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Updated: May 24, 2026

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

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Automatic Placement Within a Hierarchical Clinical Decision Support Terminology.

Skyler Resendez1,2, Frank LeHouillier1,2, Guresh Mehta1

  • 1Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

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Automated algorithms accurately place clinical terms in decision support terminologies, reducing clinician burden. This improves electronic health record (EHR) systems by identifying correct clinical groupers efficiently.

Area of Science:

  • Informatics
  • Natural Language Processing
  • Clinical Decision Support

Background:

  • Hierarchical terminologies are crucial for clinical decision support in electronic health record (EHR) systems.
  • Automating the placement of clinical terms within these terminologies can enhance efficiency.

Purpose of the Study:

  • To evaluate two algorithms for automating the assignment of clinical terms to a hierarchical decision support terminology.
  • To assess the accuracy and efficiency of these automated methods in reducing clinician workload.

Main Methods:

  • Utilized a feature ranking system, analogous to TF-IDF, for term placement.
  • Employed a clinical Bidirectional Encoder Representations from Transformers (BERT) machine learning system.
  • Tested algorithms on 100 clinical terms initially assigned a placeholder grouper.
Keywords:
Artificial IntelligenceClinical Decision SupportHealth Information TechnologyNatural Language ProcessingStructured Hierarchy

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Last Updated: May 24, 2026

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Main Results:

  • One of the two algorithms correctly identified the appropriate grouper in 93% of test cases.
  • When both algorithms agreed on the top grouper, accuracy reached 97.9% for 48 cases.
  • Demonstrated significant potential for reducing manual effort in terminology management.

Conclusions:

  • Automated algorithms show high accuracy in assigning clinical terms to hierarchical terminologies.
  • These methods can substantially decrease the burden on clinicians and improve EHR functionality.
  • The study highlights the potential of NLP and machine learning in optimizing clinical decision support.