# Uncertainty-aware abstention in medical diagnosis based on medical texts

arXiv: https://arxiv.org/abs/2502.18050 · HTML: https://mlsanity.com/paper/2502.18050/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 72 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [Deep Deterministic Uncertainty: A Simple Baseline](https://mlsanity.com/paper/2102.11582/) (2021)
- [Out-of-Distribution Detection and Selective Generation for Conditional Language Models](https://mlsanity.com/paper/2209.15558/) (2022)
- [LM-Polygraph: Uncertainty Estimation for Language Models](https://mlsanity.com/paper/2311.07383/) (2023)
- [Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection](https://mlsanity.com/paper/2101.03778/) (2021)
- [Analyzing the Role of Model Uncertainty for Electronic Health Records](https://mlsanity.com/paper/1906.03842/) (2019)
- [Bayesian Recurrent Neural Networks](https://mlsanity.com/paper/1704.02798/) (2017)
- [Nonparametric Uncertainty Quantification for Single Deterministic Neural Network](https://mlsanity.com/paper/2202.03101/) (2022)
- [A Comparative Study of Pretrained Language Models for Long Clinical Text](https://mlsanity.com/paper/2301.11847/) (2023)
- [Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation](https://mlsanity.com/paper/2203.01677/) (2022)
- [Uncertainty-Aware Attention for Reliable Interpretation and Prediction](https://mlsanity.com/paper/1805.09653/) (2018)
- [Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification](https://mlsanity.com/paper/2304.13998/) (2023)
- [MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining](https://mlsanity.com/paper/2012.13978/) (2020)
- [Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records](https://mlsanity.com/paper/2003.10170/) (2020)
- [RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text mining](https://mlsanity.com/paper/2204.03951/) (2022)
- [Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models](https://mlsanity.com/paper/2212.05857/) (2022)
- [Modeling the Uncertainty in Electronic Health Records: a Bayesian Deep Learning Approach](https://mlsanity.com/paper/1907.06162/) (2019)
- [Exploring Large Language Models for Detecting Mental Disorders](https://mlsanity.com/paper/2410.07129/) (2024)

## Built on by (most cited first, 6 of 6)

- [Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis](https://mlsanity.com/paper/2505.03467/) (2025)
- [Safe to Stop? Risk-Constrained Stopping for Sequential Clinical Diagnosis Agents](https://mlsanity.com/paper/2609.09678/) (2026)
- [Uncertainty-Aware Calibrated Clinical Text Classification with Large Language Models](https://mlsanity.com/paper/2509.19375/) (2025)
- [To Predict or Not to Predict? Towards reliable uncertainty estimation in the presence of noise](https://mlsanity.com/paper/2603.07330/) (2026)
- [Classification with Abstention Under Class-Conditional Error Constraints](https://mlsanity.com/paper/2609.22632/) (2026)
- [OmniMed-Jev: Calibrating LVLM Confidence for Trustworthy Medical Multimodal Decisions via System One](https://mlsanity.com/paper/2610.00381/) (2026)

## Abstract (arXiv)

This study addresses the critical issue of reliability for AI-assisted medical diagnosis. We focus on the selection prediction approach that allows the diagnosis system to abstain from providing the decision if it is not confident in the diagnosis. Such selective prediction (or abstention) approaches are usually based on the modeling predictive uncertainty of machine learning models involved. This study explores uncertainty quantification in machine learning models for medical text analysis, addressing diverse tasks across multiple datasets. We focus on binary mortality prediction from textual data in MIMIC-III, multi-label medical code prediction using ICD-10 codes from MIMIC-IV, and multi-class classification with a private outpatient visits dataset. Additionally, we analyze mental health datasets targeting depression and anxiety detection, utilizing various text-based sources, such as essays, social media posts, and clinical descriptions. In addition to comparing uncertainty methods, we introduce HUQ-2, a new state-of-the-art method for enhancing reliability in selective prediction tasks. Our results provide a detailed comparison of uncertainty quantification methods. They demonstrate the effectiveness of HUQ-2 in capturing and evaluating uncertainty, paving the way for more reliable and interpretable applications in medical text analysis.