Robust Meta-Model for Predicting the Need for Blood Transfusion in Non-traumatic ICU Patients

no code implementations • 1 Jan 2024 • Alireza Rafiei, Ronald Moore, Tilendra Choudhary, Curtis Marshall, Geoffrey Smith, John D. Roback, Ravi M. Patel, Cassandra D. Josephson, Rishikesan Kamaleswaran

This study aims to develop an advanced machine learning-based model to predict the probability of transfusion necessity over the next 24 hours for a diverse range of non-traumatic ICU patients.

Meta-learning in healthcare: A survey

no code implementations • 5 Aug 2023 • Alireza Rafiei, Ronald Moore, Sina Jahromi, Farshid Hajati, Rishikesan Kamaleswaran

We then divide the employed meta-learning approaches in the healthcare domain into two main categories of multi/single-task learning and many/few-shot learning and survey the studies.

Few-Shot Learning

Transfer Learning for Causal Effect Estimation

no code implementations • 16 May 2023 • Song Wei, Hanyu Zhang, Ronald Moore, Rishikesan Kamaleswaran, Yao Xie

We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces, aiming to improve causal effect estimation accuracy in limited data.

regression Transfer Learning

ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data

no code implementations • 13 Dec 2022 • Ronald Moore, Rishikesan Kamaleswaran

Sepsis is a deadly condition affecting many patients in the hospital.

Active Learning