https://github.com/csinva/iai-clinical-decision-rule
Interpretable clinical decision rules for predicting intra-abdominal injury.
https://github.com/csinva/iai-clinical-decision-rule
ai artificial-intelligence decision-rules explainable-ai explainable-ml interpretability machine-learning medical medicine ml statistics
Last synced: 7 months ago
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Interpretable clinical decision rules for predicting intra-abdominal injury.
- Host: GitHub
- URL: https://github.com/csinva/iai-clinical-decision-rule
- Owner: csinva
- Created: 2019-09-19T02:14:46.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-06-18T22:52:30.000Z (over 2 years ago)
- Last Synced: 2025-02-26T06:23:30.409Z (8 months ago)
- Topics: ai, artificial-intelligence, decision-rules, explainable-ai, explainable-ml, interpretability, machine-learning, medical, medicine, ml, statistics
- Language: Jupyter Notebook
- Homepage: https://csinva.io/iai-clinical-decision-rule/
- Size: 41.4 MB
- Stars: 8
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
Stable clinical rule learning.
Demonstrating the use of PDR/PCS in emergency medicine on the PECARN dataset.
Accompanies the paper "Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma
" (PLOS digital health, 2022)
Code for reproducing analysis evaluating the PECARN Clinical Decision rule for prediction Intra-abdominal injury requiring intervention (IAI-I).
Documentation for useful functions is [here](csinva.io/iai-clinical-decision-rule) and for a thorough look into the notebooks is [here](https://github.com/csinva/iai-clinical-decision-rule/tree/master/notebooks).
# PCS documentation
## 1. Domain problem formulation
Our goal is to be able to identify the risk of clinically important traumatic brain injury (ciTBI) among children. This information can be used to triage CT imaging.
The outcome is definite to be Intra-abdominal injury requiring intervention (IAI-I).
## 2. Data collection and storage
Protocol for screening subjects is given in the [PECARN paper](https://www.annemergmed.com/article/S0196-0644(12)01743-X/fulltext) (i.e. children presenting within 24 h of non-trivial head trauma) and the [PSRC paper](https://www.sciencedirect.com/science/article/abs/pii/S1072751517300376). Data is now open-source and available as a series of csv and accompanying pdf files providing details on how it was collected.
## 3. Data cleaning and preprocessing
After matching features between the PECARN and PSRC data, we get histograms as shown below:
The definition of the outcome is the most difficult part. Categorical features are one-hot encoded.
## 4. Exploratory data analysis
Split up preverbal (<2 years of age) and verbal (>=2 years of age) patients.
## 5. Modeling and Post-hoc analysis
Many different models are fit and achieve different sensitivity/specificity tradeoffs.
## 6. Interpretation of results
We are able to rederive reasonable rule-based models for IAI prediction.
# Reference
- IAI data is gratefully downloaded from the open-source [PECARN website](http://pecarn.org/studyDatasets/Default) (also available here in the [data](data) folder)
- unfortunately, PSRC data is not available open-source at this time
- makes heavy use of the [imodels](https://github.com/csinva/interpretability-implementations-demos) package
```R
@article{kornblith22iaiclinical
doi = {10.1371/journal.pdig.0000076},
author = {Kornblith, Aaron E. AND Singh, Chandan AND Devlin, Gabriel AND Addo, Newton AND Streck, Christian J. AND Holmes, James F. AND Kuppermann, Nathan AND Grupp-Phelan, Jacqueline AND Fineman, Jeffrey AND Butte, Atul J. AND Yu, Bin},
journal = {PLOS Digital Health},
publisher = {Public Library of Science},
title = {Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma},
year = {2022},
month = {08},
volume = {1},
url = {https://doi.org/10.1371/journal.pdig.0000076},
pages = {1-16},
number = {8},
}
```