Health Guardian Platform: A technology stack to accelerate discovery in Digital Health research

• URL: http://arxiv.org/abs/2211.06330v1
• Date: Thu, 10 Nov 2022 16:28:07 GMT
• Title: Health Guardian Platform: A technology stack to accelerate discovery in Digital Health research
• Authors: Bo Wen, Vince S. Siu, Italo Buleje, Kuan Yu Hsieh, Takashi Itoh, Lukas Zimmerli, Nigel Hinds, Elif Eyigoz, Bing Dang, Stefan von Cavallar, Jeffrey L. Rogers
• Abstract summary: The Health Guardian is a platform developed by the IBM Digital Health team to accelerate discoveries of new digital biomarkers and development of digital health technologies. 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.
• Score: 1.4334172557562619
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• DAMMI:Daily Activities in a Psychologically Annotated Multi-Modal IoT dataset [10.771838327042609]
  The DAMMI dataset is designed to support researchers in the field. It includes daily activity data of an elderly individual collected via home-installed sensors, smartphone data, and a wristband over 146 days. The data collection spans significant events such as the COVID-19 pandemic, New Year's holidays, and the religious month of Ramadan.
  arXiv  Detail & Related papers  (2024-10-05T13:26:54Z)
• Artificial Intelligence-based Decision Support Systems for Precision and Digital Health [0.49109372384514843]
  We discuss the opportunity offered by AI, more specifically reinforcement learning, to current trends in healthcare. We focus on the area of adaptive interventions. This article has undergone anonymous review and is intended as a book chapter for the volume "Frontiers of Statistics and Data Science"
  arXiv  Detail & Related papers  (2024-07-22T21:39:34Z)
• A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions [66.40362209055023]
  This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-based methods. By comparing the model structures, parameter estimation algorithms, model evaluation methods and applications, we provide a relatively comprehensive review of the recent trends in cognitive diagnosis models.
  arXiv  Detail & Related papers  (2024-07-07T18:02:00Z)
• OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine [55.29668193415034]
  We present OpenMEDLab, an open-source platform for multi-modality foundation models. It encapsulates solutions of pioneering attempts in prompting and fine-tuning large language and vision models for frontline clinical and bioinformatic applications. It opens access to a group of pre-trained foundation models for various medical image modalities, clinical text, protein engineering, etc.
  arXiv  Detail & Related papers  (2024-02-28T03:51:02Z)
• Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey [53.691704671844406]
  The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare. The human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body. HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed. Recently, generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data.
  arXiv  Detail & Related papers  (2024-01-22T03:17:41Z)
• Disease Insight through Digital Biomarkers Developed by Remotely Collected Wearables and Smartphone Data [3.9411499615751113]
  RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka. It provides support for study design and set-up, active (eg PROMs) and passive (eg. phone sensors, wearable devices and IoT) remote data collection capabilities. The platform has successfully collected longitudinal data for various cohorts in a number of disease areas including Multiple Sclerosis, Depression, Epilepsy, ADHD, Alzheimer, Autism and Lung diseases.
  arXiv  Detail & Related papers  (2023-08-03T22:44:48Z)
• HEAR4Health: A blueprint for making computer audition a staple of modern healthcare [89.8799665638295]
  Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems. Computer audition can be seen to be lagging behind, at least in terms of commercial interest. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data.
  arXiv  Detail & Related papers  (2023-01-25T09:25:08Z)
• A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform to Facilitate Healthcare AI Research [0.0]
  We present a system to accelerate data acquisition, dataset development and analysis, and AI model development. This system can ingest 15,000 patient records per hour, where each record represents thousands of measurements, text notes, and high resolution data.
  arXiv  Detail & Related papers  (2021-12-13T18:39:10Z)
• MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation [110.31526448744096]
  We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. We are building MedPerf, an open framework for benchmarking machine learning in the medical domain.
  arXiv  Detail & Related papers  (2021-09-29T18:09:41Z)
• A highly scalable repository of waveform and vital signs data from bedside monitoring devices [0.0]
  Machine learning is driving the appetite of the research community for various types of signal data such as patient vitals. Health care systems are ill suited for massive processing of large volumes of data. We have developed a solution that siphons off patient vital data on a nightly basis from on-premises bio-medical systems to a cloud storage location as a permanent archive.
  arXiv  Detail & Related papers  (2021-06-07T20:59:58Z)
• Challenges in biomarker discovery and biorepository for Gulf-war-disease studies: a novel data platform solution [48.7576911714538]
  We introduce a novel data platform, named ROSALIND, to overcome the challenges, foster healthy and vital collaborations and advance scientific inquiries. We follow the principles etched in the platform name - ROSALIND stands for resource organisms with self-governed accessibility, linkability, integrability, neutrality, and dependability. The deployment of ROSALIND in our GWI study in recent 12 months has accelerated the pace of data experiment and analysis, removed numerous error sources, and increased research quality and productivity.
  arXiv  Detail & Related papers  (2021-02-04T20:38:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.