Abstract
BackgroundArtificial intelligence methods applied to electronic medical records (EMRs) hold the potential to help physicians save time by sharpening their analysis and decisions, thereby improving the health of patients. On the one hand, machine learning algorithms have proven their effectiveness in extracting information and exploiting knowledge extracted from data. On the other hand, knowledge graphs capture human knowledge by relying on conceptual schemas and formalization and supporting reasoning. Leveraging knowledge graphs that are legion in the medical field, it is possible to pre-process and enrich data representation used by machine learning algorithms. Medical data standardization is an opportunity to jointly exploit the richness of knowledge graphs and the capabilities of machine learning algorithms.MethodsWe propose to address the problem of hospitalization prediction for patients with an approach that enriches vector representation of EMRs with information extracted from different knowledge graphs before learning and predicting. In addition, we performed an automatic selection of features resulting from knowledge graphs to distinguish noisy ones from those that can benefit the decision making. We report the results of our experiments on the PRIMEGE PACA database that contains more than 600,000 consultations carried out by 17 general practitioners (GPs).ResultsA statistical evaluation shows that our proposed approach improves hospitalization prediction. More precisely, injecting features extracted from cross-domain knowledge graphs in the vector representation of EMRs given as input to the prediction algorithm significantly increases the F1 score of the prediction.ConclusionsBy injecting knowledge from recognized reference sources into the representation of EMRs, it is possible to significantly improve the prediction of medical events. Future work would be to evaluate the impact of a feature selection step coupled with a combination of features extracted from several knowledge graphs. A possible avenue is to study more hierarchical levels and properties related to concepts, as well as to integrate more semantic annotators to exploit unstructured data.
Highlights
Patients are accustomed to meet with their general practitioners (GPs) for their health problems and as such, the electronic medical records (EMRs) in the GP’s possession are along the best available data sources for understanding factors related to the patient’s health condition
Selected machine learning algorithms For non sequential classification algorithms, we focus on three different machine learning algorithms which are frequently used in the literature: the logistic regression (LR) [11], random forests (RF) [12], and support vector machine (SVM) [13]
We aimed to measure the impact of enriching the vector representations of EMRs with different features extracted from knowledge graphs when predicting hospitalization
Summary
Patients are accustomed to meet with their general practitioners (GPs) for their health problems and as such, the electronic medical records (EMRs) in the GP’s possession are along the best available data sources for understanding factors related to the patient’s health condition. These records concern everyone, and each patient is unique, with regard to the biometric information (age, weight, gender...), diseases, interventions and lifestyle behavior. Our decision making support system relies on machine learning approaches trained and predicting on the enriched representations The combination of these two artificial intelligence techniques is evaluated by measuring the quality of the decision support for deciding an hospitalization. Medical data standardization is an opportunity to jointly exploit the richness of knowledge graphs and the capabilities of machine learning algorithms
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