ArticleJMIR medical informatics2016
A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences.
Article in JMIR medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- DOME: Directional medical embedding vectors from Electronic Health Records.Journal of biomedical informatics · 2025Article
- EntroLLM: Leveraging Entropy and Large Language Model Embeddings for Enhanced Risk Prediction with Wearable Device Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- PheWJAMIA open · 2024Article
- Mining for equitable health: Assessing the impact of missing data in electronic health records.Journal of biomedical informatics · 2023Article
- AdaDiag: Adversarial Domain Adaptation of Diagnostic Prediction with Clinical Event Sequences.Journal of biomedical informatics · 2022Article
- Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study.Journal of medical Internet research · 2022Article
- Modeling multivariate clinical event time-series with recurrent temporal mechanisms.Artificial intelligence in medicine · 2021Article
- Diagnostic Prediction with Sequence-of-sets Representation Learning for Clinical Events.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2020Article
- Distributed learning from multiple EHR databases: Contextual embedding models for medical events.Journal of biomedical informatics · 2019Article
- Predictive Modeling of the Hospital Readmission Risk from Patients' Claims Data Using Machine Learning: A Case Study on COPD.Scientific reports · 2019Article
- Assessing the Readability of Freely Available ICU Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Predictive Modeling of the Total Joint Replacement Surgery Risk: a Deep Learning Based Approach with Claims Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Deep learning for healthcare: review, opportunities and challenges.Briefings in bioinformatics · 2018Review
- Predictive modeling in urgent care: a comparative study of machine learning approaches.JAMIA open · 2018Article
- Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources.JMIR medical informatics · 2018Article
- Discriminative and Distinct Phenotyping by Constrained Tensor Factorization.Scientific reports · 2017Article
- Leveraging Collaborative Filtering to Accelerate Rare Disease Diagnosis.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundMedical 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).
objectiveOur 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.
methodsWe 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.
resultsWe 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.
conclusionsWe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.