Alle Papers – Machine Learning im Gesundheitswesen

AN UNSUPERVISED PATTERN (SYNDROME IN TRADITIONAL CHINESE MEDICINE) DISCOVERY ALGORITHM BASED ON ASSOCIATION DELINEATED BY REVISED MUTUAL INFORMATION IN CHRONIC RENAL FAILURE DATA

How Can We Ensure Reproducibility and Clinical Translation of Machine Learning Applications in Laboratory Medicine?

Deep Learning for Alzheimer’s Disease Prediction: A Comprehensive Review

Predict Mortality in Patients Infected with COVID-19 Virus Based on Observed Characteristics of the Patient using Logistic Regression

Automated medical histories: Factors determining patient performance

Challenges of developing artificial intelligence‐assisted tools for clinical medicine

DocOnTap: AI-based disease diagnostic system and recommendation system

Using natural language processing of clinical text to enhance identification of opioid‐related overdoses in electronic health records data

Is videopupillography useful in the diagnosis of Alzheimer's disease?

Heatmaps for Visual Explainability of CNN-Based Predictions for Multivariate Time Series with Application to Healthcare

SoFDA: an integrated web platform from syndrome ontology to network-based evaluation of disease–syndrome–formula associations for precision medicine

Making Diagnoses from DIS Data

Uncertainty-Aware Attention for Reliable Interpretation and Prediction

Detection of overdose and underdose prescriptions—An unsupervised machine learning approach

Overcoming Alzheimer’s Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies

OCTANE: Oncology Clinical Trial Annotation Engine

Explaining Black-Box Classifiers with ILP – Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules

The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions

Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data

Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients

Deep Learning Prediction of Mild Cognitive Impairment using Electronic Health Records

Symptom networks in glioma patients: understanding the multidimensionality of symptoms and quality of life

Deep models for medical knowledge engineering

Complication Risk Profiling in Diabetes Care: A Bayesian Multi-Task and Feature Relationship Learning Approach

Performance of a computable phenotype for identification of patients with diabetes within PCORnet: The Patient‐Centered Clinical Research Network