Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest X-Rays using Transfer Learning and Fusion Strategies

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.

Fairness Transfer Learning

Developing and validating multi-modal models for mortality prediction in COVID-19 patients: a multi-center retrospective study

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.

BIG-bench Machine Learning Decision Making +1

Reading Race: AI Recognises Patient's Racial Identity In Medical Images

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.

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

no code implementations • 28 Jan 2020 • Xiaoli Liu, Pan Hu, Zhi Mao, Po-Chih Kuo, Peiyao Li, Chao Liu, Jie Hu, Deyu Li, Desen Cao, Roger G. Mark, Leo Anthony Celi, Zhengbo Zhang, Feihu Zhou

This study aims to develop an interpretable and generalizable model for early mortality prediction in elderly patients with MODS.

Interpretable Machine Learning Mortality Prediction +1