Predictive Models on Pain and Severity in FM Patients
Development of Predictive Models Based on Artificial Intelligence for the Analysis of the Psychosocial Profile of the Patient With Fibromyalgia on Pain and Severity of the Disease.
The primary goal of this research project is to develop different prediction models in fibromyalgia disease through the application of machine learning techniques and to assess the explainability of the results.
As specific objectives the research project intends: to predicting Fibromyalgia severity of patients based on clinical variables; to assess the relevance of social-psycho-demographic variables on the fibromyalgia severity of the patients; to predict the pain suffered by the patients as well as the impact of the fibromyalgia on patient's life; to categorize fibromyalgia group of patients depending on their levels of Fibromyalgia severity.
Tutkimuksen yleiskatsaus
Tila
Ehdot
Yksityiskohtainen kuvaus
Fibromyalgia (FM) is a condition characterized by chronic musculoskeletal pain whose pathophysiology is still unclear. Furthermore, this pathology is frequently associated with sleep disturbances, pronounced fatigue, morning stiffness, poor quality of life, cognitive disturbances (mainly memory problems) and psychological problems (depression, anxiety and stress).
FM is associated with greater negative affect, which implies a general state of anguish composed of aversive emotions such as sadness, fear, anger and guilt. Patients with FM commonly suffer from high rates of anxiety, depression, pain catastrophizing, and stress levels, which are associated with a worsening of symptoms, including own cognitive.
Machine learning (ML) and data mining had been successfully applied, over the past few decades, to build computer-aided diagnosis (CAD) systems for diagnosing complex health issues with good accuracy and efficiency by recognizing potentially useful, original, and comprehensible patterns in health data. Thus, machine learning provides useful tools for multivariate data analysis allowing predictions based on the established models and hence offering a suitable advantage for risk assessment of many diseases including heart failure. Machine learning offers advantages not only for clinical prediction but also for feature ranking improving the interpretation of the outputs by clinical professionals.
Explainable ML models, also known as interpretable ML models, allow healthcare experts to make reasonable and data-driven decisions to provide personalized treatment that can ultimately lead to high quality of service in healthcare. These models fall into eXplainable Artificial Intelligence (XAI) field, defined as suite of ML techniques that 1) produce more explainable models while maintaining a high level of learning performance, and 2) enable humans to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.
Opintotyyppi
Ilmoittautuminen (Odotettu)
Yhteystiedot ja paikat
Opiskeluyhteys
- Nimi: Rubén Arroyo Fernández, MSc
- Puhelinnumero: 86589 925803600
- Sähköposti: rubenarroyofernandez@gmail.com
Opiskelupaikat
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Toledo
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Talavera De La Reina, Toledo, Espanja, 45600
- Hospital General Nuestra Señora del Prado
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Ottaa yhteyttä:
- Rubén Arroyo Fernández, MSc
- Puhelinnumero: 86589 925803600
- Sähköposti: rubenarroyofernandez@gmail.com
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Osallistumiskriteerit
Kelpoisuusvaatimukset
Opintokelpoiset iät
Hyväksyy terveitä vapaaehtoisia
Sukupuolet, jotka voivat opiskella
Näytteenottomenetelmä
Tutkimusväestö
Kuvaus
Inclusion Criteria:
- Age between 18 and 65 years.
- Fullfilled the 2010 American Collegue of Rheumathology criteria for fibromyalgia.
- Understanding of spoken and written Spanish.
Exclusion Criteria:
- Diagnosed psychiatric pathology.
- Rheumatic pathology not medically controlled.
- Neurological pathologies that make evaluations difficult.
Opintosuunnitelma
Miten tutkimus on suunniteltu?
Suunnittelun yksityiskohdat
Mitä tutkimuksessa mitataan?
Ensisijaiset tulostoimenpiteet
Tulosmittaus |
Toimenpiteen kuvaus |
Aikaikkuna |
|---|---|---|
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Pain intensity
Aikaikkuna: Baseline.
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It will be measured with a visual analog scale (VAS) of 100 millimeters in length.
The subject has to indicate the level ofpain he feels, being 0 the absence of pain and 100 the maximum imaginable.
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Baseline.
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Disease severity.
Aikaikkuna: Baseline.
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It will be measured using the Polysymptomatic Distress Scale (PDS) (or Fibromyalgia Severity Scale), composed of the sum of the following two scales:
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Baseline.
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Referred pain area after suprathreshold pressure stimulation.
Aikaikkuna: Baseline.
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A pressure algometer (Force Ten™, Wagner Instruments, USA) will be used. It will be performed on the infraspinatus muscle (point equidistant between the midpoint of the spine of the scapula, the inferior angle of the scapula and the midpoint of the medial border of the scapula) at a constant suprathreshold pressure (20% above the pressure pain threshold) for 60 seconds. After the stimulation, the subject should draw the induced pain area on a digital bodychart using the Navigate Pain application (Navigate Pain, Aalborg University, Denmark). |
Baseline.
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Toissijaiset tulostoimenpiteet
Tulosmittaus |
Toimenpiteen kuvaus |
Aikaikkuna |
|---|---|---|
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Fibromyalgia Impact Quality-of-Life.
Aikaikkuna: Baseline.
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It will be measured with the version adapted to the Spanish of the Fibromyalgia Impact Questionnaire (FIQ).
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Baseline.
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Anxiety.
Aikaikkuna: Baseline.
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The version adapted to Spanish from the State Scale (STAI-ES) of the State-Trait Anxiety Inventory (STAI) will be used.
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Baseline.
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Pain catastrophizing.
Aikaikkuna: Baseline.
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The Spanish version of the Pain Catastrophizing Scale (PCS) will be used.
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Baseline.
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Depression.
Aikaikkuna: Baseline.
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The adaptation to the Spanish of Beck Depression Inventory II will be used.
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Baseline.
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Yhteistyökumppanit ja tutkijat
Sponsori
Opintojen ennätyspäivät
Opi tärkeimmät päivämäärät
Opiskelun aloitus (Odotettu)
Ensisijainen valmistuminen (Odotettu)
Opintojen valmistuminen (Odotettu)
Opintoihin ilmoittautumispäivät
Ensimmäinen lähetetty
Ensimmäinen toimitettu, joka täytti QC-kriteerit
Ensimmäinen Lähetetty (Todellinen)
Tutkimustietojen päivitykset
Viimeisin päivitys julkaistu (Todellinen)
Viimeisin lähetetty päivitys, joka täytti QC-kriteerit
Viimeksi vahvistettu
Lisää tietoa
Tähän tutkimukseen liittyvät termit
Muita asiaankuuluvia MeSH-ehtoja
Muut tutkimustunnusnumerot
- IA Fibromyalgia
Lääke- ja laitetiedot, tutkimusasiakirjat
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Tutkii yhdysvaltalaista FDA sääntelemää laitetuotetta
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