# AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data

arXiv: https://arxiv.org/abs/2107.06039 · HTML: https://mlsanity.com/paper/2107.06039/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 71 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration](https://mlsanity.com/paper/1901.09290/) (2019)
- [From GAN to WGAN](https://mlsanity.com/paper/1904.08994/) (2019)
- [A Solvable High-Dimensional Model of GAN](https://mlsanity.com/paper/1805.08349/) (2018)
- [AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data](https://mlsanity.com/paper/2106.06957/) (2021)
- [On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models](https://mlsanity.com/paper/2012.09645/) (2020)

## Built on by (most cited first, 7 of 7)

- [Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies](https://mlsanity.com/paper/2107.09951/) (2021)
- [Benchmarking emergency department triage prediction models with machine learning and large public electronic health records](https://mlsanity.com/paper/2111.11017/) (2021)
- [An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models](https://mlsanity.com/paper/2204.11351/) (2022)
- [Exploring the Reliability of Self-explanation and its Relationship with Classification in Language Model-driven Financial Analysis](https://mlsanity.com/paper/2503.15985/) (2025)
- [Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learning](https://mlsanity.com/paper/2402.02561/) (2024)
- [Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories](https://mlsanity.com/paper/2404.04282/) (2024)
- [Sequence Composition in EHR Foundation Model Pretraining](https://mlsanity.com/paper/2610.16878/) (2026)

## Abstract (arXiv)

Background: Medical decision-making impacts both individual and public health. Clinical scores are commonly used among a wide variety of decision-making models for determining the degree of disease deterioration at the bedside. AutoScore was proposed as a useful clinical score generator based on machine learning and a generalized linear model. Its current framework, however, still leaves room for improvement when addressing unbalanced data of rare events. Methods: Using machine intelligence approaches, we developed AutoScore-Imbalance, which comprises three components: training dataset optimization, sample weight optimization, and adjusted AutoScore. All scoring models were evaluated on the basis of their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i.e., mean value of sensitivity and specificity). By utilizing a publicly accessible dataset from Beth Israel Deaconess Medical Center, we assessed the proposed model and baseline approaches in the prediction of inpatient mortality. Results: AutoScore-Imbalance outperformed baselines in terms of AUC and balanced accuracy. The nine-variable AutoScore-Imbalance sub-model achieved the highest AUC of 0.786 (0.732-0.839) while the eleven-variable original AutoScore obtained an AUC of 0.723 (0.663-0.783), and the logistic regression with 21 variables obtained an AUC of 0.743 (0.685-0.800). The AutoScore-Imbalance sub-model (using down-sampling algorithm) yielded an AUC of 0. 0.771 (0.718-0.823) with only five variables, demonstrating a good balance between performance and variable sparsity. Conclusions: The AutoScore-Imbalance tool has the potential to be applied to highly unbalanced datasets to gain further insight into rare medical events and to facilitate real-world clinical decision-making.