Background: Ambulatory blood #pressure monitoring (ABPM) is considered a reference standard for diagnosing #hypertension and is recommended for out-of-office blood pressure assessment, yet internal medicine residents receive limited training in its interpretation. We conducted a needs assessment to identify gaps in hypertension and ABPM education and developed an asynchronous e-learning module to address these deficiencies.… Continue reading Bridging the Gap: Using an Asynchronous E-Learning Module to Improve Internal Medicine Residents’ Confidence and Preparedness in Ambulatory Blood Pressure Monitoring Interpretation
Category: Tech
Multikingdom microbiome-based machine learning enables multiple sclerosis diagnosis
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
Data-driven decision support in hospital resource planning: an artificial intelligence-based model proposal for emergency department demand
Background: The sustainability of service quality in healthcare systems is directly related to accurate resource planning, especially in #emergency departments with high unpredictability. This study aims to analyze the impact of meteorological factors on emergency department visits and propose a highly accurate and explainable artificial intelligence-based decision support model for hospital management. Within the scope… Continue reading Data-driven decision support in hospital resource planning: an artificial intelligence-based model proposal for emergency department demand
Novelty to necessity: large language models in clinical practice-a call to action for physicians
Large language models (#LLMs ) represent a rapidly advancing subset of artificial intelligence with significant potential to augment clinical practice. These tools can assist in literature synthesis, documentation, clinical reasoning, and patient communication. Despite demonstrated capabilities, adoption in healthcare remains limited due to barriers such as limited AI literacy, trust concerns, integration challenges, and generational… Continue reading Novelty to necessity: large language models in clinical practice-a call to action for physicians
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
Using randomization to compare #AI and expert-generated formative assessment questions in medical education
Background: AI-generated content is being used across the education spectrum and is beneficial for creating complex multiple-choice questions, such as those used in medical education. However, evaluating AI-generated content is challenging, and existing testing and evaluation methods are falling short. This study uses randomization to compare medical students' performance on and subjective evaluation of AI… Continue reading Using randomization to compare #AI and expert-generated formative assessment questions in medical education
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
Optimizing temporal windows for wearable-augmented post-discharge risk prediction: a methods study
Objective: Traditional #readmission risk models relying on static discharge data have limited predictive performance and fail to capture patients' recovery trajectories after hospitalization. We sought to identify optimal modeling parameters for dynamically predicting readmission risk using post-discharge step-count data from remote monitoring devices.Methods: We combined data for adults aged 55+ from 2 studies that collected… Continue reading Optimizing temporal windows for wearable-augmented post-discharge risk prediction: a methods study
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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