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首页> 外文期刊>BMC Medical Informatics and Decision Making >A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology
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A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

机译：对临床泌尿外科专家系统和机器学习（ML）应用的系统审查

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Testing a hypothesis for ‘factors-outcome effect’ is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified. The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system. The search identified n?=?1087 articles from all databases and n?=?712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer?=?15, bladder cancer?=?13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible. ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.
机译：测试假设“因素 - 结果效应”是一个常见的任务，但标准统计回归分析工具通过污染太多噪音变量污染的数据呈现无效。专家系统可以在分析数据时提供替代方法，以识别与结果最高相关的变量。通过应用其有效的机器学习（ML）能力，可以节省重大的研究时间和成本。该研究旨在系统地审查ES在泌尿外研究中的应用和其方法模型，以实现有效多变异分析。他们的域名，发展和有效性将被确定。应用PRISMA方法以制定有效的数据收集方法方法。本研究搜索包括七个最相关的信息来源：科学网，Embase，Biosis引用指数，Scopus，PubMed，Google Scholar和Medline。如果它们应用了涉及多变量分析的清晰泌尿科研究问题，则包括符合条件的文章。仅包括ES模型中具有相关研究方法的文章。分析的数据包括系统模型，应用程序，输入/输出变量，目标用户，验证和结果。每个系统相对报告ML模型和可变分析。搜索已确定n？=？来自所有数据库的1087篇文章，n？= 712有资格抵消纳入标准。最终包括168个系统，并系统地分析了近期在特定人工神经网络中具有31个系统的学术泌尿外科摄取es的最新增加。大多数系统被研究尿道肿瘤（前列腺癌？=？15，膀胱癌？= 13），其中研究了诊断，预后和存活预测标志物。由于模型的异质性及其统计测试，Meta分析是不可行的。 ES Utility提供有效的ML潜力，并且其研究中的应用已经证明了多变异分析的有效模型。他们的发展的复杂性可以挑战他们在泌尿外科诊所的摄取，同时该领域的统计工具的限制创造了进一步研究研究的差距。计算机科学家在学术单位上融入临床泌尿外科研究中的使用。

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