Browsing by Subject "ROC curve"

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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
    Reconsider Machine Learning Method for Variable Selection and Validation with High Dimensional Data
    (2024) Liu, Lu

    The big data tendency influences how people think and inspires potential research directions. Recent feats of machine learning have seized collective attention because of its profound performance in conducting big data analysis including text analysis and image processing. Machine learning is also a popular topic in clinical medicine to implement analysis on electronic health records and medical image data, which traditional statistics model is not adequate for. However, we realize that machine learning is not panacea and its defects such as loss of interpretability and excess selection may restrict its application. And we must also recognize that for many clinical prediction analyses, the simpler approach-generalized linear model is enough for what we need.

    In this dissertation, we propose to use standard regression methods, without any penalizing approach, combined with a stepwise variable selection procedure to overcome the over-selection issue of popular machine learning methods. For model validation, we propose a permutation approach to estimate the performance of various validation methods. Finally, we propose a repeated sieving approach, extending the standard regression methods with stepwise variable selection, to handle high dimensional modeling.