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Found 47 papers, 14 papers with code
CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray
The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR).
Performance Gains of LLMs With Humans in a World of LLMs Versus Humans
Currently, a considerable research effort is devoted to comparing LLMs to a group of human experts, where the term "expert" is often ill-defined or variable, at best, in a state of constantly updating LLM releases.
BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace.
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
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, Squamish, and Tsleil-Waututh Nations in Vancouver, British Columbia, Canada.
An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes
Thus, due to worse access to primary health care, minority patients are more likely to end up in ICU for preventable conditions, causing a reduction in the mortality rates and creating an effect that appears to be protective.
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs
Current ophthalmology clinical workflows are plagued by over-referrals, long waits, and complex and heterogeneous medical records.
MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries
Medical decisions directly impact individuals' health and well-being.
Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective
The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes.
Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks
Medical knowledge is context-dependent and requires consistent reasoning across various natural language expressions of semantically equivalent phrases.
Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications
Our paper investigates the efficiency and effectiveness of using vector embeddings from single-modal foundation models and multi-modal Vision-Language Models (VLMs) for multimodal deep learning in low-resource environments, particularly in healthcare.
Towards Clinical AI Fairness: Filling Gaps in the Puzzle
The ethical integration of Artificial Intelligence (AI) in healthcare necessitates addressing fairness-a concept that is highly context-specific across medical fields.
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias
Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in their training data.
Seeds of Stereotypes: A Large-Scale Textual Analysis of Race and Gender Associations with Diseases in Online Sources
We find widespread disparities in the associations of specific racial and gender terms with the 18 diseases analyzed.
DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era
In the big data era, integrating diverse data modalities poses significant challenges, particularly in complex fields like healthcare.
A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Our contributions include a multifactorial framework for human assessment of LLM-generated answers for biases, and EquityMedQA, a collection of seven datasets enriched for adversarial queries.
DengueNet: Dengue Prediction using Spatiotemporal Satellite Imagery for Resource-Limited Countries
In this study, our aim is to improve health equity in resource-constrained countries by exploring the effectiveness of high-resolution satellite imagery as a nontraditional and readily accessible data source.
DRStageNet: Deep Learning for Diabetic Retinopathy Staging from Fundus Images
Diabetic retinopathy (DR) is a prevalent complication of diabetes associated with a significant risk of vision loss.
Benchmarking bias: Expanding clinical AI model card to incorporate bias reporting of social and non-social factors
Clinical AI model reporting cards should be expanded to incorporate a broad bias reporting of both social and non-social factors.
Generalization in medical AI: a perspective on developing scalable models
The scientific community is increasingly recognizing the importance of generalization in medical AI for translating research into practical clinical applications.
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions.
Causal thinking for decision making on Electronic Health Records: why and how
We illustrate the various choices in studying the effect of albumin on sepsis mortality in the Medical Information Mart for Intensive Care database (MIMIC-IV).
Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit
Social determinants of health (SDOH) -- the conditions in which people live, grow, and age -- play a crucial role in a person's health and well-being.
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.
Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest X-Rays using Transfer Learning and Fusion Strategies
We further propose two fusion schemes, (1) model-level fusion, including bagging and stacking methods using MIMIC-CXR, and (2) data-level fusion, including multi-site data using MIMIC-CXR and Emory-CXR, and multi-modal using MIMIC-CXRs and MIMIC-IV EHR, to improve the overall model performance.
Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event
A datathon is a time-constrained competition involving data science applied to a specific problem.
Learning to Ask Like a Physician
The questions are generated by medical experts from 100+ MIMIC-III discharge summaries.
Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations
In this study, we investigate the level of implicit race information available to ML models and human experts and the implications of model-detectable differences in clinical notes.
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation
Off-policy policy evaluation methods for sequential decision making can be used to help identify if a proposed decision policy is better than a current baseline policy.
Developing and validating multi-modal models for mortality prediction in COVID-19 patients: a multi-center retrospective study
We employed the best machine learning practices for clinical model development.
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
Prediction of Blood Lactate Values in Critically Ill Patients: A Retrospective Multi-center Cohort Study
Outcomes were defined based on increase or decrease of serum lactate levels between the groups.
Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability
While hyperkalemia is often a complication of acute kidney injury (AKI), it also occurs in the absence of AKI.
Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment
Many batch RL health applications first discretize time into fixed intervals.
Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding
To push forward research in this direction, we have organized two shared task for acronym identification and acronym disambiguation in scientific documents, named AI@SDU and AD@SDU, respectively.
Real-time Prediction of COVID-19 related Mortality using Electronic Health Records
Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients.
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
In this paper, we introduce a dataset for patient phenotyping, a task that is defined as the identification of whether a patient has a given medical condition (also referred to as clinical indication or phenotype) based on their patient note.
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deployment in high stakes settings requires ways of assessing its validity.
Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation
This study aims to develop an interpretable and generalizable model for early mortality prediction in elderly patients with MODS.
Ensembles of Locally Independent Prediction Models
Ensembles depend on diversity for improved performance.
Racial Disparities and Mistrust in End-of-Life Care
There are established racial disparities in healthcare, including during end-of-life care, when poor communication and trust can lead to suboptimal outcomes for patients and their families.
Applications
Withholding or withdrawing invasive interventions may not accelerate time to death among dying ICU patients
Comparing the period 2002-2005 vs. 2008-2011, we found a reduction in the use of vasopressors and inotropes among patients with the lowest severity who died within 30 days of ICU admission (41. 8 vs. 36. 2 hours, p<0. 001) but not among those with the highest severity.
Modeling Mistrust in End-of-Life Care
In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score.
Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare.
Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach
In this work, we propose a new approach to deduce optimal treatment policies for septic patients by using continuous state-space models and deep reinforcement learning.
Clinical Intervention Prediction and Understanding using Deep Networks
Real-time prediction of clinical interventions remains a challenge within intensive care units (ICUs).
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping
We assess the performance of deep learning algorithms and compare them with classical NLP approaches.
MIMIC-III, a freely accessible critical care database
MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital.
Ranked #1 on
Multi-Label Classification Of Biomedical Texts
on MIMIC-III
(using extra training data)
