Identification of asthma control factor in clinical notes using a hybrid deep learning model

Abstract

Background There are significant variabilities in guideline-concordant documentation in asthma care. However, assessing clinician’s documentation is not feasible using only structured data but requires labor-intensive chart review of electronic health records (EHRs). A certain guideline element in asthma control factors, such as review inhaler techniques, requires context understanding to correctly capture from EHR free text. Methods The study data consist of two sets: (1) manual chart reviewed data—1039 clinical notes of 300 patients with asthma diagnosis, and (2) weakly labeled data (distant supervision)—27,363 clinical notes from 800 patients with asthma diagnosis. A context-aware language model, Bidirectional Encoder Representations from Transformers (BERT) was developed to identify inhaler techniques in EHR free text. Both original BERT and clinical BioBERT (cBERT) were applied with a cost-sensitivity to deal with imbalanced data. The distant supervision using weak labels by rules was also incorporated to augment the training set and alleviate a costly manual labeling process in the development of a deep learning algorithm. A hybrid approach using post-hoc rules was also explored to fix BERT model errors. The performance of BERT with/without distant supervision, hybrid, and rule-based models were compared in precision, recall, F-score, and accuracy. Results The BERT models on the original data performed similar to a rule-based model in F1-score (0.837, 0.845, and 0.838 for rules, BERT, and cBERT, respectively). The BERT models with distant supervision produced higher performance (0.853 and 0.880 for BERT and cBERT, respectively) than without distant supervision and a rule-based model. The hybrid models performed best in F1-score of 0.877 and 0.904 over the distant supervision on BERT and cBERT. Conclusions The proposed BERT models with distant supervision demonstrated its capability to identify inhaler techniques in EHR free text, and outperformed both the rule-based model and BERT models trained on the original data. With a distant supervision approach, we may alleviate costly manual chart review to generate the large training data required in most deep learning-based models. A hybrid model was able to fix BERT model errors and further improve the performance.

May Artificial Intelligence Influence Future Pediatric Research?—The Case of ChatGPT

“…Similarly, in the context of pediatrics, Annapragada et al used NLP to identify child abuse from EMRs [ 12 ]. In another study, authors utilized a context-aware language model to identify inhaler techniques in electronic health records for asthma care [ 13 ]. Their work suggested that it may be possible to alleviate the costly manual chart review required for guideline-concordant documentation in asthma care by using a machine learning approach.…”

Use of ChatGPT in Pediatric Urology and its Relevance in Clinical Practice: Is it useful?

“…Numerous cardiology and oncology approaches have demonstrated the utility of AI, particularly in identifying and classifying disease phenotypes and improving predictive outcome models by incorporating unstructured data. [13][14][15][16]. Using AI to identify inhaler techniques in electronic health records for asthma care, a study suggests it may be possible to eliminate the expensive manual chart review required for guidelineconformant documentation in asthma care by employing a machine learning strategy [13].…”

“…[13][14][15][16]. Using AI to identify inhaler techniques in electronic health records for asthma care, a study suggests it may be possible to eliminate the expensive manual chart review required for guidelineconformant documentation in asthma care by employing a machine learning strategy [13]. However, to the best of our knowledge, no study has evaluated the impact of ChatGPT on medical decision-making in Pediatric Urology.…”

Lightweight Transformers for Clinical Natural Language Processing

“…2.1), transformer-based language models that have been only trained on generic and biomedical texts are not always sufficient to capture all the complexities of clinical notes. For this reason, it is common to use pre-trained models as a starting point and either use fine-tuning to adapt them to clinical notes (van Aken et al, 2021;Agnikula Kshatriya et al, 2021) or use continual learning and further pre-train a model like BERT or BioBERT (Lee et al, 2020a) on clinical texts Alsentzer et al, 2019;Qiu et al, 2020).…”