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Updated: Jun 16, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Design of a Tool for Patient Classification Based on Pharmacotherapeutic Attitudes]
Daniel Reyes Estévez1, Laura Charola Ramos2, Carlos Horas Barrera3
1Licenciado en Farmacia. Farmacéutico comunitario en Farmacia La Comunitaria Dr. Antonio Villafaina Barroso, Granadilla de Abona (Tenerife). España.
Introduction:
Perception and relief of pain exhibit variability among individuals. Age, gender, ethnicity, educational level, actual stress level, mood, or medical conditions can modify the personal interpretation of pain and responses to pharmacological treatment. These differences may play a significant role in the effects, sometimes unwanted, of analgesic treatment.
Objectives:
Define patient typologies with Failed Back Syndrome regarding attitudes toward the disease, treatment, healthcare, and the follow-up they receive from their healthcare professionals. Create a tool for patient profile identification.
Materials And Methods:
A clinical case series study, observational, descriptive, and cross-sectional. Study population: patients from the Pain Unit of Nuestra Señora de La Candelaria University Hospital (HUNSC) in Tenerife, conducted in three phases: collection of medical history data (F0), initial visit (F1), and personal interview (F2).
Results:
Five patient typologies are obtained based on responses to 17 items. Regression equations are calculated from these responses to predict the patient type. They are grouped into "Classics," "Dependents," "Critics," "Unconscious," and "Responsible." Additionally, two tools with 17 items and another with 7 optimized items are developed to simplify the process.
Conclusions:
These tools enable Community Pharmacy (CP) to identify patients based on their characteristics to direct personalized strategies for each of them.
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