This paper highlights the design philosophy and architecture of the Health Guardian, a platform developed by the IBM Digital Health team to accelerate discoveries of new digital biomarkers and development of digital health technologies. The Health Guardian allows for rapid translation of artificial intelligence (AI) research into cloud-based microservices that can be tested with data from clinical cohorts to understand disease and enable early prevention. The platform can be connected to mobile applications, wearables, or Internet of things (IoT) devices to collect health-related data into a secure database. When the analytics are created, the researchers can containerize and deploy their code on the cloud using pre-defined templates, and validate the models using the data collected from one or more sensing devices. The Health Guardian platform currently supports time-series, text, audio, and video inputs with 70+ analytic capabilities and is used for non-commercial scientific research. We provide an example of the Alzheimer's disease (AD) assessment microservice which uses AI methods to extract linguistic features from audio recordings to evaluate an individual's mini-mental state, the likelihood of having AD, and to predict the onset of AD before turning the age of 85. Today, IBM research teams across the globe use the Health Guardian internally as a test bed for early-stage research ideas, and externally with collaborators to support and enhance AI model development and clinical study efforts.
翻译:本文强调了健康卫士的设计理念和结构,这是IBM数字保健小组为加速发现新的数字生物标志和开发数字保健技术而开发的一个平台。健康卫士允许将人工智能(AI)研究迅速转换为云基微观服务,这些研究可借助临床组的数据进行测试,以了解疾病和早期预防。该平台可连接到移动应用、可穿戴设备或物品互联网(IoT)设备,将健康相关数据收集到一个安全的数据库中。当分析器建立起来时,研究人员可以使用预先定义的模板在云上储存和部署代码,并利用从一个或多个感测装置收集的数据验证模型。健康卫士平台目前支持使用70+分析能力的时间序列、文本、音频和视频投入,用于非商业科学研究。我们举了阿尔茨海默氏病(AD)评估微观服务的例子,它使用AI方法将语言特征从声音记录中提取出来,评估个人微型状态的可能性,以及使用AD的可能性,并预测在将AD模型启动之前将模型用于将IBM 和I IMRA 进行基础研究, 和基础研究的早期基础研究, 将AD 提高全球 基础研究 和基础研究 基础研究 和基础研究团队作为基础研究 和基础 基础研究 基础研究 和基础研究。