# A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study

arXiv: https://arxiv.org/abs/2201.03291 · HTML: https://mlsanity.com/paper/2201.03291/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression](https://mlsanity.com/paper/2012.01536/) (2020)
- [False Discoveries Occur Early on the Lasso Path](https://mlsanity.com/paper/1511.01957/) (2015)
- [AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data](https://mlsanity.com/paper/2106.06957/) (2021)
- [Shapley variable importance clouds for interpretable machine learning](https://mlsanity.com/paper/2110.02484/) (2021)
- [Predictive Modeling of Hospital Readmission: Challenges and Solutions](https://mlsanity.com/paper/2106.08488/) (2021)

## Built on by (most cited first, 3 of 3)

- [Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework](https://mlsanity.com/paper/2311.11055/) (2023)
- [Shapley variable importance cloud for machine learning models](https://mlsanity.com/paper/2212.08370/) (2022)
- [A roadmap to fair and trustworthy prediction model validation in healthcare](https://mlsanity.com/paper/2304.03779/) (2023)

## Abstract (arXiv)

Risk scores are widely used for clinical decision making and commonly generated from logistic regression models. Machine-learning-based methods may work well for identifying important predictors, but such 'black box' variable selection limits interpretability, and variable importance evaluated from a single model can be biased. We propose a robust and interpretable variable selection approach using the recently developed Shapley variable importance cloud (ShapleyVIC) that accounts for variability across models. Our approach evaluates and visualizes overall variable contributions for in-depth inference and transparent variable selection, and filters out non-significant contributors to simplify model building steps. We derive an ensemble variable ranking from variable contributions, which is easily integrated with an automated and modularized risk score generator, AutoScore, for convenient implementation. In a study of early death or unplanned readmission, ShapleyVIC selected 6 of 41 candidate variables to create a well-performing model, which had similar performance to a 16-variable model from machine-learning-based ranking.