肝细胞癌经动脉化疗栓塞后的生存预测:一种深入的多任务生存分析方法。

IF 5.9 Q1 Computer Science Journal of Healthcare Informatics Research Pub Date : 2023-07-31 eCollection Date: 2023-09-01 DOI:10.1007/s41666-023-00139-0
Guo Huang, Huijun Liu, Shu Gong, Yongxin Ge
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引用次数: 0

摘要

准确预测巴塞罗那临床癌症(BCLC)B期肝细胞癌(HCC)患者术后生存时间对术后健康护理具有重要意义。生存分析是医学领域中用于预测感兴趣事件发生时间的常用方法。目前,主流的生存分析模型,如Cox比例风险模型,应该对潜在的随机过程进行严格的假设,以解决审查数据,从而可能限制其在临床实践中的应用。在本文中,我们提出了一种新的深度多任务生存模型(DMSM)来分析HCC的生存数据。具体而言,DMSM将HCC患者的传统生存时间预测问题转化为多个时间点的生存概率预测问题,并应用熵正则化和排序损失来优化多任务神经网络。与传统的删除审查数据和强假设的方法相比,DMSM充分利用了审查数据中的所有信息,但不需要做出任何假设。此外,我们还确定了影响HCC患者预后的风险因素,并可视化了对这些因素进行排名的重要性。基于对BCLC B期HCC患者真实数据集的分析,在三个不同验证数据集上的实验结果表明,DMSM实现了竞争性能,一致性指数分别为0.779、0.727和0.780,综合Brier评分(IBS)分别为0.172、0.138和0.135。我们的DMSM对于自举100次的IBS具有相对较小的标准偏差(0.002、0.002和0.003)。我们提出的DMSM可以作为一种有效的生存分析模型,为准确预测BCLC B期HCC患者的术后生存时间提供了重要手段。
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Survival Prediction After Transarterial Chemoembolization for Hepatocellular Carcinoma: a Deep Multitask Survival Analysis Approach.

The accurate prediction of postoperative survival time of patients with Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC) is important for postoperative health care. Survival analysis is a common method used to predict the occurrence time of events of interest in the medical field. At present, the mainstream survival analysis models, such as the Cox proportional risk model, should make strict assumptions about the potential random process to solve the censored data, thus potentially limiting their application in clinical practice. In this paper, we propose a novel deep multitask survival model (DMSM) to analyze HCC survival data. Specifically, DMSM transforms the traditional survival time prediction problem of patients with HCC into a survival probability prediction problem at multiple time points and applies entropy regularization and ranking loss to optimize a multitask neural network. Compared with the traditional methods of deleting censored data and strong hypothesis, DMSM makes full use of all the information in the censored data but does not need to make any assumption. In addition, we identify the risk factors affecting the prognosis of patients with HCC and visualize the importance of ranking these factors. On the basis of the analysis of a real dataset of patients with BCLC stage B HCC, experimental results on three different validation datasets show that the DMSM achieves competitive performance with concordance index of 0.779, 0.727, and 0.780 and integrated Brier score (IBS) of 0.172, 0.138, and 0.135, respectively. Our DMSM has a comparatively small standard deviation (0.002, 0.002, and 0.003) for IBS of bootstrapping 100 times. The DMSM we proposed can be utilized as an effective survival analysis model and provide an important means for the accurate prediction of postoperative survival time of patients with BCLC stage B HCC.

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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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