955.51401.cd.
[77]
S. Dhar et al., Morphology Parameters for Intracranial Aneurysm Rupture Risk Assessment,” Neurosurgery, vol. 63, no. 2, pp. 185–197, 2008, doi: 10.1227/01.neu.0000316847.64140.81.
[78]
J. Xiang et al., Hemodynamic-Morphologic Discriminants for Intracranial Aneurysm Rupture,” Stroke, vol. 42, no. 1, pp. 144–152, 2011, doi: 10.1161/STROKEAHA.110.592923.
[79]
M. I. Baharoglu, A. Lauric, B.-L. Gao, and A. M. Malek, “Identification of a dichotomy in morphological predictors of rupture status between sidewall-and bifurcation-type intracranial aneurysms,” Journal of Neurosurgery, vol. 116, no. 4, pp. 871–881, 2012, doi: 10.3171/2011.11.JNS11311.
[80]
J. G. Elmore, D. Wild, D. L. Katz, and H. D. Nelson, Jekel’s Epidemiology, Biostatistics and Preventive Medicine. Elsevier Health Sciences, 2020.
[81]
T. Ittermann et al., Inverse Association Between Serum Free Thyroxine Levels and Hepatic Steatosis: Results From the Study of Health in Pomerania,” Thyroid, vol. 22, no. 6, pp. 568–574, 2012, doi: 10.1089/thy.2011.0279.
[82]
K. Lau et al., The association between fatty liver disease and blood pressure in a population-based prospective cohort study,” Journal of Hypertension, vol. 28, no. 9, pp. 1829–1835, 2010, doi: 10.1097/HJH.0b013e32833c211b.
[83]
F. Stickel, S. Buch, K. Lau, and others, Genetic variation in the PNPLA3 gene is associated with alcoholic liver injury in caucasians,” Hepatology, vol. 53, no. 1, pp. 86–95, 2011, doi: 10.1002/hep.24017.
[84]
G. Targher, C. P. Day, and E. Bonora, Risk of Cardiovascular Disease in Patients with Nonalcoholic Fatty Liver Disease,” New England Journal of Medicine, vol. 363, no. 14, pp. 1341–1350, 2010, doi: 10.1056/NEJMra0912063.
[85]
M. R. P. Markus et al., Hepatic Steatosis Is Associated With Aortic Valve Sclerosis in the General Population: The Study of Health in Pomerania (SHIP),” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 33, no. 7, pp. 1690–1695, 2013, doi: 10.1161/ATVBAHA.112.300556.
[86]
A. D. Hingorani, D. A. van der Windt, R. D. Riley, and others, Prognosis research strategy (PROGRESS) 4: Stratified medicine research,” BMJ: British Medical Journal, vol. 346, pp. 1–9, 2013, doi: 10.1136/bmj.e5793.
[87]
H. Völzke, G. Fung, T. Ittermann, and others, A new, accurate predictive model for incident hypertension,” Journal of Hypertension, vol. 31, no. 11, pp. 2142–2150, 2013, doi: 10.1097/HJH.0b013e328364a16d.
[88]
C. Zhanga and R. L. Kodell, Subpopulation-specific confidence designation for more informative biomedical classification,” Artificial Intelligence in Medicine, vol. 58, no. 3, pp. 155–163, 2013, doi: 10.1016/j.artmed.2013.04.008.
[89]
F. Pinheiro, M.-H. Kuo, A. Thomo, and J. Barnett, Extracting association rules from liver cancer data using the FP-growth algorithm,” 2013, doi: 10.1109/ICCABS.2013.6629208.
[90]
J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” in ACM SIGMOD record, 2000, vol. 29, pp. 1–12, doi: 10.1145/335191.335372.
[91]
Z. Zhang, D. Gotz, and A. Perer, “Iterative cohort analysis and exploration,” Information Visualization, vol. 14, no. 4, pp. 289–307, 2015, doi: 10.1177/1473871614526077.
[92]
K. Wongsuphasawat and D. Gotz, Exploring Flow, Factors, and Outcomes of Temporal Event Sequences with the Outflow Visualization,” Transactions on Visualization and Computer Graphics (TVCG), vol. 18, no. 12, pp. 2659–2668, 2012, doi: 10.1109/TVCG.2012.225.
[93]
S. Ebadollahi, J. Sun, D. Gotz, J. Hu, D. Sow, and C. Neti, Predicting Patient’s Trajectory of Physiological Data using Temporal Trends in Similar Patients: A System for Near-Term Prognostics,” in AMIA annual symposium proceedings, 2010, pp. 192–196, [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041306/.
[94]
J. Krause, A. Perer, and K. Ng, Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models,” in Conference on human factors in computing systems (CHI), 2016, pp. 5686–5697, doi: 10.1145/2858036.2858529.
[95]
A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin, Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation,” Journal of Computational and Graphical Statistics, vol. 24, no. 1, pp. 44–65, 2015, doi: 10.1080/10618600.2014.907095.
[96]
C. A. L. Pahins et al., COVIZ: A System for Visual Information and Exploration of Patient Cohorts,” VLDB Endowment, vol. 12, no. 12, pp. 1822–1825, 2019, doi: 10.14778/3352063.3352075.
[97]
C. A. de Lara Pahins, N. Ferreira, and J. Comba, Real-Time Exploration of Large Spatiotemporal Datasets based on Order Statistics,” Transactions on Visualization and Computer Graphics (TVCG), vol. 26, no. 11, pp. 3314–3326, 2019, doi: 10.1109/TVCG.2019.2914446.
[98]
J. Bernard, D. Sessler, T. May, T. Schlomm, D. Pehrke, and J. Kohlhammer, A Visual-Interactive System for Prostate Cancer Cohort Analysis,” IEEE Computer Graphics and Applications, vol. 35, no. 3, pp. 44–55, 2015, doi: 10.1109/MCG.2015.49.
[99]
B. Preim et al., Visual Analytics of Image-Centric Cohort Studies in Epidemiology,” in Visualization in medicine and life sciences III, Springer International Publishing, 2016, pp. 221–248.
[100]
B. Preim, S. Alemzadeh, T. Ittermann, P. Klemm, U. Niemann, and M. Spiliopoulou, Visual Analytics for Epidemiological Cohort Studies,” 2019, [Online]. Available: http://www.vismd.de/lib/exe/fetch.php?media=files:misc:preim_2019_medp.pdf.
[101]
P. Klemm et al., 3D Regression Heat Map Analysis of Population Study Data,” Transactions on Visualization and Computer Graphics (TVCG), vol. 22, no. 1, pp. 81–90, 2015, doi: 10.1109/TVCG.2015.2468291.
[102]
S. Alemzadeh et al., Subpopulation Discovery and Validation in Epidemiological Data,” 2017, doi: 10.2312/eurova.20171118.
