title: Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model.
abstract: BACKGROUND
  Clinical deterioration (ICU transfer and cardiac arrest) occurs during approximately 5-10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used routine vital signs and laboratory values obtained from the electronic medical record (EMR) along with a machine learning algorithm called a neural network to develop a prediction model that would increase the predictive accuracy and decrease false alarm rates.
  
  
  METHODS
  Retrospective cohort study.
  
  
  METHODS
  The hematologic malignancy unit in an academic medical center in the United States.
  
  
  METHODS
  Adult patients admitted to the hematologic malignancy unit from 2009 to 2010.
  
  
  METHODS
  None.
  
  
  RESULTS
  Vital signs and laboratory values were obtained from the electronic medical record system and then used as predictors (features). A neural network was used to build a model to predict clinical deterioration events (ICU transfer and cardiac arrest). The performance of the neural network model was compared to the VitalPac Early Warning Score (ViEWS). Five hundred sixty five consecutive total admissions were available with 43 admissions resulting in clinical deterioration. Using simulation, the neural network outperformed the ViEWS model with a positive predictive value of 82% compared to 24%, respectively.
  
  
  CONCLUSIONS
  We developed and tested a neural network-based prediction model for clinical deterioration in patients hospitalized in the hematologic malignancy unit. Our neural network model outperformed an existing model, substantially increasing the positive predictive value, allowing the clinician to be confident in the alarm raised. This system can be readily implemented in a real-time fashion in existing EMR systems.
authors.affiliation: Division of Pulmonary/Critical Care, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Scott B
authors.initials: SB
authors.lastname: Hu
authors.affiliation: Division of Hematology/Oncology, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Deborah J L
authors.initials: DJ
authors.lastname: Wong
authors.affiliation: Downstate College of Medicine, State University of New York, Albany, New York, United States of America.
authors.forename: Aditi
authors.initials: A
authors.lastname: Correa
authors.affiliation: Department of Biomathematics, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Ning
authors.initials: N
authors.lastname: Li
authors.affiliation: Division of Pulmonary/Critical Care, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Jane C
authors.initials: JC
authors.lastname: Deng
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015331:Cohort Studies; D003422:Critical Care / Q000379:methods; D042241:Early Diagnosis; D057286:Electronic Health Records; D005260:Female; D006323:Heart Arrest / Q000175:diagnosis* / Q000401:mortality; D019337:Hematologic Neoplasms / Q000401:mortality / Q000473:pathology* / Q000628:therapy*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D008991:Monitoring, Physiologic; D016571:Neural Networks, Computer; D011379:Prognosis; D012189:Retrospective Studies; D016896:Treatment Outcome; D055986:Vital Signs / Q000502:physiology; D055815:Young Adult