Browsing by Author "Balu, Suresh"

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    ItemOpen Access
    Machine learning for early detection of sepsis: an internal and temporal validation study.
    (JAMIA open, 2020-07) Bedoya, Armando D; Futoma, Joseph; Clement, Meredith E; Corey, Kristin; Brajer, Nathan; Lin, Anthony; Simons, Morgan G; Gao, Michael; Nichols, Marshall; Balu, Suresh; Heller, Katherine; Sendak, Mark; O'Brien, Cara

    Objective

    Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice.

    Materials and methods

    We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP-RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1, 2015, while the temporal validation dataset was from March 1, 2018 to August 31, 2018. Comparisons were made to 3 machine learning methods, random forest (RF), Cox regression (CR), and penalized logistic regression (PLR), and 3 clinical scores used to detect sepsis, SIRS, quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS). Traditional discrimination statistics such as the C-statistic as well as metrics aligned with operational implementation were assessed.

    Results

    The training set and internal validation included 42 979 encounters, while the temporal validation set included 39 786 encounters. The C-statistic for predicting sepsis within 4 h of onset was 0.88 for the MGP-RNN compared to 0.836 for RF, 0.849 for CR, 0.822 for PLR, 0.756 for SIRS, 0.619 for NEWS, and 0.481 for qSOFA. MGP-RNN detected sepsis a median of 5 h in advance. Temporal validation assessment continued to show the MGP-RNN outperform all 7 clinical risk score and machine learning comparisons.

    Conclusions

    We developed and validated a novel deep learning model to detect sepsis. Using our data elements and feature set, our modeling approach outperformed other machine learning methods and clinical scores.
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    ItemOpen Access
    The Project Baseline Health Study: a step towards a broader mission to map human health.
    (NPJ digital medicine, 2020-01) Arges, Kristine; Assimes, Themistocles; Bajaj, Vikram; Balu, Suresh; Bashir, Mustafa R; Beskow, Laura; Blanco, Rosalia; Califf, Robert; Campbell, Paul; Carin, Larry; Christian, Victoria; Cousins, Scott; Das, Millie; Dockery, Marie; Douglas, Pamela S; Dunham, Ashley; Eckstrand, Julie; Fleischmann, Dominik; Ford, Emily; Fraulo, Elizabeth; French, John; Gambhir, Sanjiv S; Ginsburg, Geoffrey S; Green, Robert C; Haddad, Francois; Hernandez, Adrian; Hernandez, John; Huang, Erich S; Jaffe, Glenn; King, Daniel; Koweek, Lynne H; Langlotz, Curtis; Liao, Yaping J; Mahaffey, Kenneth W; Marcom, Kelly; Marks, William J; Maron, David; McCabe, Reid; McCall, Shannon; McCue, Rebecca; Mega, Jessica; Miller, David; Muhlbaier, Lawrence H; Munshi, Rajan; Newby, L Kristin; Pak-Harvey, Ezra; Patrick-Lake, Bray; Pencina, Michael; Peterson, Eric D; Rodriguez, Fatima; Shore, Scarlet; Shah, Svati; Shipes, Steven; Sledge, George; Spielman, Susie; Spitler, Ryan; Schaack, Terry; Swamy, Geeta; Willemink, Martin J; Wong, Charlene A
    The Project Baseline Health Study (PBHS) was launched to map human health through a comprehensive understanding of both the health of an individual and how it relates to the broader population. The study will contribute to the creation of a biomedical information system that accounts for the highly complex interplay of biological, behavioral, environmental, and social systems. The PBHS is a prospective, multicenter, longitudinal cohort study that aims to enroll thousands of participants with diverse backgrounds who are representative of the entire health spectrum. Enrolled participants will be evaluated serially using clinical, molecular, imaging, sensor, self-reported, behavioral, psychological, environmental, and other health-related measurements. An initial deeply phenotyped cohort will inform the development of a large, expanded virtual cohort. The PBHS will contribute to precision health and medicine by integrating state of the art testing, longitudinal monitoring and participant engagement, and by contributing to the development of an improved platform for data sharing and analysis.