Machine Learning-Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study

Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults

Machine learning-based prediction of pulmonary embolism to reduce unnecessary computed tomography scans in gastrointestinal cancer patients: a retrospective multicenter study

Increased coherence predicts medical refractoriness in patients with temporal lobe epilepsy on monotherapy

PRISM: Deep metric learning based personal grouping method to reduce intersubject variability for motor imagery brain-computer interface

A novel deterioration prediction system for mild COVID-19 patients in Korea: a retrospective study

Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data

Comparative study of time-frequency transformation methods for ECG signal classification

Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning

Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population

Machine learning model for predicting immediate postoperative desaturation using spirometry signal data

Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19

Real-time prediction of massive transfusion during cesarean section using intraoperative hemodynamic monitoring data

Residual one-dimensional convolutional neural network for neuromuscular disorder classification from needle electromyography signals with explainability, COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 226

A machine learning approach for predicting suicidal ideation in post stroke patients, Scientific Reports, 12(1), 1

Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals

Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury

Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification

Automated artifact elimination of physiological signals using a deep belief network: An application for continuously measured arterial blood pressure waveforms

Machine learning model for predicting immediate postoperative desaturation using spirometry signal data

Real-time prediction of massive transfusion during cesarean section using intraoperative hemodynamic monitoring data

Residual one-dimensional convolutional neural network for neuromuscular disorder classification from needle electromyography signals with explainability, COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 226

A machine learning approach for predicting suicidal ideation in post stroke patients, Scientific Reports, 12(1), 1

Real-Time Evaluation of Cerebral Autoregulation Based on Near-Infrared Spectroscopy to Predict Clinical Outcome after Bypass Surgery in Moyamoya Disease, Biomed Research International, 2022(9), 9

sing the Axial Oblique View of Computed Tomography (CT) in Evaluating Femoral Anteversion: A Comparative Cadaveric Study, DIAGNOSTICS, 12(8)

Detection of Depression and Suicide Risk Based on Text From Clinical Interviews Using Machine Learning: Possibility of a New Objective Diagnostic Marker, FRONTIERS IN PSYCHIATRY, 13

A machine learning approach for predicting suicidal ideation in post stroke patients

Real-Time Evaluation of Cerebral Autoregulation Based on Near-Infrared Spectroscopy to Predict Clinical Outcome after Bypass Surgery in Moyamoya Disease

Using the Axial Oblique View of Computed Tomography (CT) in Evaluating Femoral Anteversion: A Comparative Cadaveric Study