Artificial Intelligence for Healthcare Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World
Part I - Personalized Medicine
Published online by Cambridge University Press: 21 April 2022
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- Artificial Intelligence for HealthcareInterdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World, pp. 13 - 80Publisher: Cambridge University PressPrint publication year: 2022
References
References
Centers for Disease Control and Prevention: Depression. www.cdc.gov/nchs/fastats/depression.htm.Google Scholar
Centers for Disease Control and Prevention: Depression morbidity. www.cdc.gov/nchs/data/nhis/earlyrelease/EarlyRelease202009-508.pdf.Google Scholar
National Institute of Mental Health: Depression. www.nimh.nih.gov/health/topics/depression/index.shtml. 2018Google Scholar
Centers for Disease Control and Prevention: Facts about suicide. www.cdc.gov/suicide/facts/index.html.Google Scholar
Insel, T.: Thomas Insel: toward a new understanding of mental illness. TED Talk. www.youtube.com/watch?v=PeZ-U0pj9LI. 2013Google Scholar
National Institute of Mental Health: Suicide. www.nimh.nih.gov/health/statistics/suicide.Google Scholar
Klein, M. Bloomberg visual data: how Americans die. www.bloomberg.com/dataview/2014-04-17/how-americans-die.html. 2014.Google Scholar
Ribeiro, J, Franklin, J, Fox, KR, et al. Self-injurious thoughts and behaviors as risk factors for future suicide ideation, attempts, and death: a meta-analysis of longitudinal studies. Psychological Medicine. 2016; 46(2):225–236.Google Scholar
USPSTF. Screening for depression in adults: U.S. preventive services task force recommendation statement. Annals of Internal Medicine. 2009; 151(11):784–792.Google Scholar
Parekh, AK, Barton, MB. The challenge of multiple comorbidity for the US health care system. JAMA. 2010; 303(13):1303–1304.Google Scholar
Simon, GE, VonKorff, M, Rutter, C, Wagner, E. Randomised trial of monitoring, feedback, and management of care by telephone to improve treatment of depression in primary care. BMJ. 2000; 320(7234):550–554.Google Scholar
Reynolds, CFR, Frank, E. US Preventive Services Task Force Recommendation Statement on Screening for Depression in Adults: not good enough. JAMA Psychiatry. 2016; 73(3):189–190.CrossRefGoogle Scholar
Pratt, L, Brody, D, Gu, Q. Antidepressant Use in Persons Aged 12 and Over: United States, 2005–2008. NCHS Data Brief, No. 76. Hyattsville, MD: National Center for Health Statistics. 2011.Google ScholarPubMed
Shechter, SM, Bailey, MD, Schaefer, AJ, Roberts, MS. The optimal time to initiate HIV therapy under ordered health states. Operations Research. 2008; 56(1):20–33.CrossRefGoogle Scholar
Alagoz, O, Maillart, LM, Schaefer, AJ, Roberts, MS. The optimal timing of living-donor liver transplantation. Management Science. 2004; 50(10):1420–1430.CrossRefGoogle Scholar
Alagoz, O, Maillart, LM, Schaefer, AJ, Roberts, MS. Determining the acceptance of cadaveric livers using an implicit model of the waiting list. Operations Research. 2007; 55(1):24–36.Google Scholar
Burhaneddin, S, Maillart, LM, Shaefer, AJ, Alagoz, O. Estimating the patient’s price of privacy in liver transplantation. Operations Research. 2008; 56(6):1393–1410.Google Scholar
Sutin, AR, Terracciano, A, Milaneschi, Y, et al. The trajectory of depressive symptoms across the adult life span. JAMA Psychiatry. 2013; 70(8):803–811.Google Scholar
Gunn, J, Elliott, P, Densley, K, et al. A trajectory-based approach to understand the factors associated with persistent depressive symptoms in primary care. Journal of Affective Disorders. 2013; 148(2-3):338–346.CrossRefGoogle ScholarPubMed
Wang, X, Sontag, D, Wang, F. Unsupervised learning of disease progression models. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014:85–94.CrossRefGoogle Scholar
Brandeau, ML, Sainfort, F, Pierskalla, WP. Operations Research and Health Care: A Handbook of Methods and Applications. Boston: Kluwer Academic 2004.CrossRefGoogle Scholar
Weinstein, MC, Siegel, JE, Gold, MR, Kamlet, MS, Russell, LB. Recommendations of the Panel on Cost-effectiveness in Health and Medicine. JAMA. 1996; 276(15):1253–1258.CrossRefGoogle ScholarPubMed
