Data-driven stochastic optimization approaches to determine decision thresholds for risk estimation models

References

• Ahsen, M.E., Ayvaci, M.U.S. and Raghunathan, S. (2019) When algorithmic predictions use human-generated data: A bias-aware classification algorithm for breast cancer diagnosis. Information Systems Research, 30(1), 97–116.
   Web of Science ®Google Scholar
• Alagoz, O., Ayer, T. and Erenay, F.S. (2011) Operations research models for cancer screening, in Wiley Encyclopedia of Operations Research and Management Science. John Wiley & Sons, Inc., Hoboken, NJ, pp. 1–14.
   Google Scholar
• Altman, D.G., Vergouwe, Y., Royston, P. and Moons, K.G.M. (2009) Prognosis and prognostic research: validating a prognostic model. British Medical Journal, 338(1), b605–b605.
   Google Scholar
• American Diabetes Society (2016) Classification and diagnosis of diabetes. Diabetes Care, 39(1), S13–S22.
   Web of Science ®Google Scholar
• Andelic, N., Hammergren, N., Bautz-Holter, E., Sveen, U., Brunborg, C. and Røe, C. (2009) Functional outcome and health-related quality of life 10 years after moderate-to-severe traumatic brain injury. Acta Neurologica Scandinavica, 120(1), 16–23.
   PubMed Web of Science ®Google Scholar
• Anderson, K.M. (1991) A nonproportional hazards Weibull accelerated failure time regression model. Biometrics, 47(1), 281–8.
   Web of Science ®Google Scholar
• Asken, B.M., Bauer, R.M., Guskiewicz, K.M., McCrea, M.A., Schmidt, J.D., Giza, C.C., Snyder, A.R., Houck, Z.M., Kontos, A.P., McAllister, T.W., Broglio, S.P., Clugston, J.R., Anderson, S., Bazarian, J., Brooks, A., Buckley, T., Chrisman, S., Collins, M., DiFiori, J., Duma, S., Dykhuizen, B., Eckner, J.T., Feigenbaum, L., Hoy, A., Kelly, L., Langford, T.D., Lintner, L., McGinty, G., Mihalik, J., Miles, C., Ortega, J., Port, N., Putukian, M., Rowson, S. and Svoboda, S. (2018) Immediate removal from activity after sport-related concussion is associated with shorter clinical recovery and less severe symptoms in collegiate student-athletes. The American Journal of Sports Medicine, 46(6), 1465–1474.
   Web of Science ®Google Scholar
• Ayer, T., Alagoz, O. and Stout, N.K. (2012) OR Forum–A POMDP approach to personalize mammography screening decisions. Operations Research, 60(5), 1019–1034.
   Web of Science ®Google Scholar
• Ayer, T., Alagoz, O., Stout, N.K. and Burnside, E.S. (2016) Heterogeneity in women’s adherence and its role in optimal breast cancer screening policies. Management Science, 62(5), 1339–1362.
   Web of Science ®Google Scholar
• Ayvaci, M.U.S., Ahsen, M.E., Raghunathan, S. and Gharibi, Z. (2017) Timing the use of breast cancer risk information in biopsy decision-making. Production and Operations Management, 26(7), 1333–1358.
   Web of Science ®Google Scholar
• Ayvaci, M.U.S., Alagoz, O. and Burnside, E.S. (2012) The effect of budgetary restrictions on breast cancer diagnostic decisions. Manufacturing & Service Operations Management, 14(4), 600–617.
   Google Scholar
• Barnett, C.L., Tomlins, S.A., Underwood, D.J., Wei, J.T., Morgan, T.M., Montie, J.E. and Denton, B.T. (2017) Two-stage biomarker protocols for improving the precision of early detection of prostate cancer. Medical Decision Making, 37(7), 815–826.
   Web of Science ®Google Scholar
• Bayati, M., Bhaskar, S. and Montanari, A. (2018) Statistical analysis of a low cost method for multiple disease prediction. Statistical Methods in Medical Research, 27(8), 2312–2328.
   Web of Science ®Google Scholar
• Bertsimas, D., Silberholz, J. and Trikalinos, T. (2018) Optimal healthcare decision making under multiple mathematical models: Application in prostate cancer screening. Health Care Management Science, 21(1), 105–118.
   Web of Science ®Google Scholar
• Broglio, S.P., Ferrara, M.S., Sopiarz, K. and Kelly, M.S. (2008). Reliable change of the sensory organization test. Clinical Journal of Sport Medicine, 18(2), 148–154.
   PubMed Web of Science ®Google Scholar
• Broglio, S.P., McCrea, M., McAllister, T., Harezlak, J., Katz, B., Hack, D. and Hainline, B. (2017) A national study on the effects of concussion in collegiate athletes and US Military Service Academy members: The NCAA–DoD Concussion Assessment, Research and Education (CARE) consortium structure and methods. Sports Medicine, 47(7), 1437–1451.
   PubMed Web of Science ®Google Scholar
• Chin, E.Y., Nelson, L.D., Barr, W.B., McCrory, P. and McCrea, M.A. (2016) Reliability and validity of the sport concussion assessment tool-3 (SCAT3) in high school and collegiate athletes. American Journal of Sports Medicine, 44(9), 2276–2285.
   PubMed Web of Science ®Google Scholar
• Collins, M.W., Kontos, A.P., Reynolds, E., Murawski, C.D. and Fu, F.H. (2014) A comprehensive, targeted approach to the clinical care of athletes following sport-related concussion. Knee Surgery, Sports Traumatology, Arthroscopy, 22(2), 235–246.
   PubMed Web of Science ®Google Scholar
• Daníelsson, J. and de Vries, C.G. (1997). Tail index and quantile estimation with very high frequency data. Journal of Empirical Finance, 4(2-3), 241–257.
   Google Scholar
• Degeling, K., Koffijberg, H. and Ijzerman, M.J. (2017) A systematic review and checklist presenting the main challenges for health economic modeling in personalized medicine: Towards implementing patient-level models. Expert Review of Pharmacoeconomics and Outcomes Research, 17(1), 17–25.
   PubMed Web of Science ®Google Scholar
• Deneef, P. and Kent, D.L. (1993) Using treatment-tradeoff preferences to select diagnostic strategies. Medical Decision Making, 13(2), 126–132.
   Web of Science ®Google Scholar
• Deo, S., Rajaram, K., Rath, S., Karmarkar, U.S. and Goetz, M.B. (2015) Planning for HIV screening, testing, and care at the Veterans Health Administration. Operations Research, 63(2), 287–304.
   Web of Science ®Google Scholar
• Deo, S. and Sohoni, M. (2015) Optimal decentralization of early infant diagnosis of HIV in resource-limited settings. Manufacturing & Service Operations Management, 17(2), 191–207.
   Google Scholar
• Ebell, M. (2010) AHRQ White Paper: Use of clinical decision rules for point-of-care decision support. Medical Decision Making, 30(6), 712–721.
   Web of Science ®Google Scholar
• El-Amine, H., Bish, E.K. and Bish, D.R. (2018) Robust postdonation blood screening under prevalence rate uncertainty. Operations Research, 66(1), 1–17.
   Web of Science ®Google Scholar
• Erenay, F.S., Alagoz, O. and Said, A. (2014) Optimizing colonoscopy screening for colorectal cancer prevention and surveillance. Manufacturing & Service Operations Management, 16(3), 381–400.
   Web of Science ®Google Scholar
• Felder, S. and Mayrhofer, T. (2014) Risk preferences: Consequences for test and treatment thresholds and optimal cutoffs. Medical Decision Making, 34(1), 33–41.
