AIMC Topic: Machine Learning

Clear Filters Showing 14091 to 14100 of 34417 articles

Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design.

Journal of neuroengineering and rehabilitation
BACKGROUND: Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising appro...

A framework for prediction of personalized pediatric nuclear medical dosimetry based on machine learning and Monte Carlo techniques.

Physics in medicine and biology
A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for a...

Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning.

BMC medical informatics and decision making
BACKGROUND: With the prevalence of cerebrovascular disease (CD) and the increasing strain on healthcare resources, forecasting the healthcare demands of cerebrovascular patients has significant implications for optimizing medical resources.

A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.

Journal of medical Internet research
BACKGROUND: Numerous studies have identified risk factors for physical restraint (PR) use in older adults in long-term care facilities. Nevertheless, there is a lack of predictive tools to identify high-risk individuals.

Pediatric Deterioration Detection Using Machine Learning.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies

A pharmacokinetic-pharmacodynamic model based on the SSA-1DCNN-Attention network and the semicompartment method.

Biotechnology & genetic engineering reviews
To solve the problem of inaccurate prediction caused by the lack of representativeness of samples due to the small sample size of the collected clinical data when using machine learning methods to predict drug concentration in plasma and describe the...

Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches.

Molecular diversity
Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several research...

Applications of artificial intelligence-machine learning for detection of stress: a critical overview.

Molecular psychiatry
Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, ...

Symbolic knowledge extraction for explainable nutritional recommenders.

Computer methods and programs in biomedicine
This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must...

Systematic review finds "spin" practices and poor reporting standards in studies on machine learning-based prediction models.

Journal of clinical epidemiology
OBJECTIVES: We evaluated the presence and frequency of spin practices and poor reporting standards in studies that developed and/or validated clinical prediction models using supervised machine learning techniques.