Employing a low-code machine learning approach to predict in-hospital mortality and length of stay in patients with community-acquired pneumonia

Employing a low-code machine learning approach to predict in-hospital mortality and length of stay in patients with community-acquired pneumonia

Abstract Community-acquired pneumonia (CAP) is associated with high mortality rates and often results in prolonged hospital stays. The potential of machine learning to enhance prediction accuracy in this context is significant, yet clinicians often lack the programming skills required for effective...

Full description

Saved in:
Bibliographic Details
Main Authors: Hao Chen, Shurui Zhang, Hiromi Matsumoto, Nanami Tsuchiya, Chihiro Yamada, Shunsuke Okasaki, Atsushi Miyasaka, Kentaro Yumoto, Daiki Kanou, Fumihiro Kashizaki, Harumi Koizumi, Kenichi Takahashi, Masato Shimizu, Nobuyuki Horita, Takeshi Kaneko
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Low-coding
Machine learning
PyCaret
Length of stay
Mortality
Artificial intelligence
Online Access:https://doi.org/10.1038/s41598-024-82615-0
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items