Clinical Context–Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation

Abstract

Background Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization. Objective Traditional approaches in ATS for biomedical text suffer from fundamental issues such as an inability to capture clinical context, quality of evidence, and purpose-driven selection of passages for the summary. We aimed to circumvent these limitations through achieving precise, succinct, and coherent information extraction from credible published biomedical resources, and to construct a simplified summary containing the most informative content that can offer a review particular to clinical needs. Methods In our proposed approach, we introduce a novel framework, termed Biomed-Summarizer, that provides quality-aware Patient/Problem, Intervention, Comparison, and Outcome (PICO)-based intelligent and context-enabled summarization of biomedical text. Biomed-Summarizer integrates the prognosis quality recognition model with a clinical context–aware model to locate text sequences in the body of a biomedical article for use in the final summary. First, we developed a deep neural network binary classifier for quality recognition to acquire scientifically sound studies and filter out others. Second, we developed a bidirectional long-short term memory recurrent neural network as a clinical context–aware classifier, which was trained on semantically enriched features generated using a word-embedding tokenizer for identification of meaningful sentences representing PICO text sequences. Third, we calculated the similarity between query and PICO text sequences using Jaccard similarity with semantic enrichments, where the semantic enrichments are obtained using medical ontologies. Last, we generated a representative summary from the high-scoring PICO sequences aggregated by study type, publication credibility, and freshness score. Results Evaluation of the prognosis quality recognition model using a large dataset of biomedical literature related to intracranial aneurysm showed an accuracy of 95.41% (2562/2686) in terms of recognizing quality articles. The clinical context–aware multiclass classifier outperformed the traditional machine-learning algorithms, including support vector machine, gradient boosted tree, linear regression, K-nearest neighbor, and naïve Bayes, by achieving 93% (16127/17341) accuracy for classifying five categories: aim, population, intervention, results, and outcome. The semantic similarity algorithm achieved a significant Pearson correlation coefficient of 0.61 (0-1 scale) on a well-known BIOSSES dataset (with 100 pair sentences) after semantic enrichment, representing an improvement of 8.9% over baseline Jaccard similarity. Finally, we found a highly positive correlation among the evaluations performed by three domain experts concerning different metrics, suggesting that the automated summarization is satisfactory. Conclusions By employing the proposed method Biomed-Summarizer, high accuracy in ATS was achieved, enabling seamless curation of research evidence from the biomedical literature to use for clinical decision-making.

Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning

“…Afzal et al proposed using deep neural network model for the summarization of biomedical texts. The objective of the work was to verify the performance of deep neural networks and recurrent networks in the field of ATS since these are previously known to outperform machine learning models in NLP tasks [25]. The results of these experiments indeed indicated that the deep neural network models performance showed greater accuracy.…”

Anticipating Ambulatory Automation: Potential Applications of Administrative and Clinical Automation in Outpatient Healthcare Delivery

“…Additionally, the application of text summarization can be extended and customized to the needs of different specialties, training machine learning algorithms on critical areas of focus in prior documentation relevant to a particular clinical field. 8,10,11 NLP is a currently available form of technology that can be utilized to discover previously missed or improperly coded patient conditions including hierarchical condition category codes. 6,7 However, improperly coded information could also be a limitation, relying on incorrect inputs that result in inappropriate decisions which are perpetuated over time.…”

“…20 Studies on text summarization have demonstrated its utility in clinical information extraction and may serve as a potential decision support aid. 8,23 NLP could be applied to extract information from clinical encounters for text summarization and offer clinical decision support by helping identify a correct diagnosis. 8,23…”

“…8,23 NLP could be applied to extract information from clinical encounters for text summarization and offer clinical decision support by helping identify a correct diagnosis. 8,23…”

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“…Recent efforts in summarization have focused on neural methods (See et al, 2017;Gehrmann et al, 2018) using benchmark datasets compiled from news articles, such as the CNN-DailyMail dataset (CNN-DM) (Hermann et al, 2015). However, despite its importance, fewer efforts have tackled text summarization in the biomedical domain for both consumer and clinical text and its applications in Question Answering (QA) (Afantenos et al, 2005;Mishra et al, 2014;Afzal et al, 2020).…”