Objective: Pituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient’s physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs. Methods: The clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods. Results: Experimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%. Conclusions: In this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts.

Background: The global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses. Objective: The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph–based approach. Methods: We propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism. Results: The constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33 potential targets and 18 drug candidates were identified for coronaviruses. Among them, 7 novel drugs (ie, quinine, nelfinavir, ivermectin, asunaprevir, tylophorine, Artemisia annua extract, and resveratrol) and 3 highly ranked targets (ie, angiotensin converting enzyme 2, transmembrane serine protease 2, and M protein) were further discussed. Conclusions: We showed the effectiveness of a knowledge graph–based approach in potential target discovery and drug repurposing for coronaviruses. Our approach can be extended to other viruses or diseases for biomedical knowledge discovery and relevant applications.

Background: Pituitary adenoma is one of the most common central nervous system tumors. The diagnosis and treatment of pituitary adenoma remain very difficult. Misdiagnosis and recurrence often occur, and experienced neurosurgeons are in serious shortage. A knowledge graph can help interns quickly understand the medical knowledge related to pituitary tumor. Objective: The aim of this study was to develop a data fusion method suitable for medical data using data of pituitary adenomas integrated from different sources. The overall goal was to construct a knowledge graph for pituitary adenoma (KGPA) to be used for knowledge discovery. Methods: A complete framework suitable for the construction of a medical knowledge graph was developed, which was used to build the KGPA. The schema of the KGPA was manually constructed. Information of pituitary adenoma was automatically extracted from Chinese electronic medical records (CEMRs) and medical websites through a conditional random field model and newly designed web wrappers. An entity fusion method is proposed based on the head-and-tail entity fusion model to fuse the data from heterogeneous sources. Results: Data were extracted from 300 CEMRs of pituitary adenoma and 4 health portals. Entity fusion was carried out using the proposed data fusion model. The F1 scores of the head and tail entity fusions were 97.32% and 98.57%, respectively. Triples from the constructed KGPA were selected for evaluation, demonstrating 95.4% accuracy. Conclusions: This paper introduces an approach to fuse triples extracted from heterogeneous data sources, which can be used to build a knowledge graph. The evaluation results showed that the data in the KGPA are of high quality. The constructed KGPA can help physicians in clinical practice.

Objective: Faced with the challenges of differential diagnosis caused by the complex clinical manifestations and high pathological heterogeneity of pituitary adenomas, this study aims to construct a high-quality annotated corpus to characterize pituitary adenomas in clinical notes containing rich diagnosis and treatment information. Methods: A dataset from a pituitary adenomas neurosurgery treatment center of a tertiary first-class hospital in China was retrospectively collected. A semi-automatic corpus construction framework was designed. A total of 2000 documents containing 9430 sentences and 524,232 words were annotated, and the text corpus of pituitary adenomas (TCPA) was constructed and analyzed. Its potential application in large language models (LLMs) was explored through fine-tuning and prompting experiments. Results: TCPA had 4782 medical entities and 28,998 tokens, achieving good quality with the inter-annotator agreement value of 0.862–0.986. The LLMs experiments showed that TCPA can be used to automatically identify clinical information from free texts, and introducing instances with clinical characteristics can effectively reduce the need for training data, thereby reducing labor costs. Conclusion: This study characterized pituitary adenomas in clinical notes, and the proposed method were able to serve as references for relevant research in medical natural language scenarios with highly specialized language structure and terminology.

Nutrition is closely associated with public health, and more and more studies have demonstrated that there are many correlations between the gut microbiota and nutritional disorders. These studies constitute the basic source of medical knowledge. However, discovering knowledge from massive amounts of data is not easy only by manpower. This study aims at discovering the relationships between nutritional disorders and gut microbiota from the biomedical literatures with knowledge graph. In this study, literature content information from massive data is automatically and instantly extracted using SPARQL query and logic reasoning technologies. Then, an annotated corpus is obtained with the categorization and definition of relationships between nutritional disorders and gut microbiota. Thus, a knowledge graph of semantic relationship between nutritional disorders and gut microbiota is constructed, and semantic relationships are discovered, verifying the feasibility and high efficiency of relationships discovery with knowledge graphs. Moreover, the analysis of evidence case shows that it is necessary and valuable to use the importance of evidence to exclude conflicting conclusions.

Background: Malnutrition in critically ill patients is associated with increased morbidity and mortality, yet traditional screening tools such as the modified NUTRIC (mNUTRIC) score often rely on subjective assessments or delayed data, limiting their utility for early intervention in the dynamic intensive care unit (ICU) environment. Real-time, data-driven approaches using electronic health records offer a promising solution for automated and objective risk stratification. Objective: This study aimed to develop and validate a machine learning model, the E-NUTRIC (Ensemble-NUTRIC), for the early prediction of malnutrition risk within the first 24 hours of ICU admission. By integrating multiple algorithms through stacking ensemble learning, we sought to improve predictive performance over traditional scoring systems and individual machine learning models while maintaining clinical interpretability. Methods: We conducted a retrospective cohort study using data from the Medical Information Mart for Intensive Care (MIMIC-IV, version 3.1). Adult ICU stays exceeding 24 hours were included, and the primary outcome was malnutrition diagnosis. Variables from the first 24 hours (demographics, vitals, and laboratory tests) were extracted and harmonized. Missingness was addressed with k-nearest neighbors imputation, features were standardized, and class imbalance was mitigated via random undersampling. The proposed E-NUTRIC model used a stacking ensemble with 4 base learners—Logistic Regression, Random Forest, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine—and a logistic metalearner. Performance was assessed on a stratified 80/20 holdout test set using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and calibration curves. The mNUTRIC score served as the clinical benchmark. Model interpretability was derived by applying Shapley Additive Explanations (SHAP) specifically to the highly predictive XGBoost component, while clinical utility was assessed using Platt scaling recalibration. Results: The final cohort comprised 51,483 patients, of whom 4384 (8.5%) were classified as being at malnutrition risk. The E-NUTRIC model showed superior discrimination with an AUROC of 0.875 (95% CI 0.864‐0.885), outperforming the mNUTRIC score (AUROC=0.635, P <.001). Relative to individual base learners, E-NUTRIC achieved the best overall performance, exceeding those of the best-performing individual models XGBoost (AUROC=0.871) and Light Gradient Boosting Machine (AUROC=0.866). The area under the precision-recall curve of E-NUTRIC was 0.424, representing approximately a 3.4-fold increase over mNUTRIC (0.126). SHAP analysis highlighted minimum serum albumin, admission weight, early hypokalemia, and specific ICU admission types as key nonlinear predictors of malnutrition risk. Unlike the traditional mNUTRIC score, which compressed predictions into a low-risk tier, the recalibrated E-NUTRIC model effectively spanned the full probability spectrum, thereby isolating high-risk phenotypes. Conclusions: The E-NUTRIC stacking ensemble provides an interpretable approach for nutritional risk screening in the ICU using routinely available electronic health records data. In this retrospective cohort study, it demonstrated superior discrimination to the mNUTRIC score and offered clinically consistent feature attributions.