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IntroductionEarly autism diagnosis remains challenging due to reliance on clinical observation and limited specialist availability. Addressing these barriers through automated diagnostic labeling and the integration of parental input may help mitigate the problem.MethodsWe trained a BioBERT machine learning model to label individual autism behavioral descriptions using the seven DSM-5 diagnostic criteria (A1-A3, B1-B4). This approach offers transparent clinical decision-making by providing detailed diagnostic information for individual behaviors and avoiding final case-level black-box decisions. We evaluated the model's performance on labeling lay (N = 35,971) and clinical (N = 145,603) behavior descriptions, as well as its transferability between the two. In addition, we compared the data sources by evaluating the diagnostic utility of lay and clinical examples across four dimensions, and of AI-generated summaries across two dimensions.ResultsWe found that BioBERT can label both types of input, although it achieved higher precision (69%) on clinical descriptions and higher recall (83%) on lay descriptions. Sample size did not explain differences in performance. Transferring models from one data type to another results in a performance drop. Overall, training first on clinical data yielded the best-performing diagnostic models. When evaluating the examples from both data sources, the results show similar scores for the Utility, Specificity, Clinical Relevance, and Impact on Daily Life dimensions, and the cosine similarity analysis revealed substantial overlap (0.42) in vocabulary between the two. The utility of examples for A diagnostic behaviors was generally scored higher than that for B diagnostic behaviors. AI-generated summary scores showed a similar pattern between A and B examples but they were only moderately representative of these examples.DiscussionThese results demonstrate that lay behavioral descriptions can provide diagnostically valuable information comparable to clinical observations, although they are not readily summarized by AI. The integration of lay information into the diagnostic workflows could accelerate autism diagnosis without compromising clinical utility.

The rapid increase in biomedical publications necessitates efficient systems to automatically handle Biomedical Named Entity Recognition (BioNER) tasks in unstructured text. However, accurately detecting biomedical entities is quite challenging due to the complexity of their names and the frequent use of abbreviations. In this paper, we propose BioBBC, a deep learning (DL) model that utilizes multi-feature embeddings and is constructed based on the BERT-BiLSTM-CRF to address the BioNER task. BioBBC consists of three main layers; an embedding layer, a Long Short-Term Memory (Bi-LSTM) layer, and a Conditional Random Fields (CRF) layer. BioBBC takes sentences from the biomedical domain as input and identifies the biomedical entities mentioned within the text. The embedding layer generates enriched contextual representation vectors of the input by learning the text through four types of embeddings: part-of-speech tags (POS tags) embedding, char-level embedding, BERT embedding, and data-specific embedding. The BiLSTM layer produces additional syntactic and semantic feature representations. Finally, the CRF layer identifies the best possible tag sequence for the input sentence. Our model is well-constructed and well-optimized for detecting different types of biomedical entities. Based on experimental results, our model outperformed state-of-the-art (SOTA) models with significant improvements based on six benchmark BioNER datasets.

PANoptosis has recently been discovered as a new type of cell death. PANoptosis mainly refers to the significant interaction among the three programmed cell death pathways of apoptosis, necroptosis, and pyroptosis. Despite this, only a few studies have examined the systematic literature in this area. By analyzing the bibliometric data for PANoptosis, we can visualize the current hotspots and predicted trends in research. This study analyzed bibliometric indicators using the Histcite Pro 2.0 tool, which searches the Web of Science for PANoptosis literature published between 2016 and 2022. A bibliometric analysis was performed using Histcite Pro 2.0, while research trends and hotspots were visualized using VOSviewer, CiteSpace and BioBERT. The output of related literature was low in the four years from the first presentation of PANoptosis in 2016 to 2020. The volume of relevant literature grew exponentially between 2020 and 2022. The United States and China play a leading role in this field. Although China started late, its research in this field is developing rapidly. As research progressed, more focus was placed on the relationship between PANoptosis and pyroptosis, as well as apoptosis and necrosis. Now is a rapid development stage of PANoptosis research. Most of the research focuses on the cellular level, and the focus is more on the treatment of tumor-related diseases. The current focus of this area is PANoptosis mechanisms in cancer and inflammation. It can be seen from the burst analysis of keywords that caspase1 and host defense have consistently been research hotspots in the field of PANoptosis, while the frequency of NLRC4, causes of autoinflammation, recognition, NLRP3, and Gasdermin D has gradually increased, all of which have become research hotspots in recent years. Finally, we used the BioBERT biomedical language model to mine the most documented genes and diseases in the PANoptosis field articles, pointing out the direction for subsequent research steps. According to a bibliometric analysis, researchers have shown an increased interest in PANoptosis over the past few years. Researchers initially focused on the molecular mechanism of PANoptosis and pyroptosis, apoptosis, and necroptosis. The role of PANoptosis in diseases and conditions such as inflammation and tumors is one of the current research hotspots in this area. The focus is more on treating inflammation-related diseases, which will become the key development direction of future research.

