Publication Date: 2024/02/06
Abstract: Entering free-form text notes into Electronic Health Records (EHR) systems takes a lot of time from clinicians. A large portion of this paper work is viewed as a burden, which cuts into the amount of time doctors spend with patients and increases the risk of burnout. We will see how machine learning and computational linguistics can be infused in the processing of taking clinical notes. We are presenting a new language modeling task that predicts the content of notes conditioned on historical data from a patient's medical record, such as patient demographics, lab results, medications, and previous notes, with the goal of enabling AI-assisted note-writing. Using the publicly available, de-identified MIMIC-III dataset, we will train generative models and perform multiple measures of comparison between the generated notes and the dataset.We will have detailed discussionabouthow thesemodels can help with assistivenote-writing functions like auto- complete and error-detection.
Keywords: No Keywords Available
DOI: https://doi.org/10.5281/zenodo.10623437
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN1195.pdf
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