NLM Informatics Lecture Series: Bridging the Semantic Gap between Clinical Research Eligibility Criteria and Clinical Data

With the burgeoning adoption of electronic health records (EHRs), vast amounts of clinical data are increasingly available for computational reuse. It is imperative that the scientific community leverage Big Data to accelerate clinical and translational science at low cost and large scale. A critical step toward this goal is matching clinical research eligibility criteria to clinical data for cohort identification. However, this task is complicated by the semantic gap between free-text eligibility criteria and raw clinical data: each criterion has many ways to describe it and a myriad of clinical data points that represent it. In fact, the semantic gap is a significant multifactorial problem because of the central role that clinical research eligibility criteria play in clinical and translational research. In a typical study, they undergo a complex evolution: perceived, defined, interpreted, implemented, and adapted by various stakeholders for a series of clinical research tasks. During the design phase, investigators choose eligibility criteria to define a study’s target population. During screening and recruitment, the criteria are used and interpreted by clinical research coordinators, query analysts, and even research volunteers themselves, each possessing different decision support needs for using the criteria. Later, they are summarized in meta-analyses for developing clinical practice guidelines and, eventually, interpreted by physicians to screen patients for ...
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