Projects per year
Abstract
This review critically analyzes how machine learning is being utilized to support clinical decision-making in the management of potentially resectable pancreatic cancer. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, electronic searches of MEDLINE, Embase, PubMed and Cochrane Database were undertaken. Studies were assessed using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies (CHARMS) checklist. In total 89,959 citations were retrieved.
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.
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.
Original language | English |
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Pages (from-to) | 598-604 |
Number of pages | 7 |
Journal | Pancreas |
Volume | 48 |
Issue number | 5 |
DOIs | |
Publication status | Published - 1 May 2019 |
Keywords
- pancreatic cancer
- personalised medicine
- machine learning
- decision making
Fingerprint
Dive 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.Projects
- 1 Finished
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Improving Outcomes for Patients with Pancreatic Cancer
van der Meer, R. (Principal Investigator) & Morton, A. (Co-investigator)
1/08/16 → 31/07/19
Project: Research
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Digital twins in healthcare
van der Meer, R., 24 Jun 2024. 12 p.Research output: Contribution to conference › Keynote
Open AccessFile -
Computer simulated comparison of neoadjuvant versus upfront surgery for resectable pancreatic cancer: the application of machine-learning algorithms to support personalised decision-making
Bradley, 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 › Conference abstract › peer-review
Open 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 strategies
Bradley, 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 › Conference abstract › peer-review
Activities
- 1 Participation in workshop, seminar, course
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Inspiring Innovation in Cancer Care III
van der Meer, R. (Participant)
14 Nov 2023Activity: Participating in or organising an event types › Participation in workshop, seminar, course