Background Automatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining, which is the foundation of extracting biomedical knowledge from unstructured texts into structured formats. Using the sequence labeling framework and deep neural networks to implement biomedical named entity recognition (BioNER) is a common method at present. However, the above method often underutilizes syntactic features such as dependencies and topology of sentences. Therefore, it is an urgent problem to be solved to integrate semantic and syntactic features into the BioNER model. Results In this paper, we propose a novel biomedical named entity recognition model, named BioByGANS ( BioB ERT/SpaC y - G raph A ttention N etwork- S oftmax), which uses a graph to model the dependencies and topology of a sentence and formulate the BioNER task as a node classification problem. This formulation can introduce more topological features of language and no longer be only concerned about the distance between words in the sequence. First, we use periods to segment sentences and spaces and symbols to segment words. Second, contextual features are encoded by BioBERT, and syntactic features such as part of speeches, dependencies and topology are preprocessed by SpaCy respectively. A graph attention network is then used to generate a fusing representation considering both the contextual features and syntactic features. Last, a softmax function is used to calculate the probabilities and get the results. We conduct experiments on 8 benchmark datasets, and our proposed model outperforms existing BioNER state-of-the-art methods on the BC2GM, JNLPBA, BC4CHEMD, BC5CDR-chem, BC5CDR-disease, NCBI-disease, Species-800, and LINNAEUS datasets, and achieves F1-scores of 85.15%, 78.16%, 92.97%, 94.74%, 87.74%, 91.57%, 75.01%, 90.99%, respectively. Conclusion The experimental results on 8 biomedical benchmark datasets demonstrate the effectiveness of our model, and indicate that formulating the BioNER task into a node classification problem and combining syntactic features into the graph attention networks can significantly improve model performance.

Background The biomedical literature is growing rapidly, and it is increasingly important to extract meaningful information from the vast amount of literature. Biomedical named entity recognition (BioNER) is one of the key and fundamental tasks in biomedical text mining. It also acts as a primitive step for many downstream applications such as relation extraction and knowledge base completion. Therefore, the accurate identification of entities in biomedical literature has certain research value. However, this task is challenging due to the insufficiency of sequence labeling and the lack of large-scale labeled training data and domain knowledge. Results In this paper, we use a novel word-pair classification method, design a simple attention mechanism and propose a novel architecture to solve the research difficulties of BioNER more efficiently without leveraging any external knowledge. Specifically, we break down the limitations of sequence labeling-based approaches by predicting the relationship between word pairs. Based on this, we enhance the pre-trained model BioBERT, through the proposed prefix and attention map dscrimination fusion guided attention and propose the E-BioBERT. Our proposed attention differentiates the distribution of different heads in different layers in the BioBERT, which enriches the diversity of self-attention. Our model is superior to state-of-the-art compared models on five available datasets: BC4CHEMD, BC2GM, BC5CDR-Disease, BC5CDR-Chem, and NCBI-Disease, achieving F1-score of 92.55%, 85.45%, 87.53%, 94.16% and 90.55%, respectively. Conclusion Compared with many previous various models, our method does not require additional training datasets, external knowledge, and complex training process. The experimental results on five BioNER benchmark datasets demonstrate that our model is better at mining semantic information, alleviating the problem of label inconsistency, and has higher entity recognition ability. More importantly, we analyze and demonstrate the effectiveness of our proposed attention.

