Back-UP: Personalised Prognostic Models To Improve Well-Being And return To Work After Neck and Low Back Pain

Ana Miriam Cabrita, Wendy Oude Nijeweme - d'Hollosy, S. Jansen-Kosterink

    Research output: Contribution to conferencePaper

    Abstract

    Patients with Neck and/or Low Back Pain (NLBP) constitute a heterogeneous group with the prognosis and precise mix of factors involved varying substantially between individuals. This means that a one-size-fits-all approach is not recommended, but methods to tailor treatment to the individual needs are still relatively under-developed. Moreover, the fragmentation of disciplines involved in its study hampers achieving sound answers to clinical questions. Data mining techniques open new horizons by combining data from existing datasets, in order to select the best treatment at each moment in time to a patient based on the individual characteristics.
    Within the Back-UP project (H2020 #777090) a multidisciplinary consortium is creating a prognostic model to support more effective and efficient management of NLBP, based on the digital representation of multidimensional clinical information. Patient-specific models provide a personalized evaluation of the patient case, using multidimensional health data from the following sources: (1) psychological, behavioral, and socioeconomic factors, (2) biological patient characteristics, including musculoskeletal structures and function, and molecular data, (3) workplace and lifestyle risk factors.
    The Back-UP system leverages shared-decision making, not only by enabling interoperability between all professionals involved in the care trajectory, but also empowering the patient in the decisions related to his/her care path. Furthermore, dynamic intervention models ensure that the patient receives the most beneficial treatment at each moment in time, having into account the current position of the patient in the care path (i.e. within clinical rehabilitation, in return-to-work process or through motivational strategies that support self-management in daily life).

    Original languageEnglish
    Pages26
    Number of pages1
    Publication statusPublished - 15 Nov 2018
    EventSBPR 2018: Understanding the mechanisms of back pain: work, rest and play - University Medical Centre , Groningen, Netherlands
    Duration: 15 Nov 201816 Nov 2018

    Conference

    ConferenceSBPR 2018
    Abbreviated titleSBPR
    CountryNetherlands
    CityGroningen
    Period15/11/1816/11/18

    Fingerprint

    Return to Work
    Low Back Pain
    Neck
    Data Mining
    Information Storage and Retrieval
    Self Care
    Molecular Structure
    Workplace
    Life Style
    Decision Making
    Patient Care
    Therapeutics
    Rehabilitation
    Psychology
    Health

    Keywords

    • Data science
    • Machine Learning

    Cite this

    Cabrita, A. M., Oude Nijeweme - d'Hollosy, W., & Jansen-Kosterink, S. (2018). Back-UP: Personalised Prognostic Models To Improve Well-Being And return To Work After Neck and Low Back Pain. 26. Paper presented at SBPR 2018, Groningen, Netherlands.
    Cabrita, Ana Miriam ; Oude Nijeweme - d'Hollosy, Wendy ; Jansen-Kosterink, S. / Back-UP: Personalised Prognostic Models To Improve Well-Being And return To Work After Neck and Low Back Pain. Paper presented at SBPR 2018, Groningen, Netherlands.1 p.
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    abstract = "Patients with Neck and/or Low Back Pain (NLBP) constitute a heterogeneous group with the prognosis and precise mix of factors involved varying substantially between individuals. This means that a one-size-fits-all approach is not recommended, but methods to tailor treatment to the individual needs are still relatively under-developed. Moreover, the fragmentation of disciplines involved in its study hampers achieving sound answers to clinical questions. Data mining techniques open new horizons by combining data from existing datasets, in order to select the best treatment at each moment in time to a patient based on the individual characteristics. Within the Back-UP project (H2020 #777090) a multidisciplinary consortium is creating a prognostic model to support more effective and efficient management of NLBP, based on the digital representation of multidimensional clinical information. Patient-specific models provide a personalized evaluation of the patient case, using multidimensional health data from the following sources: (1) psychological, behavioral, and socioeconomic factors, (2) biological patient characteristics, including musculoskeletal structures and function, and molecular data, (3) workplace and lifestyle risk factors. The Back-UP system leverages shared-decision making, not only by enabling interoperability between all professionals involved in the care trajectory, but also empowering the patient in the decisions related to his/her care path. Furthermore, dynamic intervention models ensure that the patient receives the most beneficial treatment at each moment in time, having into account the current position of the patient in the care path (i.e. within clinical rehabilitation, in return-to-work process or through motivational strategies that support self-management in daily life).",
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    Cabrita, AM, Oude Nijeweme - d'Hollosy, W & Jansen-Kosterink, S 2018, 'Back-UP: Personalised Prognostic Models To Improve Well-Being And return To Work After Neck and Low Back Pain' Paper presented at SBPR 2018, Groningen, Netherlands, 15/11/18 - 16/11/18, pp. 26.