[103]
T. Hielscher, M. Spiliopoulou, H. Völzke, and J.-P. Kühn, Identifying Relevant Features for a Multi-factorial Disorder with Constraint-Based Subspace Clustering,” in Computer-based medical systems (CBMS), 2016, pp. 207–212, doi: 10.1109/CBMS.2016.42.
[104]
T. Hielscher, U. Niemann, B. Preim, H. Völzke, T. Ittermann, and M. Spiliopoulou, A Framework for Expert-Driven Subpopulation Discovery and Evaluation Using Subspace Clustering for Epidemiological Data,” Expert Systems with Applications, vol. 113, pp. 147–160, 2018, doi: 10.1016/j.eswa.2018.07.003.
[105]
M. Hall, HotSpot (Weka Package).” 2012, [Online]. Available: https://weka.sourceforge.io/packageMetaData/hotSpot/1.0.4.html.
[106]
J. R. Quinlan, Learning with Continuous Classes,” in Artificial intelligence (AI), 1992, pp. 343–348.
[107]
E. Frank, M. A. Hall, and I. H. Witten, The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques". Morgan Kaufmann, 2016.
[108]
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, SMOTE: synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, no. 1, pp. 321–357, 2002, doi: 10.1613/jair.953.
[109]
J. Fürnkranz, D. Gamberger, and N. Lavrač, Foundations of Rule Learning. Springer Science & Business Media, 2012.
[110]
F. Herrera, C. J. Carmona, P. González, and M. J. Del Jesus, An overview on subgroup discovery: foundations and applications,” Knowledge and Information Systems, vol. 29, no. 3, pp. 495–525, 2011, doi: 10.1007/s10115-010-0356-2.
[111]
D. Gilbert, JFreeChart (Free Java class library for creating charts).” 2005-2021, [Online]. Available: http://www.jfree.org/jfreechart/.
[112]
D. W. Scott, On optimal and data-based histograms,” Biometrika, vol. 66, no. 3, pp. 605–610, 1979, doi: 10.1093/biomet/66.3.605.
[113]
J.-P. Kühn, M. Evert, N. Friedrich, and others, Noninvasive quantification of hepatic fat content using three-echo dixon magnetic resonance imaging with correction for T2\(*\) relaxation effects,” Investive Radiology, vol. 46, no. 12, pp. 783–789, 2011, doi: 10.1097/RLI.0b013e31822b124c.
[114]
M. Schleicher, T. Ittermann, U. Niemann, H. Völzke, and M. Spiliopoulou, ICE: Interactive Classification Rule Exploration on Epidemiological Data,” in Computer-based medical systems (CBMS), 2017, pp. 606–611, doi: 10.1109/CBMS.2017.127.
[115]
G. Bedogni et al., The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population,” BMC Gastroenterology, vol. 6, no. 33, pp. 1–7, 2006, doi: 10.1186/1471-230X-6-33.
[116]
X. Yuan et al., Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes,” The American Journal of Human Genetics, vol. 83, no. 4, pp. 520–528, 2008, doi: 10.1016/j.ajhg.2008.09.012.
[117]
S. E. Baumeister et al., Impact of Fatty Liver Disease on Health Care Utilization and Costs in a General Population: A 5-Year Observation,” Gastroenterology, vol. 134, no. 1, pp. 85–94, 2008, doi: 10.1053/j.gastro.2007.10.024.
[118]
S. Bellentani et al., Prevalence of and Risk Factors for Hepatic Steatosis in Northern Italy,” BMC Gastroenterology, vol. 132, no. 2, pp. 112–117, 2000, doi: 10.7326/0003-4819-132-2-200001180-00004.
[119]
H. Völzke et al., Menopausal status and hepatic steatosis in a general female population,” Gut, vol. 56, no. 4, pp. 594–595, 2007, doi: 10.1136/gut.2006.115345.
[120]
M. Atzmüller, “Subgroup discovery,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 5, no. 1, pp. 35–49, 2015, doi: 10.1002/widm.1144.
[121]
T.-P. Nguyen, C. Priami, and L. Caberlotto, “Novel drug target identification for the treatment of dementia using multi-relational association mining,” Scientific Reports, vol. 5, pp. 1–13, 2015, doi: 10.1038/srep11104.
[122]
J. C. Vick et al., Data-Driven Subclassification of Speech Sound Disorders in Preschool Children,” Journal of Speech, Language, and Hearing Research, vol. 57, no. 6, pp. 2033–2050, 2014, doi: 10.1044/2014_JSLHR-S-12-0193.
[123]
C. J. Carmona, P. González, M. J. Del Jesus, M. Navío-Acosta, and L. Jiménez-Trevino, “Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department,” Soft Computing, vol. 15, no. 12, pp. 2435–2448, 2011, doi: 10.1007/s00500-010-0670-3.
[124]
W. Klösgen and M. May, Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database,” in Principles of data mining and knowledge discovery, 2002, pp. 275–286, doi: 10.1007/3-540-45681-3_23.
[125]
D. Gamberger and N. Lavrac, Expert-Guided Subgroup Discovery: Methodology and Application,” Journal of Artificial Intelligence Research, vol. 17, pp. 501–527, 2002, doi: 10.1613/jair.1089.
[126]
N. Lavrač, B. Kavšek, P. Flach, and L. Todorovski, Subgroup Discovery with CN2-SD,” Journal of Machine Learning Research, vol. 5, pp. 153–188, 2004.
[127]
N. Lavrač, P. Flach, and B. Zupan, Rule Evaluation Measures: A Unifying View,” in Inductive logic programming (ILP), 1999, pp. 174–185, doi: 10.1007/3-540-48751-4_17.
[128]
M. van Leeuwen and A. Knobbe, “Diverse subgroup set discovery,” Data Mining and Knowledge Discovery, vol. 25, no. 2, pp. 208–242, 2012, doi: 10.1007/s10618-012-0273-y.
[129]
P. Clark and T. Niblett, The CN2 induction algorithm,” Machine Learning, vol. 3, no. 4, pp. 261–283, 1989, doi: 10.1007/BF00116835.
[130]
W. W. Cohen, Fast effective rule induction,” in International conference on machine learning, 1995, pp. 115–123, doi: 10.1016/B978-1-55860-377-6.50023-2.
[131]
L. R. Dice, Measures of the Amount of Ecologic Association Between Species,” Ecology, vol. 26, no. 3, pp. 297–302, 1945, doi: 10.2307/1932409.
[132]
R. Gutekunst, W. Becker, R. Hehrmann, T. Olbricht, and others, Ultrasonic diagnosis of the thyroid gland,” Deutsche Medizinische Wochenschrift, vol. 113, no. 27, pp. 1109–1112, 1988, doi: 10.1055/s-2008-1067777.
[133]
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, The WEKA Data Mining Software: An Update,” ACM SIGKDD Explorations, vol. 11, no. 1, pp. 10–18, 2009, doi: 10.1145/1656274.1656278.
