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Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry
  1. Sunil Gupta1,
  2. Truyen Tran1,2,
  3. Wei Luo1,
  4. Dinh Phung1,
  5. Richard Lee Kennedy3,
  6. Adam Broad4,
  7. David Campbell4,
  8. David Kipp4,
  9. Madhu Singh4,
  10. Mustafa Khasraw3,4,
  11. Leigh Matheson5,
  12. David M Ashley3,4,5,
  13. Svetha Venkatesh1
  1. 1Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, Victoria, Australia
  2. 2Department of Computing, Curtin University, Perth, Western Australia, Australia
  3. 3School of Medicine, Deakin University, Geelong, Victoria, Australia
  4. 4Andrew Love Cancer Centre, Barwon Health, Geelong, Victoria, Australia
  5. 5Barwon Southwest Integrated Cancer Service, Geelong, Victoria, Australia
  1. Correspondence to Professor Svetha Venkatesh; svetha.venkatesh{at}deakin.edu.au

Abstract

Objectives Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes.

Setting A regional cancer centre in Australia.

Participants Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data.

Primary and secondary outcome measures Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC).

Results The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours.

Conclusions Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems.

  • Cancer
  • Survival
  • Prediction
  • Machine Learning
  • Electronic Medical Record

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/

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