Weinstein, MC, O’Brien, B, Hornberger, J, et al. Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices – modeling studies. Value in Health. 2003; 6(1):9–17.CrossRefGoogle ScholarPubMed
Twisk, J, Hoekstra, T. Classifying developmental trajectories over time should be done with great caution: a comparison between methods. Journal of Clinical Epidemiology, 2012; 65(10):1078–1087.CrossRefGoogle ScholarPubMed
Craig, BA, Sendi, PP. Estimation of the transition matrix of a discrete-time Markov chain. Health Economics, 2002; 11(1):33–42.CrossRefGoogle ScholarPubMed
Musliner, KL, Munk-Olsen, T, Eaton, WW, Zandi, PP. Heterogeneity in long-term trajectories of depressive symptoms: patterns, predictors and outcomes. Journal of Affective Disorders, 2016; 192:199–211.Google Scholar
Russell, SJ, Norvig, P, Davis, E. Artificial Intelligence: A Modern Approach. 3rd ed. Upper Saddle River, NJ: Prentice Hall 2010.Google Scholar
Drummond, M. Methods for the Economic Evaluation of Health Care Programmes. 4th ed. Oxford: Oxford University Press 2015.Google Scholar
Kroenke, K, Spitzer, RL, Williams, JB. The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine. 2001; 16(9):606-613.Google Scholar
Charlson, ME, Pompei, P, Ales, KL, MacKenzie, CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of Chronic Diseases. 1987; 40(5):373–383.Google Scholar
Lasko, TA, Denny, JC, Levy, MA. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PloS One. 2013; 8(6):e66341.CrossRefGoogle ScholarPubMed
Shechter, S. When to initiate, when to switch, and how to sequence HIV therapies: a Markov decision process approach. Doctoral thesis, University of Pittsburgh, Pittsburgh, PA; 2006.Google Scholar
Gong, J, Simon, GE, Liu, S. Machine learning discovery of longitudinal patterns of depression and suicidal ideation. PloS One. 2019; 14(9):e0222665.Google Scholar
Lin, Y, Huang, S, Simon, GE, Liu, S. Analysis of depression trajectory patterns using collaborative learning. Mathematical Biosciences. 2016; 282:191–203.Google Scholar
Lin, Y, Liu, K, Byon, E, et al. A collaborative learning framework for estimating many individualized regression models in a heterogeneous population. IEEE Transactions on Reliability. 2018; 67(1):328–341.Google Scholar
Lin, Y, Liu, S, Huang, S. Selective sensing of a heterogeneous population of units with dynamic health conditions. IISE Transactions. 2018; 50(12):1076–1088.Google Scholar
Lin, Y, Huang, S, Simon, GE, Liu, S. Data-based decision rules to personalize depression follow-up. Scientific Reports. 2018; 8(1):5064.CrossRefGoogle ScholarPubMed
Kosorok, MR, Moodie, EE. Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine. ASA-SIAM Series on Statistics and Applied Mathematics. Philadelphia: Society for Industrial and Applied Mathematics 2016.Google Scholar
Schaefer, AJ, Bailey, MD, Shechter, SM, Roberts, MS. Modeling medical treatment using Markov decision processes. In: Brandeau, ML, Sainfort, F, Pierskalla, WP, eds. Operations Research and Health Care: A Handbook of Methods and Applications. Boston: Springer 2004:593–612.Google Scholar
Vincent, RD, Pineau, J, Ybarra, N, Naqa, IE. Chapter 16: Practical reinforcement learning in dynamic treatment regimes. In: Adaptive Treatment Strategies in Practice 2016:263–296.Google Scholar
Negoescu, D, Bimpikis, K, Brandeau, M, Iancu, DA. Dynamic learning of patient response types: an application to treating chronic diseases. Management Science. 2017; 64(8):3469–3970.Google Scholar
Gong, J, Liu, S. Partially observable collaborative model for optimizing personalized treatment selection. Paper presented at the 40th SMDM Annual Meeting; 2018; Montreal, Quebec, Canada.Google Scholar
Monahan, GE. A survey of partially observable Markov decision processes: theory, models, and algorithms. Management Science. 1982; 28(1):1–16.Google Scholar
Gold, M. Panel on cost-effectiveness in health and medicine. Medical Care. 1996; 34(12 Suppl):DS197–DS199.Google ScholarPubMed