[134]
M. Atzmüller and F. Puppe, SD-Map – A Fast Algorithm for Exhaustive Subgroup Discovery,” in Machine learning and knowledge discovery in databases, 2006, pp. 6–17, doi: 10.1007/11871637_6.
[135]
M. Atzmüller and F. Lemmerich, VIKAMINE – Open-Source Subgroup Discovery, Pattern Mining, and Analytics,” in Machine learning and knowledge discovery in databases, 2012, pp. 842–845, doi: 10.1007/978-3-642-33486-3_60.
[136]
U. M. Fayyad and K. B. Irani, Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning,” in International joint conference on artificial intelligence (IJCAI), 1993, pp. 1022–1027.
[137]
W. S. Cleveland, Robust Locally Weighted Regression and Smoothing Scatterplots,” Journal of the American Statistical Association, vol. 74, no. 368, pp. 829–836, 1979, doi: 10.1080/01621459.1979.10481038.
[138]
Z. G. Jiang, S. C. Robson, and Z. Yao, “Lipoprotein metabolism in nonalcoholic fatty liver disease,” Journal of Biomedical Research, vol. 27, no. 1, pp. 1–13, 2013, doi: 10.7555/JBR.27.20120077.
[139]
T. Poynard, A. Abella, J.-P. Pignon, S. Naveau, R. Leluc, and J.-C. Chaput, Apolipoprotein AI and alcoholic liver disease,” Hepatology, vol. 6, no. 6, pp. 1391–1395, 1986, doi: 10.1002/hep.1840060628.
[140]
J. Lizardi-Cervera, N. C. Chavez-Tapia, O. Pérez-Bautista, M. H. Ramos, and M. Uribe, Association among C-reactive protein, Fatty liver disease, and cardiovascular risk,” Digestive Diseases and Sciences, vol. 52, no. 9, pp. 2375–2379, 2007, doi: 10.1007/s10620-006-9262-6.
[141]
T. Keenan et al., Relation of uric acid to serum levels of high-sensitivity C-reactive protein, triglycerides, and high-density lipoprotein cholesterol and to hepatic steatosis,” The American Journal of Cardiology, vol. 110, no. 12, pp. 1787–1792, 2012, doi: 10.1016/j.amjcard.2012.08.012.
[142]
N. Takamura et al., “Thyroid function is associated with carotid intima-media thickness in euthyroid subjects,” Atherosclerosis, vol. 204, no. 2, pp. 77–81, 2009, doi: 10.1016/j.atherosclerosis.2008.09.022.
[143]
N. Gao, W. Zhang, Y. Zhang, Q. Yang, and S. Chen, Carotid intima-media thickness in patients with subclinical hypothyroidism: a meta-analysis,” Atherosclerosis, vol. 227, no. 1, pp. 18–25, 2013, doi: 10.1016/j.atherosclerosis.2012.10.070.
[144]
E. Unal, A. Akın, R. Yıldırım, V. Demir, İ. Yildiz, and Y. K. Haspolat, Association of Subclinical Hypothyroidism with Dyslipidemia and Increased Carotid Intima-Media Thickness in Children,” Journal of Clinical Research in Pediatric Endocrinology, vol. 9, no. 2, pp. 144–149, 2017, doi: 10.4274/jcrpe.3719.
[145]
A. Jabbar, A. Pingitore, S. H. S. Pearce, A. Zaman, G. Iervasi, and S. Razvi, Thyroid hormones and cardiovascular disease,” Nature Reviews Cardiology, vol. 14, no. 1, pp. 39–55, 2017, doi: 10.1038/nrcardio.2016.174.
[146]
S. Fazio, E. A. Palmieri, G. Lombardi, and B. Biondi, Effects of Thyroid Hormone on the Cardiovascular System,” Recent Progress in Hormone Research, vol. 59, no. 1, pp. 31–50, 2004, doi: 10.1210/rp.59.1.31.
[147]
A. A. Erikci et al., The effect of subclinical hypothyroidism on platelet parameters,” Hematology, vol. 14, no. 2, pp. 115–117, 2009, doi: 10.1179/102453309X385124.
[148]
G. Hesse, “Evidence and evidence gaps in tinnitus therapy,” GMS Current Topics in Otorhinolaryngology - Head and Neck Surgery, vol. 15, pp. 1–42, 2016, doi: 10.3205/cto000131.
[149]
S. M. Theodoroff, A. Schuette, S. Griest, and J. A. Henry, Individual Patient Factors Associated with Effective Tinnitus Treatment,” Journal of the American Academy of Audiology, vol. 25, no. 7, pp. 631–643, 2014, doi: 10.3766/jaaa.25.7.2.
[150]
M. Schecklmann et al., “Cluster analysis for identifying sub-types of tinnitus: A positron emission tomography and voxel-based morphometry study,” Brain Research, vol. 1485, pp. 3–9, 2012, doi: 10.1016/j.brainres.2012.05.013.
[151]
E. Genitsaridi, D. J. Hoare, T. Kypraios, and D. A. Hall, A Review and a Framework of Variables for Defining and Characterizing Tinnitus Subphenotypes,” Brain Sciences, vol. 10, no. 12, pp. 1–21, 2020, doi: 10.3390/brainsci10120938.
[152]
W. Schlee, D. A. Hall, N. K. Edvall, B. Langguth, B. Canlon, and C. R. Cederroth, Visualization of Global Disease Burden for the Optimization of Patient Management and Treatment,” Frontiers in Medicine, vol. 4, pp. 1–12, 2017, doi: 10.3389/fmed.2017.00086.
[153]
H. Hotelling, Analysis of a complex of statistical variables into principal components,” Journal of Educational Psychology, vol. 24, no. 6, p. 417, 1933, doi: 10.1037/h0071325.
[154]
J. C. Gower, Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis,” Biometrika, vol. 53, no. 3/4, pp. 325–338, 1966, doi: 10.2307/2333639.
[155]
L. van der Maaten and G. Hinton, Visualizing Data using t-SNE,” Journal of Machine Learning Research, vol. 9, no. 86, pp. 2579–2605, 2008.
[156]
L. McInnes, J. Healy, and J. Melville, UMAP: Uniform manifold approximation and projection for dimension reduction.” 2018.
[157]
J.-F. Im, M. J. McGuffin, and R. Leung, GPLOM: The Generalized Plot Matrix for Visualizing Multidimensional Multivariate Data,” Transactions on Visualization and Computer Graphics (TVCG), vol. 19, no. 12, pp. 2606–2614, 2013, doi: 10.1109/TVCG.2013.160.
[158]
A. Mayorga and M. Gleicher, Splatterplots: Overcoming Overdraw in Scatter Plots,” Transactions on Visualization and Computer Graphics (TVCG), vol. 19, no. 9, pp. 1526–1538, 2013, doi: 10.1109/TVCG.2013.65.
