A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences
| Autor: | Wael Farhan, Xiaoqian Jiang, Fei Wang, Yingxiang Huang, Shuang Wang, Zhimu Wang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
clinical decision support
early classification Computer science Health Informatics 02 engineering and technology Semantics Machine learning computer.software_genre clinical event context embedding Clinical decision support system 03 medical and health sciences 0302 clinical medicine Health Information Management Intensive care Similarity (psychology) 0202 electrical engineering electronic engineering information engineering 030212 general & internal medicine Medical diagnosis Representation (mathematics) Original Paper Receiver operating characteristic Event (computing) business.industry temporal phenotyping 3. Good health 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer |
| Zdroj: | JMIR Medical Informatics |
| ISSN: | 2291-9694 |
| DOI: | 10.2196/medinform.5977 |
| Popis: | 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. [JMIR Med Inform 2016;4(4):e39] |
| Databáze: | OpenAIRE |
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