101 citations · 161 across the 22 of their papers we have counts for
22 papers
Toward Better Assessment of LLMs' Performance in Clinical Error Detection
Yifan Zhang, Rahmatollah Beheshti
Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that eval…
AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization
Fahmida Liza Piya, Rahmatollah Beheshti
Large language models (LLMs) offer substantial promise for automating clinical text summarization, yet maintaining factual consistency remains challenging due to the length, noise,…
NAST: Improving Negation Handling in Medical Vision-Language Models through Negation-Aware Selective Training
Ali Abbasi, Mehdi Taghipour, Rahmatollah Beheshti
Negation is a fundamental linguistic operation in clinical reporting, yet vision-language models (VLMs) frequently fail to distinguish affirmative from negated medical statements.…
A Multimodal Data Processing Pipeline for MIMIC-IV Dataset
Farzana Islam Adiba, Varsha Danduri, Fahmida Liza Piya +3
The MIMIC-IV dataset is a large, publicly available electronic health record (EHR) resource widely used for clinical machine learning research. It comprises multiple modalities, in…
Reward Hacking Mitigation using Verifiable Composite Rewards
Mirza Farhan Bin Tarek, Rahmatollah Beheshti
Reinforcement Learning from Verifiable Rewards (RLVR) has recently shown that large language models (LLMs) can develop their own reasoning without direct supervision. However, appl…
Toward Revealing Nuanced Biases in Medical LLMs
Farzana Islam Adiba, Rahmatollah Beheshti
Large language models (LLMs) used in medical applications are known to be prone to exhibiting biased and unfair patterns. Prior to deploying these in clinical decision-making, it i…
ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs
Fahmida Liza Piya, Rahmatollah Beheshti
Unstructured clinical data can serve as a unique and rich source of information that can meaningfully inform clinical practice. Extracting the most pertinent context from such data…
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
Amin Adibi, Xu Cao, Zongliang Ji +39
The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam,…
Enabling Scalable Evaluation of Bias Patterns in Medical LLMs
Hamed Fayyaz, Raphael Poulain, Rahmatollah Beheshti
Large language models (LLMs) have shown impressive potential in helping with numerous medical challenges. Deploying LLMs in high-stakes applications such as medicine, however, brin…
Aligning (Medical) LLMs for (Counterfactual) Fairness
Raphael Poulain, Hamed Fayyaz, Rahmatollah Beheshti
Large Language Models (LLMs) have emerged as promising solutions for a variety of medical and clinical decision support applications. However, LLMs are often subject to different t…
Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Tasks
Mirza Farhan Bin Tarek, Raphael Poulain, Rahmatollah Beheshti
Among various aspects of ensuring the responsible design of AI tools for healthcare applications, addressing fairness concerns has been a key focus area. Specifically, given the wi…
Bias patterns in the application of LLMs for clinical decision support: A comprehensive study
Raphael Poulain, Hamed Fayyaz, Rahmatollah Beheshti
Large Language Models (LLMs) have emerged as powerful candidates to inform clinical decision-making processes. While these models play an increasingly prominent role in shaping the…
HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks
Fahmida Liza Piya, Mehak Gupta, Rahmatollah Beheshti
While electronic health records (EHRs) are widely used across various applications in healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or si…
Multimodal Sleep Apnea Detection with Missing or Noisy Modalities
Hamed Fayyaz, Abigail Strang, Niharika S. D'Souza +1
Polysomnography (PSG) is a type of sleep study that records multimodal physiological signals and is widely used for purposes such as sleep staging and respiratory event detection.…
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods
Raphael Poulain, Mirza Farhan Bin Tarek, Rahmatollah Beheshti
Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes applications such as those in healthcare. However, health AI models' overall predi…
An Extensive Data Processing Pipeline for MIMIC-IV
Mehak Gupta, Brennan Gallamoza, Nicolas Cutrona +3
An increasing amount of research is being devoted to applying machine learning methods to electronic health record (EHR) data for various clinical purposes. This growing area of re…
Who will Leave a Pediatric Weight Management Program and When? -- A machine learning approach for predicting attrition patterns
Hamed Fayyaz, Thao-Ly T. Phan, H. Timothy Bunnell +1
Childhood obesity is a major public health concern. Multidisciplinary pediatric weight management programs are considered standard treatment for children with obesity and severe ob…
Identifying the Leading Factors of Significant Weight Gains Using a New Rule Discovery Method
Mina Samizadeh, Jessica C Jones-Smith, Bethany Sheridan +1
Overweight and obesity remain a major global public health concern and identifying the individualized patterns that increase the risk of future weight gains has a crucial role in p…
Subtyping patients with chronic disease using longitudinal BMI patterns
Md Mozaharul Mottalib, Jessica C Jones-Smith, Bethany Sheridan +1
Obesity is a major health problem, increasing the risk of various major chronic diseases, such as diabetes, cancer, and stroke. While the role of obesity identified by cross-sectio…
Time-series Imputation and Prediction with Bi-Directional Generative Adversarial Networks
Mehak Gupta, Rahmatollah Beheshti
Multivariate time-series data are used in many classification and regression predictive tasks, and recurrent models have been widely used for such tasks. Most common recurrent mode…
Obesity Prediction with EHR Data: A deep learning approach with interpretable elements
Mehak Gupta, Thao-Ly T. Phan, Timothy Bunnell +1
Childhood obesity is a major public health challenge. Early prediction and identification of the children at a high risk of developing childhood obesity may help in engaging earlie…
Multi-modal Predictive Models of Diabetes Progression
Ramin Ramazi, Christine Perndorfer, Emily Soriano +2
With the increasing availability of wearable devices, continuous monitoring of individuals' physiological and behavioral patterns has become significantly more accessible. Access t…