Emerging evidence suggests a role for the gut bacteria in the pathogenesis of multiple sclerosis (#MS ); however, the role of other microorganisms and their diagnostic potential for MS remain poorly explored. Here, we analyzed large-scale metagenomic data derived from fecal samples (discovery cohort n = 1152; total n = 1306 across 3 geographically diverse… Continue reading Multikingdom microbiome-based machine learning enables multiple sclerosis diagnosis
Tag: machine-learning
Machine learning prediction of sudden cardiac death incorporating multiple lipid markers: evidence from the Taiwan Chin Shan community cohort
Background: Sudden cardiac death (SCD) is a major contributor to cardiovascular mortality, but reliable long-term risk prediction in community-based populations remains limited. Machine learning (#ML ) offers potential advantages, yet its application to #SCD prediction remains comparatively limited, particularly in community-based populations.Methods: We used data from the Chin-Shan Community Cardiovascular Cohort (CCCC), a prospective community-based… Continue reading Machine learning prediction of sudden cardiac death incorporating multiple lipid markers: evidence from the Taiwan Chin Shan community cohort
Machine Learning-Based Prediction Model for Infectious Complications in Trauma and Its Association With In-Hospital Mortality
Background: Infectious complications, such as #sepsis or catheter-related infections, are common and serious sequelae after trauma. Despite their clinical significance, existing risk-prediction models are limited by reliance on in-hospital data that fail to capture complex physiological interactions. Thus, this study aimed to develop and validate an interpretable ensemble machine learning (ML) model integrating both prehospital… Continue reading Machine Learning-Based Prediction Model for Infectious Complications in Trauma and Its Association With In-Hospital Mortality
A Novel, Interpretable Machine Learning Model Predicts Furosemide Dosing After Congenital Cardiac Surgery
Fluid overload is common after neonatal congenital #cardiac surgery (CCS) and is frequently managed with continuous furosemide infusions requiring iterative dose titration. An interpretable prediction model could support more consistent early postoperative dosing decisions. We hypothesized that a novel, interpretable machine learning approach could accurately predict furosemide dosing decisions in neonates following CCS. We identified… Continue reading A Novel, Interpretable Machine Learning Model Predicts Furosemide Dosing After Congenital Cardiac Surgery
Comprehensive validation of machine learning models predicting chemotherapy related electrolyte disorders in a multicenter study
Background: Electrolyte abnormalities following #chemotherapy are common and clinically significant complications in cancer patients and are often associated with treatment delays and adverse outcomes. This study aims to develop and validate machine learning models to predict eight electrolyte abnormalities in cancer patients receiving chemotherapy.Methods: We retrospectively analyze medical records of cancer patients from two tertiary… Continue reading Comprehensive validation of machine learning models predicting chemotherapy related electrolyte disorders in a multicenter study
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