Publications
1.
Mauer, Elizabeth; Lee, Jihui; Choi, Justin; Zhang, Hongzhe; Hoffman, Katherine L.; Easthausen, Imaani J.; Rajan, Mangala; Weiner, Mark G.; Kaushal, Rainu; Safford, Monika M.; Steel, Peter; Banerjee, Samprit
A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories Journal Article
In: Journal of Biomedical Informatics, vol. 118, pp. 103794, 2021.
Abstract | Links | BibTeX | Tags: COVID-19, machine learning
@article{nokey,
title = {A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories},
author = {Elizabeth Mauer and Jihui Lee and Justin Choi and Hongzhe Zhang and Katherine L. Hoffman and Imaani J. Easthausen and Mangala Rajan and Mark G. Weiner and Rainu Kaushal and Monika M. Safford and Peter Steel and Samprit Banerjee},
doi = {10.1016/j.jbi.2021.103794},
year = {2021},
date = {2021-04-30},
journal = {Journal of Biomedical Informatics},
volume = {118},
pages = {103794},
abstract = {From early March through mid-May 2020, the COVID-19 pandemic overwhelmed hospitals in New York City. In anticipation of ventilator shortages and limited ICU bed capacity, hospital operations prioritized the development of prognostic tools to predict clinical deterioration. However, early experience from frontline physicians observed that some patients developed unanticipated deterioration after having relatively stable periods, attesting to the uncertainty of clinical trajectories among hospitalized patients with COVID-19. Prediction tools that incorporate clinical variables at one time-point, usually on hospital presentation, are suboptimal for patients with dynamic changes and evolving clinical trajectories. Therefore, our study team developed a machine-learning algorithm to predict clinical deterioration among hospitalized COVID-19 patients by extracting clinically meaningful features from complex longitudinal laboratory and vital sign values during the early period of hospitalization with an emphasis on informative missing-ness. To incorporate the evolution of the disease and clinical practice over the course of the pandemic, we utilized a time-dependent cross-validation strategy for model development. Finally, we validated our prediction model on an external validation cohort of COVID-19 patients served in a demographically distinct population from the training cohort. The main finding of our study is the identification of risk profiles of early, late and no clinical deterioration during the course of hospitalization. While risk prediction models that include simple predictors at ED presentation and clinical judgement are able to identify any deterioration vs. no deterioration, our methodology is able to isolate a particular risk group that remain stable initially but deteriorate at a later stage of the course of hospitalization. We demonstrate the superior predictive performance with the utilization of laboratory and vital sign data during the early period of hospitalization compared to the utilization of data at presentation alone. Our results will allow efficient hospital resource allocation and will motivate research in understanding the late deterioration risk group.},
keywords = {COVID-19, machine learning},
pubstate = {published},
tppubtype = {article}
}
From early March through mid-May 2020, the COVID-19 pandemic overwhelmed hospitals in New York City. In anticipation of ventilator shortages and limited ICU bed capacity, hospital operations prioritized the development of prognostic tools to predict clinical deterioration. However, early experience from frontline physicians observed that some patients developed unanticipated deterioration after having relatively stable periods, attesting to the uncertainty of clinical trajectories among hospitalized patients with COVID-19. Prediction tools that incorporate clinical variables at one time-point, usually on hospital presentation, are suboptimal for patients with dynamic changes and evolving clinical trajectories. Therefore, our study team developed a machine-learning algorithm to predict clinical deterioration among hospitalized COVID-19 patients by extracting clinically meaningful features from complex longitudinal laboratory and vital sign values during the early period of hospitalization with an emphasis on informative missing-ness. To incorporate the evolution of the disease and clinical practice over the course of the pandemic, we utilized a time-dependent cross-validation strategy for model development. Finally, we validated our prediction model on an external validation cohort of COVID-19 patients served in a demographically distinct population from the training cohort. The main finding of our study is the identification of risk profiles of early, late and no clinical deterioration during the course of hospitalization. While risk prediction models that include simple predictors at ED presentation and clinical judgement are able to identify any deterioration vs. no deterioration, our methodology is able to isolate a particular risk group that remain stable initially but deteriorate at a later stage of the course of hospitalization. We demonstrate the superior predictive performance with the utilization of laboratory and vital sign data during the early period of hospitalization compared to the utilization of data at presentation alone. Our results will allow efficient hospital resource allocation and will motivate research in understanding the late deterioration risk group.
2.
Xu, Zhenxing; Wang, Fei; Adekkanattu, Prakash; Bose, Budhaditya; Vekaria, Veer; Brandt, Pascal; Jiang, Guoqian; Kiefer, Richard C.; Luo, Yuan; Pancheco, Jennifer; Rasmussen, Luke V.; Xu, Jie; Alexopoulos, George; Pathak, Jyotishman
Subphenotyping depression using machine learning and electronic health records Journal Article
In: Learning Health Systems, vol. 4, iss. 4, pp. e10241, 2020.
Abstract | Links | BibTeX | Tags: depression, electronic health records, machine learning
@article{nokey,
title = {Subphenotyping depression using machine learning and electronic health records},
author = {Zhenxing Xu and Fei Wang and Prakash Adekkanattu and Budhaditya Bose and Veer Vekaria and Pascal Brandt and Guoqian Jiang and Richard C. Kiefer and Yuan Luo and Jennifer Pancheco and Luke V. Rasmussen and Jie Xu and George Alexopoulos and Jyotishman Pathak},
doi = {10.1002/lrh2.10241},
year = {2020},
date = {2020-08-03},
journal = {Learning Health Systems},
volume = {4},
issue = {4},
pages = {e10241},
abstract = {Objective: To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications.
Materials and methods: Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.
Results: Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype.
Conclusion: Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.},
keywords = {depression, electronic health records, machine learning},
pubstate = {published},
tppubtype = {article}
}
Objective: To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications.
Materials and methods: Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.
Results: Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype.
Conclusion: Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.
Materials and methods: Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.
Results: Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype.
Conclusion: Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.