CondBEHRT: A Conditional Probability Based Transformer for Modeling Medical Ontology

Detta är en Master-uppsats från Högskolan i Halmstad/Akademin för informationsteknologi

Sammanfattning: In recent years the number of electronic healthcare records (EHRs)has increased rapidly. EHR represents a systematized collection of patient health information in a digital format. EHR systems maintain diagnoses, medications, procedures, and lab tests associated with the patients at each time they visit the hospital or care center. Since the information is available into multiple visits to hospitals or care centers, the EHR can be used to increasing quality care. This is especially useful when working with chronic diseases because they tend to evolve. There have been many deep learning methods that make use of these EHRs to solve different prediction tasks. Transformers have shown impressive results in many sequence-to-sequence tasks within natural language processing. This paper will mainly focus on using transformers, explicitly using a sequence of visits to do prediction tasks. The model presented in this paper is called CondBEHRT. Compared to previous state-of-art models, CondBEHRT will focus on using as much available data as possible to understand the patient’s trajectory. Based on all patients, the model will learn the medical ontology between diagnoses, medications, and procedures. The results show that the inferred medical ontology that has been learned can simulate reality quite well. Having the medical ontology also gives insights about the explainability of model decisions. We also compare the proposed model with the state-of-the-art methods using two different use cases; predicting the given codes in the next visit and predicting if the patient will be readmitted within 30 days.

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