AIMC Topic: Prodromal Symptoms
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Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer's disease.
Scientific reports
May 17, 2025
The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer's disease (AD) has created a pressing need for the ability to accurately identify optimal candidates for anti-amyloid therapy, specifically those with evidence for inc...
Multiclass classification of Alzheimer's disease prodromal stages using sequential feature embeddings and regularized multikernel support vector machine.
NeuroImage
Nov 19, 2024
The detection of patients in the cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) stages of neurodegeneration is crucial for early treatment interventions. However, the heterogeneity of MCI data samples poses a cha...
Automated linguistic analysis in youth at clinical high risk for psychosis.
Schizophrenia research
Sep 17, 2024
Identifying individuals at clinical high risk for psychosis (CHRP) is crucial for preventing psychosis and improving the prognosis for schizophrenia. Individuals at CHR-P may exhibit mild forms of formal thought disorder (FTD), making it possible to ...
Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.
Molecular psychiatry
Feb 9, 2024
Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psy...
Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram.
Scientific reports
Jul 29, 2023
Little is known about electrocardiogram (ECG) markers of Parkinson's disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the...
Prediction of tau accumulation in prodromal Alzheimer's disease using an ensemble machine learning approach.
Scientific reports
Mar 11, 2021
We developed machine learning (ML) algorithms to predict abnormal tau accumulation among patients with prodromal AD. We recruited 64 patients with prodromal AD using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Supervised ML approa...
Preictal state detection using prodromal symptoms: A machine learning approach.
Epilepsia
Jan 19, 2021
A reliable identification of a high-risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML...
Humans
Machine Learning
Female
Young Adult
Middle Aged
Speech
Surveys and Questionnaires
Adult
Male
Electroencephalography
Support Vector Machine
Seizures
Vision Disorders
Attention
Noise
Video Recording
Area Under Curve
Reading
Prodromal Symptoms
Drug Resistant Epilepsy
Photophobia
Tinnitus
Affect
Comprehension
Hearing Loss
Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms.
European child & adolescent psychiatry
Dec 23, 2019
To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by considering all CAARMS' symptoms at first presentation and using a multivariate machine learning method known as logistic regression with Elastic-net shri...
The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning.
Schizophrenia research
Jan 19, 2018
Machine learning techniques were used to identify highly informative early psychosis self-report items and to validate an early psychosis screener (EPS) against the Structured Interview for Psychosis-risk Syndromes (SIPS). The Prodromal Questionnaire...
AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: Defining a summary measure.
NeuroImage
Jul 26, 2017
Utilizing [18F]-AV-1451 tau positron emission tomography (PET) as an Alzheimer disease (AD) biomarker will require identification of brain regions that are most important in detecting elevated tau pathology in preclinical AD. Here, we utilized an uns...
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