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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Maximizing clinical cohort size using free text queries.

Adi V Gundlapalli1, Doug Redd2, Bryan Smith Gibson1

  • 1IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.

Computers in Biology and Medicine
|March 10, 2015
PubMed
Summary
This summary is machine-generated.

Adding free text queries to structured data significantly enhances patient cohort identification. This approach improves the yield of patient cohorts for both clinical research and population health management.

Keywords:
Clinical notesCohort identificationDiabetesGingkoOverweightStructured dataText queryUnstructured dataWarfarin

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Area of Science:

  • Health Informatics
  • Clinical Research Informatics
  • Data Science in Healthcare

Background:

  • Cohort identification is crucial for population health management and research.
  • This study evaluated the utility of text queries for cohort identification.
  • The research specifically assessed the added value of unstructured data queries to structured queries.

Purpose of the Study:

  • To determine the incremental value of unstructured (free text) data queries when combined with structured data queries for patient cohort identification.
  • To assess the impact of text queries on cohort size and positive predictive value across different clinical scenarios.

Main Methods:

  • Three cohort identification tasks were evaluated: Gingko/Warfarin, overweight individuals, and uncontrolled diabetes (UCD).
  • The increase in cohort size by adding unstructured data queries to structured data queries was assessed.
  • The positive predictive value of unstructured data queries was determined through manual chart review of 500 patients.

Main Results:

  • Text queries substantially increased cohort sizes across all evaluated tasks, for example, from 9 to 28,924 for Gingko/Warfarin.
  • For weight and UCD cohorts, text search increased identification by 5-29% and 2-43% respectively, compared to structured queries alone.
  • Positive predictive values for text searches ranged from 19% to 94%, varying by cohort.

Conclusions:

  • Free text queries offer significant value and demonstrate limitations in identifying patient cohorts from large datasets.
  • The effectiveness of free text queries is influenced by the clinical domain and the prevalence of specific patient criteria.