2019
DOI: 10.1177/0962280219841082
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Integration of elicited expert information via a power prior in Bayesian variable selection: Application to colon cancer data

Abstract: Background Building tools to support personalized medicine needs to model medical decision-making. For this purpose, both expert and real world data provide a rich source of information. Currently, machine learning techniques are developing to select relevant variables for decision-making. Rather than using data-driven analysis alone, eliciting prior information from physicians related to their medical decision-making processes can be useful in variable selection. Our framework is electronic health records dat… Show more

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Cited by 7 publications
(8 citation statements)
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“…Of the 148 studies, 21 (14.2%) used AI to adapt doses in antidrug regimen [ 35 ], assess effect and combination of dose, evaluate cancer therapeutic procedures, and recommend treatment schemes based on EHRs. The precision, recall, specificity, F-score, accuracy, and AUC were above 0.67, except in a model for drug repurposing reported by Wu et al [ 36 ].…”
Section: Resultsmentioning
confidence: 99%
“…Of the 148 studies, 21 (14.2%) used AI to adapt doses in antidrug regimen [ 35 ], assess effect and combination of dose, evaluate cancer therapeutic procedures, and recommend treatment schemes based on EHRs. The precision, recall, specificity, F-score, accuracy, and AUC were above 0.67, except in a model for drug repurposing reported by Wu et al [ 36 ].…”
Section: Resultsmentioning
confidence: 99%
“…Higher number of spatial knots placed strategically or equidistantly over  improves the performance of the model. Hence, (9) becomes…”
Section: Model Formulationmentioning
confidence: 99%
“…It is a clever way to borrow information about a parameter from a previous study to a current study in situations where historical data are available. It is a useful statistical tool in clinical research, [6][7][8] public health studies, 9,10 epidemiological studies, 11 and quality control. 12 The incorporation of historical data into a current study can be helpful in results interpretation.…”
Section: Introductionmentioning
confidence: 99%
“…A number of guidelines on developing risk prediction tools 1,10–12 have recommended using clinical expert knowledge to guide the selection of predictors. If done informally, there is a concern that this might reinforce expert cognitive biases without fully capturing their uncertainties, which would result in biased risk scores, limited generalizability, and highly variable practice 13,14 . Expert judgment (belief or intuition) is often a combination of fact‐based knowledge and subjective interpretation.…”
Section: Introductionmentioning
confidence: 99%