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MLD3
FIDDLE
Commits
83a6d599
Commit
83a6d599
authored
Jun 15, 2019
by
Shengpu Tang (tangsp)
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init experiments
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mimic3_experiments/README.md
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## Overview
-
Requires: the raw csv files of
[
MIMIC-III database
](
https://mimic.physionet.org/about/mimic/
)
-
Extract and format data from structured tables in MIMIC-III as input to FIDDLE
-
Goal: using data from all tables, generate Time-Invariant features
**s**
and Time Series features __X__.
## Usage
0.
Modify
`config.yaml`
to specify
`mimic3_path`
and
`data_path`
.
1.
Data Extraction
-
Execute
`python -c "from extract_data import *; check_nrows();"`
to verify the integrity of raw csv files.
-
Run
`python extract_data.py`
.
1.
Labels & Analyses
-
Run
`python generate_labels.py`
to generate the event onset time and labels for two outcomes: ARF and shock.
-
Run the following notebooks in order:
`LabelDistribution.ipynb`
,
`InclusionExclusion.ipynb`
and
`PopulationSummary.ipynb`
.
1.
Generate features
-
Run
`python prepare_input.py --task={task} --T={T} --dt={dt}`
-
Run
`python make_features.py --task={task} --T={T} --dt={dt}`
Note: a bash script is provided for generating features.
The generated features and associated metadata are located in
`{data_path}/features/task={task}.T={T}.dt={dt}/`
:
-
`s.npz`
: a sparse array of shape (N, d)
-
`X.npz`
: a sparse tensor of shape (N, L, D)
-
`s.feature_names.txt`
: names of _d_ time-invariant features
-
`X.feature_names.txt`
: names of _D_ time-series features
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