Elena Tseli

Elena Tseli

Universitetsadjunkt | Biträdande Lektor
E-postadress: elena.tseli@ki.se
Besöksadress: Alfred Nobels Allé 23, 14183 Huddinge
Postadress: H1 Neurobiologi, vårdvetenskap och samhälle, H1 Fysioterapi, 171 77 Stockholm

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Alla övriga publikationer

Forskningsbidrag

  • Swedish Research Council
    1 January 2023 - 31 December 2025
    With the purpose of enhancing the effectiveness of specialised interdisciplinary treatment (IDT) in patients with chronic pain we will develop and validate a new intelligent Clinical Decision Support System (CDSS) and, with the support of a registry based randomised trial, evaluate and implement the system in future IDT.Chronic pain is a leading cause of disability worldwide and has a huge impact on public health. IDT is the established form of treatment but only modest effects have been reported. Both patient selection processes and individual treatment precision need improvement.We will create a CDSS using artificial intelligence and clinical data from the FRIDA-database, which includes over 60,000 patients with chronic pain across 40 specialist units in Sweden. FRIDA synchronises unique and longitudinal data from the National Register of Pain Rehabilitation with four other national registers. The CDSS will facilitate patient selection and guide individualised treatment strategies with the use of systematic machine-learning loops to identify patient-specific patterns.This support system has the potential to improve clinical praxis, which will result in enhanced quality of life and reduced sick leave, emotional suffering and pain medication in patients with chronic pain, and hence be of great socio-economic value to society. Our multi-professional research team has the expertise to successfully complete the project, working in close collaboration with stakeholders.
  • Swedish Research Council for Health Working Life and Welfare
    1 January 2023 - 31 December 2025
    Research problem Chronic pain is a leading cause of disability worldwide with huge impact on public health and welfare systems. Interdisciplinary treatment (IDT) is currently the established treatment in Swedish specialist care, but recent research shows that treatment effects are small or non-existent. It is recognised that both selection processes and individualised treatment need improvement! With the purpose to enhance the effectiveness of Swedish specialised IDT in patients with chronic pain we plan for three Objectives:To develop an intelligent Clinical Decision Support System for use in treatment individualisation of specialised IDT and validate its design, performance, and feasibility.To prospectively evaluate the system’s clinical effectiveness and cost-utility in a registry-based clustered-randomised control trial (RRCT).To implement the support system in Swedish specialised IDT. The system will facilitate patient selection and guide context-specific individualised treatment strategies by systematically learning patient-specific patterns from big-data. Data and methodWe will apply artificial intelligence (AI) methods to clinical data from over 60,000 patients with chronic pain across 40 specialist units in Sweden for the period 2009-2022 (the FRIDA-database). FRIDA is today one of the world´s largest rehabilitation databases that synchronises unique and longitudinal data from the National Register of Pain Rehabilitation with four other national registers. The project will be carried out in close collaboration with healthcare providers, patient organisations, policymakers and the business community. Plan for project realisationOur multi-professional research team has all the necessary expertise to successfully complete each project stage, from system development to evaluation of effectiveness and cost utility, and implementation. The timeframe of this Forte application includes Objective 1 and the major part of the RRCT. RelevanceA decision support system that can now be developed in FRIDA has great potential to streamline Swedish multimodal specialist care
    AI methods in combination with “big-data” are recommended in precision medicine to optimize treatment. This AI support system can significantly improve health-related quality of life, reduce emotional suffering and sick leave for patients with chronic pain and be of great socio-economic value to society.

Anställningar

  • Universitetsadjunkt, Neurobiologi, vårdvetenskap och samhälle, Karolinska Institutet, 2002-
  • Biträdande Lektor, Neurobiologi, vårdvetenskap och samhälle, Karolinska Institutet, 2022-2028

Examina och utbildning

  • Medicine Doktorsexamen, Institutionen för neurobiologi, vårdvetenskap och samhälle, Karolinska Institutet, 2019
  • Medicine Magisterexamen, Karolinska Institutet, 2006

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