Novel IRL method identifies suboptimal medical decisions in ICU data.
problem Identifying suboptimal medical decisions in clinical settings.
method Incorporates Inverse Reinforcement Learning with a pruning step to identify and remove suboptimal actions.
result Pruning step effectively identifies clinical priorities and values from suboptimal data.
Paper detects bias in AI medical models using CART.
problem Ensuring fairness in AI medical decision support systems.
method Uses Classification and Regression Trees (CART) algorithm to identify bias.
result Validated the CART approach in both synthetic and real-world data.
CASCADE improves uncertainty communication in Parkinson's disease medication management.
problem Uncertainty in clinical decision-making for Parkinson's disease patients.
method CASCADE uses a novel conformal prediction framework to adaptively scale prediction intervals based on classification uncertainty.
result CASCADE produces more efficient and robust prediction intervals for Parkinson's disease patients.
A new reinforcement learning method for medical decisions with limited data.
problem Learning high-performing policies from partially observed data in healthcare.
method Optimization objective that combines policy and generative model quality, suitable for batch off-policy settings.
result Demonstrated improved performance on synthetic and medical decision-making problems.
RL algorithms with medical integration improve personalized treatment recommendations.
problem Developing effective personalized treatment strategies for chronic diseases.
method Integrating medical knowledge into RL algorithms for DTR.
result Enhanced treatment recommendations with increased confidence.
Prediction models can harm patients even when accurate, leading to self-fulfilling prophecies.
problem Prediction models can lead to harmful decisions that worsen patient outcomes.
method Formal characterization of harmful prediction models and analysis of their impact.
result Well-calibrated models are ineffective for decision-making as they do not change the data distribution.
The study evaluates AI model performance measures for medical use.
problem Selecting appropriate performance measures for AI models in medical practice.
method Assessed 32 performance measures across five domains for binary outcomes.
result 17 measures are both proper and reflect decision-analytic performance.
The medical field stands to see significant benefits from the recent advances in deep learning. Knowing the uncertainty in the decision made by any machine learning algorithm is of utmost importance for medical practitioners. This study demonstrates the utility of using Bayesian LSTMs for classification of medical time…
MCU-Net combines U-Net and Monte Carlo Dropout for uncertainty in medical image segmentation.
problem Lack of uncertainty representation in deep learning methods for patient-centered healthcare decisions.
method MCU-Net framework using U-Net and Monte Carlo Dropout with four uncertainty metrics.
result MCU-Net maximizes automated performance and refers truly uncertain cases.
Paper shows how to quantify uncertainty in medical ML models.
problem Uncertainty in opaque ML models can lead to safety risks in medical applications.
method Introduces Uncertainty Wrapper to quantify uncertainty transparently.
result Demonstrates practical utility of Uncertainty Wrapper in flow cytometry.
AI predicts medical specialty diagnostic choices from EHR records.
problem Predicting timely medical specialty diagnostic workups for patients.
method Ensemble of feed-forward neural networks trained on EHR data.
result Significantly higher accuracy compared to traditional checklists.
Study compares non-parametric models for predicting medical insurance reimbursement delays.
problem Estimating the time-lapse between medical insurance reimbursement.
method Comparative study of four non-parametric regression models (KNNs, SVMs, Decision Trees, Random Forests) using R-squared metric.
result Each model's performance varies with training data size, feature space, and hyperparameters.
The paper investigates interpretability techniques for deep learning models in medical data.
problem Understanding the logic behind predictions of black-box models in medical decision-making.
method Applied deep neural networks and random forests to a medical dataset. Used autoencoders and local interpretable models to provide insights.
result Local interpretable models and autoencoders provide meaningful insights into cancer predictions, identifying distinct and non-generalizable features.
A new framework designs experiments for better decision-making.
problem Suboptimal experimental designs for downstream decision-making.
method Amortized decision-aware Bayesian Experimental Design (BED) with Transformer Neural Decision Process (TNDP).
result TNDP effectively designs experiments and facilitates accurate decision-making.
Paper uses AI to improve medical diagnosis accuracy.
problem Improving accuracy of medical diagnoses.
method Heuristic frequentist and Bayesian approaches applied to a nationwide dataset.
result Algorithm outperforms human doctors in detecting abnormal births.