   Web of Science ®Google Scholar
• Galie, N., Hoeper, M.M., Humbert, M., Torbicki, A., Vachiery, J.-L., Barbera, J.A., Beghetti, M., Corris, P., Gaine, S., Gibbs, J.S., Gomez-Sanchez, M.A., Jondeau, G., Klepetko, W., Opitz, C., Peacock, A., Rubin, L., Zellweger, M. and Simonneau, G. (2009) Guidelines for the diagnosis and treatment of pulmonary hypertension. European Heart Journal, 30(20), 2493–2537.
   PubMed Web of Science ®Google Scholar
• Garcia, G.-G. P., Broglio, S.P., Lavieri, M.S., McCrea, M. and McAllister, T. (2018) Quantifying the value of multidimensional assessment models for acute concussion: An analysis of data from the NCAA-DoD CARE consortium. Sports Medicine, 48(7), 1739–1749.
   Web of Science ®Google Scholar
• Glasziou, P. and Hilden, J. (1986) Threshold analysis of decision tables. Medical Decision Making, 6(3), 161–168.
   Web of Science ®Google Scholar
• Godbole, S. and Sarawagi, S. (2004) Discriminative methods for multi-labeled classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3056, 22–30.
   Web of Science ®Google Scholar
• Greiner, M., Pfeiffer, D. and Smith, R. (2000) Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Preventive Veterinary Medicine, 45(1-2), 23–41.
   PubMed Web of Science ®Google Scholar
• Greiner, M., Sohr, D. and Göbel, P. (1995) A modified ROC analysis for the selection of cut-off values and the definition of intermediate results of serodiagnostic tests. Journal of Immunological Methods, 185(1), 123–132.
   Web of Science ®Google Scholar
• Güneş, E.D., Örmeci, E.L. and Kunduzcu, D. (2015) Preventing and diagnosing colorectal cancer with a limited colonoscopy resource. Production and Operations Management, 24(1), 1–20.
   Web of Science ®Google Scholar
• Guskiewicz, K.M., Marshall, S.W., Bailes, J., McCrea, M., Cantu, R.C., Randolph, C. and Jordan, B.D. (2005) Association between recurrent concussion and late-life cognitive impairment in retired professional football players. Neurosurgery, 57(4), 719–726.
   PubMed Web of Science ®Google Scholar
• Guskiewicz, K.M., Marshall, S.W., Bailes, J., McCrea, M.A., Harding, H.P., Matthews, A., Register-Mihalik, J.K. and Cantu, R.C. (2007) Recurrent concussion and risk of depression in retired professional football players. Medicine & Science in Sports & Exercise, 39(6), 903–909.
   PubMed Web of Science ®Google Scholar
• Hänninen, T., Tuominen, M., Parkkari, J., Vartiainen, M., Öhman, J., Iverson, G.L. and Luoto, T.M. (2016) Sport concussion assessment tool – 3rd edition – normative reference values for professional ice hockey players. Journal of Science and Medicine in Sport, 19(8), 636–641.
   PubMed Web of Science ®Google Scholar
• Harrell, F.E. and Davis, C.E. (1982) A new distribution-free quantile estimator. Biometrika, 69(3), 635–640.
   Web of Science ®Google Scholar
• Hartz, A., McKinney, W.P., Centor, R., Krieg, A., Simms, G. and Henck, S. (1986) Stochastic thresholds. Medical Decision Making, 6(3), 145–148.
   Web of Science ®Google Scholar
• Helm, J.E., Lavieri, M.S., van Oyen, M.P., Stein, J.D. and Musch, D.C. (2015) Dynamic forecasting and control algorithms of glaucoma progression for clinician decision support. Operations Research, 63(5), 979–999.
   Web of Science ®Google Scholar
• Humphreys, I., Wood, R.L., Phillips, C.J., and Macey, S. (2013) The costs of traumatic brain injury: A literature review. ClinicoEconomics and Outcomes Research, 5(1), 281–287.
