Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective Observational Study by Using the Common Data Model

<b>Background/Objectives</b>: Earlier detection of severe immune-related hematological adverse events (irHAEs) in cancer patients treated with a PD-1 or PD-L1 inhibitor is critical to improving treatment outcomes. The study aimed to develop a simple machine learning (ML) model for predic...

Full description

Saved in:

Bibliographic Details
Main Authors:	Seok Jun Park, Seungwon Yang, Suhyun Lee, Sung Hwan Joo, Taemin Park, Dong Hyun Kim, Hyeonji Kim, Soyun Park, Jung-Tae Kim, Won Gun Kwack, Sung Wook Kang, Yun-Kyoung Song, Jae Myung Cha, Sang Youl Rhee, Eun Kyoung Chung
Format:	Article
Language:	English
Published:	MDPI AG 2025-01-01
Series:	Diagnostics
Subjects:	immune checkpoint inhibitor PD-1 inhibitor PD-L1 inhibitor observational medical outcomes partnership (OMOP) common data model (CDM) real-world data (RWD)
Online Access:	https://www.mdpi.com/2075-4418/15/2/226
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective Observational Study by Using the Common Data Model

Similar Items