Real-Time Identification of Pancreatic Cancer Cases Using Artificial Intelligence Developed on Danish Nationwide Registry Data.
Anders Bo BojesenFrank Viborg MortensenJakob KirkegårdPublished in: JCO clinical cancer informatics (2023)
Currently available nationwide live data and computational resources are sufficient for real-time identification of individuals with at least 10.1% risk of having undiagnosed pancreatic cancer and 17.7% risk of any GI cancer in the Danish population. For prospective identification of high-risk patients, the area under the curve is not a useful indication of the positive predictive values achieved. Viable design solutions are demonstrated, which address the main shortfalls of the existing cancer prediction efforts in relation to temporal biases, leaks, and performance metric inflation. Efficacy evaluations with resection rates and mortality as end points are needed.
Keyphrases
- artificial intelligence
- big data
- papillary thyroid
- machine learning
- end stage renal disease
- squamous cell
- electronic health record
- bioinformatics analysis
- deep learning
- chronic kidney disease
- cross sectional
- prognostic factors
- lymph node metastasis
- cardiovascular events
- risk factors
- childhood cancer
- quality improvement
- data analysis