Alle Papers – Machine Learning im Gesundheitswesen
104.164 Papers insgesamt · Seite 360 von 400
Alzheimer-type dementia prediction by sparse logistic regression using claim data
The relationship between electronic health records user interface features and data quality of patient clinical information: an integrative review
Validating the Knowledge Base of a Therapy Planning System
Explaining Time Series Predictions with Dynamic Masks
FedMood: Federated Learning on Mobile Health Data for Mood Detection
An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission
Privacy-Preserving Multi-Source Domain Adaptation for Medical Data
Automatic DPC Code Selection from Electronic Medical Records
Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions
EHR-Independent Predictive Decision Support Architecture Based on OMOP
Predicting Adverse Drug Events by Analyzing Electronic Patient Records
Multi-modality Hierarchical Recall based on GBDTs for Bipolar Disorder Classification
Value-based Healthcare: Can Artificial Intelligence Provide Value in Orthopaedic Surgery?
Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials
Intelligent Personal Health Record: Experience and Open Issues
Confidence Predictions for the Diagnosis of Acute Abdominal Pain
Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study
Statistical Analysis and Predicting Kidney Diseases using Machine Learning Algorithms
Neural maps for faithful data modelling in medicine — state-of-the-art and exemplary applications
Exploring the potential of federated learning in mental health research: a systematic literature review
Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities
C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
The role of big data in healthcare: A review of implications for patient outcomes and treatment personalization
Identifying prescription patterns with a topic model of diseases and medications
A Supervised Learning Approach to Predicting Coronary Heart Disease Complications in Type 2 Diabetes Mellitus Patients