Buxton, MJ, Drummond, MF, et al. Modelling in economic evaluation: an unavoidable fact of life. Health Economics. 1997; 6(3):217–227.Google Scholar
Lin, Y, Huang, S, Simon, GE, Liu, S. Cost-effectiveness analysis of prognostic-based depression monitoring. IIE Transactions on Healthcare Systems Engineering. 2019; 9(1):41–54.CrossRefGoogle Scholar
Sun, X, Liu, S. A decision-analytic framework to evaluate the cost-effectiveness of adaptive monitoring technology: the case of chronic depression. Paper presented at the 42nd Annual SMDM Annual Meeting; 2020; virtual.Google Scholar
Wu, S, Vidyanti, I, Liu, P, et al. Patient-centered technological assessment and monitoring of depression for low-income patients. Journal of Ambulatory Care Management. 2014; 37(2):138–147.Google Scholar
References
Alba, Ana Carolina, Agoritsas, Thomas, Walsh, Michael, Hanna, Steven, Iorio, Alfonso, Devereaux, P. J., McGinn, Thomas, and Guyatt, Gordon. 2017. Discrimination and calibration of clinical prediction models: Users’ guides to the medical literature. JAMA, 318(14), 1377–1384.Google Scholar
Athey, Susan, and Imbens, Guido. 2016. Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353–7360.Google Scholar
Basu, Sanjay, Sussman, Jeremy B., Rigdon, Joseph, Steimle, Lauren, Denton, Brian T., and Hayward, Rodney A. 2017. Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOs Medicine, 14(10), e1002410.Google Scholar
Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esther, Hansen, Christian, Newey, Whitney, and Robins, James. 2018. Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68.Google Scholar
Coppock, Alexander, Leeper, Thomas J., and Mullinix, Kevin J. 2018. Generalizability of heterogeneous treatment effect estimates across samples. Proceedings of the National Academy of Sciences, 115(49), 12441–12446.CrossRefGoogle ScholarPubMed
Dahabreh, Issa J., Trikalinos, Thomas A., Kent, David M., and Schmid, Christopher H. 2017. Heterogeneity of treatment effects. Pages 227–272 of: Methods in Comparative Effectiveness Research, edited by Gatsonis, Constantine and Morton, Sally C.. New York: Chapman and Hall/CRC.Google Scholar
Davidoff, Frank. 2017. Can knowledge about heterogeneity in treatment effects help us choose wisely? Annals of Internal Medicine, 166(2), 141–142.Google Scholar
Davis, Jesse, and Goadrich, Mark. 2006. The relationship between precision-recall and ROC curves. Pages 233–240 of: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, Pittsburgh, Pennsylvania, USA. New York: ACM.CrossRefGoogle Scholar
Duan, Tony, Pranav, Rajpurkar, Dillon, Laird, Ng, Andrew Y., and Sanjay, Basu. 2019. Clinical value of predicting individual treatment effects for intensive blood pressure therapy. Circulation: Cardiovascular Quality and Outcomes, 12(3), e005010.Google Scholar
Friedman, Jerome H. 2001. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.CrossRefGoogle Scholar
Gottesman, Omer, Johansson, Fredrik, Komorowski, Matthieu, Faisal, Aldo, Sontag, David, Doshi-Velez, Finale, and Celi, Leo Anthony. 2019. Guidelines for reinforcement learning in healthcare. Nature Medicine, 25(1), 16–18.Google Scholar
Harrell, Frank. 2001. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer Series in Statistics. New York: Springer-Verlag.Google Scholar
Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer Series in Statistics. New York: Springer-Verlag.CrossRefGoogle Scholar
Hayward, Rodney A., Kent, David M., Vijan, Sandeep, and Hofer, Timothy P. 2006. Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis. BMC Medical Research Methodology, 6(1), 18.CrossRefGoogle ScholarPubMed
Imbens, Guido W., and Rubin, Donald B. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. New York: Cambridge University Press.Google Scholar
Kent, David M., Rothwell, Peter M., Ioannidis, John P. A., Altman, Doug G., and Hayward, Rodney A. 2010. Assessing and reporting heterogeneity in treatment effects in clinical trials: A proposal. Trials, 11(August), 85.Google Scholar
