title: Visualizing patient journals by combining vital signs monitoring and natural language processing.
abstract: This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admission, which is assessed by electronically monitoring vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on the existing patient journal to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see drastic changes in the patients' health, and thereby enabling staff to see where in the journal critical events have taken place.
authors.forename: Adnan
authors.initials: A
authors.lastname: Vilic
authors.forename: John Asger
authors.initials: JA
authors.lastname: Petersen
authors.forename: Karsten
authors.initials: K
authors.lastname: Hoppe
authors.forename: Helge B D
authors.initials: HB
authors.lastname: Sorensen
mesh_terms: D000368:Aged; D001794:Blood Pressure; D003422:Critical Care / Q000379:methods; D003718:Denmark; D006323:Heart Arrest / Q000175:diagnosis; D006760:Hospitalization; D006761:Hospitals; D006801:Humans; D007362:Intensive Care Units; D008490:Medical Informatics / Q000379:methods; D008875:Middle Aged; D015233:Models, Statistical; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods*; D009323:Natural Language Processing; D010100:Oxygen / Q000737:chemistry; D010343:Patient Admission; D012131:Respiratory Insufficiency / Q000175:diagnosis; D018805:Sepsis / Q000175:diagnosis; D055986:Vital Signs
chemical_list: D010100:Oxygen