Clinical BioBERT Hyperparameter Optimization using Genetic Algorithm

TL;DR

This paper presents a method for optimizing Clinical BioBERT hyperparameters using a genetic algorithm to improve the extraction of Social Determinants of Health from clinical notes.

Contribution

It introduces a genetic algorithm-based hyperparameter tuning approach for Clinical BioBERT in SDoH extraction from unstructured clinical text.

Findings

AdamW optimizer achieved the highest accuracy

Hyperparameter optimization improved model performance

The pipeline effectively classifies SDoH issues in clinical notes

Abstract

Clinical factors account only for a small portion, about 10-30%, of the controllable factors that affect an individual's health outcomes. The remaining factors include where a person was born and raised, where he/she pursued their education, what their work and family environment is like, etc. These factors are collectively referred to as Social Determinants of Health (SDoH). The majority of SDoH data is recorded in unstructured clinical notes by physicians and practitioners. Recording SDoH data in a structured manner (in an EHR) could greatly benefit from a dedicated ontology of SDoH terms. Our research focuses on extracting sentences from clinical notes, making use of such an SDoH ontology (called SOHO) to provide appropriate concepts. We utilize recent advancements in Deep Learning to optimize the hyperparameters of a Clinical BioBERT model for SDoH text. A genetic algorithm-based hyperparameter tuning regimen was implemented to identify optimal parameter settings. To implement a complete classifier, we pipelined Clinical BioBERT with two subsequent linear layers and two dropout layers. The output predicts whether a text fragment describes an SDoH issue of the patient. We compared the AdamW, Adafactor, and LAMB optimizers. In our experiments, AdamW outperformed the others in terms of accuracy.