Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search

Designing Formulae for Ranking Search Results: Mixed Methods Evaluation Study

Background

A major factor in the success of any search engine is the relevance of the search results; a tool should sort the search results to present the most relevant documents first. Assessing the performance of the ranking formula is an important part of search engine evaluation. However, the methods currently used to evaluate ranking formulae mainly collect quantitative data and do not gather qualitative data, which help to understand what needs to be improved to tailor the formulae to their end users.

Objective

This study aims to evaluate 2 different parameter settings of the ranking formula of LiSSa (the French acronym for scientific literature in health care; Department of Medical Informatics and Information), a tool that provides access to health scientific literature in French, to adapt the formula to the needs of the end users.

Methods

To collect quantitative and qualitative data, user tests were carried out with representative end users of LiSSa: 10 general practitioners and 10 registrars. Participants first assessed the relevance of the search results and then rated the ranking criteria used in the 2 formulae. Verbalizations were analyzed to characterize each criterion.

Results

A formula that prioritized articles representing a consensus in the field was preferred. When users assess an article’s relevance, they judge its topic, methods, and value in clinical practice.

Conclusions

Following the evaluation, several improvements were implemented to give more weight to articles that match the search topic and to downgrade articles that have less informative or scientific value for the reader. Applying a qualitative methodology generates valuable user inputs to improve the ranking formula and move toward a highly usable search engine.

The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis

BACKGROUND

The Unified Medical Language System (UMLS) has been a critical tool in biomedical and health informatics, and the year 2021 marks its 30th anniversary. The UMLS brings together many broadly used vocabularies and standards in the biomedical field to facilitate interoperability among different computer systems and applications.

OBJECTIVE

Despite its longevity, there is no comprehensive publication analysis of the use of the UMLS. Thus, this review and analysis is conducted to provide an overview of the UMLS and its use in English-language peer-reviewed publications, with the objective of providing a comprehensive understanding of how the UMLS has been used in English-language peer-reviewed publications over the last 30 years.

METHODS

PubMed, ACM Digital Library, and the Nursing & Allied Health Database were used to search for studies. The primary search strategy was as follows: UMLS was used as a Medical Subject Headings term or a keyword or appeared in the title or abstract. Only English-language publications were considered. The publications were screened first, then coded and categorized iteratively, following the grounded theory. The review process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

A total of 943 publications were included in the final analysis. Moreover, 32 publications were categorized into 2 categories; hence the total number of publications before duplicates are removed is 975. After analysis and categorization of the publications, UMLS was found to be used in the following emerging themes or areas (the number of publications and their respective percentages are given in parentheses): natural language processing (230/975, 23.6%), information retrieval (125/975, 12.8%), terminology study (90/975, 9.2%), ontology and modeling (80/975, 8.2%), medical subdomains (76/975, 7.8%), other language studies (53/975, 5.4%), artificial intelligence tools and applications (46/975, 4.7%), patient care (35/975, 3.6%), data mining and knowledge discovery (25/975, 2.6%), medical education (20/975, 2.1%), degree-related theses (13/975, 1.3%), digital library (5/975, 0.5%), and the UMLS itself (150/975, 15.4%), as well as the UMLS for other purposes (27/975, 2.8%).

CONCLUSIONS

The UMLS has been used successfully in patient care, medical education, digital libraries, and software development, as originally planned, as well as in degree-related theses, the building of artificial intelligence tools, data mining and knowledge discovery, foundational work in methodology, and middle layers that may lead to advanced products. Natural language processing, the UMLS itself, and information retrieval are the 3 most common themes that emerged among the included publications. The results, although largely related to academia, demonstrate that UMLS achieves its intended uses successfully, in addition to achieving uses broadly beyond its original intentions.

Hypermethylation of DRD5 Promoter Is a Biomarker Across 12 Cancer Types

Aberrant DNA methylation is thought to be an early event in cancer development. Thus, identification of DNA methylation-based markers may provide valuable evidence in clinical decision-making. In this study, a DNA methylation dataset from 514 normal-tumor pairs is used to explore possible shared differentially methylated regions (DMRs) across 12 cancer types. Results showed that DMR in Dopamine receptor D5 (DRD5) promoter may be serviced as a good candidate biomarker across different cancer types. We further validated the extended DMR (292bp) in DRD5 promoter using SEQUENOM MassARRAY platform. Detection of DRD5 promoter dynamic methylation will allow rapid risk assessment at diagnosis, for suspicious tumor with the tissue biopsies.

Multi-omics Approach Reveals Distinct Differences in Left- and Right-Sided Colon Cancer

Increasing evidence suggests that left-sided colon cancer (LCC) and right-sided colon cancer (RCC) are emerging as two different colorectal cancer types with distinct clinical characteristics. However, the discrepancy in the underlying molecular event between these types of cancer has not been thoroughly elucidated to date and warrants comprehensive investigation. To this end, an integrated dataset from The Cancer Genome Atlas was used to compare and contrast LCC and RCC, covering mutation, DNA methylation, gene expression, and miRNA. Briefly, the signaling pathway cross-talk is more prevalent in RCC than LCC, such as RCC-specific PI3K pathway, which often exhibits cross-talk with the RAS and P53 pathways. Meanwhile, methylation signatures revealed that RCC was hypermethylated relative to LCC. In addition, differentially expressed genes (n = 253) and differentially expressed miRNAs (n = 16) were determined between LCC and RCC. Especially for Prostate Cancer Susceptibility Candidate 1 (PRAC1), a gene that was closely associated with hypermethylation, was the top significantly downregulated gene in RCC. Multi-omics comparison of LCC and RCC suggests that there are more aggressive markers in RCC and that tumor heterogeneity occurs within the location-based subtypes of colon cancer. These results clarify the debate regarding the conflicting prognosis between LCC and RCC, as proposed by different studies.Implications: The underlying molecular features present in LCC and RCC identified in this study are beneficial for adopting reasonable therapeutic approaches to prolong overall survival and progression-free survival in colorectal cancer patients. Mol Cancer Res; 16(3); 476-85. ©2017 AACR.