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OMOP: add observation_period and visit_detail to the default config

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Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
42/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
data, machine-learning

Línea de trabajo

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.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

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?

Lenguaje dominante
Python
Estrellas
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Forks
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Merge medio
5 d 7 h
PR fusionados (30 d)
58

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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