AMIA Annu Symp Proc - ADESSA: A Real-Time Decision Support Service for Delivery of Semantically Coded Adverse Drug Event Data.

Tópicos

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{ signal(2180) analysi(812) frequenc(800) }
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{ group(2977) signific(1463) compar(1072) }
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{ patient(1821) servic(1111) care(1106) }
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{ analysi(2126) use(1163) compon(1037) }
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{ cancer(2502) breast(956) screen(824) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Evaluating medications for potential adverse events is a time-consuming process, typically involving manual lookup of information by physicians. This process can be expedited by CDS systems that support dynamic retrieval and filtering of adverse drug events (ADE's), but such systems require a source of semantically-coded ADE data. We created a two-component system that addresses this need. First we created a natural language processing application which extracts adverse events from Structured Product Labels and generates a standardized ADE knowledge base. We then built a decision support service that consumes a Continuity of Care Document and returns a list of patient-specific ADE's. Our database currently contains 534,125 ADE's from 5602 product labels. An NLP evaluation of 9529 ADE's showed recall of 93% and precision of 95%. On a trial set of 30 CCD's, the system provided adverse event data for 88% of drugs and returned these results in an average of 620ms.

Resumo Limpo

evalu medic potenti advers event timeconsum process typic involv manual lookup inform physician process can expedit cds system support dynam retriev filter advers drug event ade system requir sourc semanticallycod ade data creat twocompon system address need first creat natur languag process applic extract advers event structur product label generat standard ade knowledg base built decis support servic consum continu care document return list patientspecif ade databas current contain ade product label nlp evalu ade show recal precis trial set ccds system provid advers event data drug return result averag ms

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