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Updated: May 14, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Published on: May 15, 2020

Predicting AD conversion: comparison between prodromal AD guidelines and computer assisted PredictAD tool.

Yawu Liu¹, Jussi Mattila, Miguel Ángel Muñoz Ruiz

¹Department of Neurology, University of Eastern Finland, Kuopio University Hospital, Kuopio, Finland.

|February 21, 2013

Summary

This summary is machine-generated.

The PredictAD tool improves the accuracy of predicting Alzheimer's disease (AD) conversion in patients with mild cognitive impairment (MCI). Clinicians using PredictAD achieved better diagnostic accuracy than current research criteria alone.

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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

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Last Updated: May 14, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Published on: May 15, 2020

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

Area of Science:

Neurology
Medical Imaging
Biomarkers

Background:

Mild cognitive impairment (MCI) is a transitional stage to Alzheimer's disease (AD).
Accurate prediction of AD conversion is crucial for timely intervention.
Current diagnostic criteria rely on clinical assessments, imaging, and biomarkers.

Purpose of the Study:

To compare the accuracy of the PredictAD decision support system against current research criteria for predicting AD conversion.
To evaluate the efficacy of PredictAD in identifying prodromal AD using episodic memory, MRI, and CSF biomarkers.

Main Methods:

391 MCI cases from the ADNI cohort were analyzed.
Baseline data included cognitive tests, MRI, and CSF biomarkers (Aβ1-42, Tau).
PredictAD tool and current guidelines were used to predict AD conversion three years later.

Main Results:

PredictAD achieved 72% accuracy (73% sensitivity, 71% specificity) in predicting AD diagnosis.
Clinician prediction with PredictAD assistance reached 71% accuracy (75% sensitivity, 68% specificity).
PredictAD significantly outperformed biomarkers alone or combined clinical/biomarker criteria (p≤0.037).

Conclusions:

The PredictAD tool enhances the accuracy of predicting AD conversion.
Clinicians can achieve more accurate AD predictions with PredictAD assistance compared to current diagnostic criteria.