title: Predicting patient acuity from electronic patient records.
abstract: BACKGROUND
  The ability to predict acuity (patients' care needs), would provide a powerful tool for health care managers to allocate resources. Such estimations and predictions for the care process can be produced from the vast amounts of healthcare data using information technology and computational intelligence techniques. Tactical decision-making and resource allocation may also be supported with different mathematical optimization models.
  
  
  METHODS
  This study was conducted with a data set comprising electronic nursing narratives and the associated Oulu Patient Classification (OPCq) acuity. A mathematical model for the automated assignment of patient acuity scores was utilized and evaluated with the pre-processed data from 23,528 electronic patient records. The methods to predict patient's acuity were based on linguistic pre-processing, vector-space text modeling, and regularized least-squares regression.
  
  
  RESULTS
  The experimental results show that it is possible to obtain accurate predictions about patient acuity scores for the coming day based on the assigned scores and nursing notes from the previous day. Making same-day predictions leads to even better results, as access to the nursing notes for the same day boosts the predictive performance. Furthermore, textual nursing notes allow for more accurate predictions than previous acuity scores. The best results are achieved by combining both of these information sources. The developed model achieves a concordance index of 0.821 when predicting the patient acuity scores for the following day, given the scores and text recorded on the previous day.
  
  
  CONCLUSIONS
  By applying language technology to electronic patient documents it is possible to accurately predict the value of the acuity scores of the coming day based on the previous daýs assigned scores and nursing notes.
authors.affiliation: University of Turku, Department of Nursing Science, Finland; Turku University of Applied Sciences, Finland. Electronic address: elina.kontio@turkuamk.fi.
authors.forename: Elina
authors.initials: E
authors.lastname: Kontio
authors.affiliation: University of Turku, Department of Information Technology, Finland.
authors.forename: Antti
authors.initials: A
authors.lastname: Airola
authors.affiliation: University of Turku, Department of Information Technology, Finland.
authors.forename: Tapio
authors.initials: T
authors.lastname: Pahikkala
authors.affiliation: University of Turku, Department of Nursing Science, Finland; Turku University Hospital, Finland.
authors.forename: Heljä
authors.initials: H
authors.lastname: Lundgren-Laine
authors.affiliation: Hospital District of Helsinki and Uusimaa, Finland.
authors.forename: Kristiina
authors.initials: K
authors.lastname: Junttila
authors.affiliation: Turku University Hospital, Finland.
authors.forename: Heikki
authors.initials: H
authors.lastname: Korvenranta
authors.affiliation: University of Turku, Department of Information Technology, Finland.
authors.forename: Tapio
authors.initials: T
authors.lastname: Salakoski
authors.affiliation: University of Turku, Department of Nursing Science, Finland; Turku University Hospital, Finland.
authors.forename: Sanna
authors.initials: S
authors.lastname: Salanterä
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005387:Finland; D055991:Health Records, Personal; D015233:Models, Statistical; D009323:Natural Language Processing; D009730:Nursing Assessment / Q000379:methods; D009737:Nursing Records / Q000706:statistics & numerical data*; D062072:Patient Acuity
keywords: Electronic patient record; Machine learning; Patient acuity; Patient classification system