Search

Objective Most existing fine-tuned biomedical large language models (LLMs) focus on enhancing performance in monolingual biomedical question answering and conversation tasks. To investigate the effectiveness of the fine-tuned LLMs on diverse biomedical natural language processing (NLP) tasks in different languages, we present Taiyi, a bilingual fine-tuned LLM for diverse biomedical NLP tasks. Materials and Methods We first curated a comprehensive collection of 140 existing biomedical text mining datasets (102 English and 38 Chinese datasets) across over 10 task types. Subsequently, these corpora were converted to the instruction data used to fine-tune the general LLM. During the supervised fine-tuning phase, a 2-stage strategy is proposed to optimize the model performance across various tasks. Results Experimental results on 13 test sets, which include named entity recognition, relation extraction, text classification, and question answering tasks, demonstrate that Taiyi achieves superior performance compared to general LLMs. The case study involving additional biomedical NLP tasks further shows Taiyi's considerable potential for bilingual biomedical multitasking. Conclusion Leveraging rich high-quality biomedical corpora and developing effective fine-tuning strategies can significantly improve the performance of LLMs within the biomedical domain. Taiyi shows the bilingual multitasking capability through supervised fine-tuning. However, those tasks such as information extraction that are not generation tasks in nature remain challenging for LLM-based generative approaches, and they still underperform the conventional discriminative approaches using smaller language models. [ABSTRACT FROM AUTHOR]

Symptom inference is a critical component of the medical dialogue system for automatic diagnosis. With the improvement of online diagnosis in recent years, the number of doctor- patient dialogue texts has continued to increase. The initial research on symptom inference based on electronic health records has gradually shifted to doctor-patient dialogue texts. However, most existing studies ignore the special role and symptom entity structure prior knowledge in dialogue, which can help the model learn contextual associations better. Therefore, this paper proposes an improved selfattention network based on the prior knowledge of role and entity structure and combines it with a pre-trained language model. The proposed model integrates the prior knowledge of role and entity structure into the encoding stage of the text, and can more accurately infer the attributes of symptom entities. Experimental results on the CHIP-MDCFNPC dataset of the CBLUE2.0 show that the proposed model outperforms the baseline model, which verifies the effectiveness of prior knowledge and model structure. [ABSTRACT FROM AUTHOR]

This paper presents a numerical modeling approach for investigating crescent‐type internal gear pumps (CIGPs) considering radial micromotions of both gears. The modeling approach couples a lumped parameter pressure solver on the bulk fluid domain, with a transient computational fluid dynamics film simulator based on transient gap geometries solved from the gear loading forces. Simulation results on a commercial unit shows that for a wide range of operating conditions, the model gives volumetric efficiency predictions with errors less than 2 % comparing to measurements. To highlight the importance of considering radial micromotions, the paper also provides the results achieved assuming nominal position for both gears. [ABSTRACT FROM AUTHOR]

The BioRED track at BioCreative VIII calls for a community effort to identify, semantically categorize, and highlight the novelty factor of the relationships between biomedical entities in unstructured text. Relation extraction is crucial for many biomedical natural language processing (NLP) applications, from drug discovery to custom medical solutions. The BioRED track simulates a real-world application of biomedical relationship extraction, and as such, considers multiple biomedical entity types, normalized to their specific corresponding database identifiers, as well as defines relationships between them in the documents. The challenge consisted of two subtasks: (i) in Subtask 1, participants were given the article text and human expert annotated entities, and were asked to extract the relation pairs, identify their semantic type and the novelty factor, and (ii) in Subtask 2, participants were given only the article text, and were asked to build an end-to-end system that could identify and categorize the relationships and their novelty. We received a total of 94 submissions from 14 teams worldwide. The highest F-score performances achieved for the Subtask 1 were: 77.17% for relation pair identification, 58.95% for relation type identification, 59.22% for novelty identification, and 44.55% when evaluating all of the above aspects of the comprehensive relation extraction. The highest F-score performances achieved for the Subtask 2 were: 55.84% for relation pair, 43.03% for relation type, 42.74% for novelty, and 32.75% for comprehensive relation extraction. The entire BioRED track dataset and other challenge materials are available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC8-BioRED-track/ and https://codalab.lisn.upsaclay.fr/competitions/13377 and https://codalab.lisn.upsaclay.fr/competitions/13378. Database URL: https://ftp.ncbi.nlm.nih.gov/pub/lu/BC8-BioRED-track/https://codalab.lisn.upsaclay.fr/competitions/13377https://codalab.lisn.upsaclay.fr/competitions/13378., (Published by Oxford University Press 2024. This work is written by (a) US Government employee(s) and is in the public domain in the US.)