    Back-UP: Personalised Prognostic Models To Improve Well-Being And return To Work After Neck and Low Back Pain. / Cabrita, Ana Miriam; Oude Nijeweme - d'Hollosy, Wendy; Jansen-Kosterink, S.

    2018. 26 Paper presented at SBPR 2018, Groningen, Netherlands.

    Research output: Contribution to conferencePaper

    TY - CONF

    T1 - Back-UP: Personalised Prognostic Models To Improve Well-Being And return To Work After Neck and Low Back Pain

    AU - Cabrita, Ana Miriam

    AU - Oude Nijeweme - d'Hollosy, Wendy

    AU - Jansen-Kosterink, S.

    PY - 2018/11/15

    Y1 - 2018/11/15

    N2 - Patients with Neck and/or Low Back Pain (NLBP) constitute a heterogeneous group with the prognosis and precise mix of factors involved varying substantially between individuals. This means that a one-size-fits-all approach is not recommended, but methods to tailor treatment to the individual needs are still relatively under-developed. Moreover, the fragmentation of disciplines involved in its study hampers achieving sound answers to clinical questions. Data mining techniques open new horizons by combining data from existing datasets, in order to select the best treatment at each moment in time to a patient based on the individual characteristics. Within the Back-UP project (H2020 #777090) a multidisciplinary consortium is creating a prognostic model to support more effective and efficient management of NLBP, based on the digital representation of multidimensional clinical information. Patient-specific models provide a personalized evaluation of the patient case, using multidimensional health data from the following sources: (1) psychological, behavioral, and socioeconomic factors, (2) biological patient characteristics, including musculoskeletal structures and function, and molecular data, (3) workplace and lifestyle risk factors. The Back-UP system leverages shared-decision making, not only by enabling interoperability between all professionals involved in the care trajectory, but also empowering the patient in the decisions related to his/her care path. Furthermore, dynamic intervention models ensure that the patient receives the most beneficial treatment at each moment in time, having into account the current position of the patient in the care path (i.e. within clinical rehabilitation, in return-to-work process or through motivational strategies that support self-management in daily life).

    AB - Patients with Neck and/or Low Back Pain (NLBP) constitute a heterogeneous group with the prognosis and precise mix of factors involved varying substantially between individuals. This means that a one-size-fits-all approach is not recommended, but methods to tailor treatment to the individual needs are still relatively under-developed. Moreover, the fragmentation of disciplines involved in its study hampers achieving sound answers to clinical questions. Data mining techniques open new horizons by combining data from existing datasets, in order to select the best treatment at each moment in time to a patient based on the individual characteristics. Within the Back-UP project (H2020 #777090) a multidisciplinary consortium is creating a prognostic model to support more effective and efficient management of NLBP, based on the digital representation of multidimensional clinical information. Patient-specific models provide a personalized evaluation of the patient case, using multidimensional health data from the following sources: (1) psychological, behavioral, and socioeconomic factors, (2) biological patient characteristics, including musculoskeletal structures and function, and molecular data, (3) workplace and lifestyle risk factors. The Back-UP system leverages shared-decision making, not only by enabling interoperability between all professionals involved in the care trajectory, but also empowering the patient in the decisions related to his/her care path. Furthermore, dynamic intervention models ensure that the patient receives the most beneficial treatment at each moment in time, having into account the current position of the patient in the care path (i.e. within clinical rehabilitation, in return-to-work process or through motivational strategies that support self-management in daily life).

    KW - Data science

    KW - Machine Learning

    UR - https://wencke4.housing.rug.nl/Cursuswinkel/public/Brochure/SBPR_program%20with%20abstracts.pdf

    M3 - Paper

    SP - 26

    ER -

    Cabrita AM, Oude Nijeweme - d'Hollosy W, Jansen-Kosterink S. Back-UP: Personalised Prognostic Models To Improve Well-Being And return To Work After Neck and Low Back Pain. 2018. Paper presented at SBPR 2018, Groningen, Netherlands.