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Effective representation learning of electronic health records is achallenging task and is becoming more important as the availability of suchdata is becoming pervasive. The data contained in these records are irregularand contain multiple modalities such as notes, and medical codes. They arepreempted by medical conditions the patient may have, and are typically jotteddown by medical staff. Accompanying codes are notes containing valuableinformation about patients beyond the structured information contained inelectronic health records. We use transformer networks and the recentlyproposed BERT language model to embed these data streams into a unified vectorrepresentation. The presented approach effectively encodes a patient's visitdata into a single distributed representation, which can be used for downstreamtasks. Our model demonstrates superior performance and generalization onmortality, readmission and length of stay tasks using the publicly availableMIMIC-III ICU dataset. Code avaialble athttps://github.com/sajaddarabi/TAPER-EHR

TAPER: Time-Aware Patient EHR Representation