Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances

By Sumithra Velupillai, H. Suominen, Maria Liakata, Angus Roberts, Anoop D. Shah, K. Morley, D. Osborn, Joseph F. Hayes, R. Stewart, J. Downs and 2 more. 2018

214 citations recorded by the source corpus.

The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e.g., diagnoses, symptoms, or treatments) or semantic attributes (e.g., negation, severity, or temporality). From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient- or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches. Here we provide a broad summary and outline of the challenging issues involved in defining appropriate intrinsic and extrinsic evaluation methods for NLP research that is to be used for clinical outcomes research, and vice versa. A particular focus is placed on mental health research, an area still relatively understudied by the clinical NLP research community, but where NLP methods are of notable relevance. Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jingqi Wang
2. Feixiong Cheng
3. Kirk Roberts
4. Hua Xu
5. Yuqi Si
6. Zhenxing Xu

In Molecular Biology

1. Jingqi Wang
2. Feixiong Cheng
3. Kirk Roberts
4. Hua Xu

Citations in the corpus

• Multimodal machine learning in precision health: A scoping review (2022, background): “In general, clinical notes can provide complementary information to structured EHR data, where natural language processing (NLP) is often needed to extract such information 161 – 163 .”
• Enhancing Clinical Concept Extraction with Contextual Embedding (2019, background): “So far, word2vec has widely established its effectiveness for achieving state-of-the-art performances in a variety of clinical NLP tasks (Wang et al., 2018a).”