The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression

dc.contributor.advisorJones, Josette
dc.contributor.authorKasthurirathne, Suranga N.
dc.contributor.otherGrannis, Shaun
dc.contributor.otherBiondich, Paul
dc.contributor.otherPurkayastha, Saptarshi
dc.contributor.otherVest, Joshua
dc.date.accessioned2018-11-13T13:36:58Z
dc.date.available2018-11-13T13:36:58Z
dc.date.issued2018-05-30
dc.degree.date2018en_US
dc.degree.discipline
dc.degree.grantorIndiana Universityen_US
dc.degree.levelPh.D.en_US
dc.descriptionIndiana University-Purdue University Indianapolis (IUPUI)en_US
dc.description.abstractDepression is the most commonly occurring mental illness the world over. It poses a significant health and economic burden across the individual and community. Not all occurrences of depression require the same level of treatment. However, identifying patients in need of advanced care has been challenging and presents a significant bottleneck in providing care. We developed a knowledge-driven depression taxonomy comprised of features representing clinical, behavioral, and social determinants of health (SDH) that inform the onset, progression, and outcome of depression. We leveraged the depression taxonomy to build decision models that predicted need for referrals across: (a) the overall patient population and (b) various high-risk populations. Decision models were built using longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded significantly high predictive performance. However, models predicting need of treatment across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models representing the overall patient population (ROC of 78.87%). Next, we assessed the value of adding SDH into each model. For each patient population under study, we built additional decision models that incorporated a wide range of patient and aggregate-level SDH and compared their performance against the original models. Models that incorporated SDH yielded high predictive performance. However, use of SDH did not yield statistically significant performance improvements. Our efforts present significant potential to identify patients in need of advanced care using a limited number of clinical and behavioral features. However, we found no benefit to incorporating additional SDH into these models. Our methods can also be applied across other datasets in response to a wide variety of healthcare challenges.en_US
dc.identifier.urihttps://hdl.handle.net/1805/17765
dc.identifier.urihttp://dx.doi.org/10.7912/C2/910
dc.language.isoen_USen_US
dc.subjectDepressionen_US
dc.subjectHealthcare deliveryen_US
dc.subjectMachine learningen_US
dc.subjectSocial determinants of healthen_US
dc.titleThe use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depressionen_US
dc.typeThesis
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