title: Functionality of Triggers for Epilepsy Patients Assessed by Text and Data Mining of Medical and Nursing Records.
abstract: A trigger is a powerful tool for identifying adverse events to measure the level of any kind of harm caused in patient care. Studies with epilepsy patients have illustrated that using triggers as a methodology with data mining may increase patient well-being. The purpose of this study is to test the functionality and validity of the previously defined triggers to describe the status of epilepsy patient's well-being. In both medical and nursing data, the triggers described patients' well-being comprehensively. The narratives showed that there was overlapping in triggers. The preliminary results of triggers encourage us to develop some reminders to the documentation of epilepsy patient well-being. These provide healthcare professionals with further and more detailed information when necessary.
authors.affiliation: University of Eastern Finland.
authors.forename: Eija
authors.initials: E
authors.lastname: Kivekäs
authors.affiliation: University of Eastern Finland.
authors.forename: Ulla-Mari
authors.initials: UM
authors.lastname: Kinnunen
authors.affiliation: University of Eastern Finland.
authors.forename: Pekka
authors.initials: P
authors.lastname: Paananen
authors.affiliation: Kuopio University Hospital.
authors.forename: Reetta
authors.initials: R
authors.lastname: Kälviäinen
authors.affiliation: Kuopio University Hospital.
authors.forename: Kaisa
authors.initials: K
authors.lastname: Haatainen
authors.affiliation: University of Eastern Finland.
authors.forename: Kaija
authors.initials: K
authors.lastname: Saranto
mesh_terms: D015984:Causality; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D004827:Epilepsy / Q000175:diagnosis* / Q000453:epidemiology / Q000517:prevention & control; D005387:Finland / Q000453:epidemiology; D006801:Humans; D000069550:Machine Learning; D019300:Medical Errors / Q000517:prevention & control* / Q000706:statistics & numerical data; D009323:Natural Language Processing; D009737:Nursing Records / Q000706:statistics & numerical data*; D061214:Patient Safety; D011379:Prognosis; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D009626:Terminology as Topic