Refines diagnostic prediction using causal relationships.
problem Improving accuracy of pain diagnostics prediction.
method Two approaches: 1) Inference of causal relationships, 2) Post-processing refinement.
result Potential for improving pain diagnostics prediction accuracy.
Paper improves predictive distributions for rare events using a simple framework.
problem Local miscalibration of predictive distributions for rare events.
method Semiparametric diagnostic transport maps to correct tail probabilities.
result Semiparametric maps improve predictions for severe weather hazards.
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.
The paper introduces diagnostic transport maps to improve the reliability of rare event predictions.
problem Improper calibration of predictive distributions, especially for rare events.
method Diagnostic transport maps to adjust base model's probabilities for better calibration.
result Diagnostic transport maps improve predictive performance for rare events, including 24-hour rapid intensity change.
Deep CNN predicts disruptions in fusion plasmas with high accuracy.
problem Predicting plasma events in fusion devices with multi-scale, multi-physics characteristics.
method Deep convolutional neural networks (CNN) with dilated convolutions trained on ECEi diagnostic data.
result Deep CNN achieves an F1-score of ~91% on disruption prediction.
New diagnostics detect variability in individual risk estimates from machine learning models in healthcare.
problem Variability in individual risk estimates from machine learning models in healthcare, leading to unreliable treatment decisions.
method Proposed evaluation framework using empirical prediction interval width and empirical decision flip rate diagnostics.
result Randomness in optimization and initialization can lead to substantial individual-level variability in risk estimates, affecting clinical decisions.
Deep Learning model diagnoses four lymphoma categories with high accuracy.
problem Automated detection of lymphoma categories using digital pathology images.
method Convolutional neural network algorithm trained on 128 cases of lymph node images.
result Excellent diagnostic accuracy (95% image-by-image, 10% set-by-set).
New bidirectional model predicts magnetohydrodynamics fields and estimates uncertainty.
problem Predicting multiple fields in magnetohydrodynamics with uncertainty.
method Bidirectional autoregressive latent diffusion approach.
result Model can estimate uncertainty without ground truth using self-supervised consistency.
Proposes a new framework for evaluating diagnostic models with multiple co-primary endpoints.
problem Overoptimistic assessments of predictive performance in automated medical testing devices.
method Multiple testing framework for diagnostic accuracy studies with co-primary endpoints, using a parametric simultaneous test procedure and Bayesian approach to determine optimal number of models.
result Our approach leads to a better final diagnostic model and increased statistical power.
Study highlights robustness issues in healthcare diagnostic models due to distribution shifts.
problem Robustness of diagnostic models in healthcare is compromised by distribution shifts.
method Theoretical analysis and simulation studies to understand and mitigate shortcuts learned by models.
result Ignoring covariates or using invariant learning approaches leads to non-robust predictors.
Paper proposes machine learning model for early Alzheimer's diagnosis.
problem Early and accurate diagnosis of Alzheimer's Disease.
method Machine learning models, demographic, biomarker, and cognitive test data.
result 90% accuracy and 87% accuracy in predicting Alzheimer's development.
Machine learning improves diagnostic test accuracy for bovine tuberculosis.
problem Improving diagnostic test sensitivity for bovine tuberculosis.
method Machine learning to assess risk landscapes and predict infection.
result Test sensitivity improved, detecting 240 more infected herds per year.
Language models fail to process hallucinated responses, and this study diagnoses the failure.
problem Language models fail to process hallucinated responses, leading to over-concentration or diffuse attention.
method The study uses forced scoring of benchmark-labeled responses to compute attention shapes and analyze the symmetric component of the degree-normalized attention operator.
result The study proves that every transpose-invariant spectral diagnostic of the attention operator is orientation-blind and bounds the sensitivity of any Lipschitz diagnostic by the asymmetry coefficient \(G\).
A theory for interpreting black-box models in medical diagnostics.
problem Lack of computational formulation for interpreting black-box models in medical diagnostics.
method Defining interpretation as a finite communication between a known model and a black-box model, deriving an algorithm for diagnostic interpretability.
result Demonstrated the feasibility of interpreting black-box models in synthetic supervised classification scenarios.
LADaR framework calibrates machine learning models for instance-wise predictions.
problem Challenges in assessing and calibrating predictive distributions for complex inputs.
method Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR) framework and extttCal−PIT algorithm. result Achieves better instance-wise calibration than existing methods in galaxy distance estimation.
Paper presents a probabilistic diagnostic model for identifying and treating supervised learning degradation issues.
problem Degradation problems in supervised learning, including class imbalance, overlapping, small-disjuncts, noisy labels, and sparseness.
method Develops a novel probabilistic diagnostic model to identify and treat degradation issues in supervised learning.
result Early and correct diagnosis of degradation issues allows for selecting appropriate remediation treatments and unbiased performance metrics.
