Methods Inf Med
DOI: 10.1055/a-1905-5639

Ali Sanaeifar

1   Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran (the Islamic Republic of) (Ringgold ID: RIN37552)

,

Saeid Eslami

2   Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran (the Islamic Republic of) (Ringgold ID: RIN37552)

3   Medical Informatics, University of Amsterdam, Amsterdam, Netherlands

,

Mitra Ahadi

4   Department of Gastroenterology and Hepatology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran (the Islamic Republic of) (Ringgold ID: RIN37552)

,

Mohsen Kahani

5   Ferdowsi University of Mashhad, Mashhad, Iran (the Islamic Republic of) (Ringgold ID: RIN48440)

,

Hasan Vakili Arki

6   Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran (the Islamic Republic of) (Ringgold ID: RIN37552)

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 (NLP). 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 alpha 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.

Received: 10 March 2022

Accepted after revision: 17 July 2022

Accepted Manuscript online:
20 July 2022

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