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480 results on '"Winslow, Raimond L."'

1. Offline reinforcement learning with uncertainty for treatment strategies in sepsis

Author

Liu, Ran, Greenstein, Joseph L., Fackler, James C., Bergmann, Jules, Bembea, Melania M., and Winslow, Raimond L.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, I.2.1
Abstract

Guideline-based treatment for sepsis and septic shock is difficult because sepsis is a disparate range of life-threatening organ dysfunctions whose pathophysiology is not fully understood. Early intervention in sepsis is crucial for patient outcome, yet those interventions have adverse effects and are frequently overadministered. Greater personalization is necessary, as no single action is suitable for all patients. We present a novel application of reinforcement learning in which we identify optimal recommendations for sepsis treatment from data, estimate their confidence level, and identify treatment options infrequently observed in training data. Rather than a single recommendation, our method can present several treatment options. We examine learned policies and discover that reinforcement learning is biased against aggressive intervention due to the confounding relationship between mortality and level of treatment received. We mitigate this bias using subspace learning, and develop methodology that can yield more accurate learning policies across healthcare applications., Comment: 25 pages, 8 figures

Published
2021

20. Erratum to “BME 2.0: Engineering the Future of Medicine”

Author

Miller, Michael I., primary, Brightman, Andrew O., additional, Epstein, Frederick H., additional, Grande-Allen, K. Jane, additional, Green, Jordan J., additional, Haase, Eileen, additional, Laurencin, Cato T., additional, Logsdon, Elizabeth, additional, Gabhann, Feilim Mac, additional, Ogle, Brenda, additional, Wang, Chun, additional, Wodicka, George R., additional, and Winslow, Raimond L., additional

Published
2023
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23. Matching Sparse Sets of Cardiac Image Cross-Sections Using Large Deformation Diffeomorphic Metric Mapping Algorithm

Author

Ardekani, Siamak, Jain, Aastha, Jain, Saurabh, Abraham, Theodore P., Abraham, Maria R., Zimmerman, Stefan, Winslow, Raimond L., Miller, Michael I., Younes, Laurent, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Camara, Oscar, editor, Konukoglu, Ender, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2012
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24. Predicting respiratory decompensation in mechanically ventilated adult ICU patients.

Author

Yvette Tan, Young, Michael, Girish, Akanksha, Beini Hu, Kurian, Zina, Greenstein, Joseph L., Han Kim, Winslow, Raimond L, Fackler, James, and Bergmann, Jules

Subjects
MACHINE learning, ARTIFICIAL respiration, RECEIVER operating characteristic curves, FEATURE selection, INTENSIVE care units
Abstract

Introduction: Mechanical ventilation is a life-saving treatment in the Intensive Care Unit (ICU), but often causes patients to be at risk of further respiratory complication. We created a statistical model utilizing electronic health record and physiologic vitals data to predict the Center for Disease Control and Prevention (CDC) defined Ventilator Associated Complications (VACs). Further, we evaluated the effect of data temporal resolution and feature generation method choice on the accuracy of such a constructed model. Methods: We constructed a random forest model to predict occurrence of VACs using health records and chart events from adult patients in the Medical Information Mart for Intensive Care III (MIMIC-III) database. We trained the machine learning models on two patient populations of 1921 and 464 based on low and high frequency data availability. Model features were generated using both basic statistical summaries and tsfresh, a python library that generates a large number of derived time-series features. Classification to determine whether a patient will experience VAC one hour after 35 h of ventilation was performed using a random forest classifier. Two different sample spaces conditioned on five varying feature extraction techniques were evaluated to identify the most optimal selection of features resulting in the best VAC discrimination. Each dataset was assessed using K-folds cross-validation (k = 10), giving average area under the receiver operating characteristic curves (AUROCs) and accuracies. Results: After feature selection, hyperparameter tuning, and feature extraction, the best performing model used automatically generated features on high frequency data and achieved an average AUROC of 0.83 ± 0.11 and an average accuracy of 0.69 ± 0.10. Discussion: Results show the potential viability of predicting VACs using machine learning, and indicate that higher-resolution data and the larger feature set generated by tsfresh yield better AUROCs compared to lower-resolution data and manual statistical features. [ABSTRACT FROM AUTHOR]

Published
2023
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35. Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning

Author

Krachman, Joshua A., primary, Patricoski, Jessica A., additional, Le, Christopher T., additional, Park, Jina, additional, Zhang, Ruijing, additional, Gong, Kirby D., additional, Gangan, Indranuj, additional, Winslow, Raimond L., additional, Greenstein, Joseph L., additional, Fackler, James, additional, Sochet, Anthony A., additional, and Bergmann, Jules P., additional

Published
2021
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43. Estimating the Probability of Cellular Arrhythmias with Simplified Statistical Models that Account for Experimentally Observed Uncertainty in Underlying Biophysical Mechanisms

Author

Jin, Qingchu, Greenstein, Joseph L., and Winslow, Raimond L.

Abstract

Early afterdepolarizations (EADs) and ectopic beats (EBs) are cellular arrhythmias that can trigger cardiac arrhythmias. Simulations using biophysically-detailed cardiac myocyte models can reveal how cellular properties influence the probability of cellular arrhythmias, however such analyses often pose a huge computational burden. Here, we develop a simplified approach in which logistic regression models (LRMs) are used to define a mapping between cellular properties and the probability of cellular arrhythmias. Two LRMs are developed, one for predicting the probability of EADs (P(EAD)) as a function of I Ks parameters and one for predicting the probability of EBs (P(EB)) as a function of Ca 2+ dynamics, ryanodine receptor opening rate, and I K1 current density. These LRMs, which require negligible computational resources, are then used to demonstrate how uncertainties in experimentally measured model parameters influence P(EAD) and P(EB). We refer to this as arrhythmia sensitivity analysis. In the investigation of five different I Ks parameters associated with Long QT syndrome 1 (LQTS1) mutations, the predicted P(EAD), when rank ordered for 6 LQTS1 mutations, matches the trend in risk from patients with the same mutations as measured by clinical cardiac event rates. We also demonstrate the degree to which parameter uncertainties map to uncertainty of P(EAD), with I Ks conductance having the greatest impact. In the analysis of P(EB), as [Ca 2+ ] SR uncertainty is increased, P(EB) uncertainty first increases, changing from a sharp unimodal distribution to a broad uniform distribution, and then decreases towards a bimodal distribution with peaks at P(EB) values of 0 and 1.

Published
2020
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44. Large deformation diffeomorphic metric mapping of vector fields

Author

Cao, Yan, Miller, Michael I., Winslow, Raimond L., and Younes, Laurent

Subjects
Magnetic resonance imaging, Diagnostic imaging, Business, Electronics, Electronics and electrical industries, Health care industry
Abstract

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRIs) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the Large Deformation Diffeomorphic Metric Mapping framework to this type of dataset, resulting in optimizing for geodesics on the space of diffeomorphisms connecting two images. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart images. Index Terms--Diffeomorphism, diffusion tensor MRI, image registration, variational methods, vector field.

Published
2005

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