Decision support system for diagnosing Rheumatic-Musculoskeletal Disease using fuzzy cognitive map technique

Akinnuwesi, Boluwaji A.; Adegbite, Blessing A.; Adelowo, Femi; Ima-Edomwonyi, U.; Fashoto, Gbenga

doi:10.1016/j.imu.2019.100279

Cited by 21 publications

(11 citation statements)

References 32 publications

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“…Interestingly, a similar trial published in 1989 by McCrea et al, including 119 medical students diagnosing 10 rheumatology cases, showed more positive results [ 28 ]. Another DDSS specifically developed for RMDs using fuzzy cognitive map technique showed a diagnostic accuracy of 87% in a validation study with 15 cases [ 29 ]. A diagnostic support system for specialists in internal medicine suggested the correct diagnosis in 48 of 50 cases (96%) using case vignettes [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

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Accuracy and usability of a diagnostic decision support system in the diagnosis of three representative rheumatic diseases: a randomized controlled trial among medical students

et al. 2021

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Background An increasing number of diagnostic decision support systems (DDSS) exist to support patients and physicians in establishing the correct diagnosis as early as possible. However, little evidence exists that supports the effectiveness of these DDSS. The objectives were to compare the diagnostic accuracy of medical students, with and without the use of a DDSS, and the diagnostic accuracy of the DDSS system itself, regarding the typical rheumatic diseases and to analyze the user experience. Methods A total of 102 medical students were openly recruited from a university hospital and randomized (unblinded) to a control group (CG) and an intervention group (IG) that used a DDSS (Ada – Your Health Guide) to create an ordered diagnostic hypotheses list for three rheumatic case vignettes. Diagnostic accuracy, measured as the presence of the correct diagnosis first or at all on the hypothesis list, was the main outcome measure and evaluated for CG, IG, and DDSS. Results The correct diagnosis was ranked first (or was present at all) in CG, IG, and DDSS in 37% (40%), 47% (55%), and 29% (43%) for the first case; 87% (94%), 84% (100%), and 51% (98%) in the second case; and 35% (59%), 20% (51%), and 4% (51%) in the third case, respectively. No significant benefit of using the DDDS could be observed. In a substantial number of situations, the mean probabilities reported by the DDSS for incorrect diagnoses were actually higher than for correct diagnoses, and students accepted false DDSS diagnostic suggestions. DDSS symptom entry greatly varied and was often incomplete or false. No significant correlation between the number of symptoms extracted and diagnostic accuracy was seen. It took on average 7 min longer to solve a case using the DDSS. In IG, 61% of students compared to 90% in CG stated that they could imagine using the DDSS in their future clinical work life. Conclusions The diagnostic accuracy of medical students was superior to the DDSS, and its usage did not significantly improve students’ diagnostic accuracy. DDSS usage was time-consuming and may be misleading due to prompting wrong diagnoses and probabilities. Trial registration DRKS.de, DRKS00024433. Retrospectively registered on February 5, 2021.

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Section: Discussionmentioning

confidence: 99%

“…Limitations of the study include the unblinded pilot study design and relatively [ 18 , 20 , 29 ] small sample size. The trial was registered retrospectively, and we did not pre-specify an effect size nor carried out a sample size calculation.…”

Section: Discussionmentioning

confidence: 99%

Accuracy and usability of a diagnostic decision support system in the diagnosis of three representative rheumatic diseases: a randomized controlled trial among medical students

et al. 2021

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“…Patients and rheumatologists, however, still seem to believe in the positive potential impact of a patient facing Digital Diagnostic Decision Support Systems. A recent study showed that 80% of physicians agreed that an app that could diagnose symptoms of rheumatic diseases in patients will be helpful (22).…”

Section: Comparison With Prior Workmentioning

confidence: 99%

“…An RMD DDSS, based on a fuzzy cognitive map technique showed a diagnostic accuracy of 87% in a validation study with 15 vignette cases (22). In a prospective pilot study, 34 patients completed an NHS and WebMD symptom checker.…”

Section: Comparison With Prior Workmentioning

confidence: 99%

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Rheumatic? - A Digital Diagnostic Decision Support Tool for Individuals Suspecting Rheumatic Diseases: A Multicenter Validation Study

Knevel

Knitza

Hensvold

et al. 2021

Preprint

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BackgroundDigital diagnostic decision support tools promise to accelerate diagnosis and increase health care efficiency in rheumatology. Rheumatic? is an online tool developed by specialists in rheumatology and general medicine together with patients and patient organizations. It calculates a risk score for several rheumatic diseases. In the current pilot study, we retrospectively test Rheumatic? for its ability to differentiate symptoms from immune-mediated diseases from other rheumatic and musculoskeletal complaints and disorders in patients visiting rheumatology clinics. MethodsThe performance of Rheumatic? was tested using data from 175 patients from three university rheumatology centers covering two different settings:A. Risk-RA phase setting. Here, we tested whether Rheumatic? could predict the development of arthritis in 50 individuals with musculoskeletal complaints and anti-citrullinated protein antibody positivity from the KI (Karolinska Institutet)B. Early arthritis setting. Here, we tested whether Rheumatic? could predict the development of an immune-mediated rheumatic disease in i) EUMC (Erlangen) n=52 patients and ii) LUMC (Leiden) n=73 patients.In each setting, we examined the discriminative power of the total score with the Wilcoxon rank test and the area-under-the-receiver-operating-characteristic curve (AUC-ROC). Next, we calculated the test characteristics for these patients at a rheumatology setting passing the first or second threshold for at least one of the rheumatic diseases.ResultsThe total test score clearly differentiated between: A) individuals developing arthritis or not, median 245 versus 163, P < 0.0001, AUC-ROC = 75.3B) patients with an immune-mediated arthritic disease or not, in EUMC median 191 versus 107, P < 0.0001, AUC-ROC = 79.0, and LUMC median 262 versus 212, P < 0.0001, AUC-ROC = 53.6.Threshold-1 (advising on seeking primary care doctor) was highly specific in two centers (0.72, 0.87 and 0.23, respectively) and far more sensitive (0.67, 0.61 and 0.67) in KI, EUMC respectively LUMC.Threshold-2 was very specific in all three centers but not very sensitive: specificity of 1.0, 0.96 and 0.91, sensitivity 0.05, 0.07, 0.14 in KI, EUMC respectively LUMC. ConclusionsRheumatic? is a web-based patient-centered multilingual diagnostic tool capable of differentiating immune-mediated rheumatic conditions from other musculoskeletal problems. The scoring system might be further optimized, for which we will perform a prospective study.

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Smart Health Application for Remote Tracking of Ambulatory Patients

Butt

Anjum

Hassan

et al. 2021

Smart Healthcare System Design

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Decision support system for diagnosing Rheumatic-Musculoskeletal Disease using fuzzy cognitive map technique

Cited by 21 publications

References 32 publications

Accuracy and usability of a diagnostic decision support system in the diagnosis of three representative rheumatic diseases: a randomized controlled trial among medical students

Accuracy and usability of a diagnostic decision support system in the diagnosis of three representative rheumatic diseases: a randomized controlled trial among medical students

Rheumatic? - A Digital Diagnostic Decision Support Tool for Individuals Suspecting Rheumatic Diseases: A Multicenter Validation Study

Smart Health Application for Remote Tracking of Ambulatory Patients

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