• Login
    View Item 
    •   UMB Digital Archive
    • UMB Open Access Articles
    • UMB Open Access Articles
    • View Item
    •   UMB Digital Archive
    • UMB Open Access Articles
    • UMB Open Access Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UMB Digital ArchiveCommunitiesPublication DateAuthorsTitlesSubjectsThis CollectionPublication DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    Display statistics

    Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Author
    Morgan, D.J.
    Bame, B.
    Zimand, P.
    Date
    2019
    Journal
    JAMA Network Open
    Publisher
    American Medical Association
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://dx.doi.org/10.1001/jamanetworkopen.2019.0348
    Abstract
    Importance: Hospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying patients at greatest risk using prediction scores. Objective: To identify the type of score that best predicts hospital readmissions. Design, Setting, and Participants: This prognostic study included 14 062 consecutive adult hospital patients with 16 649 discharges from a tertiary care center, suburban community hospital, and urban critical access hospital in Maryland from September 1, 2016, through December 31, 2016. Patients not included as eligible discharges by the Centers for Medicare & Medicaid Services or the Chesapeake Regional Information System for Our Patients were excluded. A machine learning rank score, the Baltimore score (B score) developed using a machine learning technique, for each individual hospital using data from the 2 years before September 1, 2016, was compared with standard readmission risk assessment scores to predict 30-day unplanned readmissions. Main Outcomes and Measures: The 30-day readmission rate evaluated using various readmission scores: B score, HOSPITAL score, modified LACE score, and Maxim/RightCare score. Results: Of the 10 732 patients (5605 [52.2%] male; mean [SD] age, 54.56 [22.42] years) deemed to be eligible for the study, 1422 were readmitted. The area under the receiver operating characteristic curve (AUROC) for individual rules was 0.63 (95% CI, 0.61-0.65) for the HOSPITAL score, which was significantly lower than the 0.66 for modified LACE score (95% CI, 0.64-0.68; P < .001). The B score machine learning score was significantly better than all other scores; 48 hours after admission, the AUROC of the B score was 0.72 (95% CI, 0.70-0.73), which increased to 0.78 (95% CI, 0.77-0.79) at discharge (all P < .001). At the hospital using Maxim/RightCare score, the AUROC was 0.63 (95% CI, 0.59-0.69) for HOSPITAL, 0.64 (95% CI, 0.61-0.68) for Maxim/RightCare, and 0.66 (95% CI, 0.62-0.69) for modified LACE score. The B score was 0.72 (95% CI, 0.69-0.75) 48 hours after admission and 0.81 (95% CI, 0.79-0.84) at discharge. In directly comparing the B score with the sensitivity at cutoff values for modified LACE, HOSPITAL, and Maxim/RightCare scores, the B score was able to identify the same number of readmitted patients while flagging 25.5% to 54.9% fewer patients. Conclusions and Relevance: Among 3 hospitals in different settings, an automated machine learning score better predicted readmissions than commonly used readmission scores. More efficiently targeting patients at higher risk of readmission may be the first step toward potentially preventing readmissions.
    Keyword
    Patient Readmission
    Machine Learning
    Decision Support Techniques
    Forecasting
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062640199&doi=10.1001%2fjamanetworkopen.2019.0348&partnerID=40&md5=1e8c84d77d2e343aafc64d09460f03c8; http://hdl.handle.net/10713/8662
    ae974a485f413a2113503eed53cd6c53
    10.1001/jamanetworkopen.2019.0348
    Scopus Count
    Collections
    UMB Open Access Articles

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Policies | Contact Us | UMB Health Sciences & Human Services Library
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.