title: A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences.
abstract: BACKGROUND
  Medical concepts are inherently ambiguous and error-prone due to human fallibility, which makes it hard for them to be fully used by classical machine learning methods (eg, for tasks like early stage disease prediction).
  
  
  OBJECTIVE
  Our work was to create a new machine-friendly representation that resembles the semantics of medical concepts. We then developed a sequential predictive model for medical events based on this new representation.
  
  
  METHODS
  We developed novel contextual embedding techniques to combine different medical events (eg, diagnoses, prescriptions, and labs tests). Each medical event is converted into a numerical vector that resembles its "semantics," via which the similarity between medical events can be easily measured. We developed simple and effective predictive models based on these vectors to predict novel diagnoses.
  
  
  RESULTS
  We evaluated our sequential prediction model (and standard learning methods) in estimating the risk of potential diseases based on our contextual embedding representation. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.79 on chronic systolic heart failure and an average AUC of 0.67 (over the 80 most common diagnoses) using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset.
  
  
  CONCLUSIONS
  We propose a general early prognosis predictor for 80 different diagnoses. Our method computes numeric representation for each medical event to uncover the potential meaning of those events. Our results demonstrate the efficiency of the proposed method, which will benefit patients and physicians by offering more accurate diagnosis.
authors.affiliation: Health Sciences, Department of Biomedical Informatics, University of California - San Diego, La Jolla, CA, United States.
authors.forename: Wael
authors.identifier: http://orcid.org/0000-0002-9294-2892
authors.initials: W
authors.lastname: Farhan
authors.affiliation: Health Sciences, Department of Biomedical Informatics, University of California - San Diego, La Jolla, CA, United States.|Department of Economics, Boston University, Boston, MA, United States.
authors.forename: Zhimu
authors.identifier: http://orcid.org/0000-0002-3863-5752
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Health Sciences, Department of Biomedical Informatics, University of California - San Diego, La Jolla, CA, United States.
authors.forename: Yingxiang
authors.identifier: http://orcid.org/0000-0002-9216-164X
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Health Sciences, Department of Biomedical Informatics, University of California - San Diego, La Jolla, CA, United States.
authors.forename: Shuang
authors.identifier: http://orcid.org/0000-0001-6420-983X
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, United States.
authors.forename: Fei
authors.identifier: http://orcid.org/0000-0001-9459-9461
authors.initials: F
authors.lastname: Wang
authors.affiliation: Health Sciences, Department of Biomedical Informatics, University of California - San Diego, La Jolla, CA, United States.
authors.forename: Xiaoqian
authors.identifier: http://orcid.org/0000-0001-9933-2205
authors.initials: X
authors.lastname: Jiang
keywords: clinical decision support; clinical event context embedding; early classification; temporal phenotyping