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Knowledge-Infused Abstractive Summarization of Clinical Diagnostic Interviews: Framework Development Study.

Gaur Manas1, Vamsi Aribandi2, Ugur Kursuncu1

  • 1Artificial Intelligence Institute, University of South Carolina, Columbia, SC, United States.

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|May 10, 2021
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Summary
This summary is machine-generated.

This study introduces a knowledge-infused abstractive summarization (KiAS) method to create informative summaries of clinical interviews, aiding mental health professionals (MHPs) in patient care.

Keywords:
Patient Health Questionnaire-9abstractive summarizationdistress clinical diagnostic interviewshealthcare informaticsinterpretable evaluationsknowledge-infusion

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Clinical Psychology

Background:

  • Clinical diagnostic interviews are complex, with mental health professionals (MHPs) gathering crucial data amidst patient discomfort and social stigma.
  • Effective summarization of these interviews is vital for MHPs to make informed decisions and explore patient behavior deeply, especially in critical situations.

Purpose of the Study:

  • To propose an unsupervised, knowledge-infused abstractive summarization (KiAS) approach for clinical interviews.
  • To enhance existing summarization methods by generating more informative summaries than those based on frequency heuristics.
  • To enable MHPs to conduct better-informed follow-ups with patients.

Main Methods:

  • Incorporated domain knowledge from the Patient Health Questionnaire-9 lexicon into an integer linear programming framework.
  • Optimized for linguistic quality and informativeness in summary generation.
  • Evaluated KiAS against three baseline summarization approaches (SumBasic, abstractive ILP, abstraction over extractive) using the Distress Analysis Interview Corpus-Wizard of Oz dataset.

Main Results:

  • KiAS generated concise (7 sentences) yet informative summaries from long, ambiguous clinical interviews (58 sentences).
  • Achieved significant improvements over baselines in thematic overlap (23.3%), Flesch Reading Ease (4.4%), contextual similarity (2.5%), and Jensen Shannon divergence (2.2%).
  • Demonstrated substantial gains in ROUGE-2 (61%) and ROUGE-L (49%) metrics, validated by mental health professionals.

Conclusions:

  • KiAS shows potential utility in leveraging voluminous patient communications outside scheduled appointments.
  • The approach promises to generate semantically relevant summaries, aiding MHPs in making informed decisions about patient status.
  • This method can significantly impact clinical practice by improving the efficiency and depth of patient information analysis.