Abstract
BackgroundAcute and chronic low back pain (LBP) are different conditions with different treatments. However, they are coded in electronic health records with the same International Classification of Diseases, 10th revision (ICD-10) code (M54.5) and can be differentiated only by retrospective chart reviews. This prevents an efficient definition of data-driven guidelines for billing and therapy recommendations, such as return-to-work options.ObjectiveThe objective of this study was to evaluate the feasibility of automatically distinguishing acute LBP episodes by analyzing free-text clinical notes.MethodsWe used a dataset of 17,409 clinical notes from different primary care practices; of these, 891 documents were manually annotated as acute LBP and 2973 were generally associated with LBP via the recorded ICD-10 code. We compared different supervised and unsupervised strategies for automated identification: keyword search, topic modeling, logistic regression with bag of n-grams and manual features, and deep learning (a convolutional neural network-based architecture [ConvNet]). We trained the supervised models using either manual annotations or ICD-10 codes as positive labels.ResultsConvNet trained using manual annotations obtained the best results with an area under the receiver operating characteristic curve of 0.98 and an F score of 0.70. ConvNet’s results were also robust to reduction of the number of manually annotated documents. In the absence of manual annotations, topic models performed better than methods trained using ICD-10 codes, which were unsatisfactory for identifying LBP acuity.ConclusionsThis study uses clinical notes to delineate a potential path toward systematic learning of therapeutic strategies, billing guidelines, and management options for acute LBP at the point of care.
Highlights
Low back pain (LBP) is one of the most common causes of disability in US adults younger than 45 years [1], with 10 to20% of American workers reporting persistent back pain [2]
We used a dataset of 17,409 clinical notes from different primary care practices; of these, 891 documents were manually annotated as acute low back pain (LBP) and 2973 were generally associated with LBP via the recorded ICD-10 code
TopicModel leads to similar performance but provides a more intuitive and potentially effective way for exploring the collection, extracting meaningful patterns that are related to acute LBP episodes
Summary
Low back pain (LBP) is one of the most common causes of disability in US adults younger than 45 years [1], with 10 to20% of American workers reporting persistent back pain [2]. Acute and chronic low back pain (LBP) are different conditions with different treatments They are coded in electronic health records with the same International Classification of Diseases, 10th revision (ICD-10) code (M54.5) and can be differentiated only by retrospective chart reviews. This prevents an efficient definition of data-driven guidelines for billing and therapy recommendations, such as return-to-work options. Acuity information was only available in the progress notes and was not incorporated into the automated recommendations This prevented the research team from providing accurate feedback to PCPs based on a full picture of the patient’s condition. Similar needs arise for other musculoskeletal conditions, such as knee, elbow, and shoulder pain, where ICD-10 codes do not differentiate by pain level and acuity [23,24]
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