Effective medical test suggestions benefit both patients and physicians to conserve time and improve diagnosis accuracy. In this work, we show that an agent can learn to suggest effective medical tests. We formulate the problem as a stage-wise Markov decision process and propose a reinforcement learning method to train…
The paper investigates deep neural networks for medical imaging applications, providing interpretable results.
problem Uninterpretable decisions made by deep neural networks in medical imaging applications.
method Investigation of deep neural networks for malaria, diabetic retinopathy, brain tumor, and tuberculosis detection in various imaging modalities. Visualization of class activation mappings provided.
result Visualization of class activation mappings enhances understanding of deep neural networks and aids doctors in decision-making.
GPs' decisions can vary significantly with different kernels, even if kernels are qualitatively similar.
problem Robustness of GP decisions to kernel choice.
method Solved a constrained optimization problem over a finite-dimensional space to identify changes in GP decisions.
result Decisions made with a GP can be non-robust to kernel choice, even with qualitatively similar kernels.
Develops a two-stage conformal prediction method for Parkinson's disease medication needs.
problem Heterogeneous disease progression and treatment response in Parkinson's Disease.
method Two-stage conformal prediction framework with statistical guarantees.
result Quantifies uncertainty in medication needs predictions, improving clinical trust and quality of life.
Deep learning models are now used in many different industries, while in certain domains safety is not a critical issue in the medical field it is a huge concern. Not only, we want the models to generalize well but we also want to know the models confidence respect to its decision and which features matter the most. Ou…
Survey of deep learning methods for medical anomaly detection.
problem Medical anomaly detection using machine learning.
method Thorough review of deep learning techniques across various medical domains.
result Comparison and contrast of deep learning models and their limitations.
Deep tabular models outperform GBDT in medical diagnosis tasks.
problem Transfer learning for tabular data in medical diagnosis.
method Proposes a pseudo-feature method for transfer learning between different feature sets.
result Tabular neural networks outperform GBDT in medical diagnosis tasks.
Framework for deferring decisions to experts in sequential medical settings.
problem Myopic and non-adaptive decision-making by ML models in sequential medical contexts.
method Sequential Learning-to-Defer (SLTD) framework using model-based reinforcement learning.
result Adaptive deferral policy improves trade-off between long-term outcomes and deferral frequency.
Develops new methods for risk-aware decision-making in medical bandits.
problem Risk-averse decision-making in medical contexts with limited data.
method Safe, anytime-valid concentration bounds, risk-aware contextual bandits, nonparametric algorithms.
result Improved decision-making algorithms for postoperative patient follow-up.
This study applies neural models to automatically recognize medical entities from natural language.
problem Automated recognition of medical entities from natural language is complex and time-consuming.
method Utilizes deep neural sequence models trained on a large dataset of death certificates.
result Deep neural models can efficiently recognize medical entities from natural language.
We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, pr…
Simplifies decision-making during medical exams with cost-efficient feature acquisition.
problem Guiding physicians during examination acquisition for accurate and efficient diagnosis.
method Dropout at input layer and integrated gradients at test-time for dynamic feature importance.
result More cost- and feature-efficient than prior approaches, achieving higher overall accuracy.
FIGS improves prediction performance while maintaining interpretability, especially in medical domains.
problem Lack of interpretability in machine learning models, particularly in high-stakes domains like medicine.
method Generalizes CART algorithm to grow multiple trees in summation, combining logical rules with addition.
result FIGS achieves state-of-the-art prediction performance and derives interpretable clinical decision instruments (CDIs).
Paper uses ML to classify liver diseases from clinical data.
problem Classifying liver diseases from clinical data.
method Multiple imputation, PCA, data visualizations, binary classifier algorithms (ANN, RF, SVM).
result SVM showed better accuracy (98.23%).
This research improves uncertainty estimation for medical predictions, enhancing model trust and decision support.
problem Improving model uncertainty estimation for rare medical conditions.
method Developed and refined heuristics for selecting uncertainty estimation techniques, distinguishing them by clinical use-case. Also, compared ensembles vs. auto-encoders for detecting out-of-domain examples.
result Auto-encoders outperform ensembles in detecting out-of-domain examples, highlighting their importance for medical tabular data.
Patient journeys are compared to find clusters of similar disease trajectories.
problem Discovering shared health outcomes among patient journeys.
method Comparing longitudinal health data to identify clusters of similar patient trajectories.
result Clusters of patient journeys with similar health outcomes can be identified.