[159]
J. A. Hartigan, Printer graphics for clustering,” Journal of Statistical Computation and Simulation, vol. 4, no. 3, pp. 187–213, 1975, doi: 10.1080/00949657508810123.
[160]
D. Pelleg and A. W. Moore, X-means: Extending K-means with Efficient Estimation of the Number of Clusters,” in International conference on machine learning (ICML), 2000, pp. 727–734.
[161]
G. Schwarz and others, Estimating the Dimension of a Model,” Annals of Statistics, vol. 6, no. 2, pp. 461–464, 1978, doi: 10.1214/aos/1176344136.
[162]
T. Ishioka, An expansion of X-means for automatically determining the optimal number of clusters,” in Computational intelligence, 2005, vol. 2, pp. 91–95.
[163]
U. Niemann, P. Brueggemann, B. Boecking, M. Rose, M. Spiliopoulou, and B. Mazurek, Phenotyping chronic tinnitus patients using self-report questionnaire data: cluster analysis and visual comparison,” Scientific Reports, vol. 10, no. 1, pp. 1–10, 2020, doi: 10.1038/s41598-020-73402-8.
[164]
J. L. Hintze and R. D. Nelson, Violin Plots: A Box Plot-Density Trace Synergism,” The American Statistician, vol. 52, no. 2, pp. 181–184, 1998, doi: 10.2307/2685478.
[165]
A. C. Davison and D. V. Hinkley, Bootstrap Methods and Their Application. Cambridge University Press, 1997.
[166]
T. Kohonen, Self-Organizing Maps. Springer Science & Business Media, 2012.
[167]
R. Wehrens and J. Kruisselbrink, Flexible Self-Organizing Maps in kohonen 3.0,” Journal of Statistical Software, vol. 87, no. 1, pp. 1–18, 2018, doi: 10.18637/jss.v087.i07.
[168]
P. Sarlin, Self-organizing time map: An abstraction of temporal multivariate patterns,” Neurocomputing, vol. 99, no. 1, pp. 496–508, 2013, doi: 10.1016/j.neucom.2012.07.011.
[169]
P. C. Austin, J. V. Tu, J. E. Ho, D. Levy, and D. S. Lee, “Using methods from the data-mining and machine-learning literature for disease classification and prediction: A case study examining classification of heart failure subtypes,” Journal of Clinical Epidemiology, vol. 66, no. 4, pp. 398–407, 2013, doi: 10.1016/j.jclinepi.2012.11.008.
[170]
D. Raju, X. Su, P. A. Patrician, L. A. Loan, and M. S. McCarthy, Exploring factors associated with pressure ulcers: A data mining approach,” International Journal of Nursing Studies, vol. 52, no. 1, pp. 102–111, 2014, doi: 10.1016/j.ijnurstu.2014.08.002.
[171]
I. Valavanis, E. G. Sifakis, P. Georgiadis, S. Kyrtopoulos, and A. A. Chatziioannou, Derivation of Cancer Related Biomarkers from DNA Methylation Data from an Epidemiological Cohort,” in Engineering applications of neural networks, Springer, 2013, pp. 249–256.
[172]
V. Unnikrishnan et al., “Entity-level stream classification: Exploiting entity similarity to label the future observations referring to an entity,” International Journal of Data Science and Analytics, vol. 9, no. 1, pp. 1–15, 2020, doi: 10.1007/s41060-019-00177-1.
[173]
C. C. Aggarwal, Data Classification: Algorithms and Applications,” CRC Press, 2014, pp. 245–273.
[174]
H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P.-A. Muller, Deep learning for time series classification: a review,” Data Mining and Knowledge Discovery, vol. 33, no. 4, pp. 917–963, 2019, doi: 10.1007/s10618-019-00619-1.
[175]
R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice. OTexts, 2018.
[176]
J. Zhao et al., Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction,” Scientific Reports, vol. 9, no. 1, pp. 1–10, 2019, doi: 10.1038/s41598-018-36745-x.
[177]
F. Bagattini, I. Karlsson, J. Rebane, and P. Papapetrou, “A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records,” BMC Medical Informatics and Decision Making, vol. 19, no. 1, pp. 1–20, 2019, doi: 10.1186/s12911-018-0717-4.
[178]
J.-J. Beunza et al., Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease),” Journal of Biomedical Informatics, vol. 97, no. 103257, pp. 1–6, 2019, doi: 10.1016/j.jbi.2019.103257.
[179]
M. Pechenizkiy, E. Vasilyeva, I. Zliobaite, A. Tesanovic, and G. Manev, Heart Failure Hospitalization Prediction in Remote Patient Management Systems,” in Computer-based medical systems (CBMS), 2010, pp. 44–49, doi: 10.1109/CBMS.2010.6042612.
[180]
J. Sun, D. Sow, J. Hu, and S. Ebadollahi, A System for Mining Temporal Physiological Data Streams for Advanced Prognostic Decision Support,” in International Conference on Data Mining (ICDM), 2010, pp. 1061–1066, doi: 10.1109/ICDM.2010.102.
[181]
C. Combi, E. Keravnou-Papailiou, and Y. Shahar, Temporal Information Systems in Medicine. Springer, 2010.
[182]
T. Hielscher, M. Spiliopoulou, H. Völzke, and J.-P. Kühn, Mining Longitudinal Epidemiological Data to Understand a Reversible Disorder,” in Intelligent data analysis (IDA), 2014, pp. 120–130, doi: 10.1007/978-3-319-12571-8_11.
[183]
Z. F. Siddiqui, G. Krempl, M. Spiliopoulou, J. M. Pena, N. Paul, and F. Maestu, Predicting the post-treatment recovery of the patients suffering from TBI,” Brain Informatics, vol. 2, pp. 33–44, 2015, doi: 10.1007/s40708-015-0010-6.
[184]
J. D. Singer and J. B. Willett, Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press, 2003.
[185]
M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” in Knowledge discovery and data mining (KDD), 1996, vol. 96, pp. 226–231.
[186]
D. R. Wilson and T. R. Martinez, Improved Heterogeneous Distance Functions,” Journal of Artificial Intelligence Research, vol. 6, no. 1, pp. 1–34, 1997, doi: 10.1613/jair.346.
[187]
P.-N. Tan, M. Steinbach, A. Karpatne, and V. Kumar, Introduction to Data Mining, Second edition. Pearson, 2019.
[188]
J. L. Leevy, T. M. Khoshgoftaar, R. A. Bauder, and N. Seliya, A survey on addressing high-class imbalance in big data,” Journal of Big Data, vol. 5, pp. 1–42, 2018, doi: 10.1186/s40537-018-0151-6.
[189]
T. Hielscher, M. Spiliopoulou, H. Völzke, and J.-P. Kühn, Using Participant Similarity for the Classification of Epidemiological Data on Hepatic Steatosis,” in Computer-based medical systems (CBMS), 2014, pp. 1–7, doi: 10.1109/CBMS.2014.28.
