Ensemble pretrained language models to extract biomedical knowledge from literature.

OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking. MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and taxonomy names and annotated 70 more abstracts as additional corpus. We further finetuned the T0pp model, with 11 billion parameters, to boost the performance on relation extraction and leveraged entites' location information (eg, title, background) to enhance novelty prediction performance in relation extraction (RE). RESULTS: Our pioneering NLP system designed for this challenge secured first place in Phase I-NER and second place in Phase II-relation extraction and novelty prediction, outpacing over 200 teams. We tested OpenAI ChatGPT 3.5 and ChatGPT 4 in a Zero-Shot setting using the same test set, revealing that our finetuned model considerably surpasses these broad-spectrum large language models. DISCUSSION AND CONCLUSION: Our outcomes depict a robust NLP system excelling in NER and RE across various biomedical entities, emphasizing that task-specific models remain superior to generic large ones. Such insights are valuable for endeavors like knowledge graph development and hypothesis formulation in biomedical research.

Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records.

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. The study highlighted the importance of seed quality rather than quantity in feeding NER models. This research reports on an efficient and accurate way to mine clinical notes for periodontal diagnoses, allowing researchers to easily and quickly build a NER model with the prompt generation approach.

Quantification of Nocturnal Blood Pressure Oscillations Induced by Sleep Disordered Breathing.

We present an approach to quantifying nocturnal blood pressure (BP) variations that are elicited by sleep disordered breathing (SDB). A sample-by-sample aggregation of the dynamic BP variations during normal breathing and BP oscillations prompted by apnea episodes is performed. This approach facilitates visualization and analysis of BP oscillations. Preliminary results from analysis of a full night study of 7 SDB subjects (5 Male 2 Female, 52±5.6 yrs., Body Mass Index 36.4±7.4 kg/m2, Apnea-Hypopnea Index 69.1±26.8) are presented. Aggregate trajectory and quantitative values for changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP) concomitant with obstructive apnea episodes are presented. The results show 19.4 mmHg (15.3%) surge in SBP and 9.4 mmHg (13.6%) surge in DBP compared to their respective values during normal breathing (p<0.05). Further, the peak of the surge in SBP and DBP occurred about 9s and 7s, respectively, post the end of apnea events. The return of SBP and DBP to baseline values displays a decaying oscillatory pattern.