scholarly journals Interpretable Visualization of Scientific Hypotheses in Literature-based Discovery

2021 ◽  
Author(s):  
Ilya Tyagin ◽  
Ilya Safro
Keyword(s):  
Topic Modeling ◽  
Hypothesis Generation ◽  
Decision Making Process ◽  
Semantic Concept ◽  
Domain Experts ◽  
Unified Medical Language System ◽  
Modeling Methods ◽  
Medical Language ◽  
Literature Based Discovery ◽  
Scientific Hypotheses

In this paper we present an approach for interpretable visualization of scientific hypotheses that is based on the idea of semantic concept interconnectivity, network-based and topic modeling methods. Our visualization approach has numerous adjustable parameters which provides the domain experts with additional flexibility in their decision making process. We also make use of the Unified Medical Language System metadata by integrating it directly into the resulting topics, and adding the variability into hypotheses resolution. To demonstrate the proposed approach in action, we deployed end-to-end hypothesis generation pipeline AGATHA, which was evaluated by BioCreative VII experts with COVID-19-related queries.

scholarly journals A transformation-based method for auditing the IS-A hierarchy of biomedical terminologies in the Unified Medical Language System

2020 ◽  
Vol 27 (10) ◽  
pp. 1568-1575 ◽  
Author(s):  
Fengbo Zheng ◽  
Jay Shi ◽  
Yuntao Yang ◽  
W Jim Zheng ◽  
Licong Cui
Keyword(s):  
Gene Ontology ◽  
Information Systems ◽  
Random Sample ◽  
English Language ◽  
Snomed Ct ◽  
Domain Experts ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Original Concept

Abstract Objective The Unified Medical Language System (UMLS) integrates various source terminologies to support interoperability between biomedical information systems. In this article, we introduce a novel transformation-based auditing method that leverages the UMLS knowledge to systematically identify missing hierarchical IS-A relations in the source terminologies. Materials and Methods Given a concept name in the UMLS, we first identify its base and secondary noun chunks. For each identified noun chunk, we generate replacement candidates that are more general than the noun chunk. Then, we replace the noun chunks with their replacement candidates to generate new potential concept names that may serve as supertypes of the original concept. If a newly generated name is an existing concept name in the same source terminology with the original concept, then a potentially missing IS-A relation between the original and the new concept is identified. Results Applying our transformation-based method to English-language concept names in the UMLS (2019AB release), a total of 39 359 potentially missing IS-A relations were detected in 13 source terminologies. Domain experts evaluated a random sample of 200 potentially missing IS-A relations identified in the SNOMED CT (U.S. edition) and 100 in Gene Ontology. A total of 173 of 200 and 63 of 100 potentially missing IS-A relations were confirmed by domain experts, indicating that our method achieved a precision of 86.5% and 63% for the SNOMED CT and Gene Ontology, respectively. Conclusions Our results showed that our transformation-based method is effective in identifying missing IS-A relations in the UMLS source terminologies.

scholarly journals Robust diagnostic classification via Q-learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Victor Ardulov ◽  
Victor R. Martinez ◽  
Krishna Somandepalli ◽  
Shuting Zheng ◽  
Emma Salzman ◽  
...  
Keyword(s):  
Autism Spectrum ◽  
Decision Function ◽  
Diagnostic Classification ◽  
Decision Making Process ◽  
Domain Experts ◽  
Q Learning ◽  
School Aged Children ◽  
Hyperactivity Disorder ◽  
Clinical Diagnoses ◽  
Learning Frameworks

AbstractMachine learning (ML) models have demonstrated the power of utilizing clinical instruments to provide tools for domain experts in gaining additional insights toward complex clinical diagnoses. In this context these tools desire two additional properties: interpretability, being able to audit and understand the decision function, and robustness, being able to assign the correct label in spite of missing or noisy inputs. This work formulates diagnostic classification as a decision-making process and utilizes Q-learning to build classifiers that meet the aforementioned desired criteria. As an exemplary task, we simulate the process of differentiating Autism Spectrum Disorder from Attention Deficit-Hyperactivity Disorder in verbal school aged children. This application highlights how reinforcement learning frameworks can be utilized to train more robust classifiers by jointly learning to maximize diagnostic accuracy while minimizing the amount of information required.

Leveraging the UMLS As a Data Standard for Rare Disease Data Normalization and Harmonization

2020 ◽  
Author(s):  
Qian Zhu ◽  
Dac-Trung Nguyen ◽  
Eric Sid ◽  
Anne Pariser
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Mendelian Inheritance ◽  
Data Normalization ◽  
Community Settings ◽  
Unified Medical Language System ◽  
Data Standard ◽  
Medical Language ◽  
Disease Concepts ◽  
High Level

Abstract Objective In this study, we aimed to evaluate the capability of the Unified Medical Language System (UMLS) as one data standard to support data normalization and harmonization of datasets that have been developed for rare diseases. Through analysis of data mappings between multiple rare disease resources and the UMLS, we propose suggested extensions of the UMLS that will enable its adoption as a global standard in rare disease. Methods We analyzed data mappings between the UMLS and existing datasets on over 7,000 rare diseases that were retrieved from four publicly accessible resources: Genetic And Rare Diseases Information Center (GARD), Orphanet, Online Mendelian Inheritance in Men (OMIM), and the Monarch Disease Ontology (MONDO). Two types of disease mappings were assessed, (1) curated mappings extracted from those four resources; and (2) established mappings generated by querying the rare disease-based integrative knowledge graph developed in the previous study. Results We found that 100% of OMIM concepts, and over 50% of concepts from GARD, MONDO, and Orphanet were normalized by the UMLS and accurately categorized into the appropriate UMLS semantic groups. We analyzed 58,636 UMLS mappings, which resulted in 3,876 UMLS concepts across these resources. Manual evaluation of a random set of 500 UMLS mappings demonstrated a high level of accuracy (99%) of developing those mappings, which consisted of 414 mappings of synonyms (82.8%), 76 are subtypes (15.2%), and five are siblings (1%). Conclusion The mapping results illustrated in this study that the UMLS was able to accurately represent rare disease concepts, and their associated information, such as genes and phenotypes, and can effectively be used to support data harmonization across existing resources developed on collecting rare disease data. We recommend the adoption of the UMLS as a data standard for rare disease to enable the existing rare disease datasets to support future applications in a clinical and community settings.

IAIMS and UMLS at Columbia-Presbyterian Medical Center

1991 ◽  
Vol 11 (4_suppl) ◽  
pp. S89-S93 ◽  
Author(s):  
James J. Cimino ◽  
Soumitra Sengupta
Keyword(s):  
Information Management ◽  
Management System ◽  
Medical Center ◽  
Information Management System ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Academic Information

The authors use an example to illustrate combining Integrated Academic Information Management System (IAIMS) components (applications) into an integral whole, to facilitate using the components simultaneously or in sequence. They examine a model for classifying IAIMS systems, proposing ways in which the Unified Medical Language System (UMLS) can be exploited in them.

Sign in / Sign up

Export Citation Format

Share Document