Supervised topic models can help clinical researchers find interpretable cooccurence patterns in count data that are relevant for diagnostics. However, standard formulations of supervised Latent Dirichlet Allocation have two problems. First, when documents have many more words than labels, the influence of the labels w…
ABC improves uncertainty quantification in LLMs for clinical diagnostics.
problem Overconfident and poorly calibrated estimates of LLMs in clinical domains.
method Approximate Bayesian Computation (ABC) for likelihood-free inference.
result Improves accuracy by up to 46.9%, reduces Brier scores by 74.4%, and enhances calibration.
Improves reliability of medical diagnosis uncertainty estimates.
problem Label uncertainty in medical diagnosis.
method Post-hoc alpha-calibration method for neural network classifiers. result Significantly enhances reliability of uncertainty estimates.
Proposes PRMs for interpreting financial risk concept drift.
problem Concept drift in high-stakes predictions like credit risk.
method Probabilistic Rule Models (PRMs) using Markov Logic Networks.
result Interpretable rules explain borrower risk changes.
Machine learning can predict cancer with 100% accuracy on a dataset.
problem Accuracy of cancer predictions using machine learning.
method Extensive experiments on the Wisconsin Diagnostic Breast Cancer dataset.
result Machine learning algorithms can be easily misled to achieve 100% accuracy.
Proposes FACT, a diagnostic for understanding group fairness trade-offs.
problem Group fairness notions often conflict with each other, requiring a cost in model performance.
method Characterizes trade-offs via the fairness-confusion tensor and optimizes accuracy and fairness objectives.
result Demonstrates the use of FACT on synthetic and real datasets to understand accuracy-fairness trade-offs.
This paper simplifies deep ReLU networks into local linear models for better interpretability.
problem Limited transparency and interpretability of deep neural networks, especially ReLU networks.
method Local linear representation and equivalent set of local linear models (LLMs).
result Simplified deep ReLU networks for better interpretability and diagnostics.
MRI method predicts glioma features, survival, and endothelial proliferation.
problem Invasive biopsy limits detection of glioma features due to tumor heterogeneity.
method Voxel-wise, multiparametric MRI radiomics with k-NN classifier.
result Model accurately predicts disease compositions, survival, and endothelial proliferation.
Spectral measurements reveal hidden representation geometry in language model training.
problem Hidden internal representation in language model training is hard to examine.
method Empirical protocol using activation covariance and per-sample gradient SVD spectra.
result Batch size affects representation geometry, and activation spectra predict token efficiency.
Paper distills ensemble ENSO forecasts into simpler models for better diagnostics.
problem Interpreting complex ensemble ENSO forecasts.
method Aggregates eSPA ensemble members that correctly predict ENSO phase.
result Compact distilled models maintain forecast performance and enable diagnostics.
Bayesian model averaging under predictor redundancy
problem Reporting Bayesian model averaging posterior without changing the Bayesian target
method Using hard or soft regions of support space
result Region reports often give shorter and clearer summaries while preserving the main posterior information
Trimming helps in conformal prediction when it separates anomaly scores.
problem Effectiveness of trimming in conformal prediction under contamination.
method Analyse fixed-threshold trimming as a replacement of the contaminated calibration law with a retained law.
result Trimming helps when it separates anomaly scores, reducing clean-target coverage to a one-dimensional score-CDF transfer problem.
New framework uses geometry of embeddings to predict robustness.
problem Monitoring robustness in models without OOD labels.
method Constructs graphs from embeddings, measures spectral complexity and curvature.
result Representation geometry predicts robustness reliably.
Improves PARAFAC tensor decomposition rank estimation for better interpretability and accuracy.
problem Estimating the optimal number of latent factors in PARAFAC tensor decomposition.
method Automatically determines the rank using Core Consistency Diagnostic (CORCONDIA) and explores the trade-off between interpretability and predictive accuracy.
result Striking a good balance between interpretability and accuracy benefits rank estimation.
Paper classifies lung nodules in CT scans, improving diagnostic accuracy.
problem Early detection of lung nodules for better treatment planning.
method Proposes four 3D neural networks for direct mapping from 3D images to class labels.
result 3D multi-output DenseNet achieves state-of-the-art classification accuracy.
GeoTop resolves topological ambiguity in diagnostic imaging using geometric-topological analysis.
problem Topological equivalence between benign and malignant structures in diagnostic images.
method Combines Topological Data Analysis and Lipschitz-Killing Curvatures to resolve ambiguity.
result Achieves 3.6% accuracy improvement and reduces false positives/negatives by 15-18%.
New diagnostic method detects misspecified models in inverse PDE problems.
problem Misleading residual-norm diagnostics in inverse PDE problems.
method Structure-sensitive sequential diagnostic using e-processes.
result Rejects fitted models that produce biased predictions.
DECAT framework evaluates multimodal models for shared biology, detecting confounders and false positives.
problem Determining if multimodal models learn shared biology or just confounders.
method DECAT framework classifies multimodal representations into four diagnostic scenarios using null-referenced metrics.
result DECAT detects confounders and false positives in multimodal models, improving with larger cohorts and stronger representations.
Study finds AI can predict diverse cardiac and non-cardiac diagnoses from a single ECG.
problem Narrow focus of ECG analysis models for diverse medical conditions.
method Exploratory study using a single AI model to predict multiple ICD codes.
result Model achieved AUROC scores > 0.8 for 253 cardiac and 172 non-cardiac diagnoses.
We propose a novel method to forecast the future from the present using time-reversed data.
problem Forecasting the future from past data, exploiting temporal asymmetry.
method Retrodictive forecasting via inverse MAP optimization over a Conditional Variational Autoencoder (CVAE).
result The method successfully predicts future events in time-reversible and irreversible processes.
Hidden Markov model predicts sepsis progression stages.
problem Sepsis diagnostic criteria fail to account for patient heterogeneity.
method Introduced a hidden Markov model to account for heterogeneity.
result Model uncovers patient's latent sepsis trajectory.
Novel nonparametric method for GLMs improves prediction and inference performance.
problem Improving prediction and inference in GLMs with minimal assumptions.
method Combines binary regression and latent variable formulations, extends parametric versions, introduces new classification statistic.
result Uniformly better prediction and inference performance over parametric formulation, especially with asymmetric data.
Framework for monitoring ML model performance in production without labels.
problem Monitoring real-time prediction quality of ML models in production without labels.
method ML Health framework using diagnostic methods to generate alerts for further investigation.
result The method outperforms standard distance metrics at detecting issues with mismatched data sets.
Paper uses transfer learning and Bayesian optimization to reduce DNA sequence design experiments.
problem Designing many similar DNA sequences for specific applications is expensive and time-consuming.
method Combines transfer learning with Bayesian optimization to reduce experiment count.
result Total number of experiments can be significantly reduced by sharing information between tasks.
New method predicts y distributions from imperfect data.
problem Predicting y from imperfect data (discrete, truncated, censored).
method Optimal transformations to estimate p(y|x).
result Estimates location, scale, and shape of y distribution.
L-C2ST improves local diagnostics for SBI approximations.
problem Evaluating trustworthiness of posterior approximations in SBI.
method Local evaluation of posterior estimators at any observation.
result Offers better statistical power and interpretability.
Deep chest X-ray classifiers show bias in predicting diagnoses.
problem Bias in deep learning classifiers predicting diagnoses from chest X-rays.
method Trained convolutional neural networks on multiple public datasets to predict 14 diagnostic labels.
result True positive rates vary significantly among different protected attributes, indicating bias.
New method clusters health codes for better risk adjustment.
problem Improving insurer incentives to attract lower-cost enrollees.
method Markov Chain Monte Carlo methods for clustering diagnostic codes.
result Methodology outperforms common alternatives in health expenditure prediction.
Establishes statistical and computational bounds for influence diagnostics.
problem Identifying influential datapoints or subsets in machine learning models.
method Finite-sample statistical bounds and computational complexity for influence functions and approximate maximum influence perturbations.
result Established statistical and computational guarantees for influence diagnostics.
The article explains how to estimate confusion matrices for classifiers using unlabeled data.
problem Estimating sensitivity and specificity of binary medical diagnostic tests without gold standard tests.
method Modifying diagnostic test solutions to estimate confusion matrices for classifiers on unlabeled data.
result The approach can be used to estimate accuracy statistics for supervised or unsupervised binary classifiers on unlabeled data.
Deep learning improves AD diagnosis and prognosis from neuroimaging data.
problem Early detection and accurate classification of Alzheimer's disease.
method Deep learning models applied to neuroimaging data for AD diagnosis and prognosis.
result Deep learning models can achieve high accuracy in AD diagnosis and prognosis.
PPI uses predictions to improve inference from incomplete data.
problem Incomplete or costly-to-measure outcomes in research fields.
method Leverages large unlabeled datasets for improved statistical efficiency with bias correction.
result PPI variants produce tighter confidence intervals than complete-case analysis.