Skip to content

Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationKDD'18
Subtitle of host publicationProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher or commissioning bodyAssociation for Computing Machinery (ACM)
Pages243-252
Number of pages10
ISBN (Print)9781450355520
DOIs
DateAccepted/In press - 18 May 2018
DatePublished (current) - 19 Jul 2018
Event24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - London, United Kingdom
Duration: 19 Aug 201823 Aug 2018

Conference

Conference24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Abbreviated titleSIGKDD
CountryUnited Kingdom
CityLondon
Period19/08/1823/08/18

Abstract

The SPHERE project is devoted to advancing eHealth in a smart-home context, and supports full-scale sensing and data analysis to enable a generic healthcare service. We describe, from a data-science perspective, our experience of taking the system out of the laboratory into more than thirty homes in Bristol, UK. We describe the infrastructure and processes that had to be developed along the way, describe how we train and deploy ML systems in this context, and give a realistic appraisal of the state of the deployed systems.

    Research areas

  • Machine learning, Health informatics, Sensor applications and deployments, Sensor networks, Data streaming, Remote medicine

Event

24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Abbreviated titleSIGKDD
Duration19 Aug 201823 Aug 2018
CityLondon
CountryUnited Kingdom
Degree of recognitionInternational event

Event: Conference

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via ACM at https://dl.acm.org/citation.cfm?id=3219819.3219883 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 2 MB, PDF-document

DOI

View research connections

Related faculties, schools or groups