   Google Scholar
• Jacobson, S.H., Yu, G. and Jokela, J.A. (2016) A double-risk monitoring and movement restriction policy for Ebola entry screening at airports in the United States. Preventive Medicine, 88, 33–38.
   Web of Science ®Google Scholar
• Jónasson, J.O., Deo, S. and Gallien, J. (2017) Improving HIV early infant diagnosis supply chains in Sub-Saharan Africa: Models and application to Mozambique. Operations Research, 65(6), 1479–1493.
   Web of Science ®Google Scholar
• Jund, J., Rabilloud, M., Wallon, M. and Ecochard, R. (2005) Methods to estimate the optimal threshold for normally or log-normally distributed biological tests. Medical Decision Making, 25(4), 406–415.
   Web of Science ®Google Scholar
• Kerr, Z.Y., Evenson, K.R., Rosamond, W.D., Mihalik, J.P., Guskiewicz, K.M. and Marshall, S.W. (2014) Association between concussion and mental health in former collegiate athletes. Injury Epidemiology, 1(1), 28.
   PubMedGoogle Scholar
• Kerr, Z.Y., Marshall, S.W., Harding, H.P. and Guskiewicz, K.M. (2012) Nine-year risk of depression diagnosis increases with increasing self-reported concussions in retired professional football players. The American Journal of Sports Medicine, 40(10), 2206–2212.
   PubMed Web of Science ®Google Scholar
• Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V. and Fotiadis, D.I. (2015) Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13,8–17.
   PubMed Web of Science ®Google Scholar
• Kutcher, J.S. and Giza, C.C. (2014) Sports concussion diagnosis and management. Continuum, 20(6), 1552–1569.
   Google Scholar
• Lau, B.C., Kontos, A.P., Collins, M.W., Mucha, A. and Lovell, M.R. (2011) Which on-field signs/symptoms predict protracted recovery from sport-related concussion among high school football players? The American Journal of Sports Medicine, 39(11), 2311–2318.
   PubMed Web of Science ®Google Scholar
• Lee, E., Lavieri, M.S., Volk, M.L. and Xu, Y. (2015) Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity. Health Care Management Science, 18(3), 363–375.
   Web of Science ®Google Scholar
• Li, Y., Zhu, M., Klein, R. and Kong, N. (2014) Using a partially observable Markov chain model to assess colonoscopy screening strategies – A cohort study. European Journal of Operational Research, 238(1), 313–326.
   Web of Science ®Google Scholar
• Maillart, L.M., Ivy, J.S., Ransom, S. and Diehl, K. (2008) Assessing dynamic breast cancer screening policies. Operations Research, 56(6), 1411–1427.
   Web of Science ®Google Scholar
• Mangasarian, O.L., Street, W.N. and Wolberg, W.H. (1995) Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), 570–577.
   Web of Science ®Google Scholar
• Maruta, J., Lumba-Brown, A. and Ghajar, J. (2018) Concussion subtype identification with the Rivermead post-concussion symptoms questionnaire. Frontiers in Neurology, 9,1–7.
   Web of Science ®Google Scholar
• McCrory, P., Meeuwisse, W., Dvorak, J., Aubry, M., Bailes, J., Broglio, S., Cantu, R.C., Cassidy, D., Echemendia, R.J., Castellani, R.J., Davis, G.A., Ellenbogen, R., Emery, C., Engebretsen, L., Feddermann-Demont, N., Giza, C.C., Guskiewicz, K.M., Herring, S., Iverson, G.L., Johnston, K.M., Kissick, J., Kutcher, J., Leddy, J.J., Maddocks, D., Makdissi, M., Manley, G.T., McCrea, M., Meehan, W.P., Nagahiro, S., Patricios, J., Putukian, M., Schneider, K.J., Sills, A., Tator, C.H., Turner, M. and Vos, P.E. (2017) Consensus statement on concussion in sport—the 5th international conference on concussion in sport held in Berlin, October 2016. British Journal of Sports Medicine, 51, 838–847.
