만성 요통에 대한 맞춤형 상황 인지 시스템
Personalized Context-Aware System for Chronic Low Back Pain
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초록
Treatment and management of chronic low back pain (CLBP) should be tailored to the patient’s individual context. However, there are limited resources available in which to find and manage the causes and mechanisms for each patient. In this study, we designed and developed a personalized context awareness system that uses machine learning techniques to understand the relationship between a patient’s lower back pain and the surrounding environment. A pilot study was conducted to verify the context awareness model. The performance of the lower back pain prediction model was successful enough to be practically usable. It was possible to use the information from the model to understand how the variables influence the occurrence of lower back pain.
Abstract
Treatment and management of chronic low back pain (CLBP) should be tailored to the patient’s individual context. However, there are limited resources available in which to find and manage the causes and mechanisms for each patient. In this study, we designed and developed a personalized context awareness system that uses machine learning techniques to understand the relationship between a patient’s lower back pain and the surrounding environment. A pilot study was conducted to verify the context awareness model. The performance of the lower back pain prediction model was successful enough to be practically usable. It was possible to use the information from the model to understand how the variables influence the occurrence of lower back pain.
참고문헌 (16)
- Airaksinen, O., "Chapter 4 European Guidelines for the Management of Chronic Low Back Pain in Primary Care", Eur Spine J, 15, pp.5192~5300 (2006)
- Chou, R., "Diagnosis and Treatment of Low Back Pain: A Joint Clinical Practice Guideline from the American College of Physicians and the American Pain Society", Annals of Internal Medicine, 147(7), pp.478~491 (2007)
- Dekker-van Weering, M. G., "Do Personalized Feedback Messages about Activity Patterns Stimulate Patients with Chronic Low Back Pain to Change Their Activity Behavior on a Short Term Notice?", Applied Psychophysiology and Biofeedback, 37(2), pp.81~89 (2012)
- Edmond, S. L., "Prevalence of Back Symptoms in Elders", The Journal of Rheumatology, 27(1), pp.220~225 (2000)
- Maluf, K. S., "Use of a Classification System to Guide Nonsurgical Management of a Patient with Chronic Low Back Pain", Physical Therapy, 80(11), pp.1097~1111 (2000)
- Liddle, S. D., "Advice for the Management of Low Back Pain : A Systematic Review of Randomised Controlled Trials", Manual Therapy, 12(4), pp.310~327 (2007)
- O’Sullivan, P., "It’s Time for Change with the Management of Non-Specific Chronic Low Back Pain" (2012), BMJ Publishing Group Ltd and British Association of Sport and Exercise Medicine
- O’Sullivan, P. B., "The Relationship Beween Posture and Back Muscle Endurance in Industrial Workers with Flexion-Related Low Back Pain", Manual Therapy, 11(4), pp.264~271 (2006)
- Park, J. Y., "Diagnosis and Management of Chronic Low Back Pain", Journal of the Korean Academy of Family Medicine, 22(9), pp.1349~1362 (2001)
- Park, J. Y., "Nonsurgical Management of Chronic Low Back Pain", Journal of the Korean Medical Association, 50(6), pp.507~522 (2007)
- Takala, E. -P., "Do Functional Tests Predict Low Back Pain?", Spine, 25(16), pp.2126~2132 (2000)
- Vingård, E., "To What Extent Do Current and Past Physical and Psychosocial Occupational Factors Explain Care-Seeking for Low Back Pain in a Working Population? : Results from the Musculoskeletal Intervention Center-Norrtälje Study", Spine, 25(4), pp.493~500 (2000)
- Von Korff, M., "Stepped Care for Back Pain: Activating Approaches for Primary Care", Annals of Internal Medicine, 134(9_Part_2), pp.911~917 (2001)
- Wallden, M., "The Neutral Spine Principle", Journal of Bodywork and Movement Therapies, 13(4), pp.350~361 (2009)
- Wyke, B., "The Lumbar Spine and Back Pain", pp.265~339 (1980)
- Yoon, D, "Development of situational awareness system for patient-tailored low back pain warning based on real-time sensing and big data analysis", pp.2017