Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters

Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters

This study aimed to investigate the patterns of anticoagulation therapy and coagulation parameters and to develop a prediction model to predict the type of anticoagulation therapy in geriatric patients with traumatic brain injury. A retrospective analysis was performed using the nationwide neurotrau...

Full description

Saved in:
Bibliographic Details
Main Authors: Gaku FUJIWARA, Yohei OKADA, Eiichi SUEHIRO, Hiroshi YATSUSHIGE, Shin HIROTA, Shu HASEGAWA, Hiroshi KARIBE, Akihiro MIYATA, Kenya KAWAKITA, Kohei HAJI, Hideo AIHARA, Shoji YOKOBORI, Motoki INAJI, Takeshi MAEDA, Takahiro ONUKI, Kotaro OSHIO, Nobukazu KOMORIBAYASHI, Michiyasu SUZUKI, Naoto SHIOMI
Format: Article
Language:English
Published: The Japan Neurosurgical Society 2025-02-01
Series:Neurologia Medico-Chirurgica
Subjects:
machine learning
anticoagulant therapy
vitamin k antagonist
direct oral anticoagulants
traumatic brain injury
Online Access:https://www.jstage.jst.go.jp/article/nmc/65/2/65_2024-0066/_pdf/-char/en
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items