Abstract：
UNASSIGNED: Although evidence-based medicine proposes personalized care that considers the best evidence, it still fails to address personal treatment in many real clinical scenarios where the complexity of the situation makes none of the available evidence applicable. \"Medicine-based evidence\" (MBE), in which big data and machine learning techniques are embraced to derive treatment responses from appropriately matched patients in real-world clinical practice, was proposed. However, many challenges remain in translating this conceptual framework into practice.
UNASSIGNED: This study aimed to technically translate the MBE conceptual framework into practice and evaluate its performance in providing general decision support services for outcomes after congenital heart disease (CHD) surgery.
UNASSIGNED: Data from 4774 CHD surgeries were collected. A total of 66 indicators and all diagnoses were extracted from each echocardiographic report using natural language processing technology. Combined with some basic clinical and surgical information, the distances between each patient were measured by a series of calculation formulas. Inspired by structure-mapping theory, the fusion of distances between different dimensions can be modulated by clinical experts. In addition to supporting direct analogical reasoning, a machine learning model can be constructed based on similar patients to provide personalized prediction. A user-operable patient similarity network (PSN) of CHD called CHDmap was proposed and developed to provide general decision support services based on the MBE approach.
UNASSIGNED: Using 256 CHD cases, CHDmap was evaluated on 2 different types of postoperative prognostic prediction tasks: a binary classification task to predict postoperative complications and a multiple classification task to predict mechanical ventilation duration. A simple poll of the k-most similar patients provided by the PSN can achieve better prediction results than the average performance of 3 clinicians. Constructing logistic regression models for prediction using similar patients obtained from the PSN can further improve the performance of the 2 tasks (best area under the receiver operating characteristic curve=0.810 and 0.926, respectively). With the support of CHDmap, clinicians substantially improved their predictive capabilities.
UNASSIGNED: Without individual optimization, CHDmap demonstrates competitive performance compared to clinical experts. In addition, CHDmap has the advantage of enabling clinicians to use their superior cognitive abilities in conjunction with it to make decisions that are sometimes even superior to those made using artificial intelligence models. The MBE approach can be embraced in clinical practice, and its full potential can be realized.
UNASSIGNED: This study aimed to technically translate the MBE conceptual framework into practice and evaluate its performance in providing general decision support services for outcomes after congenital heart disease (CHD) surgery.
UNASSIGNED: Data from 4774 CHD surgeries were collected. A total of 66 indicators and all diagnoses were extracted from each echocardiographic report using natural language processing technology. Combined with some basic clinical and surgical information, the distances between each patient were measured by a series of calculation formulas. Inspired by structure-mapping theory, the fusion of distances between different dimensions can be modulated by clinical experts. In addition to supporting direct analogical reasoning, a machine learning model can be constructed based on similar patients to provide personalized prediction. A user-operable patient similarity network (PSN) of CHD called CHDmap was proposed and developed to provide general decision support services based on the MBE approach.
UNASSIGNED: Using 256 CHD cases, CHDmap was evaluated on 2 different types of postoperative prognostic prediction tasks: a binary classification task to predict postoperative complications and a multiple classification task to predict mechanical ventilation duration. A simple poll of the k-most similar patients provided by the PSN can achieve better prediction results than the average performance of 3 clinicians. Constructing logistic regression models for prediction using similar patients obtained from the PSN can further improve the performance of the 2 tasks (best area under the receiver operating characteristic curve=0.810 and 0.926, respectively). With the support of CHDmap, clinicians substantially improved their predictive capabilities.
UNASSIGNED: Without individual optimization, CHDmap demonstrates competitive performance compared to clinical experts. In addition, CHDmap has the advantage of enabling clinicians to use their superior cognitive abilities in conjunction with it to make decisions that are sometimes even superior to those made using artificial intelligence models. The MBE approach can be embraced in clinical practice, and its full potential can be realized.
摘要：
■尽管循证医学提出了考虑最佳证据的个性化护理，在许多真实的临床场景中，这种情况的复杂性使得现有的证据都不适用，它仍然无法解决个人治疗问题。“基于医学的证据”(MBE)，在现实世界的临床实践中，大数据和机器学习技术被采用，从适当匹配的患者那里获得治疗反应，被提议了。然而,在将这个概念框架转化为实践方面仍然存在许多挑战。
■本研究旨在将MBE概念框架在技术上转化为实践，并评估其在为先天性心脏病（CHD）手术后的结果提供一般决策支持服务方面的表现。
■收集来自4774例CHD手术的数据。使用自然语言处理技术从每个超声心动图报告中提取总共66个指标和所有诊断。结合一些基本的临床和手术资料，通过一系列计算公式测量每位患者之间的距离.受结构映射理论的启发，不同维度之间的距离融合可以由临床专家调节。除了支持直接类比推理，基于相似患者构建机器学习模型,提供个性化预测。提出并开发了一种称为CHDmap的CHD用户可操作的患者相似性网络（PSN），以提供基于MBE方法的通用决策支持服务。
■使用256例冠心病病例，CHDmap对2种不同类型的术后预后预测任务进行了评估：用于预测术后并发症的二元分类任务和用于预测机械通气持续时间的多分类任务。对PSN提供的k个最相似的患者进行简单的民意调查，可以获得比3名临床医生的平均表现更好的预测结果。使用从PSN获得的相似患者构建用于预测的逻辑回归模型可以进一步提高2项任务的性能（接收器工作特征曲线下的最佳面积分别为0.810和0.926）。在CHDmap的支持下，临床医生大大提高了他们的预测能力。
■没有单独优化，与临床专家相比，CHDmap具有竞争力。此外,CHDmap的优势是使临床医生能够利用他们优越的认知能力来做出有时甚至优于使用人工智能模型做出的决策。MBE方法可以在临床实践中采用，它的全部潜力可以实现。
■本研究旨在将MBE概念框架在技术上转化为实践，并评估其在为先天性心脏病（CHD）手术后的结果提供一般决策支持服务方面的表现。
■收集来自4774例CHD手术的数据。使用自然语言处理技术从每个超声心动图报告中提取总共66个指标和所有诊断。结合一些基本的临床和手术资料，通过一系列计算公式测量每位患者之间的距离.受结构映射理论的启发，不同维度之间的距离融合可以由临床专家调节。除了支持直接类比推理，基于相似患者构建机器学习模型,提供个性化预测。提出并开发了一种称为CHDmap的CHD用户可操作的患者相似性网络（PSN），以提供基于MBE方法的通用决策支持服务。
■使用256例冠心病病例，CHDmap对2种不同类型的术后预后预测任务进行了评估：用于预测术后并发症的二元分类任务和用于预测机械通气持续时间的多分类任务。对PSN提供的k个最相似的患者进行简单的民意调查，可以获得比3名临床医生的平均表现更好的预测结果。使用从PSN获得的相似患者构建用于预测的逻辑回归模型可以进一步提高2项任务的性能（接收器工作特征曲线下的最佳面积分别为0.810和0.926）。在CHDmap的支持下，临床医生大大提高了他们的预测能力。
■没有单独优化，与临床专家相比，CHDmap具有竞争力。此外,CHDmap的优势是使临床医生能够利用他们优越的认知能力来做出有时甚至优于使用人工智能模型做出的决策。MBE方法可以在临床实践中采用，它的全部潜力可以实现。
