AMIA Annu Symp Proc - Automatically Detecting Acute Myocardial Infarction Events from EHR Text: A Preliminary Study.

Tópicos

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Resumo

The Worcester Heart Attack Study (WHAS) is a population-based surveillance project examining trends in the incidence, in-hospital, and long-term survival rates of acute myocardial infarction (AMI) among residents of central Massachusetts. It provides insights into various aspects of AMI. Much of the data has been assessed manually. We are developing supervised machine learning approaches to automate this process. Since the existing WHAS data cannot be used directly for an automated system, we first annotated the AMI information in electronic health records (EHR). With strict inter-annotator agreement over 0.74 and un-strict agreement over 0.9 of Cohen's , we annotated 105 EHR discharge summaries (135k tokens). Subsequently, we applied the state-of-the-art supervised machine-learning model, Conditional Random Fields (CRFs) for AMI detection. We explored different approaches to overcome the data sparseness challenge and our results showed that cluster-based word features achieved the highest performance.

Resumo Limpo

worcest heart attack studi whas populationbas surveil project examin trend incid inhospit longterm surviv rate acut myocardi infarct ami among resid central massachusett provid insight various aspect ami much data assess manual develop supervis machin learn approach autom process sinc exist whas data use direct autom system first annot ami inform electron health record ehr strict interannot agreement unstrict agreement cohen annot ehr discharg summari k token subsequ appli stateoftheart supervis machinelearn model condit random field crfs ami detect explor differ approach overcom data spars challeng result show clusterbas word featur achiev highest perform

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