Case Studies

Hooman H. Rashidi, John Pepper, Taylor Howard, Karina Klein, Larissa May, Samer Albahra, Brett Phinney, Michelle R. Salemi, Nam K. Tran


July 2022

PLoS One

Hooman H Rashidi, Imran H Khan, Luke T Dang, Samer Albahra, Ujjwal Ratan, Nihir Chadderwala, Wilson To, Prathima Srinivas, Jeffery Wajda, Nam K Tran

Prediction of tuberculosis using an automated machine learning platform for models trained on synthetic data

Jan 2022

J. Path Informatics

Rashidi HH, Tran N, Albahra S, Dang LT

Machine learning in health care and laboratory medicine: General overview of supervised learning and Auto-ML

July 2021

Int J Lab Hematology

Tran NK, Howard T, Walsh R, Pepper J, Loegering J, Phinney B, Salemi MR, Rashidi HH

Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept

April 2021

Nature's Scientific Reports

Hooman H. Rashidi, MD, FASCP, Amy Makley, MD; Tina L. Palmieri, MD, FACS, FCCM; Samer Albahra, MD; Julia Loegering, BS; Lei Fang, PhD; Kensuke Yamaguchi, PhD; Travis Gerlach, MD; Dario Rodriquez Jr, MSc, RRT, FAARC; and Nam K. Tran, PhD, HCLD (ABB), FAACC

Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction through an Automated Machine Learning Platform and Point-of-Care Testing

February 2021

Archives of Pathology & Laboratory Medicine

Kuang-Yu Jen, Samer Albahra, Felicia Yen, Junichiro Sageshima, Ling-Xin Chen, Nam Tran, Hooman H. Rashidi

Automated En Masse Machine Learning Model Generation and Optimization for Predicting Delayed Graft Function in Renal Allografts

Several groups have previously developed logistic regression models for predicting delayed graft function (DGF). In this study, we used an automated machine learning (ML) modeling pipeline to generate and optimize DGF prediction models en masse.

December 2020


Nam K. Tran, PhD, Samer Albahra, MD, Tam N. Pham, MD, James Holmes IV, MD, David Greenhalgh, MD, Tina L. Palmieri, MD, Jeffrey Wajda, and Hooman H. Rashidi, MD

Novel Application of An Automated-Machine Learning Development Tool For Predicting Burn Sepsis

We conducted a retrospective analysis of 211 adult patients (age ≥ 18 years) with severe burn injury (≥ 20% total body surface area) to generate training and test datasets for ML applications. The MILO-ML approach was compared against an exhaustive “non-automated” ML approach as well as standard statistical methods.

July 2020

Nature’s Scientific Reports

Hooman H. Rashidi, MD, FASCP; Soman Sen, MD, FACS; Tina L. Palmieri, MD, FACS, FCCM; Thomas Blackmon, BS; Jeffrey Wajda, DO; and Nam K. Tran, PhD, HCLD (ABB), FACB

Early Recognition of Acute Kidney Injury In Trauma Surgery And Severely Burned Patients By Artificial Intelligence: Generalization Of Machine Learning Techniques

The study objective was to assess the theoretical performance of artificial intelligence (AI)/machine learning (ML) algorithms to augment AKI recognition using the novel biomarker, neutrophil gelatinase associated lipocalin (NGAL), combined with contemporary biomarkers such as N-terminal pro B-type natriuretic peptide (NT-proBNP), urine output (UOP), and plasma creatinine.

January 2020

Nature’s Scientific Reports

Hooman H Rashidi, MD, Nam K. Tran, PhD, Elham Vali Betts, MD, Lydia P. Howell, MD, Ralph Green, MD, PhD.

Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods.

This review provides definitions and basic knowledge of machine learning categories, introduces the underlying concept of the bias-variance trade-off as an important foundation in supervised machine learning, and discusses approaches to the supervised machine learning study design along with an overview and description of common supervised machine learning algorithms.

September 2019

Academic Pathology