跳至主導覽
跳至搜尋
跳過主要內容
國立陽明交通大學研發優勢分析平台 首頁
English
中文
首頁
人員
單位
研究成果
計畫
獎項
活動
貴重儀器
影響
按專業知識、姓名或所屬機構搜尋
Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients
Tien Yun Yang
, Pin Yu Kuo
, Yaoru Huang
, Hsiao Wei Lin
, Shwetambara Malwade
, Long Sheng Lu
, Lung Wen Tsai
, Shabbir Syed-Abdul
*
,
Chia Wei Sun
*
, Jeng Fong Chiou
*
*
此作品的通信作者
光電工程學系
生醫工程研究所
研究成果
:
Article
›
同行評審
15
引文 斯高帕斯(Scopus)
總覽
指紋
指紋
深入研究「Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients」主題。共同形成了獨特的指紋。
排序方式
重量
按字母排序
Keyphrases
Cancer Patients
100%
Deep Learning
100%
Survival Outcomes
100%
Actigraphy
100%
Performance Status
60%
Prediction Model
40%
Status Assessment
40%
Wristband
40%
Long Short-term Memory
40%
Prognostic Accuracy
40%
Clinical Practice
20%
Performance Improvement
20%
Performance Measures
20%
Monitoring System
20%
Stable Conditions
20%
Performance Practice
20%
Current Performance
20%
Model Performance
20%
Activity Data
20%
Evaluation Tool
20%
End-of-life Care
20%
Hospital Stay
20%
Survival Prediction
20%
Wearable Technology
20%
Hospital Admission
20%
Patient Activity
20%
Clinical Patients
20%
Palliative Care
20%
In-hospital Death
20%
Hospice Care
20%
Clinical Trial Registry
20%
Karnofsky Performance Status
20%
Care Unit
20%
Patient Performance
20%
Performance Status Score
20%
Medicine and Dentistry
Cancer Staging
100%
Actigraphy
100%
Short Term Memory
40%
Clinical Trial
20%
Survival Prediction
20%
Symptomatic Treatment
20%
Prognostication
20%
End of Life Care
20%
Karnofsky Performance Status
20%