A Deep Learning Approach to Predict Bleeding Risk Over Time in Patients on Extended Anticoagulation Therapy

医学 重症监护医学
作者
Soroush Shahryari Fard,Theodore J. Perkins,Philip S. Wells
出处
期刊:Journal of Thrombosis and Haemostasis [Wiley]
卷期号:22 (7): 1997-2008
标识
DOI:10.1016/j.jtha.2024.04.005
摘要

Background Thus far, all the clinical models developed to predict major bleeding in patients on extended anticoagulation therapy use the baseline predictors to stratify patients into different risk groups. Therefore, these models do not account for the clinical changes and events that occur after the baseline visit, which can modify risk of bleeding. However, it is difficult to develop predictive models from the routine follow-up clinical interviews which are irregular sequences of multivariate time series data. Objectives To demonstrate that deep learning can incorporate patient time-series follow-up data to improve prediction of major bleeding. Method We used the baseline and follow-up data that was collected over 8 years in a longitudinal cohort study of 2542 patients, of whom 118 had major bleeding. Four supervised neural network-based machine learning models were trained on the baseline, or the follow-up, or both datasets on 70% of the data. The performance of these models was evaluated, along with modified versions of previously developed clinical models (CHAP, ACCP, RIETE, VTE-BLEED, HAS-BLED, and OBRI), on the remaining 30% of the data. Results An ensemble of feedforward and recurrent neural networks that used the baseline and follow-up data was the best-performing model, achieving a sensitivity and a specificity of 61% and 82%, respectively, in identifying major bleeding, and it outperformed the previously developed clinical models in terms of area under the ROC curve (82%) and area under the precision-recall curve (14%). Conclusion Time series follow-up data can improve major bleeding prediction in patients on extended anticoagulation therapy.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
无我完成签到,获得积分10
刚刚
mlle完成签到,获得积分10
1秒前
WX完成签到,获得积分10
1秒前
咿呀完成签到,获得积分10
1秒前
夏虫完成签到,获得积分10
1秒前
羞涩的蘑菇完成签到,获得积分10
2秒前
zero完成签到,获得积分10
2秒前
pw完成签到 ,获得积分10
2秒前
ff完成签到 ,获得积分10
3秒前
华仔应助simomo采纳,获得10
3秒前
O泡果奶完成签到 ,获得积分10
4秒前
思源应助石头采纳,获得10
4秒前
a雪橙完成签到 ,获得积分10
4秒前
selfevidbet完成签到,获得积分10
4秒前
Ww完成签到,获得积分10
5秒前
5秒前
goldenfleece发布了新的文献求助10
5秒前
无情的牛马完成签到,获得积分10
6秒前
平淡纸飞机完成签到 ,获得积分10
6秒前
阔达语柔完成签到,获得积分10
6秒前
安东尼奥完成签到,获得积分10
7秒前
抹缇卡完成签到 ,获得积分10
7秒前
夜幕完成签到,获得积分10
8秒前
biubiu完成签到,获得积分10
8秒前
豆丁小猫完成签到,获得积分10
8秒前
梁子奥里给完成签到,获得积分10
9秒前
梁正强完成签到,获得积分10
9秒前
花花完成签到,获得积分10
9秒前
娇气的春天完成签到 ,获得积分10
9秒前
10秒前
过时的谷丝完成签到,获得积分10
10秒前
嗣音发布了新的文献求助10
10秒前
ccc应助you采纳,获得10
10秒前
sharon完成签到,获得积分10
11秒前
GuGuGaGaAH完成签到 ,获得积分10
11秒前
yuanyuan完成签到,获得积分10
11秒前
zh完成签到,获得积分10
11秒前
13秒前
wzy5508完成签到 ,获得积分10
13秒前
野性的胡萝卜完成签到,获得积分10
14秒前
高分求助中
Production Logging: Theoretical and Interpretive Elements 2500
Healthcare Finance: Modern Financial Analysis for Accelerating Biomedical Innovation 2000
Applications of Emerging Nanomaterials and Nanotechnology 1111
Agaricales of New Zealand 1: Pluteaceae - Entolomataceae 1040
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Neuromuscular and Electrodiagnostic Medicine Board Review 700
지식생태학: 생태학, 죽은 지식을 깨우다 600
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3467001
求助须知:如何正确求助?哪些是违规求助? 3059773
关于积分的说明 9068088
捐赠科研通 2750239
什么是DOI,文献DOI怎么找? 1509127
科研通“疑难数据库(出版商)”最低求助积分说明 697126
邀请新用户注册赠送积分活动 696953