Universidade Federal Rural do Rio de Janeiro - Nova Iguaçu (RJ), Brazil.
Serviços de Exames Ambulatoriais do Coração - Niterói (RJ), Brazil.
Rev Assoc Med Bras (1992). 2023 Apr 14;69(4):e20221394. doi: 10.1590/1806-9282.20221394. eCollection 2023.
COVID-19 has brought emerging public health emergency and new challenges. It configures a complex panorama that has been requiring a set of coordinated actions and has innovation as one of its pillars. In particular, the use of digital tools plays an important role. In this context, this study presents a screening algorithm that uses a machine learning model to assess the probability of a diagnosis of COVID-19 based on clinical data.
This algorithm was made available for free on an online platform. The project was developed in three phases. First, an machine learning risk model was developed. Second, a system was developed that would allow the user to enter patient data. Finally, this platform was used in teleconsultations carried out during the pandemic period.
The number of accesses during the period was 4,722. A total of 126 assistances were carried out from March 23, 2020, to June 16, 2020, and 107 satisfaction survey returns were received. The response rate to the questionnaires was 84.92%, and the ratings obtained regarding the satisfaction level were higher than 4.8 (on a 0-5 scale). The Net Promoter Score was 94.4.
To the best of our knowledge, this is the first online application of its kind that presents a probabilistic assessment of COVID-19 using machine learning models exclusively based on the symptoms and clinical characteristics of users. The level of satisfaction was high. The integration of machine learning tools in telemedicine practice has great potential.
COVID-19 带来了新的突发公共卫生事件和挑战。它构成了一个复杂的局面，需要采取一系列协调行动，创新是其中的一个支柱。特别是，数字工具的使用发挥了重要作用。在这种情况下，本研究提出了一种使用机器学习模型根据临床数据评估 COVID-19 诊断概率的筛选算法。
该算法在一个在线平台上免费提供。该项目分三个阶段开发。首先，开发了机器学习风险模型。其次，开发了一个允许用户输入患者数据的系统。最后，该平台在大流行期间进行远程咨询时使用。
在此期间，访问量为 4722 次。从 2020 年 3 月 23 日至 2020 年 6 月 16 日，共进行了 126 次援助，收到了 107 份满意度调查回复。问卷调查的回复率为 84.92%，对满意度的评分高于 4.8（0-5 分制）。净推荐值为 94.4。
据我们所知，这是第一个在线应用程序，它使用机器学习模型仅根据用户的症状和临床特征对 COVID-19 进行概率评估。满意度很高。将机器学习工具集成到远程医疗实践中有很大的潜力。
