000 04104nam a22005175i 4500
001 978-3-031-71452-8
003 DE-He213
005 20251030113306.0
007 cr nn 008mamaa
008 241110s2024 sz | s |||| 0|eng d
020 _a9783031714528
_9978-3-031-71452-8
024 7 _a10.1007/978-3-031-71452-8
_2doi
050 4 _aRG1-991
072 7 _aMJT
_2bicssc
072 7 _aMED033000
_2bisacsh
072 7 _aMKC
_2thema
100 1 _aBalayla, Jacques.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987291
245 1 0 _aTheorems on the Prevalence Threshold and the Geometry of Screening Curves
_h[electronic resource] :
_bA Bayesian Approach to Clinical Decision-Making /
_cby Jacques Balayla.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXXI, 308 p. 64 illus., 50 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1. Introductory remarks on screening -- 2. Clinical judgement and decision-making -- 3. Bayes' theorem -- 4. Odds, probability and likelihood ratios -- 5. Prevalence threshold, screening curves and screening probability square plane S -- 6. Sequential Bayesian updating -- 7. Orthogonal Bayesian updating, parallel testing and asymptotic convergence -- 8. Theorems on the geometric definition of the positive likelihood ratio K -- 9. The screening paradox and dynamic systems -- 10. Performance metrics of binary classifiers -- 11. Applications in Bayesian epistemology and artificial intelligence (AI) -- 12. What constitutes a sufficiently adequate binary classification system? -- 13. Gaussian distribution, confidence intervals, binomial (Wald) proportion, wilson score interval and delta theorem -- 14. Prevalence Threshold and Public Health -- 15. Shannon's entropy, Kullback-Leibler divergence, and mutual information in diagnostic systems -- 16. Estimating pretest probabilities -- 17. Case studies - clinical examples -- 18. Limitations and future directions.
520 _aIn Theorems on the Prevalence Threshold and the Geometry of Screening Curves, the author explores the mathematical underpinnings of screening and diagnostic testing, offering a unique and novel perspective which employs classical differential geometry and Bayesian theory to elucidate critical aspects of clinical decision-making. Taking the reader on a mathematical journey which bridges these seemingly unrelated worlds, the author presents a quantifiable framework on clinical judgement by introducing the “prevalence threshold” – a novel statistical parameter derived from Bayesian principles by means of the study of the geometry of screening curves. As the prevalence threshold demarcates the pretest probability level beyond which additional information ceases to significantly enhance the yield and reliability of a clinical assessment, it may serve as a benchmark for confidence in clinical decision-making. Given the theorems herein described, readers will find comprehensive analyses and insightful explorations of how these geometric concepts apply to real-world diagnostic scenarios, allowing the clinician to navigate clinical care more effectively at both the individual and public health levels.
650 0 _aGynecology .
_964086
650 0 _aDiagnosis.
_964942
650 0 _aInternal medicine.
650 1 4 _aGynecology.
_964086
650 2 4 _aDiagnosis.
_964942
650 2 4 _aInternal Medicine.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031714511
776 0 8 _iPrinted edition:
_z9783031714535
776 0 8 _iPrinted edition:
_z9783031714542
856 4 0 _uhttps://doi.org/10.1007/978-3-031-71452-8
912 _aZDB-2-SME
912 _aZDB-2-SXM
999 _c64845
_d64845