AIMC Topic: Machine Learning

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Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection.

BMC health services research
Revascularization therapies, such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), alleviate symptoms and treat myocardial ischemia. Patients with multivessel disease, particularly those undergoing 3-vessel PCI,...

Constructing a fall risk prediction model for hospitalized patients using machine learning.

BMC public health
STUDY OBJECTIVES: This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model's predictions.

Machine learning-driven identification of critical gene programs and key transcription factors in migraine.

The journal of headache and pain
BACKGROUND: Migraine is a complex neurological disorder characterized by recurrent episodes of severe headaches. Although genetic factors have been implicated, the precise molecular mechanisms, particularly gene expression patterns in migraine-associ...

Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy.

Scientific reports
This study aimed to identify biomolecular differences between benign gastric tissues (gastritis/intestinal metaplasia) and gastric adenocarcinoma and to evaluate the diagnostic power of Raman spectroscopy-based machine learning in gastric adenocarcin...

Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study.

Scientific reports
The Arteriovenous Access Stage (AVAS) classification simplifies information about suitability of vessels for vascular access (VA). It's been previously validated in a clinical study. Here, AVAS performance was tested against multiple ultrasound mappi...

A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis.

Scientific reports
Elderly patients undergoing maintenance hemodialysis (MHD) face a heightened risk of cognitive frailty (CF), which significantly compromises quality of life. Early identification of at-risk individuals and timely intervention are essential. Neverthel...

The application of machine learning approaches to classify and predict fertility rate in Ethiopia.

Scientific reports
Integrating machine learning (ML) models into healthcare systems is a rapidly evolving field with the potential to revolutionize care delivery. This study aimed to classify fertility rates and identify significant predictors using ML models among rep...

Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model.

Scientific reports
Human organic cation transporter 2 (hOCT2/SLC22A2) is a key drug transporter that facilitates the transport of endogenous and exogenous organic cations. Because hOCT2 is responsible for the development of adverse effects caused by platinum-based anti...

Accelerated enzyme engineering by machine-learning guided cell-free expression.

Nature communications
Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design. To address this challenge, we develop a machine learning (ML)-guided platform that integrates cell...

Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: Delayed cerebral ischemia (DCI) is a primary contributor to death after subarachnoid hemorrhage (SAH), with significant incidence. Therefore, early determination of the risk of DCI is an urgent need. Machine learning (ML) has received muc...