How essential are unstructured clinical narratives and information fusion to clinical trial recruitment?

By Preethi Raghavan, James L. Chen, Eric Fosler-Lussier, Albert Max Lai. 2014

54 citations recorded by the source corpus.

Electronic health records capture patient information using structured controlled vocabularies and unstructured narrative text. While structured data typically encodes lab values, encounters and medication lists, unstructured data captures the physician’s interpretation of the patient’s condition, prognosis, and response to therapeutic intervention. In this paper, we demonstrate that information extraction from unstructured clinical narratives is essential to most clinical applications. We perform an empirical study to validate the argument and show that structured data alone is insufficient in resolving eligibility criteria for recruiting patients onto clinical trials for chronic lymphocytic leukemia (CLL) and prostate cancer. Unstructured data is essential to solving 59% of the CLL trial criteria and 77% of the prostate cancer trial criteria. More specifically, for resolving eligibility criteria with temporal constraints, we show the need for temporal reasoning and information integration with medical events within and across unstructured clinical narratives and structured data.

Subfields: Artificial Intelligence, Genetics, 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. Zexian Zeng
2. Tristan Naumann
3. Xiaoyu Li
4. Yu Deng
5. Yuan Luo

In Genetics

1. Yuan Luo
2. Xiaoyu Li
3. Yu Deng
4. Zexian Zeng
5. Tristan Naumann

In Molecular Biology

1. Zexian Zeng
2. Tristan Naumann
3. Xiaoyu Li
4. Yu Deng
5. Yuan Luo

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “Structured data typically capture patients’ demographic information, lab values, medications, diagnoses, and encounters [9].”
• Supporting Information Retrieval from Electronic Health Records: A Report of University of Michigan's Nine-Year Experience in Developing and Using the Electronic Medical Record Search Engine (EMERSE) (2015, unknown): “free-text entry even when coding options are provided [3,5–7], and that the free text is still needed for complex tasks such as clinical trial recruitment [8].”