Patient Similarity in Prediction Models Based on Health Data: A Scoping Review.
Patient Similarity in Prediction Models Based on Health Data: A Scoping Review.
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DOI：
10.2196/medinform.6730
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发表时间：
2017-03-03
影响因子：
3.2
通讯作者：
Lee J
中科院分区：
文献类型：
--
作者：
Sharafoddini A;Dubin JA;Lee J
医生和卫生政策制定者在对各种医疗问题进行决策时需要做出预测。在结果预测的预测建模方面已经取得了许多进展，但是这些创新针对的是普通患者，并且对于个体患者来说是不够可调整的。该领域的一个发展思路是基于患者相似性的个性化预测分析。这种方法的目标是识别与索引患者相似的患者，并从相似患者的记录中获得见解，以提供个性化的预测。其目的是总结和审查已发表的研究，描述基于计算机的方法来预测患者的未来健康状况的基础上的健康数据和患者的相似性，确定差距，并为相关的未来研究提供一个起点。该方法包括：（1）通过在Scopus、PubMed和ISI Web of Science中进行自动检索来进行综述，通过首先筛选标题和摘要然后分析全文来选择相关研究，以及（2）通过提取出版物详细信息和关于背景、预测因子、缺失数据、建模算法、结局和评价方法的信息来记录到矩阵表中，合成数据并报告结果。删除重复文献后，对1339篇文献的摘要和标题进行筛选，选择67篇进行全文综述。共有22篇文章符合纳入标准。在纳入的文章中，医院是主要数据来源（n=10）。心血管疾病（n=7）和糖尿病（n=4）是主要患者疾病。大多数研究（n=18）在设计预测模型时使用了基于邻域的方法。两项研究表明，基于患者相似性的建模优于基于人群的预测方法。对用于诊断和预后的基于患者相似性的预测建模的兴趣一直在增长。除了原始/编码的健康数据外，还采用小波变换和词频-逆文档频率方法来提取预测因子。选择有潜力突出特殊情况的预测因子和定义新的患者相似性指标是现有文献中确定的差距之一，为未来的工作提供了起点。基于患者相似性和健康数据的患者状态预测模型为个性化和最终改善医疗保健提供了令人兴奋的潜力，从而改善患者的治疗效果。
Physicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions.. The aim is to summarize and review published studies describing computer-based approaches for predicting patients’ future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research. The method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results. After duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods. Interest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health data, wavelet transform and term frequency-inverse document frequency methods were employed to extract predictors. Selecting predictors with potential to highlight special cases and defining new patient similarity metrics were among the gaps identified in the existing literature that provide starting points for future work. Patient status prediction models based on patient similarity and health data offer exciting potential for personalizing and ultimately improving health care, leading to better patient outcomes.