AIMC Topic: Aged

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A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum.

Journal of Alzheimer's disease : JAD
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict t...

Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study.

The Lancet. Digital health
BACKGROUND: Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated m...

Development and validation of an artificial neural network algorithm to predict mortality and admission to hospital for heart failure after myocardial infarction: a nationwide population-based study.

The Lancet. Digital health
BACKGROUND: Patients have an estimated mortality of 15-20% within the first year following myocardial infarction and one in four patients who survive myocardial infarction will develop heart failure, severely reducing quality of life and increasing t...

[Valuation of automatized ASPECTS as an artificial intelligence tool in daily clinical].

Revista de neurologia
AIMS: To evaluate an automated ASPECTS (ASPECTS-a) software against two radiologists' reading of CT scans requested from the Emergency Department. Describe the most frequent failures of the ASPECTS-a.

Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer's Disease.

Journal of Alzheimer's disease : JAD
BACKGROUND: In assessing the levels of clinical impairment in dementia, a summary index of neuropsychological batteries has been widely used in describing the overall functional status.

A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning.

Shock (Augusta, Ga.)
Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Eme...