Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data: Implications for Temporal Modeling With Respect to Time Before Diagnosis, Data Density, Data Quantity, and Data Type.

ABSTRACT: BACKGROUND:We determined the impact of data volume and diversity and training conditions on recurrent neural network methods compared with traditional machine learning methods. METHODS AND RESULTS:Using longitudinal electronic health record data, we assessed the relative performance of machine learning models trained to detect a future diagnosis of heart failure in primary care patients. Model performance was assessed in relation to data parameters defined by the combination of different data domains (data diversity), the number of patient records in the training data set (data quantity), the number of encounters per patient (data density), the prediction window length, and the observation window length (ie, the time period before the prediction window that is the source of features for pr

SUBMITTER: Chen R 

PROVIDER: S-EPMC6814386 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature