论文标题
基于智能手机的多输入工作流，用于使用机器学习技术对血红蛋白水平进行非侵入性估算
A smartphone based multi input workflow for non-invasive estimation of haemoglobin levels using machine learning techniques
论文作者
论文摘要
我们建议使用低成本的非侵入性医疗保健系统，该系统可测量患者的血红蛋白水平，并可以用作贫血的初步诊断测试。图像处理，机器学习和深度学习技术的结合用于开发预测模型以测量血红蛋白水平。这是通过对指甲床，骨膜结膜和患者舌头的颜色分析来实现的。然后将这种预测模型封装在医疗保健应用中。该应用程序加快了数据收集，并促进了该模型的积极学习。它还为每个患者的模型进行个性化校准，有助于持续监测患者的血红蛋白水平。在使用数据验证此框架后，它可以作为贫血的高度准确的初步诊断测试。
We suggest a low cost, non invasive healthcare system that measures haemoglobin levels in patients and can be used as a preliminary diagnostic test for anaemia. A combination of image processing, machine learning and deep learning techniques are employed to develop predictive models to measure haemoglobin levels. This is achieved through the color analysis of the fingernail beds, palpebral conjunctiva and tongue of the patients. This predictive model is then encapsulated in a healthcare application. This application expedites data collection and facilitates active learning of the model. It also incorporates personalized calibration of the model for each patient, assisting in the continual monitoring of the haemoglobin levels of the patient. Upon validating this framework using data, it can serve as a highly accurate preliminary diagnostic test for anaemia.
