AIMC Topic: Machine Learning

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Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings?

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
BACKGROUND: Identifying health problems in audio-recorded patient-nurse communication is important to improve outcomes in home healthcare patients who have complex conditions with increased risks of hospital utilization. Training machine learning cla...

ATR-FTIR spectroscopy and machine/deep learning models for detecting adulteration in coconut water with sugars, sugar alcohols, and artificial sweeteners.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Packaged coconut water offers various options, from pure to those with added sugars and other additives. While the purity of coconut water is esteemed for its health benefits, its popularity also exposes it to potential adulteration and misrepresenta...

Using Human Resources Data to Predict Turnover of Community Mental Health Employees: Prediction and Interpretation of Machine Learning Methods.

International journal of mental health nursing
This study used machine learning (ML) to predict mental health employees' turnover in the following 12 months using human resources data in a community mental health centre. The data contain 621 employees' information (e.g., demographics, job informa...

Predictive modeling for identification of older adults with high utilization of health and social services.

Scandinavian journal of primary health care
AIM: Machine learning techniques have demonstrated success in predictive modeling across various clinical cases. However, few studies have considered predicting the use of multisectoral health and social services among older adults. This research aim...

Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features.

European journal of applied physiology
PURPOSE: Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical ...

Guided image generation for improved surgical image segmentation.

Medical image analysis
The lack of large datasets and high-quality annotated data often limits the development of accurate and robust machine-learning models within the medical and surgical domains. In the machine learning community, generative models have recently demonst...

Predicting Alzheimer's disease from cognitive footprints in mid and late life: How much can register data and machine learning help?

International journal of medical informatics
BACKGROUND: Real-world data with decades-long medical records are increasingly available alongside the growing adoption of machine learning in healthcare research. We evaluated the performance of machine learning models in predicting the risk of Alzh...

A hybrid Machine learning and statistical modeling approach for analyzing the crash severity of mobility scooter users considering temporal instability.

Accident; analysis and prevention
One of the main objectives in improving the quality of life for individuals with disabilities, especially those experiencing mobility issues such as the elderly, is to enhance their day-to-day mobility. Enabling easy mobility contributes to their ind...

Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review.

Computers in biology and medicine
Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing hardware and software ...

Predicting Collision Cross-Section Values for Small Molecules through Chemical Class-Based Multimodal Graph Attention Network.

Journal of chemical information and modeling
Libraries of collision cross-section (CCS) values have the potential to facilitate compound identification in metabolomics. Although computational methods provide an opportunity to increase library size rapidly, accurate prediction of CCS values rema...