[190]
M. A. Hall, Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning,” in International conference on machine learning (ICML), 2000, pp. 359–366, [Online]. Available: http://dl.acm.org/citation.cfm?id=645529.657793.
[191]
L. Breiman, Random Forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.
[192]
R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
[193]
U. Niemann, T. Hielscher, M. Spiliopoulou, H. Völzke, and J.-P. Kühn, Can we classify the participants of a longitudinal epidemiological study from their previous evolution? in Computer-based medical systems (CBMS), 2015, pp. 121–126, doi: 10.1109/CBMS.2015.12.
[194]
U. Gerhardt, R. Breitschwerdt, and O. Thomas, mHealth Engineering: A Technology Review,” Journal of Information Technology Theory and Application, vol. 19, no. 3, pp. 82–117, 2018, [Online]. Available: https://aisel.aisnet.org/jitta/vol19/iss3/5.
[195]
E. J. Boyko, A. D. Seelig, and J. H. Ahroni, Limb- and Person-Level Risk Factors for Lower-Limb Amputation in the Prospective Seattle Diabetic Foot Study ,” Diabetes Care, vol. 41, no. 4, pp. 891–898, 2018, doi: 10.2337/dc17-2210.
[196]
A. Ming, I. Walter, A. Alhajjar, M. Leuckert, and P. R. Mertens, Study protocol for a randomized controlled trial to test for preventive effects of diabetic foot ulceration by telemedicine that includes sensor-equipped insoles combined with photo documentation,” Trials, vol. 20, no. 1, pp. 1–12, 2019, doi: 10.1186/s13063-019-3623-x.
[197]
C. A. Abbott et al., Innovative intelligent insole system reduces diabetic foot ulcer recurrence at plantar sites: a prospective, randomised, proof-of-concept study,” The Lancet Digital Health, vol. 1, no. 6, pp. e308–e318, 2019, doi: 10.1016/S2589-7500(19)30128-1.
[198]
U. Waldecker, Pedographic classification and ulcer detection in the diabetic foot,” Foot and Ankle Surgery, vol. 18, no. 1, pp. 42–49, 2012, doi: 10.1016/j.fas.2011.03.004.
[199]
M. Fernando, S. Wearing, and R. Crowther, Handbook of Human Motion,” Springer, 2018, pp. 759–787.
[200]
C. Giacomozzi and F. Martelli, Peak pressure curve: an effective parameter for early detection of foot functional impairments in diabetic patients,” Gait & Posture, vol. 23, no. 4, pp. 464–470, 2006, doi: 10.1016/j.gaitpost.2005.06.006.
[201]
A. De Cock, T. Willems, E. Witvrouw, J. Vanrenterghem, and D. De Clercq, “A functional foot type classification with cluster analysis based on plantar pressure distribution during jogging,” Gait & Posture, vol. 23, no. 3, pp. 339–347, 2006, doi: 10.1016/j.gaitpost.2005.04.011.
[202]
C. J. Bennetts, T. M. Owings, A. Erdemir, G. Botek, and P. R. Cavanagh, Clustering and Classification of Regional Peak Plantar Pressures of Diabetic Feet,” Journal of Biomechanics, vol. 46, no. 1, pp. 19–25, 2013, doi: 10.1016/j.jbiomech.2012.09.007.
[203]
K. Deschamps et al., Classification of Forefoot Plantar Pressure Distribution in Persons with Diabetes: A Novel Perspective for the Mechanical Management of Diabetic Foot? PLOS ONE, vol. 8, no. 11, pp. 1–10, 2013, doi: 10.1371/journal.pone.0079924.
[204]
P. K. Moulik, R. Mtonga, and G. V. Gill, Amputation and mortality in new-onset diabetic foot ulcers stratified by etiology,” Diabetes Care, vol. 26, no. 2, pp. 491–494, 2003, doi: 10.2337/diacare.26.2.491.
[205]
P. C. Sun et al., Impaired microvascular flow motion in subclinical diabetic feet with sudomotor dysfunction,” Microvascular Research, vol. 83, no. 2, pp. 243–248, 2012, doi: 10.1016/j.mvr.2011.06.002.
[206]
N. C. Schaper, J. J. Van Netten, J. Apelqvist, B. A. Lipsky, K. Bakker, and others, Prevention and management of foot problems in diabetes: A Summary Guidance for Daily Practice 2015, based on the IWGDF guidance documents,” Diabetes Research and Clinical Practice, vol. 124, pp. 84–92, 2017, doi: 10.1016/j.diabres.2016.12.007.
[207]
G. Rayman, R. A. Malik, A. K. Sharma, and J. L. Day, Microvascular response to tissue injury and capillary ultrastructure in the foot skin of type I diabetic patients,” Clinical Science, vol. 89, no. 5, pp. 467–474, 1995, doi: 10.1042/cs0890467.
[208]
G. Rayman, S. A. Williams, J. Gamble, and J. E. Tooke, A study of factors governing fluid filtration in the diabetic foot,” European Journal of Clinical Investigation, vol. 24, no. 12, pp. 830–836, 1994, doi: 10.1111/j.1365-2362.1994.tb02027.x.
[209]
M. L. Arts et al., “Data-driven directions for effective footwear provision for the high-risk diabetic foot,” Diabetic Medicine, vol. 32, no. 6, pp. 790–797, 2015, doi: 10.1111/dme.12741.
[210]
A. Veves, H. J. Murray, M. J. Young, and A. J. M. Boulton, The risk of foot ulceration in diabetic patients with high foot pressure: a prospective study,” Diabetologia, vol. 35, no. 7, pp. 660–663, 1992, doi: 10.1007/BF00400259.
[211]
D. G. Armstrong, E. J. G. Peters, K. A. Athanasiou, and L. A. Lavery, “Is there a critical level of plantar foot pressure to identify patients at risk for neuropathic foot ulceration?” The Journal of Foot and Ankle Surgery, vol. 37, no. 4, pp. 303–307, 1998, doi: 10.1016/S1067-2516(98)80066-5.
[212]
L. A. Lavery, D. G. Armstrong, R. P. Wunderlich, J. Tredwell, and A. J. M. Boulton, Predictive value of foot pressure assessment as part of a population-based diabetes disease management program,” Diabetes Care, vol. 26, no. 4, pp. 1069–1073, 2003, doi: 10.2337/diacare.26.4.1069.
[213]
R. G. Frykberg et al., “Diabetic foot disorders: A clinical practice guideline (2006 revision),” The journal of foot and ankle surgery, vol. 45, no. 5, pp. 1–66, 2006.
[214]
P. R. Cavanagh and S. A. Bus, “Off-loading the diabetic foot for ulcer prevention and healing,” Journal of Vascular Surgery, vol. 52, no. 3, pp. 37S–43S, 2010, doi: 10.1016/j.jvs.2010.06.007.
[215]
L. Rizzo et al., Custom-made orthesis and shoes in a structured follow-up program reduces the incidence of neuropathic ulcers in high-risk diabetic foot patients,” International Journal of Lower Extremity Wounds, vol. 11, no. 1, pp. 59–64, 2012, doi: 10.1177/1534734612438729.
[216]
U. Niemann et al., Comparative Clustering of Plantar Pressure Distributions in Diabetics with Polyneuropathy May Be Applied to Reveal Inappropriate Biomechanical Stress,” PLOS ONE, vol. 11, no. 8, pp. 1–12, 2016, doi: 10.1371/journal.pone.0161326.
[217]
L. Kaufman and P. J. Rousseeuw, Clustering by Means of Medoids,” Statistical Data Analysis based on the L1-Norm and Related Methods, pp. 405–416, 1987.
[218]
S. S. Singh and N. C. Chauhan, K-means v/s K-medoids: A Comparative Study,” in National conference on recent trends in engineering & technology, 2011, pp. 839–844.
[219]
D. C. Hoaglin, John W. Tukey and Data Analysis,” Statistical Science, vol. 18, no. 3, pp. 311–318, 2003, doi: 10.1214/ss/1076102418.
[220]
R. Barn, R. Waaijman, F. Nollet, J. Woodburn, and S. A. Bus, Predictors of Barefoot Plantar Pressure during Walking in Patients with Diabetes, Peripheral Neuropathy and a History of Ulceration,” PLOS ONE, vol. 10, no. 2, pp. 1–12, 2015, doi: 10.1371/journal.pone.0117443.
[221]
R. Waaijman et al., Risk Factors for Plantar Foot Ulcer Recurrence in Neuropathic Diabetic Patients,” Diabetes Care, vol. 37, no. 6, pp. 1697–1705, 2014, doi: 10.2337/dc13-2470.
[222]
S. A. Bus, “Priorities in offloading the diabetic foot,” Diabetes/Metabolism Research and Reviews, vol. 28, no. S1, pp. 54–59, 2012, doi: 10.1002/dmrr.2240.
[223]
R. Dahmen, R. Haspels, B. Koomen, and A. F. Hoeksma, Therapeutic Footwear for the Neuropathic Foot: An algorithm,” Diabetes Care, vol. 24, no. 4, pp. 705–709, 2001, doi: 10.2337/diacare.24.4.705.
[224]
S. Tonekaboni, S. Joshi, M. D. McCradden, and A. Goldenberg, What Clinicians Want: Contextualizing Explainable MachineLearning for Clinical End Use,” in Machine learning for healthcare, 2019, pp. 359–380, [Online]. Available: http://proceedings.mlr.press/v106/tonekaboni19a.html.
[225]
C. Molnar, Interpretable Machine Learning. 2020.
[226]
R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi, A Survey of Methods for Explaining Black Box Models,” ACM Computing Surveys, vol. 51, no. 5, pp. 1–42, 2018, doi: 10.1145/3236009.
[227]
J. Kazemitabar, A. Amini, A. Bloniarz, and A. S. Talwalkar, Variable Importance Using Decision Trees,” in Neural information processing systems (NIPS), 2017, vol. 30, pp. 426–435, [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/5737c6ec2e0716f3d8a7a5c4e0de0d9a-Paper.pdf.
[228]
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media, 2009.
[229]
M. T. Ribeiro, S. Singh, and C. Guestrin, Why Should I Trust You?: Explaining the Predictions of Any Classifier,” in ACM SIGKDD Knowledge Discovery and Data Mining, 2016, pp. 1135–1144, doi: 10.1145/2939672.2939778.
[230]
S. M. Lundberg and S.-I. Lee, A Unified Approach to Interpreting Model Predictions,” in Neural information processing systems (NIPS), 2017, pp. 4765–4774, [Online]. Available: http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf.
[231]
D. Alvarez-Melis and T. S. Jaakkola, On the Robustness of Interpretability Methods.” 2018.
[232]
G. Visani, E. Bagli, and F. Chesani, OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms.” 2020.
[233]
W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, and K.-R. Müller, Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond.” 2020.
[234]
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek, On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation,” PLOS ONE, vol. 10, no. 7, pp. 1–46, 2015, doi: 10.1371/journal.pone.0130140.
[235]
S. Lipovetsky and M. Conklin, Analysis of regression in game theory approach,” Applied Stochastic Models in Business and Industry, vol. 17, no. 4, pp. 319–330, 2001, doi: 10.1002/asmb.446.
[236]
E. Štrumbelj and I. Kononenko, Explaining prediction models and individual predictions with feature contributions,” Knowledge and Information Systems, vol. 41, no. 3, pp. 647–665, 2014, doi: 10.1007/s10115-013-0679-x.
[237]
L. S. Shapley, A Value for n-Person Games,” Contributions to the Theory of Games, vol. 2, no. 28, pp. 307–317, 1953, doi: 10.1515/9781400881970-018.
[238]
S. M. Lundberg et al., Explainable AI for Trees: From Local Explanations to Global Understanding.” 2019.
[239]
B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers,” in Workshop on Computational Learning Theory, 1992, pp. 144–152, doi: 10.1145/130385.130401.
[240]
W. N. Venables and B. D. Ripley, Modern Applied Statistics with S, Fourth edition. Springer, 2002.
[241]
I. Guyon and A. Elisseeff, An Introduction to Variable and Feature Selection,” Journal of Machine Learning Research, vol. 3, no. Mar, pp. 1157–1182, 2003.
[242]
M. Kuhn and K. Johnson, Applied Predictive Modeling. Springer, 2013.
[243]
J. Friedman, T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent,” Journal of Statistical Software, vol. 33, no. 1, pp. 1–22, 2010, doi: 10.18637/jss.v033.i01.
[244]
A. E. Hoerl and R. W. Kennard, “Ridge regression: Biased estimation for nonorthogonal problems,” Technometrics, vol. 12, no. 1, pp. 55–67, 1970, doi: 10.2307/1271436.
[245]
K. Kira and L. A. Rendell, The Feature Selection Problem: Traditional Methods and a New Algorithm,” in AAAI artificial intelligence, 1992, vol. 2, pp. 129–134, [Online]. Available: https://www.aaai.org/Library/AAAI/1992/aaai92-020.php.
[246]
G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Computers & Electrical Engineering, vol. 40, no. 1, pp. 16–28, 2014, doi: 10.1016/j.compeleceng.2013.11.024.
[247]
B. Ding and R. Gentleman, Classification Using Generalized Partial Least Squares,” Journal of Computational and Graphical Statistics, vol. 14, no. 2, pp. 280–298, 2005, doi: 10.1198/106186005X47697.
