AIMC Topic: Machine Learning

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An artificial intelligence model to predict mortality among hemodialysis patients: A retrospective validated cohort study.

Scientific reports
Hemodialysis stands as the most prevalent renal replacement therapy globally. Accurately identifying mortality among hemodialysis patients is paramount importance, as it enables the formulation of tailored interventions and facilitates timely managem...

Transfer learning prediction of type 2 diabetes with unpaired clinical and genetic data.

Scientific reports
The prevalence of type 2 diabetes mellitus (T2DM) in Korea has risen in recent years, yet many cases remain undiagnosed. Advanced artificial intelligence models using multi-modal data have shown promise in disease prediction, but two major challenges...

Epigenomic diagnosis and prognosis of Acute Myeloid Leukemia.

Nature communications
Despite the critical role of DNA methylation, clinical implementations harnessing its promise have not been described in acute myeloid leukemia. Utilizing DNA methylation from 3314 leukemia patient samples across 11 harmonized cohorts, we describe th...

Urea-mediated growth engineering of Au@Ag core-shell nanostructures: an enzymatic detection strategy with machine learning-assisted comparative analysis.

Mikrochimica acta
A non-invasive, enzyme-based colorimetric biosensor was developed for urea detection in saliva, utilizing a growth-based method with Au@Ag core-shell nanostructures, including CTAB-coated gold nanoparticles (AuNPs) and CTAB-coated gold nanorods with ...

Evaluating the efficacy of using large language models in preoperative prediction of microvascular invasion in HCC: a multicenter study.

Scientific reports
Primary liver cancer is the sixth most commonly diagnosed cancer globally and the third leading cause of cancer-related deaths. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and microvascular invasion (MVI) is a sign...

Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors.

Scientific reports
The Ki67 score is a crucial prognostic biomarker for neuroendocrine tumors, but its manual assessment is labor-intensive, requiring the counting of 500-2,000 cells in hotspots. Digital image analysis could streamline this process, yet few comprehensi...

IoT enabled health monitoring system using rider optimization algorithm and joint process estimation.

Scientific reports
The timely detection of abnormal health conditions is crucial in achieving successful medical intervention and enhancing patient outcomes. Despite advances in health monitoring, existing methods often struggle with achieving high accuracy, sensitivit...

Evidence Based Gait Analysis Interpretation Tools (EB-GAIT) treatment recommendation and outcome prediction models to support decision-making based on clinical gait analysis data.

PloS one
Clinical gait analysis (CGA) has historically relied on clinician experience and judgment, leading to modest, stagnant, and unpredictable outcomes. This paper introduces Evidence-Based Gait Analysis Interpretation Tools (EB-GAIT), a novel framework l...

Relative importance of socioecological domains to predicting opioid-involved mortality.

PloS one
BACKGROUND: The opioid crisis in the United States is a complex issue with interconnected factors that lead to opioid misuse and opioid-involved mortality. This study assessed the relative importance of different risk factor domains in predicting fat...

Hysterectomy as a predictor of depression: A comprehensive analysis using logistic regression and machine learning.

Journal of affective disorders
BACKGROUND: An increasing number of studies have shown that there is an inseparable connection between hysterectomy and occurrence of depression, and the impact on patient's mental health cannot be ignored. Therefore, this study utilized the National...