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Updated: Jun 5, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Development of ICF code selection tools for mental health care.

S Manabe¹, Y Miura, T Takemura

¹Department of Integrated Medicine, Medical Informatics, Osaka University Graduate School of Medicine, Suita,Osaka, Japan.

Methods of Information in Medicine

|December 21, 2010

Summary

New tools simplify coding for the International Classification of Functioning, Disability and Health (ICF) in mental health care. These tools improve the efficiency of applying ICF codes to activity and participation data.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

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Published on: June 20, 2020

Area of Science:

Health Informatics
Rehabilitation Medicine
Mental Health Services

Background:

The International Classification of Functioning, Disability and Health (ICF) is a valuable tool for coding life functions.
The extensive number of ICF codes makes the manual coding process complex and time-consuming.
Efficient coding is crucial for accurately representing patient data in healthcare settings.

Purpose of the Study:

To develop and evaluate tools that simplify the selection of ICF codes for activity and participation data.
To enhance the usability of ICF coding in the context of domiciliary mental health care reports.
To improve the efficiency and accuracy of ICF code application in clinical practice.

Main Methods:

Development of a directory tool for navigating ICF code conceptual trees.
Morphological analysis of training data to correlate nouns with ICF codes.
Creation of a full-text search tool based on an analysis corpus of noun-ICF code associations.
Evaluation of the full-text search tool's performance on a test dataset.

Main Results:

The full-text search tool identified correct ICF codes in the first candidate list for 54.2% of sentences.
Correct ICF codes were found in the combined candidate lists for 90.1% of sentences.
The top three candidate lists included correct ICF codes for 71.7% of sentences, with 100% accuracy for 'General Tasks and Demands'.

Conclusions:

Developed ICF code selection tools effectively support coding processes in mental health care.
Full automation of ICF coding remains challenging, but tools significantly improve efficiency.
ICF codes can be more effectively applied to mental health care with these developed tools.