Datasets:
row_id string | series_id string | timepoint_h int64 | organism string | strain_id string | drug_a string | drug_b string | stress_index float64 | exposure_a_index float64 | exposure_b_index float64 | mic_a_mg_L float64 | mic_b_mg_L float64 | mic_a_fold_vs_baseline float64 | mic_b_fold_vs_baseline float64 | resistance_marker_a float64 | resistance_marker_b float64 | fail_a_flag int64 | fail_b_flag int64 | first_failure_label string | breakthrough_prediction_label string | earliest_breakthrough_prediction int64 | notes string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ABXCT009-TR-0001 | S1 | 0 | Escherichia coli | EC-CLIN101 | cefepime | amikacin | 0.1 | 0.1 | 0.1 | 1 | 2 | 1 | 1 | 0.05 | 0.05 | 0 | 0 | A | A | 0 | baseline |
ABXCT009-TR-0002 | S1 | 12 | Escherichia coli | EC-CLIN101 | cefepime | amikacin | 0.9 | 0.9 | 0.9 | 1.5 | 2 | 1.5 | 1 | 0.55 | 0.1 | 0 | 0 | A | A | 1 | early A drift |
ABXCT009-TR-0003 | S1 | 24 | Escherichia coli | EC-CLIN101 | cefepime | amikacin | 0.9 | 0.9 | 0.9 | 2 | 2 | 2 | 1 | 0.7 | 0.1 | 0 | 0 | A | A | 0 | drift persists |
ABXCT009-TR-0004 | S1 | 72 | Escherichia coli | EC-CLIN101 | cefepime | amikacin | 0.9 | 0.9 | 0.9 | 32 | 2 | 32 | 1 | 0.9 | 0.1 | 1 | 0 | A | A | 0 | A fails |
ABXCT009-TR-0005 | S2 | 0 | Pseudomonas aeruginosa | PA-CLIN220 | piperacillin_tazobactam | ciprofloxacin | 0.1 | 0.1 | 0.1 | 2 | 0.25 | 1 | 1 | 0.05 | 0.05 | 0 | 0 | B | B | 0 | baseline |
ABXCT009-TR-0006 | S2 | 12 | Pseudomonas aeruginosa | PA-CLIN220 | piperacillin_tazobactam | ciprofloxacin | 0.9 | 0.9 | 0.9 | 2 | 0.4 | 1 | 1.6 | 0.1 | 0.6 | 0 | 0 | B | B | 1 | early B drift |
ABXCT009-TR-0007 | S2 | 24 | Pseudomonas aeruginosa | PA-CLIN220 | piperacillin_tazobactam | ciprofloxacin | 0.9 | 0.9 | 0.9 | 2 | 0.5 | 1 | 2 | 0.1 | 0.75 | 0 | 0 | B | B | 0 | drift persists |
ABXCT009-TR-0008 | S2 | 48 | Pseudomonas aeruginosa | PA-CLIN220 | piperacillin_tazobactam | ciprofloxacin | 0.9 | 0.9 | 0.9 | 2 | 8 | 1 | 32 | 0.1 | 0.9 | 0 | 1 | B | B | 0 | B fails |
ABXCT009-TR-0009 | S3 | 0 | Staphylococcus aureus | SA-CLIN600 | vancomycin | rifampin | 0.1 | 0.1 | 0.1 | 1 | 0.03 | 1 | 1 | 0.05 | 0.05 | 0 | 0 | none | none | 0 | baseline only |
ABXCT009-TR-0010 | S4 | 0 | Klebsiella pneumoniae | KP-CLIN330 | meropenem | amikacin | 0.1 | 0.1 | 0.1 | 16 | 2 | 1 | 1 | 0.05 | 0.05 | 0 | 0 | none | none | 0 | baseline only |
ABX-CT-009 Resistance Breakthrough Prediction
Purpose
Predict which drug in a combination will fail first.
Core pattern
- stress_index high
- both exposure indices high
- one drug shows early resistance drift
- mic fold rises
- resistance marker rises
- later fail flag confirms the order
Files
- data/train.csv
- data/test.csv
- scorer.py
Schema
Each row is one timepoint in a within strain series.
Required columns
- row_id
- series_id
- timepoint_h
- organism
- strain_id
- drug_a
- drug_b
- stress_index
- exposure_a_index
- exposure_b_index
- mic_a_fold_vs_baseline
- mic_b_fold_vs_baseline
- resistance_marker_a
- resistance_marker_b
- fail_a_flag
- fail_b_flag
- first_failure_label
- breakthrough_prediction_label
- earliest_breakthrough_prediction
Labels
first_failure_label
- A or B or none
- repeated on all rows in the series
breakthrough_prediction_label
- A or B or none
- repeated on all rows in the series
earliest_breakthrough_prediction
- 1 only on the first row where a prediction becomes valid
Scorer logic in v1
- series level true label comes from the earliest fail flag
- prediction point requires
- stress_index at least 0.80
- exposure_a_index and exposure_b_index at least 0.80
- for A
- mic_a_fold_vs_baseline at least 1.50
- mic_b_fold_vs_baseline at most 1.20
- resistance_marker_a at least 0.50
- resistance_marker_a exceeds resistance_marker_b by 0.15
- symmetric for B
Evaluation
Run
- python scorer.py --path data/test.csv
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