AIMC Topic: Machine Learning

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Detection of breast cancer using machine learning and explainable artificial intelligence.

Scientific reports
Breast cancer is characterized by the proliferation of abnormal breast cells that eventually turn into malignant tumors. These cancer cells can metastasize to be life-threatening and fatal. An intricate mix of environmental factors and individual gen...

Machine learning analysis of molecular dynamics properties influencing drug solubility.

Scientific reports
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource cons...

A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study.

Scientific reports
This study aims to identify depressive risks in elderly individuals with subjective cognitive decline (SCD) and develop a predictive model using machine learning algorithms to enable timely interventions.Data from the 2015 and 2018 waves of the China...

Predicting mortality risk in Alzheimer's disease using machine learning based on lifestyle and physical activity.

Scientific reports
Alzheimer's disease (AD), a progressive neurodegenerative disorder, significantly impacts patient survival, prompting the need for accurate prognostic tools. Lifestyle factors and physical activity levels have been identified as critical modifiable r...

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC.

Nature communications
Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy (ICI-Chemo), improves survival in advanced non-small cell lung cancer (NSCLC). However, prospective guidance for choosing between these options remain...

Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study.

JMIR medical informatics
BACKGROUND: By analyzing electronic health record snapshots of similar patients, physicians can proactively predict disease onsets, customize treatment plans, and anticipate patient-specific trajectories. However, the modeling of electronic health re...

A Weighted Voting Approach for Traditional Chinese Medicine Formula Classification Using Large Language Models: Algorithm Development and Validation Study.

JMIR medical informatics
BACKGROUND: Several clinical cases and experiments have demonstrated the effectiveness of traditional Chinese medicine (TCM) formulas in treating and preventing diseases. These formulas contain critical information about their ingredients, efficacy, ...

Improving Large Language Models' Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation.

JMIR medical informatics
BACKGROUND: The American Medical Association recommends that electronic health record (EHR) notes, often dense and written in nuanced language, be made readable for patients and laypeople, a practice we refer to as the simplification of discharge not...

Fast identification of influenza using label-free SERS combined with machine learning algorithms clinical nasal swab samples.

Analytical methods : advancing methods and applications
Influenza virus outbreaks, which have become more frequent in recent years, have attracted global attention. Reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA), as the "gold standard" methods for vi...

Calcium ion dynamic trajectory is associated with prognosis in patients with sepsis: A potential class mixture modeling study.

International immunopharmacology
Disturbed calcium homeostasis in patients with sepsis is associated with poor prognosis; however, its dynamic pattern and clinical significance remain unclear. This study aimed to establish an early warning system for calcium homeostasis in sepsis by...