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.

翻译：临床因素仅占影响个体健康结果的可控因素的约10-30%。其余因素包括一个人的出生和成长地、教育背景、工作与家庭环境等。这些因素统称为健康社会决定因素（SDoH）。大部分SDoH数据由医生和从业者记录在非结构化临床笔记中。以结构化方式（在电子健康记录中）记录SDoH数据将极大受益于专门的SDoH术语本体。我们的研究专注于从临床笔记中提取句子，利用此类SDoH本体（称为SOHO）提供相应的概念。我们采用深度学习的最新进展，针对SDoH文本优化临床BioBERT模型的超参数。我们实现了一种基于遗传算法的超参数调优方案，以确定最优参数设置。为构建完整分类器，我们将临床BioBERT与两个后续线性层及两个丢弃层进行流水化连接。输出结果用于预测文本片段是否描述了患者的SDoH问题。我们比较了AdamW、Adafactor和LAMB优化器。实验表明，AdamW在准确率方面优于其他优化器。