IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0209909.html
   My bibliography  Save this article

Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia

Author

Listed:
  • Shirin Enshaeifar
  • Ahmed Zoha
  • Severin Skillman
  • Andreas Markides
  • Sahr Thomas Acton
  • Tarek Elsaleh
  • Mark Kenny
  • Helen Rostill
  • Ramin Nilforooshan
  • Payam Barnaghi

Abstract

Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers.

Suggested Citation

  • Shirin Enshaeifar & Ahmed Zoha & Severin Skillman & Andreas Markides & Sahr Thomas Acton & Tarek Elsaleh & Mark Kenny & Helen Rostill & Ramin Nilforooshan & Payam Barnaghi, 2019. "Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-22, January.
  • Handle: RePEc:plo:pone00:0209909
    DOI: 10.1371/journal.pone.0209909
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0209909
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0209909&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0209909?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Shirin Enshaeifar & Ahmed Zoha & Andreas Markides & Severin Skillman & Sahr Thomas Acton & Tarek Elsaleh & Masoud Hassanpour & Alireza Ahrabian & Mark Kenny & Stuart Klein & Helen Rostill & Ramin Nilf, 2018. "Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-20, May.
    2. Jeffrey A. Kaye & Shoshana A. Maxwell & Nora Mattek & Tamara L. Hayes & Hiroko Dodge & Misha Pavel & Holly B. Jimison & Katherine Wild & Linda Boise & Tracy A. Zitzelberger, 2011. "Intelligent Systems for Assessing Aging Changes: Home-Based, Unobtrusive, and Continuous Assessment of Aging," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 66(suppl_1), pages 180-190.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Belfiore, Alessandra & Cuccurullo, Corrado & Aria, Massimo, 2022. "IoT in healthcare: A scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shirin Enshaeifar & Ahmed Zoha & Andreas Markides & Severin Skillman & Sahr Thomas Acton & Tarek Elsaleh & Masoud Hassanpour & Alireza Ahrabian & Mark Kenny & Stuart Klein & Helen Rostill & Ramin Nilf, 2018. "Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-20, May.
    2. Belfiore, Alessandra & Cuccurullo, Corrado & Aria, Massimo, 2022. "IoT in healthcare: A scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Lee-Nam Kwon & Dong-Hun Yang & Myung-Gwon Hwang & Soo-Jin Lim & Young-Kuk Kim & Jae-Gyum Kim & Kwang-Hee Cho & Hong-Woo Chun & Kun-Woo Park, 2021. "Automated Classification of Normal Control and Early-Stage Dementia Based on Activities of Daily Living (ADL) Data Acquired from Smart Home Environment," IJERPH, MDPI, vol. 18(24), pages 1-24, December.
    4. Manuel Prado-Velasco & Rafael Ortiz MarĂ­n & Gloria Del Rio Cidoncha, 2013. "Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm," IJERPH, MDPI, vol. 10(10), pages 1-23, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0209909. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.