FADEL: Ensemble Learning Enhanced by Feature Augmentation and Discretization.

In recent years, data augmentation techniques have become the predominant approach for addressing highly imbalanced classification problems in machine learning. Algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Tabular Generative Adversarial Network (CTGAN) have proven effective in synthesizing minority class samples. However, these methods often introduce distributional bias and noise, potentially leading to model overfitting, reduced predictive performance, increased computational costs, and elevated cybersecurity risks.

A Multi-Stage Framework for Kawasaki Disease Prediction Using Clustering-Based Undersampling and Synthetic Data Augmentation: Cross-Institutional Validation with Dual-Center Clinical Data in Taiwan.

Kawasaki disease (KD) is a rare yet potentially life-threatening pediatric vasculitis that, if left undiagnosed or untreated, can result in serious cardiovascular complications. Its heterogeneous clinical presentation poses diagnostic challenges, often failing to meet classical criteria and increasing the risk of oversight. Leveraging routine laboratory tests with AI offers a promising strategy for enhancing early detection.

Predicting Fetal Growth with Curve Fitting and Machine Learning.

Monitoring fetal growth throughout pregnancy is essential for early detection of developmental abnormalities. This study developed a Taiwan-specific fetal growth reference using a web-based data collection platform and polynomial regression modeling. We analyzed ultrasound data from 980 pregnant women, encompassing 8350 prenatal scans, to model six key fetal biometric parameters: abdominal circumference, crown-rump length, estimated fetal weight, head circumference, biparietal diameter, and femur length.

Enhancing generalization in a Kawasaki Disease prediction model using data augmentation: Cross-validation of patients from two major hospitals in Taiwan.

Kawasaki Disease (KD) is a rare febrile illness affecting infants and young children, potentially leading to coronary artery complications and, in severe cases, mortality if untreated. However, KD is frequently misdiagnosed as a common fever in clinical settings, and the inherent data imbalance further complicates accurate prediction when using traditional machine learning and statistical methods. This paper introduces two advanced approaches to address these challenges, enhancing prediction accuracy and generalizability.

Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.

Background: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk for individuals with chronic kidney disease and end-stage renal disease (ESRD). Traditional laboratory test monitoring is resource-heavy, invasive, and unable to provide continuous tracking. Wearable technologies like smartwatches with electrocardiogram (ECG) capabilities are emerging as valuable tools for remote monitoring, potentially allowing for personalized monitoring with artificial intelligence (AI)-ECG interpretation.

Explainable Deep Learning Model for Predicting Serious Adverse Events in Hospitalized Geriatric Patients Within 72 Hours.

Background: The global aging population presents a significant challenge, with older adults experiencing declining physical and cognitive abilities and increased vulnerability to chronic diseases and adverse health outcomes. This study aims to develop an interpretable deep learning (DL) model to predict adverse events in geriatric patients within 72 hours of hospitalization.

Methods: The study used retrospective data (2017-2020) from a major medical center in Taiwan.

Development and validation of a deep learning pipeline to measure pericardial effusion in echocardiography.

Objectives: The aim of this study was to develop a deep-learning pipeline for the measurement of pericardial effusion (PE) based on raw echocardiography clips, as current methods for PE measurement can be operator-dependent and present challenges in certain situations.

Methods: The proposed pipeline consisted of three distinct steps: moving window view selection (MWVS), automated segmentation, and width calculation from a segmented mask. The MWVS model utilized the ResNet architecture to classify each frame of the extracted raw echocardiography files into selected view types.

Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus.

Several risk factors are related to glycemic control in patients with type 2 diabetes mellitus (T2DM), including demographics, medical conditions, negative emotions, lipid profiles, and heart rate variability (HRV; to present cardiac autonomic activity). The interactions between these risk factors remain unclear. This study aimed to use machine learning methods of artificial intelligence to explore the relationships between various risk factors and glycemic control in T2DM patients.

Use of Machine Learning to Differentiate Children With Kawasaki Disease From Other Febrile Children in a Pediatric Emergency Department.

Importance: Early awareness of Kawasaki disease (KD) helps physicians administer appropriate therapy to prevent acquired heart disease in children. However, diagnosing KD is challenging and relies largely on subjective diagnosis criteria.

Objective: To develop a prediction model using machine learning with objective parameters to differentiate children with KD from other febrile children.

Explainable deep learning model to predict invasive bacterial infection in febrile young infants: A retrospective study.

Background: Machine learning models have demonstrated superior performance in predicting invasive bacterial infection (IBI) in febrile infants compared to commonly used risk stratification criteria in recent studies. However, the black-box nature of these models can make them difficult to apply in clinical practice. In this study, we developed and validated an explainable deep learning model that can predict IBI in febrile infants ≤ 60 days of age visiting the emergency department.

Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring.

Blood glucose (BG) monitoring is important for critically ill patients, as poor sugar control has been associated with increased mortality in hospitalized patients. However, constant BG monitoring can be resource-intensive and pose a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia from electrocardiogram (ECG) data.

Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach.

