Application of the Bidirectional Encoder Representations from Transformers Model for Predicting the Abbreviated Injury Scale in Patients with Trauma: Algorithm Development and Validation Study
Bibliographic record
Abstract
Background: Deaths related to physical trauma impose a heavy burden on society, and the Abbreviated Injury Scale (AIS) is an important tool for injury research. AIS covers injuries to various parts of the human body and scores them based on the severity of the injury. In practical applications, the complex AIS coding rules require experts to encode by consulting patient medical records, which inevitably increases the difficulty, time, and cost of evaluation of patient and also puts higher demands on the workload of information collection and processing. In some cases, the sheer number of patients or the inability to access detailed medical records necessary for coding further complicates independent AIS codes. Objective: This study aims to use advanced deep learning techniques to predict AIS codes based on easily accessible diagnostic information of patients to improve the accuracy of trauma assessment. Methods: We used a dataset of patients with trauma (n=26,810) collected by the Chongqing Daping Hospital between October 2013 and June 2024. We mainly selected the patient's diagnostic information, injury description, cause of injury, injury region, injury types, and present illness history as the key feature inputs. We used a robust optimization Bidirectional Encoder Representations from Transformers (BERT) pretraining method to embed these features and constructed a prediction model based on BERT. This model aims to predict AIS codes and comprehensively evaluate its performance through a 5-fold cross-validation. We compared the BERT model with previous research results and current mainstream machine learning methods to verify its advantages in prediction tasks. In addition, we also conducted external validation of the model using 244 external data points from the Chongqing Emergency Center. Results: The BERT model proposed in this paper performs significantly better than the comparison model on independent test datasets with an accuracy of 0.8971, which surpassed the previous study by 10 % points. In addition, the area under the curve (AUC value of the BERT model is 0.9970, and the F1-score is 0.8434. In the external dataset, the accuracy, AUC, and F1-score results of the model are 0.7131, 0.8586, and 0.6801, respectively. These results indicate that our model has high generalization ability and prediction accuracy. Conclusions: The BERT model we proposed is mainly based on diagnostic information to predict AIS codes, and its prediction accuracy is superior to previous investigations and current mainstream machine learning methods. It has a high generalization ability in external datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".