  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.
