AIMC Topic: Machine Learning

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models: Quantitative Study Using Large Language Models.

Journal of medical Internet research
BACKGROUND: Data collected in controlled settings typically results in high-quality datasets. However, in real-world applications, the quality of data collection is often compromised. It is well established that the quality of a dataset significantly...

Predicting Ultra-High Risk Outcomes Using Linguistic and Acoustic Measures From High-Risk Social Challenge Recordings: mHealth Longitudinal Cohort Exploratory Study.

JMIR formative research
BACKGROUND: Early detection of individuals at ultra-high risk (UHR) for psychosis is critical for timely intervention and improving clinical outcomes. However, current UHR assessments, which rely heavily on psychometric tools, often suffer from low s...

Prediction of Respiratory Decompensation in Patients Receiving Home Mechanical Ventilation: Machine Learning Model Development and Validation Study.

JMIR formative research
BACKGROUND: Chronic respiratory diseases often require long-term ventilatory support, leading to a growing number of patients treated with home mechanical ventilation (HMV). Despite advancements in telemonitoring with real-time tracking of noninvasiv...

Integrating aperture shape controller and machine learning prediction to improve gamma passing rates in lattice radiotherapy.

Physics in medicine and biology
This study proposes a workflow integrating the aperture shape controller (ASC) in the Varian Eclipse system with a machine learning-based verification prediction model to improve gamma passing rates (GPRs) of LATTICE Radiotherapy (LRT) plans and redu...

Identification of PANoptosis-associated genes in hepatic ischemia-reperfusion injury by integrated bioinformatics analysis and machine learning.

PloS one
BACKGROUND: In the context of liver resection and transplantation, hepatic ischemia-reperfusion injury (hepatic IRI) remains a significant clinical challenge, profoundly impacting both postoperative short- and long-term recovery. A novel cell death p...

Development of a machine learning-based model for predicting the functional outcome of patients with proximal femur fractures.

Scientific reports
Early-stage rehabilitation is crucial for the functional recovery of patients with proximal femur fractures. Predicting functional prognosis at such an early stage can simplify the process of planning for transfers and discharge destinations, as well...

Validation of a modified oxygen nebulized inhalation method in airway surface anesthesia by comparative analysis via scalable broad learning.

Scientific reports
In clinical practice for the diagnosis of pulmonary tuberculosis (PTB), bronchoscopy is typically performed under airway surface anaesthesia. The effectiveness of this anaesthesia is closely associated with the smoothness of bronchoscopy diagnosis, a...

Intelligent delignification: leveraging explainable AI for ozone transport modeling and optimization.

Scientific reports
Biomass is mainly composed of cellulose, hemicellulose, and lignin, where lignin is almost one-third of the amount of biomass. Lignin is removed from the biomass matrix because its complex, recalcitrant structure acts as a physical and chemical barri...

Multi-omics and machine learning refine HCC molecular subtypes and prognosis based on liquid-liquid phase separation related genes.

Scientific reports
Accumulating evidence has demonstrated that biological processes associated with liquid-liquid phase separation (LLPS) play a critical role in cancer development. However, the effect of LLPS on hepatocellular carcinoma (HCC) remains largely unknown. ...

Predicting Left Ventricular Ejection Fraction Recovery After Percutaneous Coronary Intervention in Patients With Chronic Coronary Syndrome by Using Interpretable Machine Learning Models: Retrospective Study.

JMIR medical informatics
BACKGROUND: Accurately predicting left ventricular ejection fraction (LVEF) recovery after percutaneous coronary intervention (PCI) in patients with chronic coronary syndrome (CCS) is crucial for clinical decision-making.