AIMC Topic: Machine Learning

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Kinome-Wide Virtual Screening by Multi-Task Deep Learning.

International journal of molecular sciences
Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform...

A stroke prediction framework using explainable ensemble learning.

Computer methods in biomechanics and biomedical engineering
The death of brain cells occurs when blood flow to a particular area of the brain is abruptly cut off, resulting in a stroke. Early recognition of stroke symptoms is essential to prevent strokes and promote a healthy lifestyle. FAST tests (looking fo...

Retinal OCT biomarkers and their association with cognitive function-clinical and AI approaches.

Die Ophthalmologie
Retinal optical coherence tomography (OCT) biomarkers have the potential to serve as early, noninvasive, and cost-effective markers for identifying individuals at risk for cognitive impairments and neurodegenerative diseases. They may also aid in mon...

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning.

Biochemical genetics
Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate th...

Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial.

Internal and emergency medicine
Machine learning (ML) has been applied in sepsis recognition across different healthcare settings with outstanding diagnostic accuracy. However, the advantage of ML-assisted sepsis alert in expediting clinical decisions leading to enhanced quality fo...

Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial.

Critical care medicine
OBJECTIVES: Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent d...

Large language models for science and medicine.

European journal of clinical investigation
Large language models (LLMs) are a type of machine learning model that learn statistical patterns over text, such as predicting the next words in a sequence of text. Both general purpose and task-specific LLMs have demonstrated potential across diver...

A Review of Machine Learning Algorithms for Biomedical Applications.

Annals of biomedical engineering
As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models ...

A journey from omics to clinicomics in solid cancers: Success stories and challenges.

Advances in protein chemistry and structural biology
The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading...