# Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/deep-patient-an-unsupervised-representation-to-predict-the-future-of-patients/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Riccardo Miotto,Li Li,Brian Kidd,Joel T. Dudley |
| Citations | 1,761 |
| DOI | 10.1038/srep26094 |
| Fields | Biochemistry, Genetics and Molecular Biology,Computer Science,Health Professions |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.nature.com/articles/srep26094.pdf |
| OpenAlex ID | https://openalex.org/W2404901863 |
| PMID | 27185194 |
| Type | article |
| Year | 2016 |

## Paper authors

- [Li Li](https://scholariq.org/researchers/li-li/)

## Paper journal

- [Scientific Reports](https://scholariq.org/journals/scientific-reports/)

## Paper primary topic

- [Machine Learning in Healthcare](https://scholariq.org/topics/machine-learning-in-healthcare/)

## Paper topics

- [Machine Learning in Healthcare](https://scholariq.org/topics/machine-learning-in-healthcare/)
- [Artificial Intelligence in Healthcare](https://scholariq.org/topics/artificial-intelligence-in-healthcare/)
- [Biomedical Text Mining and Ontologies](https://scholariq.org/topics/biomedical-text-mining-and-ontologies/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.