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Using a Semi-Automated Modeling Environment to Construct a Bayesian, Sepsis Diagnostic System

Published: 02 October 2016 Publication History

Abstract

We have developed an analytics environment, which uses clinical data from an Enterprise Data Warehouse to construct diagnostic models for use in clinical settings. We have focused on models based on Bayesian networks. The resulting system allows flexible development and testing of different Bayesian models based on 1) varying the data made available for use in the model and 2) manually and programmatically altering the models to improve their behavior. Here we illustrate the use of this system in exploring a group of models designed to identify sepsis patients in an emergency department setting. Varying the data elements used in the models and the structure of the models provides a range of diagnostic models whose operating behavior can be compared and contrasted.

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Cited By

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  • (2024)Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysisTechnology and Health Care10.3233/THC-24008732:6(4291-4307)Online publication date: 8-Nov-2024
  • (2023)Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum studyFrontiers in Public Health10.3389/fpubh.2023.109926311Online publication date: 20-Mar-2023
  • (2021)Modeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networks2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)10.1109/ICSET53708.2021.9612523(134-138)Online publication date: 6-Nov-2021
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  1. Using a Semi-Automated Modeling Environment to Construct a Bayesian, Sepsis Diagnostic System

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    cover image ACM Conferences
    BCB '16: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
    October 2016
    675 pages
    ISBN:9781450342254
    DOI:10.1145/2975167
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 02 October 2016

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    Author Tags

    1. Bayesian Networks
    2. Data Mining
    3. Diagnostic Models
    4. Sepsis

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    Cited By

    View all
    • (2024)Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysisTechnology and Health Care10.3233/THC-24008732:6(4291-4307)Online publication date: 8-Nov-2024
    • (2023)Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum studyFrontiers in Public Health10.3389/fpubh.2023.109926311Online publication date: 20-Mar-2023
    • (2021)Modeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networks2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)10.1109/ICSET53708.2021.9612523(134-138)Online publication date: 6-Nov-2021
    • (2020)Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracyIntensive Care Medicine10.1007/s00134-019-05872-yOnline publication date: 21-Jan-2020
    • (2018)Recent Temporal Pattern Mining for Septic Shock Early Prediction2018 IEEE International Conference on Healthcare Informatics (ICHI)10.1109/ICHI.2018.00033(229-240)Online publication date: Jun-2018

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