Improving deep learning method for biomedical named entity recognition by using entity definition information

Design and development of a disease-specific clinical database system to increase the availability of hospital data in China

Purpose

In order to meet restrictions and difficulties in the development of hospital medical informatization and clinical databases in China, in this study, a disease-specific clinical database system (DSCDS) was designed and built. It provides support for the full utilization of real world medical big data in clinical research and medical services for specific diseases.

Methods

The development of DSCDS involved (1) requirements analysis on precision medicine, medical big data, and clinical research; (2) design schematics and basic architecture; (3) standard datasets of specific diseases consisting of common data elements (CDEs); (4) collection and aggregation of specific disease data scattered in various medical business systems of the hospital; (5) governance and quality improvement of specific disease data; (6) data storage and computing; and (7) design of data application modules.

Results

A DSCDS for liver cirrhosis was created in the gastrointestinal department of a 3A grade hospital in China and had more than nine data application modules. Based on this DSCDS, a series of clinical studies are being carried out, such as retrospective or prospective cohorts, prognostic studies using multimodal data, and follow-up studies.

Conclusion

The development of the DSCDS for liver cirrhosis in this paper provides experience and reference for the design and development of DSCDSs for other specific diseases in China; it can even expand to the development of DSCDSs in other countries if they have the demand for DSCDS and the same or better medical informatization foundation. DSCDS has more accurate, standard, comprehensive, multimodal and usable data of specific diseases than the general clinical database system and clinical data repository (CDR) and provides a credible data foundation for medical research, clinical decision-making and improving the medical service quality of specific diseases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-023-00211-4.

Integrating domain knowledge for biomedical text analysis into deep learning: A survey

The past decade has witnessed an explosion of textual information in the biomedical field. Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision-making. Over the same period, deep learning has achieved remarkable performance in biomedical natural language processing, however, its development has been limited by well-annotated datasets and interpretability. To solve this, researchers have considered combining domain knowledge (such as biomedical knowledge graph) with biomedical data, which has become a promising means of introducing more information into biomedical datasets and following evidence-based medicine. This paper comprehensively reviews more than 150 recent literature studies on incorporating domain knowledge into deep learning models to facilitate typical biomedical text analysis tasks, including information extraction, text classification, and text generation. We eventually discuss various challenges and future directions.

Probiotics Inhibit Cartilage Damage and Progression of Osteoarthritis in Mice

Increasing interest has focussed on the possible role of alterations in the microbiome in the pathogenesis of metabolic disease, inflammatory disease, and osteoporosis. Here we examined the role of the microbiome in a preclinical model of osteoarthritis in mice subjected to destabilisation of medical meniscus (DMM). The intestinal microbiome was depleted by broad-spectrum antibiotics from 1 week before birth until the age of 6 weeks when mice were subjected reconstitution of the microbiome with faecal microbial transplant (FMT) followed by the administration of a mixture of probiotic strains Lacticaseibacillus paracasei 8700:2, Lactiplantibacillus plantarum HEAL9 and L. plantarum HEAL19 or vehicle. All mice were subjected to DMM at the age of 8 weeks. The severity of osteoarthritis was evaluated by histological analysis and effects on subchondral bone were investigated by microCT analyses. The combination of FMT and probiotics significantly inhibited cartilage damage at the medial femoral condyle such that the OARSI score was 4.64 ± 0.32 (mean ± sem) in the FMT and probiotic group compared with 6.48 ± 0.53 in the FMT and vehicle group (p = 0.007). MicroCT analysis of epiphyseal bone from the femoral condyle showed that the probiotic group had higher BV/TV, increased Tb.Th, and moderately thicker subchondral bone plates than the control group. There was no difference between groups in joint inflammation or in serum concentrations of inflammatory cytokines and chemokines. We conclude that treatment with probiotics following FMT in mice where the microbiome has been depleted inhibits DMM-induced cartilage damage and impacts on the structure of subchondral bone particularly at the femoral condyle. While further studies are required to elucidate the mechanism of action, our research suggests that these probiotics may represent a novel intervention for the treatment of osteoarthritis.