title: A framework for feature extraction from hospital medical data with applications in risk prediction.
abstract: BACKGROUND
  Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities.
  
  
  RESULTS
  Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72).
  
  
  CONCLUSIONS
  The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks.
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, VIC, 3220, Australia. truyen.tran@deakin.edu.au.|Department of Computing, Curtin University, Perth, WA, Australia. truyen.tran@deakin.edu.au.
authors.forename: Truyen
authors.initials: T
authors.lastname: Tran
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, VIC, 3220, Australia. wei.luo@deakin.edu.au.
authors.forename: Wei
authors.initials: W
authors.lastname: Luo
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, VIC, 3220, Australia. dinh.phung@deakin.edu.au.
authors.forename: Dinh
authors.initials: D
authors.lastname: Phung
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, VIC, 3220, Australia. sunil.gupta@deakin.edu.au.
authors.forename: Sunil
authors.initials: S
authors.lastname: Gupta
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, VIC, 3220, Australia. santu.rana@deakin.edu.au.
authors.forename: Santu
authors.initials: S
authors.lastname: Rana
authors.affiliation: School of Medicine, Deakin University, Geelong, VIC, Australia. lee.kennedy@deakin.edu.au.
authors.forename: Richard Lee
authors.initials: RL
authors.lastname: Kennedy
authors.affiliation: Barwon Health, Geelong, VIC, Australia. annla@barwonhealth.org.au.
authors.forename: Ann
authors.initials: A
authors.lastname: Larkins
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, VIC, 3220, Australia. svetha.venkatesh@deakin.edu.au.
authors.forename: Svetha
authors.initials: S
authors.lastname: Venkatesh
mesh_terms: D000368:Aged; D019540:Area Under Curve; D015897:Comorbidity; D016208:Databases, Factual; D003920:Diabetes Mellitus / Q000209:etiology*; D005260:Female; D006761:Hospitals; D006801:Humans; D016015:Logistic Models; D008297:Male; D001523:Mental Disorders / Q000209:etiology*; D008962:Models, Theoretical; D011014:Pneumonia / Q000209:etiology*; D029424:Pulmonary Disease, Chronic Obstructive / Q000209:etiology*; D018570:Risk Assessment; D012984:Software