Finance Section, The Second Affiliated Hospital of Qiqihar Medical University, Qiqihar, 161006 Heilongjiang, China.
Computer Centre, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, 161006 Heilongjiang, China.
Comput Math Methods Med. 2022 Jun 14;2022:1366407. doi: 10.1155/2022/1366407. eCollection 2022.
Hospital information system (HIS) can provide a full range of information support for various hospital business activities and information collection, processing, and transmission, helping medical service providers. And HIS can reduce medical service costs and improve work efficiency, greatly reducing errors in diagnosis and treatment. Although the advantages of using the HIS are obvious, there are still some challenges in its use, the most prominent being how to make the medical staff use HIS effectively. Based on this background, this paper uses machine learning (ML) technology to predict and analyze the satisfaction of HIS use in hospitals and completes the following work: firstly, introduce the situation and development trend of HIS construction at home and abroad and provide theoretical basis for model design. The related development technologies are discussed and studied in detail. Second, the ML algorithm is used to provide a prediction strategy. The support vector machine (SVM) can handle small data sets well, and this study applies the AdaBoost technique to improve the model's generalization ability and accuracy. Lastly, a diversity metric is included to guarantee that the basic learner has good variety in order to increase the algorithm's performance. Accuracy rates may reach more than 95% in the case of tiny data sets, according to the self-built data set used for testing. This proves the superiority of the model proposed in this paper.
医院信息系统（HIS）可为各种医院业务活动和信息收集、处理和传输提供全面的信息支持，帮助医疗服务提供者。并且 HIS 可以降低医疗服务成本并提高工作效率，大大减少诊断和治疗中的错误。虽然使用 HIS 的优势显而易见，但在使用过程中仍然存在一些挑战，最突出的是如何使医务人员有效地使用 HIS。基于此背景，本文使用机器学习（ML）技术预测和分析医院 HIS 使用的满意度，并完成了以下工作：首先，介绍国内外 HIS 建设的现状和发展趋势，为模型设计提供理论依据。详细讨论和研究了相关的开发技术。其次，使用 ML 算法提供预测策略。支持向量机（SVM）可以很好地处理小数据集，本研究应用 AdaBoost 技术提高模型的泛化能力和准确性。最后，包含多样性度量以确保基本学习者具有良好的多样性，从而提高算法的性能。根据用于测试的自建数据集，在小数据集的情况下，准确率可能达到 95%以上。这证明了本文提出的模型的优越性。
