Datasets:
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license: mit
language:
- en
pretty_name: ABX-PD-005 Time-Kill Curve Shape Distortion
tags:
- antibiotics
- pharmacodynamics
- resistance
- time-kill
- tabular
task_categories:
- tabular-classification
size_categories:
- n<1k
---
# ABX-PD-005: Time-Kill Curve Shape Distortion
This dataset tests whether you can detect loss of log linear kill coherence.
The curve stops behaving like a clean log linear decline.
It becomes plateaued or multiphasic.
## Time grid
v1 uses
- 0, 2, 4, 6, 24 hours
## Files
- data/train.csv
- data/test.csv
- scorer.py
## Schema
Each row is one timepoint in a time kill series.
Required columns
- row_id
- series_id
- timepoint_h
- organism
- strain_id
- antibiotic_name
- antibiotic_class
- drug_conc_mg_L
- cfu_log10
- media
- assay_method
- source_type
- shape_distortion_signal
- earliest_distortion
## Labels
- shape_distortion_signal
- 1 for rows at or after distortion
- earliest_distortion
- 1 only for the first detected row in that series
## Evaluation
Run
- python scorer.py --path data/test.csv
The scorer detects distortion using
- plateau segments
- multiphasic sign changes in slopes
- poor linear fit
The scorer avoids a common mistake.
It does not call distortion when drug concentration changes too much.
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