A Software Tool for Calculating the Uncertainty of Diagnostic Accuracy Measures

Chatzimichail, Theodora; Hatjimihail, Aristides T.

doi:10.3390/diagnostics11030406

Cited by 11 publications

(14 citation statements)

References 39 publications

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“…These systems are designed to assist medical decision making through suggestions and alerts, and can even help professionals reach a diagnosis. [87][88][89] These systems are bound to grow in the future, especially since scientific studies have shown their interest for various conditions such as common chronic diseases. 87 It is essential to work jointly on these aspects to ensure the development of programs that promote optimized diagnosis that is compatible with these new systems.…”

Section: Discussionmentioning

confidence: 99%

“…Future studies should also examine these various concepts while recontextualizing them in decision support approaches, namely clinical decision support systems (CDSS 87 ). These systems are designed to assist medical decision making through suggestions and alerts, and can even help professionals reach a diagnosis 87–89 . These systems are bound to grow in the future, especially since scientific studies have shown their interest for various conditions such as common chronic diseases 87 .…”

Section: Discussionmentioning

confidence: 99%

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Impact of uncertainty intolerance on clinical reasoning: A scoping review of the 21st‐century literature

Brun

Zerhouni

Akinyemi

et al. 2022

Evaluation Clinical Practice

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Rationale, Aims and Objectives Clinical reasoning is currently extensively studied to find out how to make proper diagnoses. Literature indicates that intolerance of uncertainty (IU) may have a strong negative impact on clinical reasoning. We summarize the various consequences of IU on clinical reasoning. Methods A scoping review was conducted using relevant keywords to scientific databases (i.e., Google Scholar, Medline, PsycINFO and PBSC) from September to November 2021. Complementary research included relevant articles and articles retrieved through Google Scholar's alert system. We included articles about healthcare professionals as defined by the French Public Health Code (As defined here: https://www.vie-publique.fr/fiches/37855-categories-de-professionnels-de-sante-code-se-la-sante-publique), and articles reporting on the impact of IU or uncertainty management on clinical reasoning. Results We retrieved 1853 articles, of which 8 were kept for final analysis considering our inclusion criteria. Two behaviour categories were affected by uncertainty intolerance: investigative and prescriptive behaviours. Regarding the investigation process, mismanagement of uncertainty appeared to lead to reasoning bias, potentially resulting in diagnostic errors. IU was associated with withholding information, more referrals to peers and less use of new medical interventions. Regarding prescription behaviours, IU among health professionals could lead to overprescribing unnecessary or dangerous tests. IU was also associated with more antibiotic prescriptions for conditions where antibiotics are to be used carefully. Conclusion Few studies have yet addressed the impact of IU on clinical reasoning. IU's influence is primarily observed on investigative and prescribing behaviours. More studies are needed to fully understand the impact of IU on clinical reasoning itself, and not only on practical consequences.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Impact of uncertainty intolerance on clinical reasoning: A scoping review of the 21st‐century literature

Brun

Zerhouni

Akinyemi

et al. 2022

Evaluation Clinical Practice

View full text Add to dashboard Cite

show abstract

“…Interestingly, computing the first-order time-dependent derivatives of the signals not only stationarizes the signals but also provides an unbiased standpoint for extracting discriminative features for diagnosis/FDI. On the other hand, recent solutions on uncertainty modeling (when properly harnessed) may offer reliable solutions [43]. Particularly for wavelet choice, it may be exhaustive to discover an optimal wavelet considering the numerous wavelets (and their variants) available for exploration; however, non-complex wavelets offer a reliable kick-start to choose from (if the E-SER approach seems time-consuming/computationally expensive).…”

Section: Discussion and Drawn Insightsmentioning

confidence: 99%

A Wavelet-Based Diagnostic Framework for CRD Engine Injection Systems under Emulsified Fuel Conditions

Akpudo

Hur

2021

Electronics

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The impact of the constituent oxides of nitrogen, carbon, sulphur, and other particulate matter which make up the gas emissions from diesel engines has motivated several control techniques for these pollutants. Water-in-diesel emulsions provide a reliable solution, but the wear effects on the fuel injection system (FIS) still pose remarkable concerns. Because pressure signals from the common rail (CR) reflect the dynamics associated with varying emulsion compositions and at varying engine RPMs, an investigative (and diagnostic) study was conducted on a KIA Sorento 2004 four-cylinder line engine at various water-in-diesel emulsion compositions and engine speeds. Alongside visual/microscopic inspections and spectral analyses, the diagnostic framework proposed herein functions on the use of standardized first-order differentials of the CR pressure signals to generate reliable continuous wavelet coefficients (CWCs) which capture discriminative spectral and transient information for accurate diagnosis. The results show that by extracting the CWCs from the first-order CR pressure differentials up to the 512th scale on a Mexican hat wavelet, adequate fault parameters can be extracted for use by a deep neural network (DNN) whose hyperparameters were globally optimized following a grid search. With a test accuracy of 92.3% against other widely-used ML-based diagnostic tools, the proposed DNN-based diagnostics tool was empirically assessed using several performance evaluation metrics.

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“…In the original publication [1], there was a mistake in the legend for Figure 13. The term submodule was misprinted, and the term relative was omitted from the first sentence of the legend.…”

Section: Figure Legendmentioning

confidence: 99%

“…There was an error in the original publication regarding an equation in an uncertainty propagation rule. This error has been corrected in the original publication [1], specifically in the last line (Appendix B.1.). The corrected equation is as follows:…”

Section: Text Correctionmentioning

confidence: 99%

Correction: Chatzimichail, T.; Hatjimihail, A.T. A Software Tool for Calculating the Uncertainty of Diagnostic Accuracy Measures. Diagnostics 2021, 11, 406

Chatzimichail,

Hatjimihail

2024

Diagnostics

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A Software Tool for Calculating the Uncertainty of Diagnostic Accuracy Measures

Cited by 11 publications

References 39 publications

Impact of uncertainty intolerance on clinical reasoning: A scoping review of the 21st‐century literature

Impact of uncertainty intolerance on clinical reasoning: A scoping review of the 21st‐century literature

A Wavelet-Based Diagnostic Framework for CRD Engine Injection Systems under Emulsified Fuel Conditions

Correction: Chatzimichail, T.; Hatjimihail, A.T. A Software Tool for Calculating the Uncertainty of Diagnostic Accuracy Measures. Diagnostics 2021, 11, 406

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