Abstract:
The integration of patient data is crucial in healthcare informatics. It involves organizing and integrating heterogeneous health
data from various Electronic Health Records (EHRs). Attribute alignment is a fundamental step in data integration. It involves mapping
data attributes across different datasets. Most of the data maintained in EHRs does not follow standard terminologies in healthcare.
Therefore, it becomes difficult to integrate patient health data from diverse data sources for generating historic medical records. The
research work carried out overcomes this problem by developing a vital sign ontology using OpenEHR health standards. It helps to
map the vital signs observations of the patients from its proprietary sources uniformly. The work also leverages the power of supervised
learning algorithms to automate the mapping of different health datasets to the proposed ontology. The approach is evaluated on patient
health datasets, considering both standard and non-standard datasets. The research work employs different machine learning algorithms,
such as Support Vector Machine (SVM), Naive Bayes, Logistic Regression, k-nearest neighbor (KNN), AdaBoost, and Neural network,
in order to evaluate the best algorithm for the proposed approach. The evaluation results conclude that Naive Bayes exhibits the highest
accuracy, with minimum misclassification rate, in both the training and validation phases for automatically mapping the health datasets
with the proposed ontology.
