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| license: cc-by-4.0 |
| pretty_name: >- |
| Topic model data from Finding Long-COVID: temporal topic modeling of |
| electronic health records from the N3C and RECOVER programs |
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| <span style="color: darkred">*Note: very small numbers are rounded to 0 in the HuggingFace dataset viewer.*</span> |
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| These data represent medical concept probabilities for 300 topics generated via Latent Direchlet Allocation applied |
| to 387M electronic health record conditions for 7.9M patients as described in |
| [Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs |
| ](https://www.nature.com/articles/s41746-024-01286-3#Sec7). |
| *Data were quality filtered and cleaned prior to modeling, including removal of COVID-19 and MIS-C as confounders (see publication).* |
| Topic `T-23` was most strongly associated with Long-COVID across all demographics. |
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| **If you use these data please cite the publication above.** |
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| Terms are encoded as OMOP CDM standard `concept_id` values; |
| see details at [OHDSI](https://ohdsi.org/) and [OHDSI ATHENA](https://athena.ohdsi.org). |
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| Columns: |
| - `topic_name`: topics are named T-1 to T-300, in order of weighted usage; also included in topic name are `U` (aggregate usage of topic in data), `H` (a measure of topic usage uniformity across data contributing sites), and `C` (a z-score normalized coherence value indicating relative topic quality) |
| - `concept_name`: human-readable name of the OMOP CDM concept |
| - `concept_id`: OMOP CDM standard concept ID |
| - `term_weight`: the probability of the `concept_id` being generated by the topic `topic_name` |
| - `relevance`: A measure of topic-relative weight - positive values indicate concepts more highly weighted in the topic than over all data, negative values less. |
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