vitaldb-resolution¶
Every recorded VitalDB time and value as a probability distribution of the true value: native resolution, or a mixture of resolutions recovered from heaped data.
| Repository | github.com/metrology-atlas/vitaldb-resolution |
| Branch | main |
| Visibility | public |
| Licence | MIT |
| Language | Python (numpy, scipy) |
| Related | vitaldb-bench |
What it is¶
Models are JSON files evaluated by one generic engine. For timing, it describes the resolution of each kind of time stamp: the second axis of the accuracy-against-resolution chart.
Studies that use it¶
| Study | Code | Summary |
|---|---|---|
| Body mass index, derived from its inputs | vitaldb_resolution/engine.py |
Body mass index computed from the posteriors of height, weight. |
| How the anesthesia end time was charted | vitaldb_resolution/engine.py |
The charted anesthesia end time is a mixture of 4 ways of writing a clock time (ceiling_30min, ceiling_10min, ceiling_5min, clock_minute), modelled as an offset from a device time. |
| How the anesthesia start time was charted | vitaldb_resolution/engine.py |
The charted anesthesia start time is a mixture of 5 ways of writing a clock time (nominal_0740, nominal_0730, nearest_10min, nearest_5min, clock_minute), modelled as an offset from a device time. |
| How the operation end time was charted | vitaldb_resolution/engine.py |
The charted operation end time is a mixture of 4 ways of writing a clock time (floor_30min, floor_10min, floor_5min, clock_minute), modelled as an offset from a device time. |
| How the operation start time was charted | vitaldb_resolution/engine.py |
The charted operation start time is a mixture of 3 ways of writing a clock time (nearest_10min, nearest_5min, clock_minute), modelled as an offset from a device time. |
| Rounding and heaping of estimated blood loss | vitaldb_resolution/engine.py |
Recorded estimated blood loss is a mixture of 5 rounding modes (preferred small values, 500 mL, 100 mL, 50 mL, 10 mL (base)); the model gives the true value's distribution for each record. |
| Rounding and heaping of height | vitaldb_resolution/engine.py |
Recorded height is a mixture of 5 rounding modes (tens, whole_cm, half_cm, round_up_from_x9, measured_0.1); the model gives the true value's distribution for each record. |
| Rounding and heaping of intraoperative crystalloid | vitaldb_resolution/engine.py |
Recorded intraoperative crystalloid is a mixture of 3 rounding modes (100 mL, 50 mL, 10 mL (base)); the model gives the true value's distribution for each record. |
| Rounding and heaping of intraoperative urine output | vitaldb_resolution/engine.py |
Recorded intraoperative urine output is a mixture of 4 rounding modes (100 mL, 50 mL, 10 mL, 5 mL (base)); the model gives the true value's distribution for each record. |
| Rounding and heaping of propofol bolus | vitaldb_resolution/engine.py |
Recorded propofol bolus is a mixture of 3 rounding modes (preferred values, 50 mg, 10 mg (base)); the model gives the true value's distribution for each record. |
| Rounding and heaping of weight | vitaldb_resolution/engine.py |
Recorded weight is a mixture of 3 rounding modes (whole_kg, scale_0.1, scale_0.05); the model gives the true value's distribution for each record. |
Mentioned in¶
References¶
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- Bradbury J, Frostig R, Hawkins P, Johnson MJ, Katariya Y, Leary C, Maclaurin D, Necula G, Paszke A, VanderPlas J, Wanderman-Milne S, Zhang Q. JAX: composable transformations of Python+NumPy programs. 2018. ↗
- Krekel H, Oliveira B, Pfannschmidt R, Bruynooghe F, Laugher B, Bruhin F. pytest. 2004. ↗