   Web of Science ®Google Scholar
• McDonald, W.I., Compston, A., Edan, G., Goodkin, D., Hartung, H.-P., Lublin, F.D., McFarland, H.F., Paty, D.W., Polman, C.H., Reingold, S.C., Sandberg-Wollheim, M., Sibley, W., Thompson, A., van Den Noort, S., Weinshenker, B.Y. and Wolinsky, J.S. (2001) Recommended diagnostic criteria for multiple sclerosis: Guidelines from the international panel on the diagnosis of multiple sclerosis. Annals of Neurology, 50(1), 121–127.
   PubMed Web of Science ®Google Scholar
• McGregor, M. and Caro, J.J. (2006) QALYs. PharmacoEconomics, 24(10), 947–952.
   PubMed Web of Science ®Google Scholar
• McKhann, G.M., Knopman, D.S., Chertkow, H., Hyman, B.T., Jack, C.R., Kawas, C.H., Klunk, W.E., Koroshetz, W.J., Manly, J.J., Mayeux, R., Mohs, R.C., Morris, J. C., Rossor, M.N., Scheltens, P., Carrillo, M.C., Thies, B., Weintraub, S. and Phelps, C.H. (2011) The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 263–269.
   PubMed Web of Science ®Google Scholar
• McLay, L.A., Foufoulides, C. and Merrick, J.R.W. (2010) Using simulation-optimization to construct screening strategies for cervical cancer. Health Care Management Science, 13(4), 294–318.
   Web of Science ®Google Scholar
• Mohajerin Esfahani, P. and Kuhn, D. (2018) Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations. Mathematical Programming, 171(1-2), 115–166.
   Web of Science ®Google Scholar
• Moons, K.G., Stijnen, T., Michel, B.C., Büller, H.R., Van Es, G.-A., Grobbee, D.E. and Habbema, J.D.F. (1997) Application of treatment thresholds to diagnostic-test evaluation. Medical Decision Making, 17(4), 447–454.
   Web of Science ®Google Scholar
• Moons, K.G.M., Altman, D.G., Vergouwe, Y. and Royston, P. (2009) Prognosis and prognostic research: Application and impact of prognostic models in clinical practice. British Medical Journal, 338(7799), 1487–1490.
   Google Scholar
• Nease, R.F., Owens, D.K. and Sox, H.C. (1989) Threshold analysis using diagnostic tests with multiple results. Medical Decision Making, 9(2), 91–103.
   Web of Science ®Google Scholar
• Nord, E., Daniels, N. and Kamlet, M. (2009) QALYs: Some challenges. Value in Health, 12(S1), S10–S15.
   Google Scholar
• Odetola, F.O., Bruski, L., Zayas-Caban, G. and Lavieri, M. (2016) An innovative framework to improve efficiency of interhospital transfer of children in respiratory failure. Annals of the American Thoracic Society, 13(5), 671–677.
   Web of Science ®Google Scholar
• Pauker, S.G. and Kassirer, J.P. (1975) Therapeutic decision making: A cost-benefit analysis. New England Journal of Medicine, 293(5), 229–234.
   PubMed Web of Science ®Google Scholar
• Pauker, S.G. and Kassirer, J.P. (1980) The threshold approach to clinical decision making. New England Journal of Medicine, 302(20), 1109–1117.
   PubMed Web of Science ®Google Scholar
• Peck, J.S., Benneyan, J.C., Nightingale, D.J. and Gaehde, S.A. (2012). Predicting emergency department inpatient admissions to improve same-day patient flow. Academic Emergency Medicine, 19(9), E1045–E1054.
