AIMC Topic: Machine Learning

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New approach of prediction of recurrence in thyroid cancer patients using machine learning.

Medicine
Although papillary thyroid cancers are known to have a relatively low risk of recurrence, several factors are associated with a higher risk of recurrence, such as extrathyroidal extension, nodal metastasis, and BRAF gene mutation. However, predicting...

Obesity as a Risk Factor for Accelerated Brain Ageing in First-Episode Psychosis-A Longitudinal Study.

Schizophrenia bulletin
BACKGROUND: Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosis, possibly through links between obesity and brain structure. In this longitudinal study in first episode of psychosis (FEP), we used machine learnin...

Protein matchmaking through representation learning.

Cell systems
Sledzieski, Singh, Cowen, and Berger employ representation learning to predict protein interactions and associations, additionally identifying binding residues between protein pairs. Generalizability is showcased by training on one organism while eva...

Population modeling with machine learning can enhance measures of mental health.

GigaScience
BACKGROUND: Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are ...

Regularized Bayesian transfer learning for population-level etiological distributions.

Biostatistics (Oxford, England)
Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsy) of a deceased individual, which are then aggregated to generate national and regional estimates of cause-specific ...

A survey of extant organizational and computational setups for deploying predictive models in health systems.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Artificial intelligence (AI) and machine learning (ML) enabled healthcare is now feasible for many health systems, yet little is known about effective strategies of system architecture and governance mechanisms for implementation. Our obje...

Predicting brain function status changes in critically ill patients via Machine learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: In intensive care units (ICUs), a patient's brain function status can shift from a state of acute brain dysfunction (ABD) to one that is ABD-free and vice versa, which is challenging to forecast and, in turn, hampers the allocation of hosp...