[248]
K. Hechenbichler and K. Schliep, Weighted k-Nearest-Neighbor Techniques and Ordinal Classification,” in SFB 386, ludwig-maximilians university, munich, 2004, pp. 1–16, [Online]. Available: http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-1769-9.
[249]
L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Wadsworth; Brooks, 1984.
[250]
R. C. Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2020.
[251]
B. Bischl et al., mlr: Machine Learning in R,” Journal of Machine Learning Research, vol. 17, no. 170, pp. 1–5, 2016, [Online]. Available: https://jmlr.org/papers/v17/15-066.html.
[252]
M. Kuhn, Building Predictive Models in R Using the caret Package,” Journal of Statistical Software, vol. 28, no. 5, pp. 1–26, 2008, doi: 10.18637/jss.v028.i05.
[253]
D. Meyer, E. Dimitriadou, K. Hornik, A. Weingessel, and F. Leisch, e1071: Misc Functions of the Department of Statistics, Probability Theory Group, TU Wien. 2019.
[254]
T. Therneau and B. Atkinson, rpart: Recursive Partitioning and Regression Trees. 2018.
[255]
M. Kuhn and R. Quinlan, C50: C5.0 decision trees and rule-based models. 2018.
[256]
M. N. Wright and A. Ziegler, ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R,” Journal of Statistical Software, vol. 77, no. 1, pp. 1–17, 2017, doi: 10.18637/jss.v077.i01.
[257]
T. Chen and C. Guestrin, XGBoost: A Scalable Tree Boosting System,” in Proc. Of ACM SIGKDD international conference on knowledge discovery and data mining, 2016, pp. 785–794, doi: 10.1145/2939672.2939785.
[258]
A. Fisher, C. Rudin, and F. Dominici, All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously,” Journal of Machine Learning Research, vol. 20, no. 177, pp. 1–81, 2019.
[259]
S. Glaßer, P. Berg, M. Neugebauer, and B. Preim, Reconstruction of 3D Surface Meshes for Bood Flow Simulations of Intracranial Aneurysms,” in Computer- and robot-assisted surgery, 2015, pp. 163–168.
[260]
L. Antiga, M. Piccinelli, L. Botti, B. Ene-Iordache, A. Remuzzi, and D. A. Steinman, “An image-based modeling framework for patient-specific computational hemodynamics,” Medical & Biological Engineering & Computing, vol. 46, no. 11, pp. 1097–1112, 2008, doi: 10.1007/s11517-008-0420-1.
[261]
S. Saalfeld, P. Berg, A. Niemann, M. Luz, B. Preim, and O. Beuing, “Semiautomatic neck curve reconstruction for intracranial aneurysm rupture risk assessment based on morphological parameters,” International Journal of Computer Assisted Radiology and Surgery (IJCARS), vol. 13, no. 11, pp. 1781–1793, 2018, doi: 10.1007/s11548-018-1848-x.
[262]
A. Lauric, M. I. Baharoglu, and A. M. Malek, “Ruptured status discrimination performance of aspect ratio, height/width, and bottleneck factor is highly dependent on aneurysm sizing methodology,” Neurosurgery, vol. 71, no. 1, pp. 38–46, 2012, doi: 10.1227/NEU.0b013e3182503bf9.
[263]
H. M. van Loo et al., Major Depressive Disorder Subtypes to Predict Long-term Course,” Depression & Anxiety, vol. 31, no. 9, pp. 765–777, 2014, doi: 10.1002/da.22233.
[264]
R. C. Kessler et al., Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports,” Molecular Psychiatry, vol. 21, no. 10, pp. 1366–1371, 2016, doi: 10.1038/mp.2015.198.
[265]
B. Langguth, M. Landgrebe, T. Kleinjung, G. P. Sand, and G. Hajak, “Tinnitus and depression,” The World Journal of Biological Psychiatry, vol. 12, no. 7, pp. 489–500, 2011, doi: 10.3109/15622975.2011.575178.
[266]
K. J. Trevis, N. M. McLachlan, and S. J. Wilson, A systematic review and meta-analysis of psychological functioning in chronic tinnitus,” Clinical Psychology Review, vol. 60, pp. 62–86, 2018, doi: 10.1016/j.cpr.2017.12.006.
[267]
M. A. Whooley, A. L. Avins, J. Miranda, and W. S. Browner, Case-finding instruments for depression: Two questions are as good as many,” Journal of General Internal Medicine, vol. 12, no. 7, pp. 439–445, 1997, doi: 10.1046/j.1525-1497.1997.00076.x.
[268]
S. A. Riolo, T. A. Nguyen, J. F. Greden, and C. A. King, Prevalence of depression by race/ethnicity: findings from the National Health and Nutrition Examination Survey III,” American Journal of Public Health, vol. 95, no. 6, pp. 998–1000, 2005, doi: 10.2105/AJPH.2004.047225.
[269]
A. H. Weinberger, M. Gbedemah, A. M. Martinez, D. Nash, S. Galea, and R. D. Goodwin, Trends in depression prevalence in the USA from 2005 to 2015: Widening disparities in vulnerable groups,” Psychological Medicine, vol. 48, no. 8, pp. 1308–1315, 2018, doi: 10.1017/S0033291717002781.
[270]
G. Yu, D. Witten, and J. Bien, Controlling Costs: Feature Selection on a Budget.” 2020.
[271]
M. Kachuee, K. Karkkainen, O. Goldstein, D. Zamanzadeh, and M. Sarrafzadeh, Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data.” 2019, [Online]. Available: https://arxiv.org/abs/1902.07102.
[272]
R. A. Dobie, Depression and tinnitus,” Otolaryngologic Clinics of North America, vol. 36, no. 2, pp. 383–388, 2003, doi: 10.1016/s0030-6665(02)00168-8.
[273]
R. L. Folmer, S. E. Griest, M. B. Meikle, and W. H. Martin, “Tinnitus severity, loudness, and depression,” Otolaryngology—Head and Neck Surgery, vol. 121, no. 1, pp. 48–51, 1999, doi: 10.1016/S0194-5998(99)70123-3.
[274]
J. B. S. Halford and S. D. Anderson, Anxiety and depression in tinnitus sufferers,” Journal of Psychosomatic Research, vol. 35, no. 4/5, pp. 383–390, 1991, doi: 10.1016/0022-3999(91)90033-K.
[275]
J. W. Salazar, K. Meisel, E. R. Smith, A. Quiggle, D. B. McCoy, and M. R. Amans, Depression in Patients with Tinnitus: A Systematic Review,” Otolaryngology–Head and Neck Surgery, vol. 161, no. 1, pp. 28–35, 2019, doi: 10.1177/0194599819835178.
[276]
J. R. Cebral, F. Mut, J. Weir, and C. Putman, Quantitative Characterization of the Hemodynamic Environment in Ruptured and Unruptured Brain Aneurysms,” American Journal of Neuroradiology, vol. 32, no. 1, pp. 145–151, 2011, doi: 10.3174/ajnr.A2419.
