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Prosopagnosia

Prosopagnosia

Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...

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Updated: Jul 18, 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

AI-assisted screening for mild cognitive impairment using routine EHR data: a Gradient Boosting approach.

Tao Ye¹, Jianghua Peng²

¹School of Medicine, Shaoxing University, Shaoxing, China.

Frontiers in Neurology

|March 5, 2026

Summary

A machine learning model effectively identifies mild cognitive impairment (MCI) in older adults using electronic health records (EHR). This tool shows promise for low-cost, automated screening in primary care settings.

Keywords:

calibration decision curve analysis electronic health records machine learning mild cognitive impairment risk prediction

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Last Updated: Jul 18, 2026

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Area of Science:

Gerontology
Medical Informatics
Machine Learning

Background:

Mild cognitive impairment (MCI) affects a significant portion of the aging population.
Early identification of MCI is crucial for timely intervention and management.
Routine electronic health records (EHR) contain vast amounts of data that could be leveraged for MCI detection.

Purpose of the Study:

To develop and validate a machine learning (ML) model for identifying older outpatients with MCI.
To utilize readily available EHR data for a cost-effective screening tool.
To assess the model's performance using established metrics.

Main Methods:

A retrospective cross-sectional study was conducted on community outpatients aged 60 years and older.
Structured EHR data, including demographics, comorbidities, medications, lifestyle, and visit patterns, were used as predictors.
Supervised ML classifiers were trained and evaluated using 10-fold cross-validation and an independent test set, with SMOTE addressing class imbalance.

Main Results:

The Gradient Boosting model demonstrated the best performance with a cross-validation AUC of 0.855 and test AUC of 0.850.
The model achieved an accuracy of 0.833 and an F1 score of 0.402 on the test set.
Key predictors included older age, female sex, lower education, smaller family size, and higher depression scores.

Conclusions:

An ML model utilizing routine outpatient EHR data can effectively discriminate MCI in older adults.
The model shows potential for automated, low-cost screening in primary care.
External validation is necessary to confirm clinical utility and refine operating thresholds.