CPR models complex decision processes by breaking them into context-specific policies, improving interpretability and accuracy.
problem Interpreting dynamic human decision-making processes in medical contexts.
method Develops Contextualized Policy Recovery (CPR) framework for multi-task learning, modeling each context-specific policy as a linear map.
result Achieves state-of-the-art performance in predicting medical decisions, closing the gap between interpretable and black-box methods.
This paper tackles label noise in deep learning for medical image analysis.
problem Label noise impacts deep learning models in medical image analysis.
method Review of state-of-the-art techniques and experiments with label noise in medical datasets.
result Developed new methods to combat label noise in deep models for medical image analysis.
Improving the precision of heart diseases detection has been investigated by many researchers in the literature. Such improvement induced by the overwhelming health care expenditures and erroneous diagnosis. As a result, various methodologies have been proposed to analyze the disease factors aiming to decrease the phys…
Interpretable deep learning is a fundamental building block towards safer AI, especially when the deployment possibilities of deep learning-based computer-aided medical diagnostic systems are so eminent. However, without a computational formulation of black-box interpretation, general interpretability research rely hea…
Method learns evolving policies in healthcare contexts.
problem Understanding non-stationary behavior in evolving decision-making processes.
method Inverse Contextual Bandits (ICB) approach for learning interpretable representations of non-stationary behavior.
result Demonstrated applicability and accuracy of ICB method in liver transplantation policies.
Develops a new RL algorithm for medical treatment regimes.
problem Optimal dose determination in continuous action environments.
method Quasi-optimal learning algorithm for near-optimal actions.
result Guaranteed convergence and effectiveness in real applications.
Enhances uncertainty estimation in medical image segmentation.
problem Frequency-related noise in medical imaging leads to biased uncertainty estimates.
method Extends MC-Dropout to the frequency domain for better uncertainty estimation.
result MC-Frequency Dropout improves calibration and uncertainty in semantic segmentation.
Study proposes a statistical test for Vision Transformer's attention mechanisms.
problem ViT's attention mechanisms may focus on irrelevant regions, leading to unreliable evidence.
method Selective inference framework to quantify statistical significance of attentions as p-values.
result Proposed method enables reliable quantification of false positive detection probability of attentions.
Method uses semi-supervised learning to estimate optimal treatment regimes from medical records.
problem Estimating optimal treatment regimes from electronic medical records.
method Imputation-based semi-supervised method using unlabeled data.
result Proposed method yields more efficient estimators of optimal treatment regimes.
Machine-assisted treatment recommendations hold a promise to reduce physician time and decision errors. We formulate the task as a sequence-to-sequence prediction model that takes the entire time-ordered medical history as input, and predicts a sequence of future clinical procedures and medications. It is built on the …
HRTPP improves TPP interpretability and accuracy in medical event modeling.
problem Lack of interpretability in TPPs for medical event sequences.
method Hybrid-Rule Temporal Point Processes (HRTPP) integrating temporal logic rules and numerical features.
result HRTPP outperforms state-of-the-art interpretable TPPs in predictive performance and clinical interpretability.
IML methods improve survival analysis transparency.
problem Limited interpretability in survival analysis models.
method Adapting IML techniques to survival analysis.
result Enhanced understanding of model predictions and risks.
Foundation models alter medical data science workflow, challenging veridical data science principles.
problem Foundation models disrupt traditional data science practices in medicine.
method Critically examined the medical foundation model lifecycle and its deviation from veridical data science principles.
result Foundation models challenge veridical data science principles of predictability, computability, and stability.
A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid mode…
A reinforcement learning method predicts medical outcomes from masked feature vectors.
problem Predicting medical outcomes from limited feature data.
method Reinforcement learning with iterative feature selection and a guesser network.
result The method outperforms baselines and is highly interpretable.
Paper introduces conformal prediction for reliable uncertainty quantification in landmark localization.
problem Systematic underestimation of total predictive uncertainty in landmark localization.
method Conformal prediction framework for multi-output regression, generating flexible prediction regions.
result Methods outperform existing approaches in validity and efficiency across 2D and 3D datasets.
A primary goal of computational phenotype research is to conduct medical diagnosis. In hospital, physicians rely on massive clinical data to make diagnosis decisions, among which laboratory tests are one of the most important resources. However, the longitudinal and incomplete nature of laboratory test data casts a sig…