Predicting patient outcomes with graph representation learning
Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused
heavily on the physiological time series data, largely ignoring sparse data such as …
heavily on the physiological time series data, largely ignoring sparse data such as …
GRAM: graph-based attention model for healthcare representation learning
Deep learning methods exhibit promising performance for predictive modeling in healthcare,
but two important challenges remain:-Data insufficiency: Often in healthcare predictive …
but two important challenges remain:-Data insufficiency: Often in healthcare predictive …
Kame: Knowledge-based attention model for diagnosis prediction in healthcare
The goal of diagnosis prediction task is to predict the future health information of patients
from their historical Electronic Healthcare Records (EHR). The most important and …
from their historical Electronic Healthcare Records (EHR). The most important and …
[HTML][HTML] Medical knowledge graph completion based on word embeddings
M Gao, J Lu, F Chen - Information, 2022 - mdpi.com
The aim of Medical Knowledge Graph Completion is to automatically predict one of three
parts (head entity, relationship, and tail entity) in RDF triples from medical data, mainly text …
parts (head entity, relationship, and tail entity) in RDF triples from medical data, mainly text …
Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications
HSA Khatib, S Neupane, HK Manchukonda… - arXiv preprint arXiv …, 2024 - arxiv.org
Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that
focuses on individualized patient care by mapping the patient's health information in a …
focuses on individualized patient care by mapping the patient's health information in a …
An explainable knowledge distillation method with XGBoost for ICU mortality prediction
Abstract Background and Objective: Mortality prediction is an important task in intensive care
unit (ICU) for quantifying the severity of patients' physiological condition. Currently, scoring …
unit (ICU) for quantifying the severity of patients' physiological condition. Currently, scoring …
[HTML][HTML] Development and validation of an interpretable 3 day intensive care unit readmission prediction model using explainable boosting machines
S Hegselmann, C Ertmer, T Volkert, A Gottschalk… - Frontiers in …, 2022 - frontiersin.org
Background Intensive care unit (ICU) readmissions are associated with mortality and poor
outcomes. To improve discharge decisions, machine learning (ML) could help to identify …
outcomes. To improve discharge decisions, machine learning (ML) could help to identify …
Interpretable Disease Progression Prediction Based on Reinforcement Reasoning Over a Knowledge Graph
Objective: To combine medical knowledge and medical data to interpretably predict the risk
of disease. Methods: We formulated the disease progression prediction task as a random …
of disease. Methods: We formulated the disease progression prediction task as a random …
[HTML][HTML] Leveraging representation learning for the construction and application of a knowledge graph for traditional Chinese medicine: Framework development study
H Weng, J Chen, A Ou, Y Lao - JMIR Medical Informatics, 2022 - medinform.jmir.org
Background: Knowledge discovery from treatment data records from Chinese physicians is a
dramatic challenge in the application of artificial intelligence (AI) models to the research of …
dramatic challenge in the application of artificial intelligence (AI) models to the research of …
[HTML][HTML] Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations
Background Identifying new potential treatment options for medical conditions that cause
human disease burden is a central task of biomedical research. Since all candidate drugs …
human disease burden is a central task of biomedical research. Since all candidate drugs …