Kent, David M., Nelson, Jason, Dahabreh, Issa J., Rothwell, Peter M., Altman, Douglas G., and Hayward, Rodney A. 2016. Risk and treatment effect heterogeneity: Re-analysis of individual participant data from 32 large clinical trials. International Journal of Epidemiology, 45(6), 2075–2088.Google Scholar
Kent, David M., Paulus, Jessica K., van Klaveren, David, D’Agostino, Ralph, Goodman, Steve, Hayward, Rodney, Ioannidis, John P. A., Patrick-Lake, Bray, Morton, Sally, Pencina, Michael, Raman, Gowri, Ross, Joseph S., Selker, Harry P., Varadhan, Ravi, Vickers, Andrew, Wong, John B., and Steyerberg, Ewout W. 2020. The Predictive Approaches to Treatment effect Heterogeneity (PATH) statement. Annals of Internal Medicine, 172(1), 35–45.Google Scholar
Komorowski, Matthieu, Celi, Leo A., Badawi, Omar, Gordon, Anthony C., and Faisal, A. Aldo. 2018. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 24(11), 1716–1720.Google Scholar
Künzel, Sören R., Sekhon, Jasjeet S., Bickel, Peter J., and Yu, Bin. 2019. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences, 116(10), 4156–4165.Google Scholar
Murdoch, W. James, Singh, Chandan, Kumbier, Karl, Abbasi-Asl, Reza, and Yu, Bin. 2019. Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071–22080.Google Scholar
Patel, Krishna K., Arnold, Suzanne V., Chan, Paul S., Tang, Yuanyuan, Pokharel, Yashashwi, Jones, Philip G., and Spertus, John A. 2017. Personalizing the intensity of blood pressure control: Modeling the heterogeneity of risks and benefits from SPRINT (Systolic Blood Pressure Intervention Trial). Circulation: Cardiovascular Quality and Outcomes, 10(4), e003624.Google Scholar
The ACCORD Study Group. 2010. Effects of intensive blood-pressure control in type 2 diabetes mellitus. New England Journal of Medicine, 362(17), 1575–1585.CrossRefGoogle Scholar
The SPRINT Research Group. 2015. A randomized trial of intensive versus standard blood-pressure control. New England Journal of Medicine, 373(22), 2103–2116.Google Scholar
van Klaveren, David, Steyerberg, Ewout W., Serruys, Patrick W., and Kent, David M. 2018. The proposed ‘concordance-statistic for benefit’ provided a useful metric when modeling heterogeneous treatment effects. Journal of Clinical Epidemiology, 94(February), 59–68.Google Scholar
Vock, David M., Wolfson, Julian, Bandyopadhyay, Sunayan, Adomavicius, Gediminas, Johnson, Paul E., Vazquez-Benitez, Gabriela, and O’Connor, Patrick J. 2016. Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting. Journal of Biomedical Informatics, 61(June), 119–131.Google Scholar
Wager, Stefan, and Athey, Susan. 2018. Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228–1242.Google Scholar
Wallach, Joshua D., Sullivan, Patrick G., Trepanowski, John F., Sainani, Kristin L., Steyerberg, Ewout W., and Ioannidis, John P. A. 2017. Evaluation of evidence of statistical support and corroboration of subgroup claims in randomized clinical trials. JAMA Internal Medicine, 177(4), 554–560.Google Scholar
References
Bastani, Hamsa. Predicting with proxies: transfer learning in high dimension. Management Science, 67(5), 2020.Google Scholar
Nestor, Bret, McDermott, Matthew, Boag, Willie, et al. Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks. arXiv preprint arXiv:1908.00690, 2019.Google Scholar
Caruana, Rich, Lou, Yin, Gehrke, Johannes, et al. Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1721–1730. 2015.Google Scholar
Bastani, Osbert, Kim, Carolyn, and Bastani, Hamsa. Interpreting blackbox models via model extraction. arXiv preprint arXiv:1705.08504, 2017.Google Scholar
Breiman, Leo, Friedman, Jerome, Stone, Charles J, and Olshen, Richard A. Classification and Regression Trees. CRC Press, 1984.Google Scholar
Bastani, Hamsa, Bastani, Osbert, and Kim, Carolyn. Interpreting predictive models for human-in-the-loop analytics. arXiv preprint arXiv:1705.08504, 1–45, 2018. https://hamsabastani.github.io/interp.pdf.Google Scholar
Colwell, Janet. Length of stay: timing it right. Strategies for achieving efficient, high-quality care. ACP Hospitalist, 2014. www.acphospitalist.org/archives/2014/10/los.htm.Google Scholar
Adepoju, Temidayo, Tucker, Anita, Jin, Helen, and Manasseh, Chris. The impact of two managerial responses on hospital occupancy crises. SSRN Electronic Journal, 2019.Google Scholar
