# An unsupervised machine learning model for discovering latent infectious diseases using social media data

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/an-unsupervised-machine-learning-model-for-discovering-latent-infectious/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Sunghoon Lim,Conrad S. Tucker,Soundar Kumara |
| Citations | 127 |
| DOI | 10.1016/j.jbi.2016.12.007 |
| Fields | Computer Science,Medicine |
| Open Access | true |
| OA Status | bronze |
| OA URL | https://www.sciencedirect.com/science/article/pii/S1532046416301812/pdf |
| OpenAlex ID | https://openalex.org/W2565943263 |
| PMID | 28034788 |
| Type | article |
| Year | 2016 |

## Paper authors

- [Conrad S. Tucker](https://scholariq.org/researchers/conrad-s-tucker/)

## Paper journal

- [Journal of Biomedical Informatics](https://scholariq.org/journals/journal-of-biomedical-informatics/)

## Paper primary topic

- [Data-Driven Disease Surveillance](https://scholariq.org/topics/data-driven-disease-surveillance/)

## Paper topics

- [Data-Driven Disease Surveillance](https://scholariq.org/topics/data-driven-disease-surveillance/)
- [Sentiment Analysis and Opinion Mining](https://scholariq.org/topics/sentiment-analysis-and-opinion-mining/)
- [Zoonotic diseases and public health](https://scholariq.org/topics/zoonotic-diseases-and-public-health/)

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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.