Search Results for author:
Found 16 papers, 9 papers with code
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.
Shapley variable importance clouds for interpretable machine learning
Interpretable machine learning has been focusing on explaining final models that optimize performance.
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.
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
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).
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.
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.
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.
Skew Probabilistic Neural Networks for Learning from Imbalanced Data
This paper introduces an imbalanced data-oriented approach using probabilistic neural networks (PNNs) with a skew normal probability kernel to address this major challenge.
A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens
A dynamic treatment regimen (DTR) is a set of decision rules to personalize treatments for an individual using their medical history.
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.
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
In recent years, reinforcement learning (RL) has acquired a prominent position in the space of health-related sequential decision-making, becoming an increasingly popular tool for delivering adaptive interventions (AIs).
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.
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.
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study
Mobile health (mHealth) technologies aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions.
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.
