# Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility

arXiv: https://arxiv.org/abs/2409.12116 · HTML: https://mlsanity.com/paper/2409.12116/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [The Inadequacy of Shapley Values for Explainability](https://mlsanity.com/paper/2302.08160/) (2023)
- [Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review](https://mlsanity.com/paper/2112.06459/) (2021)
- [ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU](https://mlsanity.com/paper/1901.08201/) (2019)
- [A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study](https://mlsanity.com/paper/2308.14245/) (2023)
- [PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models](https://mlsanity.com/paper/2404.15549/) (2024)
- [Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression](https://mlsanity.com/paper/2006.16942/) (2020)

## Built on by (most cited first, 1 of 1)

- [Interpretable Multiple Myeloma Prognosis with Observational Medical Outcomes Partnership Data](https://mlsanity.com/paper/2603.20341/) (2026)

## Abstract (arXiv)

Machine Learning (ML) research has increased substantially in recent years, due to the success of predictive modeling across diverse application domains. However, well-known barriers exist when attempting to deploy ML models in high-stakes, clinical settings, including lack of model transparency (or the inability to audit the inference process), large training data requirements with siloed data sources, and complicated metrics for measuring model utility. In this work, we show empirically that including stronger baseline models in healthcare ML evaluations has important downstream effects that aid practitioners in addressing these challenges. Through a series of case studies, we find that the common practice of omitting baselines or comparing against a weak baseline model (e.g. a linear model with no optimization) obscures the value of ML methods proposed in the research literature. Using these insights, we propose some best practices that will enable practitioners to more effectively study and deploy ML models in clinical settings.