[277]
P. Berg and O. Beuing, “Multiple intracranial aneurysms: A direct hemodynamic comparison between ruptured and unruptured vessel malformations,” International Journal of Computer Assisted Radiology and Surgery (IJCARS), vol. 13, no. 1, pp. 83–93, 2018, doi: 10.1007/s11548-017-1643-0.
[278]
F. J. Detmer et al., “Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics,” International Journal of Computer Assisted Radiology and Surgery (IJCARS), vol. 13, no. 11, pp. 1767–1779, 2018, doi: 10.1007/s11548-018-1837-0.
[279]
F. Doshi-Velez and B. Kim, Towards A Rigorous Science of Interpretable Machine Learning.” 2017.
[280]
S. Nolen-Hoeksema, Gender Differences in Depression,” Current Directions in Psychological Science, vol. 10, no. 5, pp. 173–176, 2001, doi: 10.1111/1467-8721.00142.
[281]
M. Piccinelli and G. Wilkinson, “Gender differences in depression: Critical review,” The British Journal of Psychiatry, vol. 177, no. 6, pp. 486–492, 2000, doi: 10.1192/bjp.177.6.486.
[282]
M. P. Matud, “Gender differences in stress and coping styles,” Personality and Individual Differences, vol. 37, no. 7, pp. 1401–1415, 2004, doi: 10.1016/j.paid.2004.01.010.
[283]
I. Jaussent et al., Insomnia Symptoms in Older Adults: Associated Factors and Gender Differences,” The American Journal of Geriatric Psychiatry, vol. 19, no. 1, pp. 88–97, 2011, doi: 10.1097/JGP.0b013e3181e049b6.
[284]
C. P. McLean, A. Asnaani, B. T. Litz, and S. G. Hofmann, Gender Differences in Anxiety Disorders: Prevalence, Course of Illness, Comorbidity and Burden of Illness,” Journal of Psychiatric Research, vol. 45, no. 8, pp. 1027–1035, 2011, doi: 10.1016/j.jpsychires.2011.03.006.
[285]
M. Asher, A. Asnaani, and I. M. Aderka, “Gender differences in social anxiety disorder: A review,” Clinical Psychology Review, vol. 56, pp. 1–12, 2017, doi: 10.1016/j.cpr.2017.05.004.
[286]
B. Langguth, P. M. Kreuzer, T. Kleinjung, and D. De Ridder, Tinnitus: causes and clinical management,” The Lancet Neurology, vol. 12, no. 9, pp. 920–930, 2013, doi: 10.1016/S1474-4422(13)70160-1.
[287]
S. I. Erlandsson and K.-M. Holgers, The impact of perceived tinnitus severity on health-related quality of life with aspects of gender,” Noise and Health, vol. 3, no. 10, pp. 39–51, 2001, [Online]. Available: http://www.noiseandhealth.org/article.asp?issn=1463-1741;year=2001;volume=3;issue=10;spage=39;epage=51;aulast=Erlandsson.
[288]
P. C. L. Pinto, T. G. Sanchez, and S. Tomita, The impact of gender, age and hearing loss on tinnitus severity,” Brazilian Journal of Otorhinolaryngology, vol. 76, no. 1, pp. 18–24, 2010, doi: 10.1590/S1808-86942010000100004.
[289]
C. Meric, M. Gartner, L. Collet, and S. Chéry-Croze, Psychopathological profile of tinnitus sufferers: evidence concerning the relationship between tinnitus features and impact on life,” Audiology and Neurotology, vol. 3, no. 4, pp. 240–252, 1998, doi: 10.1159/000013796.
[290]
C. Seydel, H. Haupt, H. Olze, A. J. Szczepek, and B. Mazurek, Gender and Chronic Tinnitus: Differences in Tinnitus-Related Distress Depend on Age and Duration of Tinnitus,” Ear and Hearing, vol. 34, no. 5, pp. 661–672, 2013, doi: 10.1097/AUD.0b013e31828149f2.
[291]
W. Hiller and G. Goebel, Factors Influencing Tinnitus Loudness and Annoyance,” Archives of Otolaryngology–Head & Neck Surgery, vol. 132, no. 12, pp. 1323–1330, 2006, doi: 10.1001/archotol.132.12.1323.
[292]
A. Lugo et al., Sex-Specific Association of Tinnitus With Suicide Attempts,” JAMA Otolaryngology–Head & Neck Surgery, vol. 145, no. 7, pp. 685–687, 2019, doi: 10.1001/jamaoto.2019.0566.
[293]
T. S. Han, J.-E. Jeong, S.-N. Park, and J. J. Kim, Gender Differences Affecting Psychiatric Distress and Tinnitus Severity,” Clinical Psychopharmacology and Neuroscience, vol. 17, no. 1, pp. 113–120, 2019, doi: 10.9758/cpn.2019.17.1.113.
[294]
A. Van der Wal et al., Sex Differences in the Response to Different Tinnitus Treatment,” Frontiers in Neuroscience, vol. 14, no. 422, pp. 1–9, 2020, doi: 10.3389/fnins.2020.00422.
[295]
T. J. DiCiccio and B. Efron, “Bootstrap confidence intervals,” Statistical Science, vol. 11, no. 3, pp. 189–212, 1996, doi: 10.1214/ss/1032280214.
[296]
A. Hannemann, N. Friedrich, K. Dittmann, and others, Age-and sex-specific reference limits for creatinine, cystatin C and the estimated glomerular filtration rate,” Clinical Chemistry and Laboratory Medicine, vol. 50, no. 5, pp. 919–926, 2012, doi: 10.1515/CCLM.2011.788.
[297]
H. Finney, D. J. Newman, and C. P. Price, Adult Reference Ranges for Serum Cystatin C, Creatinine and Predicted Creatinine Clearance,” Annals of Clinical Biochemistry, vol. 37, no. 1, pp. 49–59, 2000, doi: 10.1258/0004563001901524.
[298]
M. Bullinger, German translation and psychometric testing of the SF-36 Health Survey: Preliminary results from the IQOLA project,” Social Science & Medicine, vol. 41, no. 10, pp. 1359–1366, 1995, doi: 10.1016/0277-9536(95)00115-N.
[299]
J. Pearl and D. Mackenzie, The Book of Why: The New Science of Cause and Effect. Basic Books, 2018.
[300]
B. Schölkopf, Causality for Machine Learning.” 2019.
[301]
M. A. Hernán and J. M. Robins, Causal Inference: What If. Chapman & Hall/CRC, 2020.
[302]
K. Yu et al., Causality-based Feature Selection: Methods and Evaluations,” ACM Computing Surveys, vol. 53, no. 5, pp. 1–36, 2020, doi: 10.1145/3409382.
[303]
P. Lemberger and I. Panico, A Primer on Domain Adaptation.” 2020.