Cho Young Sik, Hong Paul C
College of Business, Jackson State University, Jackson, MS 39217, USA.
John B. and Lillian E. Neff College of Business and Innovation, The University of Toledo, Toledo, OH 43606, USA.
Healthcare (Basel). 2023 Jun 16;11(12):1779. doi: 10.3390/healthcare11121779.
The purpose of this study is to explore how machine learning technologies can improve healthcare operations management. A machine learning-based model to solve a specific medical problem is developed to achieve this research purpose. Specifically, this study presents an AI solution for malaria infection diagnosis by applying the CNN (convolutional neural network) algorithm. Based on malaria microscopy image data from the NIH National Library of Medicine, a total of 24,958 images were used for deep learning training, and 2600 images were selected for final testing of the proposed diagnostic architecture. The empirical results indicate that the CNN diagnostic model correctly classified most malaria-infected and non-infected cases with minimal misclassification, with performance metrics of precision (0.97), recall (0.99), and f1-score (0.98) for uninfected cells, and precision (0.99), recall (0.97), and f1-score (0.98) for parasite cells. The CNN diagnostic solution rapidly processed a large number of cases with a high reliable accuracy of 97.81%. The performance of this CNN model was further validated through the k-fold cross-validation test. These results suggest the advantage of machine learning-based diagnostic methods over conventional manual diagnostic methods in improving healthcare operational capabilities in terms of diagnostic quality, processing costs, lead time, and productivity. In addition, a machine learning diagnosis system is more likely to enhance the financial profitability of healthcare operations by reducing the risk of unnecessary medical disputes related to diagnostic errors. As an extension for future research, propositions with a research framework are presented to examine the impacts of machine learning on healthcare operations management for safety and quality of life in global communities.
本研究的目的是探索机器学习技术如何改善医疗运营管理。为实现这一研究目的，开发了一种基于机器学习的模型来解决特定的医学问题。具体而言，本研究通过应用卷积神经网络（CNN）算法，提出了一种用于疟疾感染诊断的人工智能解决方案。基于美国国立医学图书馆（NIH National Library of Medicine）的疟疾显微镜图像数据，总共24958张图像用于深度学习训练，2600张图像被选用于对所提出的诊断架构进行最终测试。实证结果表明，CNN诊断模型能够以最小的错误分类正确地对大多数疟疾感染和未感染病例进行分类，对于未感染细胞，精确率为0.97、召回率为0.99、F1分数为0.98；对于寄生虫细胞，精确率为0.99、召回率为0.97、F1分数为0.98。CNN诊断解决方案以97.81%的高可靠准确率快速处理了大量病例。通过k折交叉验证测试进一步验证了该CNN模型的性能。这些结果表明，在诊断质量、处理成本、交付时间和生产率方面，基于机器学习的诊断方法相对于传统手动诊断方法在提高医疗运营能力方面具有优势。此外，机器学习诊断系统更有可能通过降低与诊断错误相关的不必要医疗纠纷风险来提高医疗运营的财务盈利能力。作为未来研究的拓展，提出了带有研究框架的命题，以检验机器学习对全球社区医疗运营管理在安全性和生活质量方面的影响。
