Datasets:
row_id
string | series_id
string | timepoint_h
int64 | organism
string | strain_id
string | antibiotic_name
string | antibiotic_class
string | drug_conc_mg_L
float64 | cfu_log10
float64 | media
string | assay_method
string | source_type
string | shape_distortion_signal
int64 | earliest_distortion
int64 | notes
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ABXPD005-TR-0001
|
S1
| 0
|
Escherichia coli
|
EC-ATCC25922
|
meropenem
|
carbapenem
| 2
| 7.2
|
CAMHB
|
time_kill
|
simulated
| 0
| 0
|
baseline
|
ABXPD005-TR-0002
|
S1
| 2
|
Escherichia coli
|
EC-ATCC25922
|
meropenem
|
carbapenem
| 2
| 6.3
|
CAMHB
|
time_kill
|
simulated
| 0
| 0
|
log linear kill
|
ABXPD005-TR-0003
|
S1
| 4
|
Escherichia coli
|
EC-ATCC25922
|
meropenem
|
carbapenem
| 2
| 5.4
|
CAMHB
|
time_kill
|
simulated
| 0
| 0
|
log linear kill
|
ABXPD005-TR-0004
|
S1
| 6
|
Escherichia coli
|
EC-ATCC25922
|
meropenem
|
carbapenem
| 2
| 4.5
|
CAMHB
|
time_kill
|
simulated
| 0
| 0
|
log linear kill
|
ABXPD005-TR-0005
|
S1
| 24
|
Escherichia coli
|
EC-ATCC25922
|
meropenem
|
carbapenem
| 2
| 2.7
|
CAMHB
|
time_kill
|
simulated
| 0
| 0
|
log linear kill
|
ABXPD005-TR-0006
|
S2
| 0
|
Klebsiella pneumoniae
|
KP-CLIN061
|
ceftriaxone
|
3rd_gen_cephalosporin
| 16
| 7.1
|
CAMHB
|
time_kill
|
simulated
| 0
| 0
|
baseline
|
ABXPD005-TR-0007
|
S2
| 2
|
Klebsiella pneumoniae
|
KP-CLIN061
|
ceftriaxone
|
3rd_gen_cephalosporin
| 16
| 5.8
|
CAMHB
|
time_kill
|
simulated
| 0
| 0
|
fast early kill
|
ABXPD005-TR-0008
|
S2
| 4
|
Klebsiella pneumoniae
|
KP-CLIN061
|
ceftriaxone
|
3rd_gen_cephalosporin
| 16
| 5.75
|
CAMHB
|
time_kill
|
simulated
| 1
| 1
|
plateau begins
|
ABXPD005-TR-0009
|
S2
| 6
|
Klebsiella pneumoniae
|
KP-CLIN061
|
ceftriaxone
|
3rd_gen_cephalosporin
| 16
| 5.7
|
CAMHB
|
time_kill
|
simulated
| 1
| 0
|
plateau persists
|
ABXPD005-TR-0010
|
S2
| 24
|
Klebsiella pneumoniae
|
KP-CLIN061
|
ceftriaxone
|
3rd_gen_cephalosporin
| 16
| 6.2
|
CAMHB
|
time_kill
|
simulated
| 1
| 0
|
regrowth late
|
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.
- Downloads last month
- 21