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Personalized PHQ-9 test length using probability density estimation based on conditional probability and K-Nearest

Zahraa Abdulhussein1, Marcia Scazufca2, Pepijn van de Ven1

  • 1Department of Electronic and Computer Engineering, University of Limerick, Ireland.

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|February 24, 2026
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Summary
This summary is machine-generated.

A new dynamic Patient Health Questionnaire-9 (PHQ-9) reduces respondent burden by adapting the number of questions. This efficient depression screening tool improves accuracy compared to shorter fixed-length versions.

Keywords:
Adaptive testsConditional probabilityDepressionDynamic testsK-Nearest NeighbourKNNPHQ-2PHQ-9PHQ-DEP-4

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Area of Science:

  • Psychological assessment
  • Clinical informatics
  • Machine learning in healthcare

Background:

  • The Patient Health Questionnaire-9 (PHQ-9) is a standard depression severity assessment tool.
  • Shorter, fixed-length versions like PHQ-DEP-4 and PHQ-2 exist for rapid screening, particularly for clinical trials.
  • Current methods may not optimize efficiency or accuracy for all individuals.

Purpose of the Study:

  • To propose and evaluate a dynamic, adaptive version of the PHQ-9.
  • To reduce respondent burden by tailoring the assessment length.
  • To improve depression classification accuracy through adaptive questioning.

Main Methods:

  • Developed a dynamic PHQ-9 model that adjusts the number of questions based on respondent answers.
  • Utilized historical PHQ-9 data to inform adaptive decision-making.
  • Employed a K-Nearest Neighbours (KNN) model to estimate probability density for novel response patterns.

Main Results:

  • The dynamic PHQ-9 demonstrated superior performance over the PHQ-DEP-4.
  • Achieved higher sensitivity, specificity, and Youden index.
  • Significantly reduced the average number of questions required per respondent.

Conclusions:

  • The dynamic PHQ-9 offers a more efficient and accurate method for depression screening.
  • Adaptive questioning can effectively reduce patient burden while maintaining or improving diagnostic performance.
  • This approach holds promise for optimizing patient assessments in research and clinical settings.