Bayesian approach to incorporating different types of biomedical knowledge bases into information retrieval systems for clinical decision support in precision medicine

A hybrid algorithm for clinical decision support in precision medicine based on machine learning

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.

Knowledge-based normative safety measure approach: systematic assessment of capabilities to conquer COVID-19

In this research article, we have introduced a knowledge-based approach to regional/national security measures. Proposed Knowledge-based Normative Safety Measure algorithm for safety measures helps to take practical actions to conquer COVID-19. We analyzed based on five dimensions: the correlation between detected cases and confirmed cases, social distance, the speed of detected cases, the correlation between imported cases and inbound cases, and the proportion of masks worn. It prompts actions based on the security level of the region. Through the use of our proposed algorithm, the government has accelerated the implementation of social distancing, accelerated test cases, and policies, etc., to prevent people from contracting COVID-19. This idea can be a very effective way to realize the impending danger and take action in advance. Help speed up the process of controlling the COVID-19. In pandemic times, it can be helpful to understand better. Holding the normative safety measure at a high level leads nations to perform excellently on triple T's (testing, tracking, and treatment) policy and other safety acts. The proposed NSM approach facilitates for improve the governance of cities and communities.

An improved BM25 algorithm for clinical decision support in Precision Medicine based on co-word analysis and Cuckoo Search

BACKGROUND

Retrieving gene and disease information from a vast collection of biomedical abstracts to provide doctors with clinical decision support is one of the important research directions of Precision Medicine.

METHOD

We propose a novel article retrieval method based on expanded word and co-word analyses, also conducting Cuckoo Search to optimize parameters of the retrieval function. The main goal is to retrieve the abstracts of biomedical articles that refer to treatments. The methods mentioned in this manuscript adopt the BM25 algorithm to calculate the score of abstracts. We, however, propose an improved version of BM25 that computes the scores of expanded words and co-word leading to a composite retrieval function, which is then optimized using the Cuckoo Search. The proposed method aims to find both disease and gene information in the abstract of the same biomedical article. This is to achieve higher relevance and hence score of articles. Besides, we investigate the influence of different parameters on the retrieval algorithm and summarize how they meet various retrieval needs.

RESULTS

The data used in this manuscript is sourced from medical articles presented in Text Retrieval Conference (TREC): Clinical Decision Support (CDS) Tracks of 2017, 2018, and 2019 in Precision Medicine. A total of 120 topics are tested. Three indicators are employed for the comparison of utilized methods, which are selected among the ones based only on the BM25 algorithm and its improved version to conduct comparable experiments. The results showed that the proposed algorithm achieves better results.

CONCLUSION

The proposed method, an improved version of the BM25 algorithm, utilizes both co-word implementation and Cuckoo Search, which has been verified achieving better results on a large number of experimental sets. Besides, a relatively simple query expansion method is implemented in this manuscript. Future research will focus on ontology and semantic networks to expand the query vocabulary.

Information retrieval on oncology knowledge base using recursive paraphrase lattice

For annotation in cancer genomic medicine, oncologists have to refer to various knowledge bases worldwide and retrieve all information (e.g., drugs, clinical trials, and academic papers) related to a gene variant. However, oncologists find it difficult to search these knowledge bases comprehensively because there are multiple paraphrases containing abbreviations and foreign languages in their terminologies including diseases, drugs, and genes. In this paper, we propose a novel search method considering deep paraphrases, which helps oncologists retrieve essential annotation resources swiftly and effortlessly. Our method recursively finds paraphrases based on paraphrase corpora, expands a source document, and finally generates a paraphrase lattice. The proposed method also feedbacks beneficial information regarding the paraphrases applied for a search, which is useful for selecting search results and considering a query for the succeeding search. The results of an experiment demonstrated that our method could retrieve important annotation information that could not be retrieved using a conventional search system and simple paraphrasing. Additionally, annotation experts evaluated our method and found it to be practical.

Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval

The semantic mismatch between query and document terms—i.e., the semantic gap—is a long-standing problem in Information Retrieval (IR). Two main linguistic features related to the semantic gap that can be exploited to improve retrieval are synonymy and polysemy. Recent works integrate knowledge from curated external resources into the learning process of neural language models to reduce the effect of the semantic gap. However, these knowledge-enhanced language models have been used in IR mostly for re-ranking and not directly for document retrieval.

We propose the Semantic-Aware Neural Framework for IR (SAFIR), an unsupervised knowledge-enhanced neural framework explicitly tailored for IR. SAFIR jointly learns word, concept, and document representations from scratch. The learned representations encode both polysemy and synonymy to address the semantic gap. SAFIR can be employed in any domain where external knowledge resources are available. We investigate its application in the medical domain where the semantic gap is prominent and there are many specialized and manually curated knowledge resources. The evaluation on shared test collections for medical literature retrieval shows the effectiveness of SAFIR in terms of retrieving and ranking relevant documents most affected by the semantic gap.