File size: 1,771 Bytes
1f87cf5 5a8f995 dd26b64 1f87cf5 115b3a1 1f87cf5 bbec4b1 115b3a1 1f87cf5 115b3a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ---
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
---
<span style="color: darkred">*Note: very small numbers are rounded to 0 in the HuggingFace dataset viewer.*</span>
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.
**If you use these data please cite the publication above.**
Terms are encoded as OMOP CDM standard `concept_id` values;
see details at [OHDSI](https://ohdsi.org/) and [OHDSI ATHENA](https://athena.ohdsi.org).
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.
|