NLICE: Synthetic Medical Record Generation for Effective Primary Healthcare Differential Diagnosis

Zaid Al-Ars, Obinna Agba, Zhuoran Guo, Christiaan Boerkamp, Ziyaad Jaber, Tareq Jaber

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

This paper offers a systematic method for creating medical knowledge-grounded patient records for use in activities involving differential diagnosis. Additionally, an assessment of machine learning models that can differentiate between various conditions based on given symptoms is also provided. We use a public disease-symptom data source called SymCat in combination with Synthea to construct the patients records. In order to increase the expressive nature of the synthetic data, we use a medically-standardized symptom modeling method called NLICE to augment the synthetic data with additional contextual information for each condition. In addition, Naive Bayes and Random Forest models are evaluated and compared on the synthetic data. The paper shows how to successfully construct SymCat-based and NLICE-based datasets. We also show results for the effectiveness of using the datasets to train predictive disease models. The SymCat-based dataset is able to train a Naive Bayes and Random Forest model yielding a 58.8% and 57.1% Top-1 accuracy score, respectively. In contrast, the NLICE-based dataset improves the results, with a Top-1 accuracy of 82.0% and Top-5 accuracy values of more than 90% for both models. Our proposed data generation approach solves a major barrier to the application of artificial intelligence methods in the healthcare domain. Our novel NLICE symptom modeling approach addresses the incomplete and insufficient information problem in the current binary symptom representation approach.
Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)
Editors Cristina Ceballos
Place of PublicationPiscataway
PublisherIEEE
Pages397-402
Number of pages6
ISBN (Electronic)979-8-3503-9311-8
ISBN (Print)979-8-3503-9312-5
DOIs
Publication statusPublished - 2023
Event2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE) - Dayton, United States
Duration: 4 Dec 20236 Dec 2023
Conference number: 23rd

Publication series

NameProceedings - 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering, BIBE 2023

Conference

Conference2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)
Country/TerritoryUnited States
CityDayton
Period4/12/236/12/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • medical records
  • synthetic data
  • differential diagnosis
  • machine learning

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