Projects per year
Six studies met the inclusion criteria. Three studies were Markov decision-analysis models comparing neoadjuvant therapy versus upfront surgery. Three studies predicted survival time using Bayesian modeling (n = 1), Artificial Neural Network (n = 1), and one study explored machine learning algorithms including: Bayesian Network, decision trees, nearest neighbor, and Artificial Neural Networks.
The main methodological issues identified were: limited data sources which limits generalizability and potentiates bias, lack of external validation, and the need for transparency in methods of internal validation, consecutive sampling, and selection of candidate predictors.
The future direction of research relies on expanding our view of the multidisciplinary team to include professionals from computing and data science with algorithms developed in conjunction with clinicians and viewed as aids, not replacement, to traditional clinical decision making.
- pancreatic cancer
- personalised medicine
- machine learning
- decision making
FingerprintDive into the research topics of 'Personalized pancreatic cancer management: a systematic review of how machine learning is supporting decision-making'. Together they form a unique fingerprint.
- 1 Finished
1/08/16 → 31/07/19
Computer simulated comparison of neoadjuvant versus upfront surgery for resectable pancreatic cancer: the application of machine-learning algorithms to support personalised decision-makingBradley, A., Van Der Meer, R. & McKay, C., 7 Dec 2020, In: British Journal of Surgery. 107, S4, p. 141-141 1 p., WS15.014.
Research output: Contribution to journal › Meeting abstract › peer-reviewOpen Access
Optimising outcomes for resectable pancreatic cancer by learning lessons from military strategy and the stockmarket: creation of a prognostic Bayesian belief network that makes personalised pre and post-operative predictions of outcome across competing treatment strategiesBradley, A., van der Meer, R. & McKay, C. J., 7 Dec 2020, In: British Journal of Surgery. 107, S4, p. 141 1 p., WS15.015.
Research output: Contribution to journal › Meeting abstract › peer-review
A prognostic Bayesian network that makes personalized predictions of poor prognostic outcome post resection of pancreatic ductal adenocarcinomaBradley, A., Van der Meer, R. & McKay, C. J., 9 Sep 2019, In: PLoS ONE. 14, 9, 14 p., e0222270.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile