Abstract
In this paper we develop and study machine learning based models based on latent semantic indexing capable of automatically assigning diagnoses and diagnostic categories to patients based on structured clinical data in their Electronic Health record (EHR). These models can be either used for automatic coding of patient’s diagnoses from structured EHR data at the time of discharge, or for supporting dynamic diagnosis and summarization of the patient condition. We study the performance of our diagnostic models on MIMIC-III EHR data.
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Acknowledgement
This work was supported by NIH grant R01GM088224. The content of the paper is solely the responsibility of the authors and does not necessarily represent the official views of NIH.
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Malakouti, S., Hauskrecht, M. (2019). Predicting Patient’s Diagnoses and Diagnostic Categories from Clinical-Events in EHR Data. In: Riaño, D., Wilk, S., ten Teije, A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science(), vol 11526. Springer, Cham. https://doi.org/10.1007/978-3-030-21642-9_17
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DOI: https://doi.org/10.1007/978-3-030-21642-9_17
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