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Found 42 papers, 17 papers with code
Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification
We anticipate that our template guidance will forge a fresh approach to elucidating AI models by integrating clinical domain expertise.
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness.
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data
We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States.
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
The calibration of DeepSurv (IBS: 0. 041) performed the best, followed by RSF (IBS: 0. 042) and GBM (IBS: 0. 0421), all using the full variables.
Fine-tuning Large Language Model (LLM) Artificial Intelligence Chatbots in Ophthalmology and LLM-based evaluation using GPT-4
200 responses to the testing dataset were generated by 5 fine-tuned LLMs for evaluation.
Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
Compared to the human-generated instructions, which had an accuracy of 86. 3%, the performance of the GPT4. 0 RAG model demonstrated non-inferiority (p=0. 610).
Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias
A total of 80 simulations were evaluated for the accuracy of initial diagnosis, top differential diagnosis and final two differential diagnoses.
Some Finite-Sample Results on the Hausman Test
This paper shows that the endogeneity test using the control function approach in linear instrumental variable models is a variant of the Hausman test.
Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation
We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs.
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains.
Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist
The widespread use of ChatGPT and other emerging technology powered by generative artificial intelligence (GenAI) has drawn much attention to potential ethical issues, especially in high-stakes applications such as healthcare, but ethical discussions are yet to translate into operationalisable solutions.
Transmission line condition prediction based on semi-supervised learning
Transmission line state assessment and prediction are of great significance for the rational formulation of operation and maintenance strategy and improvement of operation and maintenance level.
Unsupervised Compositional Concepts Discovery with Text-to-Image Generative Models
Text-to-image generative models have enabled high-resolution image synthesis across different domains, but require users to specify the content they wish to generate.
Towards clinical AI fairness: A translational perspective
In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness' translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness.
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice.
A roadmap to fair and trustworthy prediction model validation in healthcare
A prediction model is most useful if it generalizes beyond the development data with external validations, but to what extent should it generalize remains unclear.
FedScore: A privacy-preserving framework for federated scoring system development
We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models.
Shapley variable importance cloud for machine learning models
Current practice in interpretable machine learning often focuses on explaining the final model trained from data, e. g., by using the Shapley additive explanations (SHAP) method.
An ensemble neural network approach to forecast Dengue outbreak based on climatic condition
In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.
A Framework for Mutual Information-based MIMO Integrated Sensing and Communication Beamforming Design
For the case of a single communication user, we consider three types of echo interference, no interference, a point interference, and an extended interference.
Integrated Sensing and Communication Beamforming Design Based on Mutual Information
A closed-form solution with low complexity and a solution based on the semidefinite relaxation (SDR) method are provided to solve these two problems, respectively.
Towards Comprehensive Representation Enhancement in Semantics-guided Self-supervised Monocular Depth Estimation
Semantics-guided self-supervised monocular depth estimation has been widely researched, owing to the strong cross-task correlation of depth and semantics.
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
We found that model backbone(s) differed among data types as well as the imputation strategy.
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics
Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics.
Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making
This study sought to investigate the effects of data imbalance on SHAP explanations for deep learning models, and to propose a strategy to mitigate these effects.
Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM
In this paper, we focus our attention on private Empirical Risk Minimization (ERM), which is one of the most commonly used data analysis method.
Compositional Visual Generation with Composable Diffusion Models
Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions.
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes
This study aims to expand the AutoScore framework to provide a tool for interpretable risk prediction for ordinal outcomes.
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study
Risk scores are widely used for clinical decision making and commonly generated from logistic regression models.
Benchmarking emergency department triage prediction models with machine learning and large public electronic health records
In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400, 000 ED visits from 2011 to 2019.
Learning to Compose Visual Relations
The visual world around us can be described as a structured set of objects and their associated relations.
Shapley variable importance clouds for interpretable machine learning
Interpretable machine learning has been focusing on explaining final models that optimize performance.
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
To some extent, current deep learning solutions can address these challenges.
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
All scoring models were evaluated on the basis of their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i. e., mean value of sensitivity and specificity).
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
We illustrated our method in a real-life study of 90-day mortality of patients in intensive care units and compared its performance with survival models (i. e., Cox) and the random survival forest.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework
We propose fill-in-the-blanks as a video understanding evaluation framework and introduce FIBER -- a novel dataset consisting of 28, 000 videos and descriptions in support of this evaluation framework.
A_Blockchain-Based_Decentralized_Federated_Learning_Framework_with_Committee_Consensus
To address these security issues, we propose a decentralized federated learning framework based on blockchain, that is, a Blockchain- based Federated Learning framework with Committee consensus (BFLC).
Fast Estimation for Privacy and Utility in Differentially Private Machine Learning
In this paper, we deduce the influence of $\epsilon$ on utility private learning models through strict mathematical derivation, and propose a novel approximate approach for estimating the utility of any $\epsilon$ value.
A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus
To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i. e., a Blockchain-based Federated Learning framework with Committee consensus (BFLC).
FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm
We propose a Chinese spell checker {--} FASPell based on a new paradigm which consists of a denoising autoencoder (DAE) and a decoder.
Time-sync Video Tag Extraction Using Semantic Association Graph
Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF).
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition
One of the main problems in webly-supervised learning is cleaning the noisy labeled data from the web.
