alan-turing-institute / alan-turing-institute/sqlsynthgen
OMOP Use Case: Features for extubation modelling
- Dominant language
- Python
- Stars
- 12
- Forks
- 4
- PR merge metrics
- No merged PRs in 30d
Description
This forms part of a series of issues to document how SqlSynthGen is used to create a synthetic version of CCHIC OMOP data. This specific issue will document how we selected and created synthetic data containing features used for extubation modelling.
These suggestions of features were taken from Slack messages:
**Initial set of suggestions**
- [ ] ethnicity
- [ ] hospital admission/discharge dates (for LoS)
- [ ] hospital mortality
- [ ] simple vitals - heart rate, blood pressure etc, RR
- [ ] simple labs - Hb, WCC, U&E
- [ ] presence of NG feeding tubes
- [ ] presence of ETT tubes
- [ ] mandatory resp rate
- [ ] airway pressures (e.g. PEEP, peak pressure)
- [ ] feeding rates
**Questions about what we can do**
- [ ] Congruency/internal logic eg Systolic Diastolic
- [ ] Preengineered variables
- [ ] Outlier events
- [ ] Temporal variation
**First set of suggestions**
- [ ] Pulse rate
- [ ] Arterial oxygen saturation
- [ ] Respiratory rate
- [ ] Systolic/Diastolic blood pressure
- [ ] Urine output per hour
- [ ] Body temperature
- [ ] Inspired oxygen concentration
- [ ] SOFA (Sequential Organ Failure Assessment) score
- [ ] Oral Fluid input
- [ ] Tidal volume
- [ ] Ventilator delivered minute volume
- [ ] End tidal carbon dioxide concentration
- [ ] Total breath rate
- [ ] ph of blood
- [ ] Carbon dioxide in blood
- [ ] Oxygen in blood
- [ ] Base excess in blood by calculation
- [ ] Chloride [Moles/volume] in blood
Contributor guide
Research direction
No file or test is named. Start by reviewing the feature lists and open questions in this issue, then define the documentation scope for selecting and creating synthetic CCHIC OMOP data for extubation modelling. Done means the selected features and handling of logic, pre-engineered variables, outliers, and temporal variation are documented.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, sql
- Domain
- data, databases, documentation
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100