AMIA Annu Symp Proc - Developing predictive models using electronic medical records: challenges and pitfalls.

Tópicos

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Resumo

While Electronic Medical Records (EMR) contain detailed records of the patient-clinician encounter - vital signs, laboratory tests, symptoms, caregivers' notes, interventions prescribed and outcomes - developing predictive models from this data is not straightforward. These data contain systematic biases that violate assumptions made by off-the-shelf machine learning algorithms, commonly used in the literature to train predictive models. In this paper, we discuss key issues and subtle pitfalls specific to building predictive models from EMR. We highlight the importance of carefully considering both the special characteristics of EMR as well as the intended clinical use of the predictive model and show that failure to do so could lead to developing models that are less useful in practice. Finally, we describe approaches for training and evaluating models on EMR using early prediction of septic shock as our example application.

Resumo Limpo

electron medic record emr contain detail record patientclinician encount vital sign laboratori test symptom caregiv note intervent prescrib outcom develop predict model data straightforward data contain systemat bias violat assumpt made offtheshelf machin learn algorithm common use literatur train predict model paper discuss key issu subtl pitfal specif build predict model emr highlight import care consid special characterist emr well intend clinic use predict model show failur lead develop model less use practic final describ approach train evalu model emr use earli predict septic shock exampl applic

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