scholarly journals Clinical Decision Analysis using Decision Tree

2014 ◽  
pp. e2014025 ◽  
Author(s):  
Jong-Myon Bae
Keyword(s):  
Decision Tree ◽  
Decision Analysis ◽  
Clinical Decision

Automated Development of Clinical Strategies Using Multistage Decision Analysis

1986 ◽  
Vol 25 (04) ◽  
pp. 207-214 ◽  
Author(s):  
P. Glasziou
Keyword(s):  
Decision Tree ◽  
Decision Analysis ◽  
Decision Trees ◽  
Optimal Strategy ◽  
Cholestatic Jaundice ◽  
Clinical Strategies ◽  
Simultaneous Study

SummaryThe development of investigative strategies by decision analysis has been achieved by explicitly drawing the decision tree, either by hand or on computer. This paper discusses the feasibility of automatically generating and analysing decision trees from a description of the investigations and the treatment problem. The investigation of cholestatic jaundice is used to illustrate the technique.Methods to decrease the number of calculations required are presented. It is shown that this method makes practical the simultaneous study of at least half a dozen investigations. However, some new problems arise due to the possible complexity of the resulting optimal strategy. If protocol errors and delays due to testing are considered, simpler strategies become desirable. Generation and assessment of these simpler strategies are discussed with examples.

scholarly journals Multiparametric MRI followed by targeted prostate biopsy for men with suspected prostate cancer: a clinical decision analysis

BMJ Open ◽  
2014 ◽  
Vol 4 (6) ◽  
pp. e004895-e004895 ◽  
Author(s):  
S. R. Willis ◽  
H. U. Ahmed ◽  
C. M. Moore ◽  
I. Donaldson ◽  
M. Emberton ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Analysis ◽  
Prostate Biopsy ◽  
Clinical Decision ◽  
Multiparametric Mri

scholarly journals Bayesian Network Models with Decision Tree Analysis for Management of Childhood Malaria in Malawi

2020 ◽  
Author(s):  
Sanya B. Taneja ◽  
Gerald P. Douglas ◽  
Gregory F. Cooper ◽  
Marian G. Michaels ◽  
Marek J. Druzdzel ◽  
...  
Keyword(s):  
Decision Tree ◽  
Bayesian Network ◽  
Clinical Features ◽  
Predictive Models ◽  
Clinical Decision Making ◽  
Performance Metrics ◽  
Expert Knowledge ◽  
Clinical Decision ◽  
Automated Method ◽  
Childhood Malaria

Abstract Background: Malaria is a major cause of death in children under five years old in low- and middle-income countries such as Malawi. Accurate diagnosis and management of malaria can help reduce the global burden of childhood morbidity and mortality. Trained healthcare workers in rural health centers manage malaria with limited supplies of malarial diagnostic tests and drugs for treatment. A clinical decision support system that integrates predictive models to provide an accurate prediction of malaria based on clinical features could aid healthcare worker in judicious use of testing and treatment. We developed Bayesian network (BN) models to predict the probability of malaria from clinical features and an illustrative decision tree to model the decision to use or not use a malaria rapid diagnostic test (mRDT).Methods: We developed two BN models from data that were collected in a national survey of outpatient encounters of children in Malawi. The target diagnosis is taken as the result of mRDT. The first BN model was created manually with expert knowledge, and the second model was derived using an automated method followed by modifications guided by expert knowledge. The performance of the BN models was compared to other statistical models on a range of performance metrics. We developed a decision tree that integrates predictions from these predictive models with the costs of mRDT and a course of recommended treatment. Results: Compared to the logistic regression and random forest models, the BN models had similar accuracy of 64% but had higher sensitivity at the cost of lower specificity at the default threshold. Sensitivity analysis of the decision tree showed that at low (below 0.04) and high (above 0.4) probabilities of malaria in a child, the preferred decision that minimizes expected costs is not to perform mRDT.Conclusion: In resource-constrained settings, judicious use of mRDT is important. Predictive models in combination with decision analysis can provide personalized guidance on when to use mRDT in the management of childhood malaria. BN models can be efficiently derived from data to support such clinical decision making.

Primer: using decision analysis to improve clinical decision making in urology

2006 ◽  
Vol 3 (8) ◽  
pp. 439-448 ◽  
Author(s):  
Elena B Elkin ◽  
Andrew J Vickers ◽  
Michael W Kattan
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Clinical Decision Making ◽  
Clinical Decision

Clinical Decision Analysis in the Treatment of Acute Otitis Media in a Child Over 2 Years of Age

2002 ◽  
Vol 31 (01) ◽  
pp. 023 ◽  
Author(s):  
Casey R.A. Manarey ◽  
Brian D. Westerberg ◽  
Stephen A. Marion
Keyword(s):  
Decision Analysis ◽  
Otitis Media ◽  
Acute Otitis Media ◽  
Clinical Decision

Clinical Decision Analysis as a Means of Technology Assessment: The Effectiveness of Intraoperative Cholangiography

1992 ◽  
Vol 8 (1) ◽  
pp. 185-197 ◽  
Author(s):  
Thomas E. Scott ◽  
Itzhak Jacoby
Keyword(s):  
Decision Analysis ◽  
Mortality Risk ◽  
Technology Assessment ◽  
Common Duct ◽  
Cost Effective ◽  
Clinical Decision ◽  
Intraoperative Cholangiography ◽  
Lower Mortality ◽  
Effective Option ◽  
Lower Mortality Risk

AbstractThree strategies for timely detection of common duct stones are examined by decision analysis: the use of intraoperative cholangiography (IOC) in ALL, NONE, or in SOME of the cases that are selected by the estimated probability of a common duct stone. Selective use of IOC is the most cost-effective option and offers a slightly lower mortality risk.

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