@article{rosenstrom_meshkinfam_ivy_goodarzi_capan_huddleston_romero-brufau_2022, title={Optimizing the First Response to Sepsis: An Electronic Health Record-Based Markov Decision Process Model}, volume={7}, ISSN={["1545-8504"]}, url={https://doi.org/10.1287/deca.2022.0455}, DOI={10.1287/deca.2022.0455}, abstractNote={ Sepsis is considered a medical emergency where delays in initial treatment are associated with increased morbidity and mortality, yet there is no gold standard for identifying sepsis onset and thus treatment timing. We leverage electronic health record (EHR) data with clinical expertise to develop a continuous-time Markov decision process (MDP) optimal stopping model that identifies the optimal first intervention action (anti-infective, fluid, or wait). To study the impact of initial treatment of patients at risk for developing sepsis, we define the delayed treatment population who received delayed treatment upon admission or during hospitalization and serves as an approximation of the natural history of sepsis. We apply the optimal first treatment policy to sample patient visits from the nondelayed treatment population. This analysis indicates the average risk of death could be reduced by approximately 2.2%, the average time until treatment could be reduced by 106 minutes, and the average severity of the treatment state could be reduced by 15.5% compared with the treatment they received in the hospital. We study the properties of the optimal policy to define an easily interpretable initial treatment heuristic that considers a patient’s organ dysfunction, location, and septic shock status. This generalizable framework can inform personalized treatment of patients at risk for sepsis. }, journal={DECISION ANALYSIS}, author={Rosenstrom, Erik and Meshkinfam, Sareh and Ivy, Julie Simmons and Goodarzi, Shadi Hassani and Capan, Muge and Huddleston, Jeanne and Romero-Brufau, Santiago}, year={2022}, month={Jul} }
 @article{jazayeri_capan_ivy_arnold_yang_2021, title={Proximity of Cellular and Physiological Response Failures in Sepsis}, volume={25}, ISSN={["2168-2208"]}, DOI={10.1109/JBHI.2021.3098428}, abstractNote={Sepsis is a devastating multi-stage health condition with a high mortality rate. Its complexity, prevalence, and dependency of its outcomes on early detection have attracted substantial attention from data science and machine learning communities. Previous studies rely on individual cellular and physiological responses representing organ system failures to predict health outcomes or the onset of different sepsis stages. However, it is known that organ systems’ failures and dynamics are not independent events. In this study, we identify the dependency patterns of significant proximate sepsis-related failures of cellular and physiological responses using data from 12,223 adult patients hospitalized between July 2013 and December 2015. The results show that proximate failures of cellular and physiological responses create better feature sets for outcome prediction than individual responses. Our findings reveal the few significant proximate failures that play the major roles in predicting patients’ outcomes. This study's results can be simply translated into clinical practices and inform the prediction and improvement of patients’ conditions and outcomes.}, number={11}, journal={IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS}, author={Jazayeri, Ali and Capan, Muge and Ivy, Julie and Arnold, Ryan and Yang, Christopher C.}, year={2021}, month={Nov}, pages={4089–4097} }
 @misc{fishbein_nambiar_mckenzie_mayorga_miller_tran_schubel_agor_kim_capan_2019, title={Objective measures of workload in healthcare: a narrative review}, volume={33}, ISSN={["1758-6542"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85077284434&partnerID=MN8TOARS}, DOI={10.1108/IJHCQA-12-2018-0288}, abstractNote={PurposeWorkload is a critical concept in the evaluation of performance and quality in healthcare systems, but its definition relies on the perspective (e.g. individual clinician-level vs unit-level workload) and type of available metrics (e.g. objective vs subjective measures). The purpose of this paper is to provide an overview of objective measures of workload associated with direct care delivery in tertiary healthcare settings, with a focus on measures that can be obtained from electronic records to inform operationalization of workload measurement.}, number={1}, journal={INTERNATIONAL JOURNAL OF HEALTH CARE QUALITY ASSURANCE}, author={Fishbein, Daniela and Nambiar, Siddhartha and McKenzie, Kendall and Mayorga, Maria and Miller, Kristen and Tran, Kevin and Schubel, Laura and Agor, Joseph and Kim, Tracy and Capan, Muge}, year={2019}, month={Dec}, pages={1–17} }
 @article{agor_ozaltin_ivy_capan_arnold_romero_2019, title={The value of missing information in severity of illness score development}, volume={97}, ISSN={["1532-0480"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85069932839&partnerID=MN8TOARS}, DOI={10.1016/j.jbi.2019.103255}, abstractNote={We aim to investigate the hypothesis that using information about which variables are missing along with appropriate imputation improves the performance of severity of illness scoring systems used to predict critical patient outcomes.We quantify the impact of missing and imputed variables on the performance of prediction models used in the development of a sepsis-related severity of illness scoring system. Electronic health records (EHR) data were compiled from Christiana Care Health System (CCHS) on 119,968 adult patients hospitalized between July 2013 and December 2015. Two outcomes of interest were considered for prediction: (1) first transfer to intensive care unit (ICU) and (2) in-hospital mortality. Five different prediction models were employed. Indicators were utilized in these prediction models to identify when variables were missing and imputed.We observed statistically significant gains in prediction performance when moving from models that did not indicate missing information to those that did. Moreover, this increase was higher in models that use summary variables as predictors compared to those that use all variables.When developing prediction models using longitudinal EHR data, researchers should explore the incorporation of indicators for missing variables along with appropriate imputation.}, journal={JOURNAL OF BIOMEDICAL INFORMATICS}, author={Agor, Joseph and Ozaltin, Osman Y. and Ivy, Julie S. and Capan, Muge and Arnold, Ryan and Romero, Santiago}, year={2019}, month={Sep} }