Search Results for author:
Found 9 papers, 7 papers with code
Representation Learning of Lab Values via Masked AutoEncoder
Accurate imputation of missing laboratory values in electronic health records (EHRs) is critical to enable robust clinical predictions and reduce biases in AI systems in healthcare.
WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation
Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide, necessitating robust benchmarks to ensure their safety, efficacy, and fairness.
Evaluating the Impact of Pulse Oximetry Bias in Machine Learning under Counterfactual Thinking
Under this counterfactual design, two models are trained with identical data, features, and settings, except for the method of measuring oxygen saturation: models using SaO2 are a "control" and models using SpO2 a "treatment".
Leveraging text data for causal inference using electronic health records
In studies that rely on data from electronic health records (EHRs), unstructured text data such as clinical progress notes offer a rich source of information about patient characteristics and care that may be missing from structured data.
CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays
Despite the progress in utilizing deep learning to automate chest radiograph interpretation and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received limited attention.
Chest ImaGenome Dataset for Clinical Reasoning
Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR) images in recent years, a quantitative evaluation of the explainability of these models is hampered by the lack of locally labeled datasets for different findings.
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
However, the adoption of such policies in practice is often challenging, as they are hard to interpret within the application context, and lack measures of uncertainty for the learned policy value and its decisions.
Migration through Machine Learning Lens -- Predicting Sexual and Reproductive Health Vulnerability of Young Migrants
In this paper, we have discussed initial findings and results of our experiment to predict sexual and reproductive health vulnerabilities of migrants in a data-constrained environment.
Understanding the Artificial Intelligence Clinician and optimal treatment strategies for sepsis in intensive care
In this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community.
