Identifying Risk Factors Associated With Lower Back Pain in Electronic Medical Record Free Text: Deep Learning Approach Using Clinical Note Annotations
Bibliographic record
Abstract
Background: Lower back pain is a common weakening condition that affects a large population. It is a leading cause of disability and lost productivity, and the associated medical costs and lost wages place a substantial burden on individuals and society. Recent advances in artificial intelligence and natural language processing have opened new opportunities for the identification and management of risk factors for lower back pain. In this paper, we propose and train a deep learning model on a data set of clinical notes that have been annotated with relevant risk factors, and we evaluate the model's performance in identifying risk factors in new clinical notes. Objective: The primary objective is to develop a novel deep learning approach to detect risk factors for underlying disease in patients presenting with lower back pain in clinical encounter notes. The secondary objective is to propose solutions to potential challenges of using deep learning and natural language processing techniques for identifying risk factors in electronic medical record free text and make practical recommendations for future research in this area. Methods: We manually annotated clinical notes for the presence of six risk factors for severe underlying disease in patients presenting with lower back pain. Data were highly imbalanced, with only 12% (n=296) of the annotated notes having at least one risk factor. To address imbalanced data, a combination of semantic textual similarity and regular expressions was used to further capture notes for annotation. Further analysis was conducted to study the impact of downsampling, binary formulation of multi-label classification, and unsupervised pretraining on classification performance. Results: Of 2749 labeled clinical notes, 347 exhibited at least one risk factor, while 2402 exhibited none. The initial analysis shows that downsampling the training set to equalize the ratio of clinical notes with and without risk factors improved the macro-area under the receiver operating characteristic curve (AUROC) by 2%. The Bidirectional Encoder Representations from Transformers (BERT) model improved the macro-AUROC by 15% over the traditional machine learning baseline. In experiment 2, the proposed BERT-convolutional neural network (CNN) model for longer texts improved (4% macro-AUROC) over the BERT baseline, and the multitask models are more stable for minority classes. In experiment 3, domain adaptation of BERTCNN using masked language modeling improved the macro-AUROC by 2%. Conclusions: Primary care clinical notes are likely to require manipulation to perform meaningful free-text analysis. The application of BERT models for multi-label classification on downsampled annotated clinical notes is useful in detecting risk factors suggesting an indication for imaging for patients with lower back pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".