Computing and Information Systems - Theses

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Subject: Natural Language Processing × Start date: 2012 × Author: Liu, Jinghui ×

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    Adapting Clinical Natural Language Processing to Contexts: Task, Framework, and Data Bias
    Liu, Jinghui ( 2023-04)
    Clinical texts contain rich amounts of valuable information about real-world patients and clinical practices that can be utilized to improve clinical care. Mining information from clinical text through Natural Language Processing is a promising research field and has attracted much attention. Recent NLP approaches for clinical text usually treat clinical texts as mere corpora from just “another” textual domain. However, clinical text is generated to serve multiple purposes in the healthcare setting and encodes variations and biases from clinical practice that are often not obvious to NLP researchers. This leads to three types of unsatisfactory applications of clinical NLP. First, some clinical NLP tasks provide solutions with limited applicability to existing clinical decision-making and clinical workflow, and they often tend to target individual patients instead of a patient cohort. Second, the output of many clinical NLP models is often a single number or label, presenting a framework that tends to replace instead of augmenting clinical reasoning in the care process. Third, most recent clinical NLP systems are trained end-to-end to manage the complexity of human language, which neglects the various biases that exist in clinical text. This thesis aims to address these three aspects of clinical NLP through three case studies, which include 1) proposing a prediction task to support clinical resource management at the cohort level, 2) examining the feasibility of patient retrieval as supplementary output for predictive analysis, and 3) evaluating the impact of clinical documentation practices on NLP modeling. The results of these studies demonstrate the importance of taking the clinical context into consideration when designing tasks, developing models, and preparing data for effective and reliable clinical NLP systems.