Purpose The objective of the manuscript is to propose a hybrid algorithm combining the improved BM25 algorithm, k-means clustering, and BioBert model to better determine biomedical articles utilizing the PubMed database so, the number of retrieved biomedical articles whose content contains much similar information regarding a query of a specific disease could grow larger. Design/methodology/approach In the paper, a two-stage information retrieval method is proposed to conduct an improved Text-Rank algorithm. The first stage consists of employing the improved BM25 algorithm to assign scores to biomedical articles in the database and identify the 1000 publications with the highest scores. The second stage is composed of employing a method called a cluster-based abstract extraction to reduce the number of article abstracts to match the input constraints of the BioBert model, and then the BioBert-based document similarity matching method is utilized to obtain the most similar search outcomes between the document and the retrieved morphemes. To realize reproducibility, the written code is made available on https://github.com/zzc1991/TREC_Precision_Medicine_Track . Findings The experimental study is conducted based on the data sets of TREC2017 and TREC2018 to train the proposed model and the data of TREC2019 is used as a validation set confirming the effectiveness and practicability of the proposed algorithm that would be implemented for clinical decision support in precision medicine with a generalizability feature. Originality/value This research integrates multiple machine learning and text processing methods to devise a hybrid method applicable to domains of specific medical literature retrieval. The proposed algorithm provides a 3% increase of P@10 than that of the state-of-the-art algorithm in TREC 2019.

The fundamental issue with drug-drug interactions (DDIs) is that they cannot be ignored or overlooked since negative drug reactions and the use of medical services as a result are detrimental to patients and increase healthcare expenses. Conventional machine learning (ML) applications to DDI extraction, such as logistic regression or support vector machines, have been less successful, as the relationships in biomedical text are difficult to describe comprehensively. Recent advances in deep learning and transformer-based models offer improved contextual insight, but their resource-intensive demands may pose a barrier to scalability. We introduce CNN-DDI, a convolutional neural network model that can extract DDIs in biomedical text efficiently. On the SemEval-2013 dataset, we performed a comparative analysis of the classical models of ML (Logistic Regression, SVM, Random Forest, Naive Bayes, Decision Trees), transformer-based models (BioBERT, RoBERTa, DeBERTa, ELECTRA, DistilBERT), and the designed CNN-DDI. All the models were trained under similar procedures of parameter tuning and preprocessing. When comparing the models, CNN-DDI shows the highest performance with 86.81 percent overall accuracy and 83.81 percent F1-score, outperforming transformer-based models (best F1-score 81.41%, BioBERT-BiLSTM) as well as traditional ML models (best F1-score 77.09 percent, Logistic Regression). CNN-DDI integrates a competitive performance and fewer computer requirements, making it a feasible option in large-scale biomedical text mining.

Background review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model. Methods Scanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy. Results The trained models’ micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF. The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI : 56.80–78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions ( AAGR  = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%). Conclusions BioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.

Drug recommendation systems (DRS) play a crucial role in personalized medicine. Traditional Collaborative Filtering (CF) methods often face challenges with data sparsity and cold-start problems due to their reliance on drug/user identifiers (IDs). To address these limitations, we propose HyBLRecs, a recommendation system leveraging content analysis and reliability enhancement. HyBLRecs eliminates the reliance on IDs, instead employing a three-branch hybrid network architecture to deeply mine content from patient reviews. The model integrates the semantic capabilities of BioBERT, sentiment analysis, and statistical features to train a high-performance LightGBM classifier (AUC ≈ 0.98). Finally, an efficient confidence-based ranking mechanism is applied to balance predictive efficacy, stability, and evidentiary reliability (N). Experimental results on the Drugs.com dataset demonstrate that HyBLRecs outperforms existing methods, achieving an F1 score of 96.91% and an overall accuracy of 95.63%, which confirms its strong generalizability even for unseen drugs.