Healthcare data integration using machine learning: A case study evaluation with health information-seeking behavior...

The amount of data in health care is rapidly rising, leading to multiple datasets generated for any given individual. Data integration involves mapping variables in different datasets together to form a combined dataset which can then be used to conduct different types of analyses. However, with increasing numbers of variables, manual mapping of a dataset can become inefficient. Another approach is to use text classification through machine learning to classify the variables to a schema.

Our aim was to create and evaluate the use of machine learning methods for the integration of data from datasets across health information-seeking behavior (HISB) databases.

Four online databases relevant to the research field were selected for integration. Two experiments were designed for dataset mapping: intra-database mapping using the one data source, and inter-database mapping to map datasets between the four databases. We compared logistic regression (LR), a random forest classifier (RFC), and neural network (NN) models by F1-score for two methods of integration. A third experiment was an ablation study that used all the available data to create a model for classifying HISB variables in a dataset.

In intra-database mapping, the mean F1 score for an LR classifier (0.787) was better than the RFC score (0.767) and fully connected NN (0.735). In inter-database mapping, the LR (0.245) scored best, however, this was dependent on which database was used as a training source. Using all the databases, these top three models were able to correctly classify 90-91% of the variables. Removing one dataset improved scores and resulted in a model able to correctly classify 95-96% of the HISB variables.

As part of data integration, a neural network can be used as an approach to map the variables of a dataset. The developed models can be used to classify the HISB terms in a database.