title: Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  A regional cancer centre in Australia.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, Victoria, Australia.
authors.forename: Sunil
authors.initials: S
authors.lastname: Gupta
authors.forename: Truyen
authors.initials: T
authors.lastname: Tran
authors.forename: Wei
authors.initials: W
authors.lastname: Luo
authors.forename: Dinh
authors.initials: D
authors.lastname: Phung
authors.forename: Richard Lee
authors.initials: RL
authors.lastname: Kennedy
authors.forename: Adam
authors.initials: A
authors.lastname: Broad
authors.forename: David
authors.initials: D
authors.lastname: Campbell
authors.forename: David
authors.initials: D
authors.lastname: Kipp
authors.forename: Madhu
authors.initials: M
authors.lastname: Singh
authors.forename: Mustafa
authors.initials: M
authors.lastname: Khasraw
authors.forename: Leigh
authors.initials: L
authors.lastname: Matheson
authors.forename: David M
authors.initials: DM
authors.lastname: Ashley
authors.forename: Svetha
authors.initials: S
authors.lastname: Venkatesh
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001315:Australia; D016208:Databases, Factual; D057286:Electronic Health Records; D004581:Electronics; D006801:Humans; D000069550:Machine Learning; D008875:Middle Aged; D008954:Models, Biological; D009369:Neoplasms / Q000175:diagnosis / Q000473:pathology / Q000628:therapy; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D011379:Prognosis; D012372:ROC Curve; D012042:Registries; D012189:Retrospective Studies; D017741:Survivors
keywords: Cancer; Electronic Medical Record; Machine Learning; Prediction; Survival