Knowledge-aware multi-center clinical dataset adaptation: Problem, method, and application.
J Biomed Inform
; 115: 103710, 2021 03.
Article
in En
| MEDLINE
| ID: mdl-33581323
ABSTRACT
Adaptable utilization of clinical data collected from multiple centers, prompted by the need to overcome the shifts between the dataset distributions, and exploit these different datasets for potential clinical applications, has received significant attention in recent years. In this study, we propose a novel approach to this task by infusing an external knowledge graph (KG) into multi-center clinical data mining. Specifically, we propose an adversarial learning model to capture shared patient feature representations from multi-center heterogeneous clinical datasets, and employ an external KG to enrich the semantics of the patient sample by providing both clinical center-specific and center-general knowledge features, which are trained with a graph convolutional autoencoder. We evaluate the proposed model on a real clinical dataset extracted from the general cardiology wards of a Chinese hospital and a well-known public clinical dataset (MIMIC III, pertaining to ICU clinical settings) for the task of predicting acute kidney injury in patients with heart failure. The achieved experimental results demonstrate the efficacy of our proposed model.
Key words
Full text:
1
Database:
MEDLINE
Main subject:
Data Mining
/
Heart Failure
Type of study:
Clinical_trials
/
Diagnostic_studies
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
J Biomed Inform
Journal subject:
INFORMATICA MEDICA
Year:
2021
Type:
Article
Affiliation country:
China