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Found 24 papers, 6 papers with code
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.
