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Updated: Mar 1, 2026
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
8.2K
Symptom severity classification with gradient tree boosting.
Yang Liu1, Yu Gu2, John Chu Nguyen1
1Med Data Quest, Inc., 505 Coast Blvd S Ste 300, La Jolla, CA 92037, United States.
Journal of Biomedical Informatics
|May 27, 2017
Summary
This study developed a system for assessing psychiatric symptom severity from clinical notes. The approach achieved a high accuracy, demonstrating potential for automated analysis of patient evaluations.
Area of Science:
- Computational psychiatry
- Natural Language Processing (NLP) in healthcare
Background:
- Automated analysis of clinical notes is crucial for efficient patient evaluation.
- Accurate symptom severity assessment aids in treatment planning and monitoring.
Purpose of the Study:
- To develop and evaluate a system for classifying psychiatric symptom severity from initial psychiatric evaluations.
- To participate in the CEGS N-GRID 2016 task 2 RDoC classification competition.
Main Methods:
- Preprocessing of psychiatric notes into a semi-structured questionnaire.
- Transformation of text answers into numerical, binary, or categorical features.
- Training weak Support Vector Regressors (SVR) and combining them with gradient tree boosting for final classification.
Main Results:
- The system achieved a macro-averaged Mean Absolute Error of 0.439.
- The submission obtained a normalized score of 81.75% in the competition.
Conclusions:
- The proposed system demonstrates effective automated symptom severity classification from clinical text.
- The methodology shows promise for improving the efficiency and accuracy of psychiatric evaluations.
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