Background: Hyperkalemia is a critical condition, especially in intensive care units. So far, there have been no accurate and noninvasive methods for recognizing hyperkalemia events on ambulatory electrocardiogram monitors.

Objective: This study aimed to improve the accuracy of hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors using a personalized transfer learning method; this would be done by training a generic model and refining it with personal data.

Machine learning models for predicting in-hospital mortality in patient with sepsis: Analysis of vital sign dynamics.

Purpose: To build machine learning models for predicting the risk of in-hospital death in patients with sepsis within 48 h, using only dynamic changes in the patient's vital signs.

Methods: This retrospective observational cohort study enrolled septic patients from five emergency departments (ED) in Taiwan. We adopted seven variables, i.

Impact of the synergistic effect of pneumonia and air pollutants on newly diagnosed pulmonary tuberculosis in southern Taiwan.

Background: An increased incidence of pulmonary tuberculosis (PTB) among patients with pulmonary diseases exposed to air pollution has been reported.

Objective: To comprehensively investigate the association between pneumonia (PN) and air pollution with PTB through a large-scale follow-up study.

Methods: We conducted a retrospective study using data from the Kaohsiung Medical University Hospital Research Database and the Taiwan Air Quality Monitoring Database.

Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory.

Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks, jointly optimizing beamforming, power control, and energy harvesting to enhance the communication performance from the base stations (BSs) (or access points (APs)) to the mobile nodes (MNs) served would be a real challenge. In this work, we formulate the joint optimization as a mixed integer nonlinear programming (MINLP) problem, which can be also realized as a complex multiple resource allocation (MRA) optimization problem subject to different allocation constraints.

Deep Learning Assisted Detection of Abdominal Free Fluid in Morison's Pouch During Focused Assessment With Sonography in Trauma.

The use of focused assessment with sonography in trauma (FAST) enables clinicians to rapidly screen for injury at the bedsides of patients. Pre-hospital FAST improves diagnostic accuracy and streamlines patient care, leading to dispositions to appropriate treatment centers. In this study, we determine the accuracy of artificial intelligence model-assisted free-fluid detection in FAST examinations, and subsequently establish an automated feedback system, which can help inexperienced sonographers improve their interpretation ability and image acquisition skills.

Machine Learning Models for Survival and Neurological Outcome Prediction of Out-of-Hospital Cardiac Arrest Patients.

Background: Out-of-hospital cardiac arrest (OHCA) is a major health problem worldwide, and neurologic injury remains the leading cause of morbidity and mortality among survivors of OHCA. The purpose of this study was to investigate whether a machine learning algorithm could detect complex dependencies between clinical variables in emergency departments in OHCA survivors and perform reliable predictions of favorable neurologic outcomes.

Methods: This study included adults (≥18 years of age) with a sustained return of spontaneous circulation after successful resuscitation from OHCA between 1 January 2004 and 31 December 2014.

Risk of motor vehicle collisions after methadone use.

Methadone maintenance treatment (MMT) can alleviate opioid dependence. However, MMT possibly increases the risk of motor vehicle collisions. The current study investigated preliminary estimation of motor vehicle collision incidence rates.

Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency Department.

Background: The aim of this study was to develop and evaluate a machine learning (ML) model to predict invasive bacterial infections (IBIs) in young febrile infants visiting the emergency department (ED).

Methods: This retrospective study was conducted in the EDs of three medical centers across Taiwan from 2011 to 2018. We included patients age in 0-60 days who were visiting the ED with clinical symptoms of fever.

Using a Multiclass Machine Learning Model to Predict the Outcome of Acute Ischemic Stroke Requiring Reperfusion Therapy.

Prediction of functional outcome in ischemic stroke patients is useful for clinical decisions. Previous studies mostly elaborate on the prediction of favorable outcomes. Miserable outcomes, which are usually defined as modified Rankin Scale (mRS) 5-6, should be considered as well before further invasive intervention.

Risk factors for depression in patients with Parkinson's disease: A nationwide nested case-control study.

Objectives: Patients with Parkinson's disease (PD) have higher prevalence of depression than the general population; however, the risk factors for depression in PD remain uncertain.

Methods/design: Using the 2000-2010 Taiwan National Health Insurance Research Database, we selected 1767 patients aged ≧ 40 years with new-onset PD during 2000-2009. Among them, 324 patients with a new incidence of depression were enrolled as cases and 972 patients without depression were randomly selected as controls.

A Multifactorial Evaluation of the Effects of Air Pollution and Meteorological Factors on Asthma Exacerbation.

In the real world, dynamic changes in air pollutants and meteorological factors coexist simultaneously. Studies identifying the effects of individual pollutants on acute exacerbation (AE) of asthma may overlook the health effects of the overall combination. A comprehensive study examining the influence of air pollution and meteorological factors is required.

Using Machine Learning to Predict Bacteremia in Febrile Children Presented to the Emergency Department.

Blood culture is frequently used to detect bacteremia in febrile children. However, a high rate of negative or false-positive blood culture results is common at the pediatric emergency department (PED). The aim of this study was to use machine learning to build a model that could predict bacteremia in febrile children.