   PubMed Web of Science ®Google Scholar
• Perk, J., De Backer, G., Gohlke, H., Graham, I., Reiner, Ž., Verschuren, W.M.M., Albus, C., Benlian, P., Boysen, G., Cifkova, R., Deaton, C., Ebrahim, S., Fisher, M., Germano, G., Hobbs, R., Hoes, A., Karadeniz, S., Mezzani, A., Prescott, E., Ryden, L., Scherer, M., Syvänne, M., Scholte Op Reimer, W.J.M., Vrints, C., Wood, D., Zamorano, J.L. and Zannad, F. (2012) European Guidelines on Cardiovascular Disease Prevention in Clinical Practice (Version 2012). International Journal of Behavioral Medicine, 19(4), 403–488.
   PubMed Web of Science ®Google Scholar
• Pierskalla, W.P. and Brailer, D.J. (1994) Operations research and the public sector. Handbooks in Operations Research and Management Science, 6, pp 469–505, Elsevier, Amsterdam, Netherlands.
   Google Scholar
• Read, J., Pfahringer, B., Holmes, G. and Frank, E. (2011) Classifier chains for multi-label classification. Machine Learning, 85(3), 333–359.
   Web of Science ®Google Scholar
• Roozenbeek, B., Lingsma, H.F., Perel, P., Edwards, P., Roberts, I., Murray, G.D., Maas, A.I. and Steyerberg, E.W. (2011) The added value of ordinal analysis in clinical trials: An example in traumatic brain injury. Critical Care, 15(3), R127.
   Web of Science ®Google Scholar
• Schell, G.J., Lavieri, M.S., Helm, J.E., Liu, X., Musch, D.C., van Oyen, M.P. and Stein, J.D. (2014) Using filtered forecasting techniques to determine personalized monitoring schedules for patients with open-angle glaucoma. Ophthalmology, 121(8), 1539–1546.
   PubMed Web of Science ®Google Scholar
• Shapiro, A., Dentcheva, D. and Ruszczyński, A. (2009) Lectures on Stochastic Programming. Society for Industrial and Applied Mathematics, Philadelphia, PA.
   Google Scholar
• Shapiro, D.E. (1999) The interpretation of diagnostic tests. Statistical Methods In Medical Research, 8(2), 113–34.
   PubMed Web of Science ®Google Scholar
• Sheppard, J.W. and Kaufman, M.A. (2005) A Bayesian approach to diagnosis and prognosis using built-in test. IEEE Transactions on Instrumentation and Measurement, 54(3), 1003–1018.
   Web of Science ®Google Scholar
• Si, B., Yakushev, I. and Li, J. (2017) A sequential tree-based classifier for personalized biomarker testing of Alzheimer’s disease risk. IISE Transactions on Healthcare Systems Engineering, 7(4), 248–260.
   Google Scholar
• Somoza, E. and Mossman, D. (1992) Comparing and optimizing diagnostic tests. Medical Decision Making, 12(3), 179–188.
   Web of Science ®Google Scholar
• Tejada, J.J., Ivy, J.S., Wilson, J.R., Ballan, M.J., Diehl, K.M. and Yankaskas, B.C. (2015) Combined DES/SD model of breast cancer screening for older women, I: Natural-history simulation. IIE Transactions, 47(6), 600–619.
   Web of Science ®Google Scholar
• Teng, Y., Kong, N. and Tu, W. (2015) Optimizing strategies for population-based chlamydia infection screening among young women: An age-structured system dynamics approach. BMC Public Health, 15(639), 1–11.
   Google Scholar
• Tschandl, P., Codella, N., Akay, B.N., Argenziano, G., Braun, R.P., Cabo, H., Gutman, D., Halpern, A., Helba, B., Hofmann-Wellenhof, R., Lallas, A., Lapins, J., Longo, C., Malvehy, J., Marchetti, M.A., Marghoob, A., Menzies, S., Oakley, A., Paoli, J., Puig, S., Rinner, C., Rosendahl, C., Scope, A., Sinz, C., Soyer, H.P., Thomas, L., Zalaudek, I. and Kittler, H. (2019) Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: An open, web-based, international, diagnostic study. The Lancet Oncology, 20(7), 938–947.
