Authors :
Funke V. Alabi; Onyeka Omose; Omotomilola Jegede
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/2nxnc4pw
Scribd :
http://tinyurl.com/3e9mpyh8
DOI :
https://doi.org/10.5281/zenodo.10623437
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.
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.
