Methods Inf Med 2022; 61(05/06): 174-184
DOI: 10.1055/a-1905-5639
Original Article

DxGenerator: An Improved Differential Diagnosis Generator for Primary Care Based on MetaMap and Semantic Reasoning

Ali Sanaeifar
1   Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
,
Saeid Eslami
1   Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
,
Mitra Ahadi
2   Department of Gastroenterology and Hepatology, School of Medicine, Mashhad University of Medical Sciences, MUMS, Iran
,
Mohsen Kahani
3   Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
,
Hassan Vakili Arki
1   Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
› Author Affiliations

Abstract

Background In recent years, researchers have used many computerized interventions to reduce medical errors, the third cause of death in developed countries. One of such interventions is using differential diagnosis generators in primary care, where physicians may encounter initial symptoms without any diagnostic presuppositions. These systems generate multiple diagnoses, ranked by their likelihood. As such, these reports' accuracy can be determined by the location of the correct diagnosis in the list.

Objective This study aimed to design and evaluate a novel practical web-based differential diagnosis generator solution in primary care.

Methods In this research, a new online clinical decision support system, called DxGenerator, was designed to improve diagnostic accuracy; to this end, an attempt was made to converge a semantic database with the unified medical language system (UMLS) knowledge base, using MetaMap tool and natural language processing. In this regard, 120 diseases of gastrointestinal organs causing abdominal pain were modeled into the database. After designing an inference engine and a pseudo-free-text interactive interface, 172 patient vignettes were inputted into DxGenerator and ISABEL, the most accurate similar system. The Wilcoxon signed ranked test was used to compare the position of correct diagnoses in DxGenerator and ISABEL. The α level was defined as 0.05.

Results On a total of 172 vignettes, the mean and standard deviation of correct diagnosis positions improved from 4.2 ± 5.3 in ISABEL to 3.2 ± 3.9 in DxGenerator. This improvement was significant in the subgroup of uncommon diseases (p-value < 0.05).

Conclusion Using UMLS knowledge base and MetaMap Tools can improve the accuracy of diagnostic systems in which terms are entered in a free text manner. Applying these new methods will help the medical community accept medical diagnostic systems better.

Protection of Human and Animal Subjects

No patients were included in this study. This study used patient vignettes to evaluate the proposed system.


Supplementary Material



Publication History

Received: 10 March 2022

Accepted: 17 July 2022

Accepted Manuscript online:
20 July 2022

Article published online:
17 November 2022

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