Bayesian Analysis in the Diagnostic Process

Velanovich, Vic

doi:10.1177/0885713x9400900405

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…2,9 The dual theory of cognition 10,11 is long established as fast thinking (system one) encompassing heuristics, pattern recognition, and intuition, with slow thinking (system two) representing cognisant analytical or algorithmic approaches to decision making. [12][13][14][15] Increasingly regarded as a false dichotomy, the cognitive continuum theory affords a middle ground, in which both systems are used to varying degrees. 16 Gut feelings have been conceptualised as unconscious system-one processes that resist incorporation into guidelines; 1,17,18 this puts them at odds with Western medical culture, which is dominated by analytical approaches epitomised by evidence-based guidelines in spite of there being evidence that slower analytical approaches do not necessarily lead to improved diagnostic decisions.…”

Section: Introductionmentioning

confidence: 99%

Understanding the role of GPs’ gut feelings in diagnosing cancer in primary care: a systematic review and meta-analysis of existing evidence

et al. 2020

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BackgroundGrowing evidence for the role of GPs’ gut feelings in cancer diagnosis raises questions about their origin and role in clinical practice.AimTo explore the origins of GPs’ gut feelings for cancer, their use, and their diagnostic utility.Design and settingSystematic review and meta-analysis of international research on GPs’ gut feelings in primary care.MethodSix databases were searched from inception to July 2019, and internet searches were conducted. A segregated method was used to analyse, then combine, quantitative and qualitative findings.ResultsTwelve articles and four online resources were included that described varied conceptualisations of gut feelings. Gut feelings were often initially associated with patients being unwell, rather than with a suspicion of cancer, and were commonly experienced in response to symptoms and non-verbal cues. The pooled odds of a cancer diagnosis were four times higher when gut feelings were recorded (OR 4.24, 95% confidence interval = 2.26 to 7.94); they became more predictive of cancer as clinical experience and familiarity with the patient increased. Despite being included in some clinical guidelines, GPs had varying experiences of acting on gut feelings as some specialists questioned their diagnostic value. Consequently, some GPs ignored or omitted gut feelings from referral letters, or chose investigations that did not require specialist approval.ConclusionGPs’ gut feelings for cancer were conceptualised as a rapid summing up of multiple verbal and non-verbal patient cues in the context of the GPs’ clinical knowledge and experience. Triggers of gut feelings not included in referral guidance deserve further investigation as predictors of cancer. Non-verbal cues that trigger gut feelings appear to be reliant on continuity of care and clinical experience; they tend to remain poorly recorded and are, therefore, inaccessible to researchers.

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Section: Introductionmentioning

confidence: 99%

Understanding the role of GPs’ gut feelings in diagnosing cancer in primary care: a systematic review and meta-analysis of existing evidence

et al. 2020

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“…The Bayesian diagnostic approach is a cornerstone in statistical inference and particularly useful in medical diagnosis [6,17,18]. The approach relies on Bayes' theorem [7].…”

Section: Bayesian Diagnostic Approachmentioning

confidence: 99%

A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis

Chatzimichail,

Hatjimihail

2023

Diagnostics

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Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a failure to capture the intricate relations between diagnostic tests and the varying prevalence of diseases. To explore this further, we have developed a freely available specialized computational tool that employs Bayesian inference to calculate the posterior probability of disease diagnosis. This novel software comprises of three distinct modules, each designed to allow users to define and compare parametric and nonparametric distributions effectively. The tool is equipped to analyze datasets generated from two separate diagnostic tests, each performed on both diseased and nondiseased populations. We demonstrate the utility of this software by analyzing fasting plasma glucose, and glycated hemoglobin A1c data from the National Health and Nutrition Examination Survey. Our results are validated using the oral glucose tolerance test as a reference standard, and we explore both parametric and nonparametric distribution models for the Bayesian diagnosis of diabetes mellitus.

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“…To take first steps toward a causal diagnostic nosology, it is important to consider the central role of causality in the diagnostic process in medicine (112)(113)(114)(115). Such consideration requires key concepts to be kept distinct.…”

Section: First Causal Principles On Diagnosis In Medicinementioning

confidence: 99%

Mental health progress requires causal diagnostic nosology and scalable causal discovery

Saxe

Bickman

et al. 2022

Front. Psychiatry

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Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other medical disorders. Progress on outcome improvement for all medical disorders, including mental disorders, requires research capable of discovering causality at sufficient scale and speed, and a diagnostic nosology capable of encoding the causal knowledge that is discovered. Accordingly, the field’s guiding paradigm limits progress by maintaining: (a) a diagnostic nosology (DSM-5) with a profound lack of causality; (b) a misalignment between mental health etiologic research and nosology; (c) an over-reliance on clinical trials beyond their capabilities; and (d) a limited adoption of newer methods capable of discovering the complex etiology of mental disorders. We detail feasible directions forward, to achieve greater levels of progress on improving outcomes for mental disorders, by: (a) the discovery of knowledge on the complex etiology of mental disorders with application of Causal Data Science methods; and (b) the encoding of the etiological knowledge that is discovered within a causal diagnostic system for mental disorders.

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Bayesian Analysis in the Diagnostic Process

Cited by 14 publications

References 19 publications

Understanding the role of GPs’ gut feelings in diagnosing cancer in primary care: a systematic review and meta-analysis of existing evidence

Understanding the role of GPs’ gut feelings in diagnosing cancer in primary care: a systematic review and meta-analysis of existing evidence

A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis

Mental health progress requires causal diagnostic nosology and scalable causal discovery

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