AIMC Topic: Humans

Clear Filters Showing 12101 to 12110 of 95995 articles

Missing-modality enabled multi-modal fusion architecture for medical data.

Journal of biomedical informatics
BACKGROUND: Fusion of multi-modal data can improve the performance of deep learning models. However, missing modalities are common in medical data due to patient specificity, which is detrimental to the performance of multi-modal models in applicatio...

Utilizing natural language processing for precision prevention of mental health disorders among youth: A systematic review.

Computers in biology and medicine
BACKGROUND: The global mental health crisis has created barriers to youth mental healthcare, leaving many disorders unaddressed. Precision prevention, which identifies individual risks, offers the potential for tailored interventions. While natural l...

Utilizing 12-lead electrocardiogram and machine learning to retrospectively estimate and prospectively predict atrial fibrillation and stroke risk.

Computers in biology and medicine
BACKGROUND: The stroke risk in patients with subclinical atrial fibrillation (AF) is underestimated. By identifying patients at high risk of embolic stroke, health-care professionals can make more informed decisions regarding anticoagulation treatmen...

Teaching AI to speak protein.

Current opinion in structural biology
Large Language Models for proteins, namely protein Language Models (pLMs), have begun to provide an important alternative to capturing the information encoded in a protein sequence in computers. Arguably, pLMs have advanced importantly to understandi...

A knowledge-based planning model to identify fraction-reduction opportunities in brain stereotactic radiotherapy.

Journal of applied clinical medical physics
OBJECTIVE: To develop and validate a HyperArc-based RapidPlan (HARP) model for three-fraction brain stereotactic radiotherapy (SRT) plans to then use to replan previously treated five-fraction SRT plans. Demonstrating the possibility of reducing the ...

A predictive study on HCV using automated machine learning models.

Computers in biology and medicine
Hepatitis C virus (HCV) infection represents a significant contributor to chronic liver disease on a global scale. The prompt identification and management of HCV are imperative in order to avert complications and to maintain control over the disease...

Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.

Cell genomics
DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low covera...

Organic Artificial Nerves: Neuromorphic Robotics and Bioelectronics.

Chemical reviews
Neuromorphic electronics are inspired by the human brain's compact, energy-efficient nature and its parallel-processing capabilities. Beyond the brain, the entire human nervous system, with its hierarchical structure, efficiently preprocesses complex...

Genetic association studies using disease liabilities from deep neural networks.

American journal of human genetics
The case-control study is a widely used method for investigating the genetic underpinnings of binary traits. However, long-term, prospective cohort studies often grapple with absent or evolving health-related outcomes. Here, we propose two methods, l...

Multi-feature fusion method combining brain functional connectivity and graph theory for schizophrenia classification and neuroimaging markers screening.

Journal of psychiatric research
BACKGROUND: The abnormalities in brain functional connectivity (FC) and graph topology (GT) in patients with schizophrenia (SZ) are unclear. Researchers proposed machine learning algorithms by combining FC or GT to identify SZ from healthy controls. ...