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    Deep attention model for triage of emergency department patients

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    1804.03240v1.pdf
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    Genre
    Conference Proceeding
    Date
    2018-01-01
    Author
    Gligorijevic, D
    Stojanovic, J
    Satz, W
    Stojkovic, I
    Schreyer, K
    Del Portal, D
    Obradovic, Z
    Subject
    cs.CY
    cs.CY
    cs.CL
    cs.LG
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/5671
    
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    DOI
    10.1137/1.9781611975321.34
    Abstract
    © 2018 by SIAM. Optimization of patient throughput and wait time in emer-gency departments (ED) is an important task for hospital systems. For that reason, Emergency Severity Index (ESI) system for patient triage was introduced to help guide man-ual estimation of acuity levels, which is used by nurses to rank the patients and organize hospital resources. However, despite improvements that it brought to managing medical resources, such triage system greatly depends on nurse's sub-jective judgment and is thus prone to human errors. Here, we propose a novel deep model based on the word attention mechanism designed for predicting a number of resources an ED patient would need. Our approach incorporates rou-tinely available continuous and nominal (structured) data with medical text (unstructured) data, including patient's chief complaint, past medical history, medication list, and nurse assessment collected for 338,500 ED visits over three years in a large urban hospital. Using both structured and unstructured data, the proposed approach achieves the AUC of ∼ 88% for the task of identifying resource intensive pa-tients (binary classification), and the accuracy of ∼ 44% for predicting exact category of number of resources (multi-class classification task), giving an estimated lift over nurses' per-formance by 16% in accuracy. Furthermore, the attention mechanism of the proposed model provides interpretabil-ity by assigning attention scores for nurses' notes which is crucial for decision making and implementation of such ap-proaches in the real systems working on human health.
    Citation to related work
    Society for Industrial and Applied Mathematics
    Has part
    SIAM International Conference on Data Mining, SDM 2018
    ADA compliance
    For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/5653
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