Frenz, David. Not too long, not too short, just right. Today’s Hospitalist, 2014. www.todayshospitalist.com/not-too-long-not-too-short-just-right/.Google Scholar
Shi, Pengyi, Helm, Jonathan, Deglise-Hawkinson, Jivan, and Pan, Julian. Timing it right: balancing inpatient congestion versus readmission risk at discharge. Operations Research, 2020. Forthcoming.Google Scholar
Bardhan, Indranil, Jeong-ha, Oh, Zheng, Zhiqiang, and Kirksey, Kirk. Predictive analytics for readmission of patients with congestive heart failure. Information Systems Research, 26(1):19–39, 2014.CrossRefGoogle Scholar
Thompson, Steven, Nunez, Manuel, Garfinkel, Robert, and Dean, Matthew D. Or practice – efficient short-term allocation and reallocation of patients to floors of a hospital during demand surges. Operations Research, 57(2):261–273, 2009.Google Scholar
Yang, Hongyu, Rudin, Cynthia, and Seltzer, Margo. Scalable Bayesian rule lists. Proceedings of the 34th International Conference on Machine Learning, 70:3921–3930, 2017.Google Scholar
Friedman, Jerome H. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29(5):1189–1232, 2001.Google Scholar
Bertsimas, Dimitris and Dunn, Jack. Optimal classification trees. Machine Learning, 106(7):1039–1082, 2017.Google Scholar
Ribeiro, Marco Tulio, Singh, Sameer, and Guestrin, Carlos. Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. 2016.Google Scholar
Simonyan, Karen, Vedaldi, Andrea, and Zisserman, Andrew. Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034, 2013.Google Scholar
Lundberg, Scott M and Lee, Su-In. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, 4765–4774, 2017.Google Scholar
Lakkaraju, Himabindu and Bastani, Osbert. “How do I fool you?”: manipulating user trust via misleading black box explanations. AIES, 2020.Google Scholar
Wei Koh, Pang and Liang, Percy. Understanding black-box predictions via influence functions. In Proceedings of the 34th International Conference on Machine Learning. Volume 70, 1885–1894. JMLR. org, 2017.Google Scholar
Berk, Emre and Moinzadeh, Kamran. The impact of discharge decisions on health care quality. Management Science, 44(3):400–415, 1998.Google Scholar
Chan, Carri W, Farias, Vivek F, Bambos, Nicholas, and Escobar, Gabriel. Optimizing intensive care unit discharge decisions with patient readmissions. Operations Research, 60(6):1323–1341, 2012.Google Scholar
Ouyang, Huiyin, Argon, Nilay Tanik, and Ziya, Serhan. Allocation of intensive care unit beds in periods of high demand. Operations Research, 2019. Forthcoming.Google Scholar
Bavafa, Hessam, Ormeci, Lerzan, Savin, Sergei, and Virudachalam, Vanitha. Surgical case-mix and discharge decisions: does within-hospital coordination matter? Working Paper, 1–40, 2019.Google Scholar
Bertsekas, Dimitri P.. Dynamic Programming and Optimal Control: Approximate Dynamic Programming. Volume II. Belmont, MA: Athena Scientific, 2012.Google Scholar
Powell, Warren B.. Approximate Dynamic Programming: Solving the Curses of Dimensionality. Wiley Series in Probability and Statistics. Hoboken, NJ: Wiley-Interscience, 2011.Google Scholar
Sutton, Richard S.. Learning to predict by the methods of temporal differences. Machine Learning, 3(1):9–44, 1988.Google Scholar
Christopher, J.C.H. Watkins and Peter Dayan. Technical note: Q-learning. Machine Learning, 8(3):279–292, 1992.Google Scholar
de Farias, D. P. and Van Roy, B.. The linear programming approach to approximate dynamic programming. Operations Research, 51(6):850–865, 2003.CrossRefGoogle Scholar
Adelman, Daniel and Mersereau, Adam J.. Relaxations of weakly coupled stochastic dynamic programs. Operations Research, 56(3):712–727, 2008.Google Scholar
Schulman, John, Levine, Sergey, Abbeel, Pieter, Jordan, Michael, and Moritz, Philipp. Trust region policy optimization. In International Conference on Machine Learning, 1889–1897, 2015.Google Scholar
Schulman, John, Wolski, Filip, Dhariwal, Prafulla, Radford, Alec, and Klimov, Oleg. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.Google Scholar