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Explainable machine learning on clinical features to predict and differentiate Alzheimer's progression by sex: Toward a clinician-tailored web interface

Fabio Massimo D'Amore, Marco Moscatelli, Antonio Malvaso, Fabrizia D’Antonio, Marta Rodini, Massimiliano Panigutti, Pierandrea Mirino, Giovanni Augusto Carlesimo, Cecilia Guariglia, Daniele Caligiore

2024Journal of the Neurological Sciences14 citationsDOIOpen Access PDF

Topics & Concepts

Interface (matter)Computer scienceMedicinePsychologyWorld Wide WebArtificial intelligenceHuman–computer interactionBubbleMaximum bubble pressure methodParallel computingMachine Learning in HealthcareChronic Disease Management StrategiesMental Health and Psychiatry
Explainable machine learning on clinical features to predict and differentiate Alzheimer's progression by sex: Toward a clinician-tailored web interface | Litcius