   Web of Science ®Google Scholar
• Van Giessen, A., de Wit, G.A., Moons, K.G., Dorresteijn, J.A. and Koffijberg, H. (2018) An alternative approach identified optimal risk thresholds for treatment indication: An illustration in coronary heart disease. Journal of Clinical Epidemiology, 94, 122–131.
   Web of Science ®Google Scholar
• Vermont, J., Bosson, J., François, P., Robert, C., Rueff, A. and Demongeot, J. (1991) Strategies for graphical threshold determination. Computer Methods and Programs in Biomedicine, 35(2), 141–150.
   PubMed Web of Science ®Google Scholar
• Wang, T.J., Massaro, J.M., Levy, D., Vasan, R.S., Wolf, P.A., D’Agostino, R.B., Larson, M.G., Kannel, W.B. and Benjamin, E.J. (2003) A risk score for predicting stroke or death in individuals with new-onset atrial fibrillation in the community: the Framingham Heart Study. Journal of the American Medical Association, 290(8), 1049–56.
   PubMed Web of Science ®Google Scholar
• Weise, K., Hübel, K., Rose, E., Schläger, M., Schrammel, D., Täschner, M. and Michel, R. (2006) Bayesian decision threshold, detection limit and confidence limits in ionising-radiation measurement. Radiation Protection Dosimetry, 121(1), 52–63.
   Web of Science ®Google Scholar
• Xue, Y., Klabjan, D. and Luo, Y. (2019) Predicting ICU readmission using grouped physiological and medication trends. Artificial Intelligence in Medicine, 95, 27–37.
   PubMed Web of Science ®Google Scholar
• Yang, Y., Goldhaber-Fiebert, J.D. and Wein, L.M. (2013) Analyzing screening policies for childhood obesity. Management Science, 59(4), 782–795.
   Web of Science ®Google Scholar
• Yao, Y. (2010) Three-way decisions with probabilistic rough sets. Information Sciences, 180(3), 341–353.
   Web of Science ®Google Scholar
• Yao, Y. and Zhou, B. (2016) Two Bayesian approaches to rough sets. European Journal of Operational Research, 251(3), 904–917.
   Web of Science ®Google Scholar
• Zhang, J., Denton, B.T., Balasubramanian, H., Shah, N.D. and Inman, B.A. (2012) Optimization of prostate biopsy referral decisions. Manufacturing & Service Operations Management, 14(4), 529–547.
   Google Scholar
• Zhang, M.L., Li, Y.K., Liu, X.Y. and Geng, X. (2018) Binary relevance for multi-label learning: An overview. Frontiers of Computer Science, 12(2), 191–202.
   Web of Science ®Google Scholar
• Zhang, M.-L. and Zhang, K. (2010) Multi-label learning by exploiting label dependency, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ‘10, ACM Press, New York, NY, pp. 999–1007.
   Google Scholar
• Zhu, Y. and Fang, J. (2016) Logistic regression–based trichotomous classification tree and its application in medical diagnosis. Medical Decision Making, 36(8), 973–989.
   PubMed Web of Science ®Google Scholar
• Zimmer, A., Marcinak, J., Hibyan, S. and Webbe, F. (2015) Normative values of major SCAT2 and SCAT3 components for a college athlete population. Applied Neuropsychology: Adult, 22(2), 132–140.
   PubMed Web of Science ®Google Scholar
• Zufferey, D., Hofer, T., Hennebert, J., Schumacher, M., Ingold, R. and Bromuri, S. (2015) Performance comparison of multi-label learning algorithms on clinical data for chronic diseases. Computers in Biology and Medicine, 65, 34–43.
   Web of Science ®Google Scholar