AIMC Topic: Humans

Clear Filters Showing 19031 to 19040 of 95995 articles

Empirical investigation of multi-source cross-validation in clinical ECG classification.

Computers in biology and medicine
Traditionally, machine learning-based clinical prediction models have been trained and evaluated on patient data from a single source, such as a hospital. Cross-validation methods can be used to estimate the accuracy of such models on new patients or...

Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.

Cell reports
Computer-vision and machine-learning (ML) approaches are being developed to provide scalable, unbiased, and sensitive methods to assess mouse behavior. Here, we used the ML-based variational animal motion embedding (VAME) segmentation platform to ass...

Development of a machine learning model for precision prognosis of rapid kidney function decline in people with diabetes and chronic kidney disease.

Diabetes research and clinical practice
AIMS: To develop a machine learning model for predicting rapid kidney function decline in people with type 2 diabetes (T2D) and chronic kidney disease (CKD) and to pinpoint key modifiable risk factors for targeted interventions.

Machine learning models of cerebral oxygenation (rcSO) for brain injury detection in neonates with hypoxic-ischaemic encephalopathy.

The Journal of physiology
The present study was designed to test the potential utility of regional cerebral oxygen saturation (rcSO) in detecting term infants with brain injury. The study also examined whether quantitative rcSO features are associated with grade of hypoxic is...

LineageFilter: Improved Proteotyping of Complex Samples Using Metaproteomics and Machine Learning.

Journal of proteome research
Metaproteomics is a powerful tool to characterize how microbiota function by analyzing their proteic content by tandem mass spectrometry. Given the complexity of these samples, accurately assessing their taxonomical composition without prior informat...

Preoperative identification of early extrahepatic recurrence after hepatectomy for colorectal liver metastases: A machine learning approach.

World journal of surgery
BACKGROUND: Machine learning (ML) may provide novel insights into data patterns and improve model prediction accuracy. The current study sought to develop and validate an ML model to predict early extra-hepatic recurrence (EEHR) among patients underg...

Generative artificial intelligence (GenAI) and decision-making: Legal & ethical hurdles for implementation in mental health.

International journal of law and psychiatry
This article argues that significant risks are being taken with using GenAI in mental health that should be assessed urgently. It recommends that guidelines for using generative artificial intelligence (GenAI) in mental health care must be establishe...

Computational screening of umami tastants using deep learning.

Molecular diversity
Umami, a fundamental human taste modality, refers to the savory flavors in meats and broths, often associated with monosodium glutamate and protein richness. With limited knowledge of umami molecules, the food industry seeks efficient approaches for ...

Transformer-Integrated Hybrid Convolutional Neural Network for Dose Prediction in Nasopharyngeal Carcinoma Radiotherapy.

Journal of imaging informatics in medicine
Radiotherapy is recognized as the major treatment of nasopharyngeal carcinoma. Rapid and accurate dose prediction can improve the efficiency of the treatment planning process and the quality of radiotherapy plans. Currently, deep learning-based metho...