AIMC Topic: Machine Learning

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Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study.

Annals of medicine
Investigation of the clinical potential of extensive phenotype data and machine learning (ML) in the prediction of mortality in acute coronary syndrome (ACS). The value of ML and extensive clinical data was analyzed in a retrospective registry stud...

Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood.

IEEE journal of biomedical and health informatics
Childhood anxiety and depression often go undiagnosed. If left untreated these conditions, collectively known as internalizing disorders, are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This p...

Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics.

European radiology
PURPOSE: To investigate the treatment response prediction feasibility and accuracy of an integrated model combining computed tomography (CT) radiomic features and dosimetric parameters for patients with esophageal cancer (EC) who underwent concurrent...

Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
People's mental workload profoundly affects their work efficiency and health. Mental workload assessment can be used to effectively avoid serious accidents caused by excessive mental workload. Both electroencephalogram (EEG) spectral features and its...

Using Machine Learning to Derive Just-In-Time and Personalized Predictors of Stress: Observational Study Bridging the Gap Between Nomothetic and Ideographic Approaches.

Journal of medical Internet research
BACKGROUND: Investigations into person-specific predictors of stress have typically taken either a population-level nomothetic approach or an individualized ideographic approach. Nomothetic approaches can quickly identify predictors but can be hinder...

Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer.

Biochimica et biophysica acta. Reviews on cancer
The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and diffi...

An Open Science Approach to Artificial Intelligence in Healthcare.

Yearbook of medical informatics
OBJECTIVES: Artificial Intelligence (AI) offers significant potential for improving healthcare. This paper discusses how an "open science" approach to AI tool development, data sharing, education, and research can support the clinical adoption of AI ...

Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications.

Yearbook of medical informatics
OBJECTIVES: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surve...

Machine learning for phenotyping opioid overdose events.

Journal of biomedical informatics
OBJECTIVE: To develop machine learning models for classifying the severity of opioid overdose events from clinical data.

Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficie...