no code implementations • 13 Nov 2022 • Ryan Wang, Li-Ching Chen, Lama Moukheiber, Mira Moukheiber, Dana Moukheiber, Zach Zaiman, Sulaiman Moukheiber, Tess Litchman, Kenneth Seastedt, Hari Trivedi, Rebecca Steinberg, Po-Chih Kuo, Judy Gichoya, Leo Anthony Celi
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.
A datathon is a time-constrained competition involving data science applied to a specific problem.
1 code implementation • • Eric Lehman, Vladislav Lialin, Katelyn Y. Legaspi, Anne Janelle R. Sy, Patricia Therese S. Pile, Nicole Rose I. Alberto, Richard Raymund R. Ragasa, Corinna Victoria M. Puyat, Isabelle Rose I. Alberto, Pia Gabrielle I. Alfonso, Marianne Taliño, Dana Moukheiber, Byron C. Wallace, Anna Rumshisky, Jenifer J. Liang, Preethi Raghavan, Leo Anthony Celi, Peter Szolovits
The questions are generated by medical experts from 100+ MIMIC-III discharge summaries.
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.
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.
1 code implementation • 1 Sep 2021 • Joy Tzung-yu Wu, Miguel Ángel Armengol de la Hoz, Po-Chih Kuo, Joseph Alexander Paguio, Jasper Seth Yao, Edward Christopher Dee, Wesley Yeung, Jerry Jurado, Achintya Moulick, Carmelo Milazzo, Paloma Peinado, Paula Villares, Antonio Cubillo, José Felipe Varona, Hyung-Chul Lee, Alberto Estirado, José Maria Castellano, Leo Anthony Celi
We employed the best machine learning practices for clinical model development.
no code implementations • 21 Jul 2021 • Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, Matthew P Lungren, Lyle Palmer, Brandon J Price, Saptarshi Purkayastha, Ayis Pyrros, Luke Oakden-Rayner, Chima Okechukwu, Laleh Seyyed-Kalantari, Hari Trivedi, Ryan Wang, Zachary Zaiman, Haoran Zhang, Judy W Gichoya
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.
Outcomes were defined based on increase or decrease of serum lactate levels between the groups.
While hyperkalemia is often a complication of acute kidney injury (AKI), it also occurs in the absence of AKI.
Many batch RL health applications first discretize time into fixed intervals.
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.
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.
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.
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.
This study aims to develop an interpretable and generalizable model for early mortality prediction in elderly patients with MODS.
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.
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.
In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score.
no code implementations • 31 May 2018 • Omer Gottesman, Fredrik Johansson, Joshua Meier, Jack Dent, Dong-hun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li-wei H. Lehman, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, Finale Doshi-Velez
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare.
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.
Real-time prediction of clinical interventions remains a challenge within intensive care units (ICUs).
no code implementations • 25 Mar 2017 • Sebastian Gehrmann, Franck Dernoncourt, Yeran Li, Eric T. Carlson, Joy T. Wu, Jonathan Welt, John Foote Jr., Edward T. Moseley, David W. Grant, Patrick D. Tyler, Leo Anthony Celi
We assess the performance of deep learning algorithms and compare them with classical NLP approaches.
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.