Background 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.. Objective 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. Methods 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. 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. Conclusions 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.
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机译：背景技术内科医生和健康政策制定者需要在做出各种医疗问题的决策时做出预测。在针对结果预测的预测建模方面已经取得了许多进步，但是这些创新针对的是普通患者，并且对于个别患者而言调整不足。该领域的一个发展中的想法是基于患者相似性的个性化预测分析。该方法的目的是识别与索引患者相似的患者，并从相似患者的记录中获取见解，从而提供个性化的预测。目的目的是总结和审查已发表的研究，这些研究描述了基于计算机的方法来预测患者的病情。根据健康数据和患者相似性确定未来健康状况，找出差距，并为相关的未来研究提供起点。方法该方法涉及（1）通过在Scopus，PubMed和ISI Web of Science中进行自动搜索进行评论，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进行文献记录将有关上下文，预测变量，缺失数据，建模算法，结果和评估方法的信息组合到矩阵表中，合成数据并报告结果。结果重复删除后，对摘要中的1339篇文章进行了摘要和标题筛选，并选择了67篇进行全文审查。总共有22篇文章符合纳入标准。在纳入的文章中，医院是主要的数据来源（n = 10）。心血管疾病（n = 7）和糖尿病（n = 4）是主要的患者疾病。大多数研究（n = 18）在设计预测模型时都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。结论对基于患者相似性的预测模型进行诊断和预后的兴趣正在增长。除了原始/编码的健康数据外，还采用小波变换和术语频率逆文档频率方法来提取预测变量。在现有文献中发现的空白中，选择有潜力突出特殊病例的预测因素以及定义新的患者相似性指标是为未来工作提供起点的空白。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，从而带来了更好的患者结果。
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