Interpretable machine learning for time-to-event prediction in medicine and healthcare (2023-03-17T00:00:00.000000Z)

TL;DR

It is shown how post-hoc interpretation methods allow for finding biases in AI systems predicting length of stay using a novel multi-modal dataset created from 1235 X-ray images with textual radiology reports annotated by human experts.

Authors

Hubert Baniecki

3 papers

P. Biecek

6 papers

Bartlomiej Sobieski

1 papers

Przemyslaw Bombi'nski

1 papers

Patryk Szatkowski

1 papers

TL;DR

Authors

References62 items

Book review: Christoph Molnar. 2020. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable

SoK: Explainable Machine Learning in Adversarial Environments

Interpretable Machine Learning for Survival Analysis

Explainable AI for survival analysis: a median-SHAP approach

Ki67 is a better marker than PRAME in risk stratification of BAP1-positive and BAP1-loss uveal melanomas

A foundation model for generalizable disease detection from retinal images

survex: an R package for explaining machine learning survival models

Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey

Towards Evaluating Explanations of Vision Transformers for Medical Imaging

CoxNAM: An interpretable deep survival analysis model

Interpretable meta-learning of multi-omics data for survival analysis and pathway enrichment

MIMIC-IV, a freely accessible electronic health record dataset

DECAF: An interpretable deep cascading framework for ICU mortality prediction

DALE: Differential Accumulated Local Effects for efficient and accurate global explanations

SurvivalCNN: A deep learning-based method for gastric cancer survival prediction using radiological imaging data and clinicopathological variables

A manifesto on explainability for artificial intelligence in medicine

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Algorithms to estimate Shapley value feature attributions

Time-to-event modeling for hospital length of stay prediction for COVID-19 patients

A systematic review of the prediction of hospital length of stay: Towards a unified framework

Evaluation of post-hoc interpretability methods in time-series classification

AI in health and medicine

Explaining artificial intelligence with visual analytics in healthcare

Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models

Benchmark of filter methods for feature selection in high-dimensional gene expression survival data

AI recognition of patient race in medical imaging: a modelling study

Grouped feature importance and combined features effect plot

SurvNAM: The machine learning survival model explanation

VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations

mlr3proba: an R package for machine learning in survival analysis

Understanding Global Feature Contributions With Additive Importance Measures

Combining structured and unstructured data for predictive models: a deep learning approach

General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models

Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach

The grammar of interactive explanatory model analysis

SurvLIME: A method for explaining machine learning survival models

Large-scale benchmark study of survival prediction methods using multi-omics data

Interpretability of machine learning‐based prediction models in healthcare

Radiological Society of North America

A deep survival analysis method based on ranking

Explaining individual predictions when features are dependent: More accurate approximations to Shapley values

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Predicting Inpatient Length of Stay After Brain Tumor Surgery: Developing Machine Learning Ensembles to Improve Predictive Performance.

Manipulating and Measuring Model Interpretability

All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Computational Radiomics System to Decode the Radiographic Phenotype.

OpenML Benchmarking Suites

Predicting Length of Stay among Patients Discharged from the Emergency Department—Using an Accelerated Failure Time Model

Visualizing the effects of predictor variables in black box supervised learning models

Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection

“Why Should I Trust You?”: Explaining the Predictions of Any Classifier

Length of stay prediction for clinical treatment process using temporal similarity

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

Fleischner Society: glossary of terms for thoracic imaging.

Greedy function approximation: A gradient boosting machine.

Hospital Length of Stay Prediction Based on Multi-modal Data Towards Trustworthy Human-AI Collaboration in Radiomics

Extracting Surrogate Decision Trees from Black-Box Models to Explain the Temporal Importance of Clinical Features in Predicting Kidney Graft Survival

Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients

Explainable AI Methods - A Brief Overview

Random Forests

Field of Study

Journal Information

Name

Page

Volume