[HTML][HTML] Deep learning prediction models based on EHR trajectories: A systematic review
Abstract Background: Electronic health records (EHRs) are generated at an ever-increasing
rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients' …
rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients' …
A meta-path, attention-based deep learning method to support hepatitis carcinoma predictions for improved cirrhosis patient management
Hepatitis carcinoma (HCC) accounts for the majority of liver cancer–related deaths globally.
Cirrhosis often precedes HCC clinically in a strong, temporal relationship. Therefore …
Cirrhosis often precedes HCC clinically in a strong, temporal relationship. Therefore …
A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients
Objective Estimating the deterioration paths of chronic hepatitis B (CHB) patients is critical
for physicians' decisions and patient management. A novel, hierarchical multilabel graph …
for physicians' decisions and patient management. A novel, hierarchical multilabel graph …
Prediction of cannabis addictive patients with graph neural networks
Neurological research is closely intertwined with public health issues, and artificial
intelligence (AI) holds substantial potential in this domain. This study aims to investigate the …
intelligence (AI) holds substantial potential in this domain. This study aims to investigate the …
KNN-Based Patient Network and Ensemble Machine Learning for Disease Prediction
Abstract Machine learning has the ability to predict outcomes and identify features that
contribute to chronic disease prediction, enabling the classification of patients based on …
contribute to chronic disease prediction, enabling the classification of patients based on …
Check for updates KNN-Based Patient Network and Ensemble Machine Learning for Disease Prediction
H Lu - … Science: 12th International Conference, HIS 2023 …, 2023 - books.google.com
Machine learning has the ability to predict outcomes and identify features that contribute to
chronic disease prediction, enabling the classification of patients based on features. The aim …
chronic disease prediction, enabling the classification of patients based on features. The aim …
[PDF][PDF] Knowledge Graph Based Trustworthy Medical Code Recommendations.
Medical coding is about assigning standardized alphanumeric codes to diagnoses,
procedures, and interventions recorded in patients' clinical notes. These codes are essential …
procedures, and interventions recorded in patients' clinical notes. These codes are essential …
Web-Based Mental Health Predicting System Using K-Nearest Neighbors and XGBoost Algorithms
Problems with mental health are common presently and have been a worry for a long time.
Mental health problems, like anxiety, depression, and panic attacks, can be caused by …
Mental health problems, like anxiety, depression, and panic attacks, can be caused by …
Stress Analysis Prediction for Coma Patient Using Machine Learning
P Alwin Infant, J Charulatha, G Sadhana… - … Conference on Data & …, 2023 - Springer
Today's working IT professionals frequently struggle with stress issues. The patient is now
more likely to experience stress due to changing lifestyle and workplace cultures. Even …
more likely to experience stress due to changing lifestyle and workplace cultures. Even …
[PDF][PDF] Tekoäly apuna mielenterveysongelmien tunnistamisessa
T Salminen - 2023 - trepo.tuni.fi
Tässä tutkielmassa selvitetään, kuinka tekoälyä voidaan käyttää apuna
mielenterveysongelmien tunnistamisessa. Tarkoituksena on tutustua miten tekoäly käyttää …
mielenterveysongelmien tunnistamisessa. Tarkoituksena on tutustua miten tekoäly käyttää …