ArticleArtificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )2019
Predicting patient's diagnoses and diagnostic categories from clinical-events in EHR data.
Article in Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Systematic Review of Topic Modeling Techniques for Electronic Health Records.Healthcare (Basel, Switzerland) · 2026Review
- A method for predicting mortality in acute mesenteric ischemia: Machine learning.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2024Article
- Personalized event prediction for Electronic Health Records.Artificial intelligence in medicine · 2023Article
- Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2021Article
- Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2021Article
- Modeling multivariate clinical event time-series with recurrent temporal mechanisms.Artificial intelligence in medicine · 2021Article
- Monitoring ICU Mortality Risk with A Long Short-Term Memory Recurrent Neural Network.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- Hierarchical Adaptive Multi-task Learning Framework for Patient Diagnoses and Diagnostic Category Classification.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In this paper we develop and study machine learning based models based on latent semantic indexing capable of automatically assigning diagnoses and diagnostic categories to patients based on structured clinical data in their Electronic Health record (EHR). These models can be either used for automatic coding of patient's diagnoses from structured EHR data at the time of discharge, or for supporting dynamic diagnosis and summarization of the patient condition. We study the performance of our diagnostic models on MIMIC-III EHR data.
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