Validating EHR clinical models using ontology patterns

Structure of Health Information With Different Information Models: Evaluation Study With Competency Questions

BACKGROUND

There is a flora of health care information models but no consensus on which to use. This leads to poor information sharing and duplicate modelling work. The amount and type of differences between models has, to our knowledge, not been evaluated.

OBJECTIVE

This work aims to explore how information structured with various information models differ in practice. Our hypothesis is that differences between information models are overestimated. This work will also assess the usability of competency questions as a method for evaluation of information models within health care.

METHODS

In this study, 4 information standards, 2 standards for secondary use, and 2 electronic health record systems were included as material. Competency questions were developed for a random selection of recommendations from a clinical guideline. The information needed to answer the competency questions was modelled according to each included information model, and the results were analyzed. Differences in structure and terminology were quantified for each combination of standards.

RESULTS

In this study, 36 competency questions were developed and answered. In general, similarities between the included information models were larger than the differences. The demarcation between information model and terminology was overall similar; on average, 45% of the included structures were identical between models. Choices of terminology differed within and between models; on average, 11% was usable in interaction with each other. The information models included in this study were able to represent most information required for answering the competency questions.

CONCLUSIONS

Different but same same; in practice, different information models structure much information in a similar fashion. To increase interoperability within and between systems, it is more important to move toward structuring information with any information model rather than finding or developing a perfect information model. Competency questions are a feasible way of evaluating how information models perform in practice.

An automatic diagnostic system based on deep learning, to diagnose hyperlipidemia

Background: Using artificial intelligence to assist in diagnosing diseases has become a contemporary research hotspot. Conventional automatic diagnostic method uses a conventional machine learning algorithm to distinguish features from which a professional doctor manually extracts features in diagnostic reports. But it can be difficult to collect large amounts of necessary medical data. Therefore, these methods face challenges with efficiency and accuracy. Method: Here, we proposed an automatic diagnostic system based on a deep learning algorithm to diagnose hyperlipidemia by using human physiological parameters. This model is a neural network which uses technologies of data extension and data correction. Firstly, we corrected and supplemented the original data by the method mentioned previously to solve the problem of lacking data. Secondly, the processed data were used to train a deep learning model. Deep learning model can automatically extract all the available information instead of artificially reducing the raw data. Therefore, it can reduce labor costs. The classifiers classify the data by using features previously mentioned. Finally, the system was evaluated with data from a test dataset. Result: It achieved 91.49% accuracy, 87.50% sensitivity, 93.33% specificity, and 87.50% precision with data from the test dataset. Conclusion: The proposed diagnostic method has a highly robust and accurate performance, and can be used for tentative diagnosis. It can automatically diagnose diseases by using human physiological parameters, thereby reducing labor cost, which results in effective improvement of clinical diagnostic efficiency.