Hacktoberfest 2026: le issue che i maintainer hanno segnato per ottobre, aperte e adatte ai principianti. Sfoglia le issue Hacktoberfest

OMOP: add observation_period and visit_detail to the default config

Aperta
  • Qualcosa non va?
#1,294 1 commento 0 reazioni 0 assegnatari Vedi su GitHub

I maintainer di solito rispondono entro 3 giorni

Nessuno ha ancora preso questa issue.

Valutazione

Difficoltà
3/5
Tempo stimato
1-2 giorni
Idoneità per principianti
42/100
Tipo di issue
Funzionalità
Chiarezza
Abbastanza chiara
Stato di attività
Attiva
Stack tecnologico
python
Ambito
data, machine-learning

Direzione di ricerca

Start with the table list in pyhealth/datasets/configs/omop.yaml and the docstring in pyhealth/datasets/omop.py near lines 38-39, which promises observation and device_exposure. Add the missing table entries, then check that passing them no longer raises "Table ... not found in config". Before writing code, wait for maintainers to answer the two open questions (config version and task option versus filter), since they decide the shape of the change.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

I am Koji Miyazawa, a data scientist based in Japan. I work with healthcare big data, including DPC inpatient data and Japanese claims data. I use Bayesian statistics and machine learning. I build data warehouses. I maintain jpomop (https://github.com/kojikojikojikoji/jpomop), which converts Japanese DPC data to the OMOP CDM.

What I found (main at b984552):

  1. The default OMOP config loads seven tables: person, visit_occurrence, death, condition_occurrence, procedure_occurrence, drug_exposure and measurement. It has no observation_period, visit_detail or observation.
    https://github.com/sunlabuiuc/PyHealth/blob/b9845528fe85ed3afb614a10b1f0b8ba32ef1668/pyhealth/datasets/configs/omop.yaml

  2. The OMOPDataset docstring says observation and device_exposure can be added. They are not in the config, so passing them raises "Table ... not found in config".
    https://github.com/sunlabuiuc/PyHealth/blob/b9845528fe85ed3afb614a10b1f0b8ba32ef1668/pyhealth/datasets/omop.py#L38-L39

  3. MortalityPredictionOMOP gives label 1 if the person has any death record, and 0 if not. It does not check how long the person was observed.
    https://github.com/sunlabuiuc/PyHealth/blob/b9845528fe85ed3afb614a10b1f0b8ba32ef1668/pyhealth/tasks/mortality_prediction.py#L1125-L1136

The CDM v5.4 docs say that outside observation_period, "absence of an Event ... cannot be construed as evidence of absence of an Event." A person who leaves the data early can get label 0 even when we do not know the outcome.
https://ohdsi.github.io/CommonDataModel/cdm54.html#observation_period

My proposal:

  • Add observation_period and visit_detail to omop.yaml. Maybe also add observation and device_exposure to match the docstring.
  • Optional: in tasks, use observation_period to drop samples without enough follow-up. Keep this off by default, so current results do not change.

I can test this with synthetic OMOP data converted from DPC by jpomop. jpomop builds observation_period. It also builds visit_detail from daily ward codes.

Questions:

  • Should the config stay at "5.3", or move to 5.4?
  • Is a task option fine, or do you prefer a separate filter?

Would a PR be welcome?

Lingua principale
Python
Stelle
1.7k
Fork
803
Merge medio
5g 20h
PR unite (30g)
56

Preparare l'ambiente

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

Altre issue di sunlabuiuc/PyHealth

Tutte le issue di sunlabuiuc/PyHealth

Issue simili

Altre issue su Python

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.