Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm

The ML algorithm, combining demographic and clinical factors, accurately predicted post-chemotherapy lung infections in cancer patients. The LR model performed well, potentially improving early detection and treatment in clinical practice.

Authors

Institutions

Publication Details

Journal
Frontiers in Oncology
Published
2024-08-09
DOI
https://doi.org/10.3389/fonc.2024.1403392
Citations
28
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
11.32
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm

Hui Yan, Jun Liu, Xin Li, Tao Sun et al.
28 citations
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
11.32
article

Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm

Hui Yan, Jun Liu, Xin Li, Tao Sun, Houqin Yuan
article en
28 citations

Abstract

The ML algorithm, combining demographic and clinical factors, accurately predicted post-chemotherapy lung infections in cancer patients. The LR model performed well, potentially improving early detection and treatment in clinical practice.

Frontiers in OncologyVol. 14
Shaoyang University (CN), The Central Hospital of Shaoyang (CN)
Good health and well-being
Openalex Percentile: Top 1%
Radiomics and Machine Learning in Medical Imaging
11.32
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.