2023-04-05 スウォンジー大学
この研究は、UCBファーマとHealth and Care Research Walesによって資金提供され、National Centre for Population Health & Wellbeing Research(NCPHWR)のデータアナリストや研究者によって行われました。
機械学習を使用して、炎症性関節炎の診断が予測されやすい人々の特徴のプロファイルを開発しました。男性では、下腰痛、虹彩炎、20歳未満の非ステロイド性抗炎症薬(NSAID)使用がASの発症と関連していることが判明しました。女性は男性に比べて、背中の痛みと多くの鎮痛剤を使用する年齢が高くなる傾向があることが示されました。
この研究により、機械学習がASの患者を特定し、彼らの診断プロセスを改善することができる可能性があることがわかりました。
<関連情報>
- https://www.swansea.ac.uk/press-office/news-events/news/2023/04/new-study-shows-the-potential-of-machine-learning-in-the-early-identification-of-people-with-inflammatory-arthritis.php
- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0279076
プライマリケア健康記録を用いた強直性脊椎炎の診断予測-機械学習によるアプローチ Predicting a diagnosis of ankylosing spondylitis using primary care health records–A machine learning approach
Jonathan Kennedy ,Natasha Kennedy,Roxanne Cooksey,Ernest Choy,Stefan Siebert,Muhammad Rahman,Sinead Brophy
PLOS ONE Published: March 31, 2023
DOI:https://doi.org/10.1371/journal.pone.0279076
Abstract
Ankylosing spondylitis is the second most common cause of inflammatory arthritis. However, a successful diagnosis can take a decade to confirm from symptom onset (via x-rays). The aim of this study was to use machine learning methods to develop a profile of the characteristics of people who are likely to be given a diagnosis of AS in future. The Secure Anonymised Information Linkage databank was used. Patients with ankylosing spondylitis were identified using their routine data and matched with controls who had no record of a diagnosis of ankylosing spondylitis or axial spondyloarthritis. Data was analysed separately for men and women. The model was developed using feature/variable selection and principal component analysis to develop decision trees. The decision tree with the highest average F value was selected and validated with a test dataset. The model for men indicated that lower back pain, uveitis, and NSAID use under age 20 is associated with AS development. The model for women showed an older age of symptom presentation compared to men with back pain and multiple pain relief medications. The models showed good prediction (positive predictive value 70%-80%) in test data but in the general population where prevalence is very low (0.09% of the population in this dataset) the positive predictive value would be very low (0.33%-0.25%). Machine learning can be used to help profile and understand the characteristics of people who will develop AS, and in test datasets with artificially high prevalence, will perform well. However, when applied to a general population with low prevalence rates, such as that in primary care, the positive predictive value for even the best model would be 1.4%. Multiple models may be needed to narrow down the population over time to improve the predictive value and therefore reduce the time to diagnosis of ankylosing spondylitis.
