AIMC Topic: Humans

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Rage Against the Machine: Advancing the study of aggression ethology via machine learning.

Psychopharmacology
RATIONALE: Aggression, comorbid with neuropsychiatric disorders, exhibits with diverse clinical presentations and places a significant burden on patients, caregivers, and society. This diversity is observed because aggression is a complex behavior th...

Development and Validation of Forecasting Next Reported Seizure Using e-Diaries.

Annals of neurology
OBJECTIVE: There are no validated methods for predicting the timing of seizures. Using machine learning, we sought to forecast 24-hour risk of self-reported seizure from e-diaries.

Radiomics in Echocardiography: Deep Learning and Echocardiographic Analysis.

Current cardiology reports
PURPOSE OF REVIEW: Recent development in artificial intelligence (AI) for cardiovascular imaging analysis, involving deep learning, is the start of a new phase in the research field. We review the current state of AI in cardiovascular field and discu...

A supervised learning framework for chromatin loop detection in genome-wide contact maps.

Nature communications
Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on ...

Interpretable clinical prediction via attention-based neural network.

BMC medical informatics and decision making
BACKGROUND: The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by the medical organizations in th...

Identifying diagnosis evidence of cardiogenic stroke from Chinese echocardiograph reports.

BMC medical informatics and decision making
BACKGROUND: Cardiogenic stroke has increasing morbidity in China and brought economic burden to patient families. In cardiogenic stroke diagnosis, echocardiograph examination is one of the most important examinations. Sonographers will investigate pa...

A semi-supervised approach for extracting TCM clinical terms based on feature words.

BMC medical informatics and decision making
BACKGROUND: A semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words.

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression.

Aging
Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer's disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathoge...

Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease.

JCI insight
BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to ide...

Advances in Telemedicine in Ophthalmology.

Seminars in ophthalmology
Telemedicine is the provision of healthcare-related services from a distance and is poised to move healthcare from the physician's office back into the patient's home. The field of ophthalmology is often at the forefront of technological advances in ...