Development of an information retrieval tool for biomedical patents

Microbiome in Lower Urinary Tract Symptoms (LUTSs): Mapping the State of the Art with Bibliometric Analysis

BACKGROUND

The main objective of this study is to provide the first characterization of the current research field of the clinical microbiome in LUTSs.

METHODS

First-of-its-kind scientometric insight into the historical development and structural state of the discipline is provided by a field analysis, mapping, and sub-analysis of articles for future research. On 22 December 2022, the entire Scopus database was searched without language or date restrictions. Search terms included "Chronic prostatitis", OR "Interstitial cystitis", OR "Lower urinary tract symptoms", OR "Lower urinary tract dysfunction", OR "Overactive bladder", OR "Incontinence", OR "Urolithiasis", OR "Urothelium", OR "Urine", OR "Urology", OR "urinary disorder", OR "Pathophysiology", OR "Benign prostatic hyperplasia", OR "Benign prostatic enlargement", AND "Microbiota", OR "Microbiome", OR "Urobio-ma", OR "Urobiota; microflora". The author and institutional data were transformed using the analytical tool Biblioshiny (a Shiny app for Bibliometrix), which took into account variations in author spelling as well as institutional naming and subgroups.

RESULTS

The specified search strategy was able to locate 529 documents from 267 sources published from 1981 to 2022. The average number of years from publication was 4.59 years. The authors with the most publications were Wolfe AJ and Brubaker I. The top three most collaborative networks were Loyola University Chicago, Loyola University Medical Center, and the University of California San Diego. The most frequently occurring words among the 50 nodes were: human, humans, nonhuman, female, adult, article, microbiology, microflora, microbiota, and controlled study. Frontiers in Cellular and Infection Microbiology and the International Urogynecology Journal, followed by Nature Reviews Urology, were the top three most relevant sources in microbiome research in urology.

CONCLUSIONS

One of the most crucial requirements for developing research policies and anticipating the scientific requirements of researchers is paying attention to the evolution of various scientific fields. Understanding research gaps and future needs in microbiome research in urology can be effectively understood by paying attention to the models, maps, and visualizations used in this research, which are the results of systematic analysis of scientific products in the most esteemed scientific journals in the world.

Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM)

With the rapid development of the patent marketplace, patent trading recommendation is required to mitigate the technology searching cost of patent buyers. Current research focuses on the recommendation based on existing patents of a company; a few studies take into account the sequential pattern of patent acquisition activities and the possible diversity of a company’s business interests. Moreover, the profiling of patents based on solely patent documents fails to capture the high-order information of patents. To bridge the gap, we propose a knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM) method for patent trading recommendation. KBiLSTM uses knowledge graph embeddings to profile patents with rich patent information. It introduces bidirectional long short-term memory network (BiLSTM) to capture the sequential pattern in a company’s historical records. In addition, to address a company’s diverse technology interests, we design an attention mechanism to aggregate the company’s historical patents given a candidate patent. Experimental results on the United States Patent and Trademark Office (USPTO) data set show that KBiLSTM outperforms state-of-the-art baselines for patent trading recommendation in terms of F1 and normalised discounted cumulative gain (nDCG). The attention visualisation of randomly selected company intuitively demonstrates the recommendation effectiveness.

An adverse drug effect mentions extraction method based on weighted online recurrent extreme learning machine

BACKGROUND AND OBJECTIVE

Automatic extraction of adverse drug effect (ADE) mentions from biomedical texts is a challenging research problem that has attracted significant attention from the pharmacovigilance and biomedical text mining communities. Indeed, deep learning based methods have recently been employed to solve this issue with great success. However, they fail to effectively identify the boundary of mentions. In this paper, we propose a weighted online recurrent extreme learning machine (WOR-ELM) based method to overcome this drawback.

METHODS

The proposed method for ADE mentions extraction from biomedical texts is divided into two stages: span detection and ADE mentions classification. At the first stage, we identify the boundary of the mentions irrespective of their types with a WOR-ELM in a given sentence. At the second stage, another WOR-ELM is used to classify the identified mentions to the appropriate type. Both stages use the concatenation of character-level and word-level embeddings as features. The character-level embedding is obtained using a modified online recurrent extreme learning machine, whereas the word-level embedding is obtained from a pre-trained model.

RESULTS

Several experiments were carried out on a well-known ADE corpus to evaluate the effectiveness and demonstrate the usefulness of the proposed method. The obtained results show that our method achieves an F-score of 87.5%, which outperforms the current state-of-the-art methods.

CONCLUSIONS

Our research results indicate that the proposed method for adverse drug effect mentions extraction from text can significantly improve performance over existing methods. Our experiments show the effectiveness of incorporating word-level and character level embeddings as features for WOR-ELM. They also illustrate the benefits of using IOU segment to represent ADE mentions.