# Learning Optimal Predictive Checklists

arXiv: https://arxiv.org/abs/2112.01020 · HTML: https://mlsanity.com/paper/2112.01020/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 85 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Supersparse Linear Integer Models for Optimized Medical Scoring Systems](https://mlsanity.com/paper/1502.04269/) (2015)
- [Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs](https://mlsanity.com/paper/2004.11440/) (2020)
- [Beyond Sparsity: Tree Regularization of Deep Models for Interpretability](https://mlsanity.com/paper/1711.06178/) (2017)
- [Learning Optimized Risk Scores](https://mlsanity.com/paper/1610.00168/) (2016)
- [Interpretable Classification Models for Recidivism Prediction](https://mlsanity.com/paper/1503.07810/) (2015)
- [Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability](https://mlsanity.com/paper/1809.04578/) (2018)
- [Optimal binning: mathematical programming formulation](https://mlsanity.com/paper/2001.08025/) (2020)
- [Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems](https://mlsanity.com/paper/1504.07614/) (2015)
- [Multi-Value Rule Sets](https://mlsanity.com/paper/1710.05257/) (2017)

## Built on by (most cited first, 10 of 10)

- [Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency](https://mlsanity.com/paper/2306.02109/) (2023)
- [The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations](https://mlsanity.com/paper/2205.03295/) (2022)
- [Perspectives on Incorporating Expert Feedback into Model Updates](https://mlsanity.com/paper/2205.06905/) (2022)
- [When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction](https://mlsanity.com/paper/2206.02058/) (2022)
- [From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes](https://mlsanity.com/paper/2507.17717/) (2025)
- [Learning predictive checklists from continuous medical data](https://mlsanity.com/paper/2211.07076/) (2022)
- [Automatic Construction of Clinical Scoring Systems with LLM Agents](https://mlsanity.com/paper/2601.22324/) (2026)
- [MISS: Multiclass Interpretable Scoring Systems](https://mlsanity.com/paper/2401.05069/) (2024)
- [Learning Predictive Checklists with Probabilistic Logic Programming](https://mlsanity.com/paper/2411.16790/) (2024)
- [Buffered AUC maximization for scoring systems via mixed-integer optimization](https://mlsanity.com/paper/2601.05544/) (2026)

## Abstract (arXiv)

Checklists are simple decision aids that are often used to promote safety and reliability in clinical applications. In this paper, we present a method to learn checklists for clinical decision support. We represent predictive checklists as discrete linear classifiers with binary features and unit weights. We then learn globally optimal predictive checklists from data by solving an integer programming problem. Our method allows users to customize checklists to obey complex constraints, including constraints to enforce group fairness and to binarize real-valued features at training time. In addition, it pairs models with an optimality gap that can inform model development and determine the feasibility of learning sufficiently accurate checklists on a given dataset. We pair our method with specialized techniques that speed up its ability to train a predictive checklist that performs well and has a small optimality gap. We benchmark the performance of our method on seven clinical classification problems, and demonstrate its practical benefits by training a short-form checklist for PTSD screening. Our results show that our method can fit simple predictive checklists that perform well and that can easily be customized to obey a rich class of custom constraints.