Computer-Based Diagnostic Expert Systems in Rheumatology: Where Do We Stand in 2014?

Alder, Hannes; Michel, Beat A.; Marx, Christian; Tamborrini, Giorgio; Langenegger, Thomas; Bruehlmann, Pius; Steurer, Johann; Wildi, Lukas

doi:10.1155/2014/672714

Cited by 31 publications

(14 citation statements)

References 54 publications

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“…A direct test of this hypothesis will require considering how best to provide physicians with EWS results or incorporate the additional cues used by physicians into EWSs. Such integration is an area for ongoing and future research, noting that prior efforts to integrate artificial intelligence systems into medical decision making have largely failed to achieve widespread adoption 25–27…”

Section: Discussionmentioning

confidence: 99%

Comparing the predictive ability of a commercial artificial intelligence early warning system with physician judgement for clinical deterioration in hospitalised general internal medicine patients: a prospective observational study

et al. 2019

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ObjectiveOur study compares physician judgement with an automated early warning system (EWS) for predicting clinical deterioration of hospitalised general internal medicine patients.DesignProspective observational study of clinical predictions made at the end of the daytime work-shift for an academic general internal medicine floor team compared with the risk assessment from an automated EWS collected at the same time.SettingInternal medicine teaching wards at a single tertiary care academic medical centre in the USA.ParticipantsIntern physicians working on the internal medicine wards and an automated EWS (Rothman Index by PeraHealth).OutcomeClinical deterioration within 24 hours including cardiac or pulmonary arrest, rapid response team activation or unscheduled intensive care unit transfer.ResultsWe collected predictions for 1874 patient days and saw 35 clinical deteriorations (1.9%). The area under the receiver operating curve (AUROC) for the EWS was 0.73 vs 0.70 for physicians (p=0.571). A linear regression model combining physician and EWS predictions had an AUROC of 0.75, outperforming physicians (p=0.016) and the EWS (p=0.05).ConclusionsThere is no significant difference in the performance of the EWS and physicians in predicting clinical deterioration at 24 hours on an inpatient general medicine ward. A combined model outperformed either alone. The EWS and physicians identify partially overlapping sets of at-risk patients suggesting they rely on different cues or decision rules for their predictions.Trial registration number NCT02648828.

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Section: Discussionmentioning

confidence: 99%

Comparing the predictive ability of a commercial artificial intelligence early warning system with physician judgement for clinical deterioration in hospitalised general internal medicine patients: a prospective observational study

et al. 2019

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“…Despite the elapsed time, such systems are still not part of standard rheumatology care. Alder et al concluded in a review in 2014 that the validation process of rheumatology DDSS was in general underappreciated and none of the systems seemed to have succeeded in daily practice (12).…”

Section: Comparison With Prior Workmentioning

confidence: 99%

“…Digital diagnostic decision support systems (DDSS) and in particular online self-referral (OSR) systems and symptom checkers (SC) promise to accelerate diagnosis in rheumatic diseases (11)(12)(13) and improve health care e ciency. Currently more than 100 SCs exist (14) and are increasingly used by patients.…”

Section: Introductionmentioning

confidence: 99%

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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“…A useful fuzzy expert system was developed by Azim and colleagues [8] for the detection of fatigue among drivers in order to prevent road accidents. Alder and colleagues [9] conducted a review on different expert systems that have been developed and applied Rheumatology. Duan and colleagues [10] applied the knowledge of prefix-tree to create a Nursing care recommender system.…”

Section: Review Of Relevant Literaturementioning

confidence: 99%

Ebinformatics: Ebola fuzzy informatics systems on the diagnosis, prediction and recommendation of appropriate treatments for Ebola virus disease (EVD)

Oluwagbemi

Abimbola

2016

Informatics in Medicine Unlocked

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a b s t r a c tEbola Virus Disease (EVD) also known as the Ebola hemorrhagic fever is a very deadly infectious disease to humankind. Therefore, a safer and complementary method of diagnosis is to employ the use of an expert system in order to initiate a platform for pre-clinical treatments, thus acting as a precursor to comprehensive medical diagnosis and treatments. This work presents a design and implementation of informatics software and a knowledge-based expert system for the diagnosis, and provision of recommendations on the appropriate type of recommended treatment to the Ebola Virus Disease (EVD).In this research an Ebola fuzzy informatics system was developed for the purpose of diagnosing and providing useful recommendations to the management of the EVD in West Africa and other affected regions of the world. It also acts as a supplementary resource in providing medical advice to individuals in Ebola -ravaged countries. This aim was achieved through the following objectives: (i) gathering of facts through the conduct of a comprehensive continental survey to determine the knowledge and perception level of the public about factors responsible for the transmission of the Ebola Virus Disease (ii) develop an informatics software based on information collated from health institutions on basic diagnosis of the Ebola Virus Disease-related symptoms (iii) adopting and marrying the knowledge of fuzzy logic and expert systems in developing the informatics software. Necessary requirements were collated from the review of existing expert systems, consultation of journals and articles, and internet sources. Online survey was conducted to determine the level at which individuals are aware of the factors responsible for the transmission of the Ebola Virus Disease (EVD). The expert system developed, was designed to use fuzzy logic as its inference mechanism along with a set of rules. A knowledge base was created to help provide diagnosis on the Ebola Virus Disease (EVD). The Root Sum Square (RSS) was adopted as a fuzzy inference method. The degree of participation of each input parameter was shown using the triangular membership function and the defuzzification technique used is the Center of Gravity (CoG).The resulting software produced a user-friendly desktop-based, Windows-based, application and the tools used were explained in the results section in three (3) separate phases. First, a comprehensive online survey was conducted over a period of about 3-9 months. 100 Participants participated in the survey on the perception and knowledge analysis of different individuals about Ebola Virus Disease (EVD) transmission factors. 31% of the participants didn't know that there is presently no cure for Ebola. 28% believed that there is presently a cure for Ebola. 43% agreed that Ebola is both air-borne and water-borne, while 33% disagreed, 24% do not know. 23% believed that insects and mosquitoes can help in transmitting the Ebola Virus Disease (EVD), while 30% were completely ignorant. We noticed that ignorance was a major limiti...

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Computer-Based Diagnostic Expert Systems in Rheumatology: Where Do We Stand in 2014?

Cited by 31 publications

References 54 publications

Comparing the predictive ability of a commercial artificial intelligence early warning system with physician judgement for clinical deterioration in hospitalised general internal medicine patients: a prospective observational study

Comparing the predictive ability of a commercial artificial intelligence early warning system with physician judgement for clinical deterioration in hospitalised general internal medicine patients: a prospective observational study

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

Ebinformatics: Ebola fuzzy informatics systems on the diagnosis, prediction and recommendation of appropriate treatments for Ebola virus disease (EVD)

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