# AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning

arXiv: https://arxiv.org/abs/2210.12090 · HTML: https://mlsanity.com/paper/2210.12090/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 87 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [HyperImpute: Generalized Iterative Imputation with Automatic Model Selection](https://mlsanity.com/paper/2206.07769/) (2022)
- [Batched High-dimensional Bayesian Optimization via Structural Kernel Learning](https://mlsanity.com/paper/1703.01973/) (2017)
- [Explaining Latent Representations with a Corpus of Examples](https://mlsanity.com/paper/2110.15355/) (2021)
- [Automated Machine Learning: State-of-The-Art and Open Challenges](https://mlsanity.com/paper/1906.02287/) (2019)
- [AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning](https://mlsanity.com/paper/1802.07207/) (2018)
- [Learning outside the Black-Box: The pursuit of interpretable models](https://mlsanity.com/paper/2011.08596/) (2020)
- [Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images](https://mlsanity.com/paper/2202.12267/) (2022)
- [CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19](https://mlsanity.com/paper/2007.13825/) (2020)
- [Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation](https://mlsanity.com/paper/1811.12583/) (2018)

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

- [Large Language Models to Enhance Bayesian Optimization](https://mlsanity.com/paper/2402.03921/) (2024)
- [SurvivalGAN: Generating Time-to-Event Data for Survival Analysis](https://mlsanity.com/paper/2302.12749/) (2023)
- [Machine Learning with Requirements: a Manifesto](https://mlsanity.com/paper/2304.03674/) (2023)
- [PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications](https://mlsanity.com/paper/2409.01635/) (2024)
- ["Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models](https://mlsanity.com/paper/2409.18594/) (2024)
- [Redefining Digital Health Interfaces with Large Language Models](https://mlsanity.com/paper/2310.03560/) (2023)
- [Deep Pipeline Embeddings for AutoML](https://mlsanity.com/paper/2305.14009/) (2023)
- [Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications](https://mlsanity.com/paper/2405.07662/) (2024)
- [Bayesian--AI Fusion for Epidemiological Decision Making: Calibrated Risk, Honest Uncertainty, and Hyperparameter Intelligence](https://mlsanity.com/paper/2511.11983/) (2025)
- [Automated Machine Learning in Radiomics: A Comparative Evaluation of Performance, Efficiency and Accessibility](https://mlsanity.com/paper/2601.08334/) (2026)

## Abstract (arXiv)

Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner. However, the use of machine learning introduces a number of technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings. To address these challenges and empower healthcare professionals, we present a machine learning framework, AutoPrognosis 2.0, to develop diagnostic and prognostic models. AutoPrognosis leverages state-of-the-art advances in automated machine learning to develop optimized machine learning pipelines, incorporates model explainability tools, and enables deployment of clinical demonstrators, without requiring significant technical expertise. Our framework eliminates the major technical obstacles to predictive modeling with machine learning that currently impede clinical adoption. To demonstrate AutoPrognosis 2.0, we provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank, a prospective study of 502,467 individuals. The models produced by our automated framework achieve greater discrimination for diabetes than expert clinical risk scores. Our risk score has been implemented as a web-based decision support tool and can be publicly accessed by patients and clinicians worldwide. In addition, AutoPrognosis 2.0 is provided as an open-source python package. By open-sourcing our framework as a tool for the community, clinicians and other medical practitioners will be able to readily develop new risk scores, personalized diagnostics, and prognostics using modern machine learning techniques.