深度学习
狼牙棒
心肌梗塞
医学
内科学
冲程(发动机)
比例危险模型
纸卷
人工智能
一致性
机器学习
随机森林
心脏病学
算法
数学
经皮冠状动脉介入治疗
计算机科学
物理
哲学
神学
热力学
作者
Doo-Young Kim,Kang‐Ho Choi,Jahae Kim,Jina Hong,Seong‐Min Choi,Man‐Seok Park,Jin‐Woong Cho
标识
DOI:10.1136/jnnp-2022-330230
摘要
Background Whether deep learning models using clinical data and brain imaging can predict the long-term risk of major adverse cerebro/cardiovascular events (MACE) after acute ischaemic stroke (AIS) at the individual level has not yet been studied. Methods A total of 8590 patients with AIS admitted within 5 days of symptom onset were enrolled. The primary outcome was the occurrence of MACEs (a composite of stroke, acute myocardial infarction or death) over 12 months. The performance of deep learning models (DeepSurv and Deep-Survival-Machines (DeepSM)) and traditional survival models (Cox proportional hazards (CoxPH) and random survival forest (RSF)) were compared using the time-dependent concordance index ( Ctd index). Results Given the top 1 to all 60 clinical factors according to feature importance, CoxPH and RSF yielded Ctd index of 0.7236–0.8222 and 0.7279–0.8335, respectively. Adding image features improved the performance of deep learning models and traditional models assisted by deep learning models. DeepSurv and DeepSM yielded the best Ctd index of 0.8496 and 0.8531 when images were added to all 39 relevant clinical factors, respectively. In feature importance, brain image was consistently ranked highly. Deep learning models automatically extracted the image features directly from personalised brain images and predicted the risk and date of future MACEs at the individual level. Conclusions Deep learning models using clinical data and brain images could improve the prediction of MACEs and provide personalised outcome prediction for patients with AIS. Deep learning models will allow us to develop more accurate and tailored prognostic prediction systems that outperform traditional models.
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