Identifying Sleep-Related Factors Associated with Cognitive Function in a Hispanics/Latinos Cohort: A Dual Random Forest Approach.

Disordered sleep is associated with poor cognitive function and cognitive decline. However, little is known regarding the association of sleep-related factors with cognitive function in underrepresented cohorts such as the Hispanic/Latino population. Leveraging the National Sleep Research Resource, one of the most comprehensive collections of sleep studies, we identified a Hispanic/Latino cohort of 1,031 lower cognitive function cases and 2,062 normal controls. We developed a novel dual random forest (DRF) approach to discriminate cases against controls for estimating the potential impact of sleep-related variables related to the decline of cognitive function. Several important sleep-related factors were identified which may be associated with cognitive function in the Hispanics/Latinos cohort, such as heart rate, sleep duration, trouble falling asleep, and apnea/hypopnea index, which are consistent with existing research findings. Our DRF approach is effective in validating the association between disordered sleep and cognitive decline in this unique minority population.

Quality Assurance of NCI Thesaurus by Mining Structural-Lexical Patterns.

Quality assurance of biomedical terminologies such as the National Cancer Institute (NCI) Thesaurus is an essential part of the terminology management lifecycle. We investigate a structural-lexical approach based on non-lattice subgraphs to automatically identify missing hierarchical relations and missing concepts in the NCI Thesaurus. We mine six structural-lexical patterns exhibiting in non-lattice subgraphs: containment, union, intersection, union-intersection, inference-contradiction, and inference union. Each pattern indicates a potential specific type of error and suggests a potential type of remediation. We found 809 non-lattice subgraphs with these patterns in the NCI Thesaurus (version 16.12d). Domain experts evaluated a random sample of 50 small non-lattice subgraphs, of which 33 were confirmed to contain errors and make correct suggestions (33/50 = 66%). Of the 25 evaluated subgraphs revealing multiple patterns, 22 were verified correct (22/25 = 88%). This shows the effectiveness of our structurallexical-pattern-based approach in detecting errors and suggesting remediations in the NCI Thesaurus.

Can SNOMED CT Changes Be Used as a Surrogate Standard for Evaluating the Performance of Its Auditing Methods?

We introduce RGT, Retrospective Ground-Truthing, as a surrogate reference standard for evaluating the performance of automated Ontology Quality Assurance (OQA) methods. The key idea of RGT is to use cumulative SNOMED CT changes derived from its regular longitudinal distributions by the official SNOMED CT editorial board as a partial, surrogate reference standard. The contributions of this paper are twofold: (1) to construct an RGT reference set for SNOMED CT relational changes; and (2) to perform a comparative evaluation of the performances of lattice, non-lattice, and randomized relational error detection methods using the standard precision, recall, and geometric measures. An RGT relational-change reference set of 32,241 IS-A changes were constructed from 5 U.S. editions of SNOMED CT from September 2014 to September 2016, with reversals and changes due to deletion or addition of new concepts excluded. 68,849 independent non-lattice fragments, 118,587 independent lattice fragments, and 446,603 relations were extracted from the SNOMED CT March 2014 distribution. Comparative performance analysis of smaller (less than 15) lattice vs. non-lattice fragments was also given to approach the more realistic setting in which such methods may be applied. Among the 32,241 IS-A changes, independent non-lattice fragments covered 52.8% changes with 26.4% precision with a G-score of 0.373. Even though this G-score is significantly lower in comparison to those in information retrieval, it breaks new ground in that such evaluations have never performed before in the highly discovery-oriented setting of OQA.

Spark-MCA: Large-scale, Exhaustive Formal Concept Analysis for Evaluating the Semantic Completeness of SNOMED CT.

The completeness of a medical terminology system consists of two parts: complete content coverage and complete semantics. In this paper, we focus on semantic completeness and present a scalable approach, called Spark-MCA, for evaluating the semantic completeness of SNOMED CT. We formulate the SNOMED CT contents into an FCA-based formal context, in which SNOMED CT concepts are used for extents, while their attributes are used as intents. We applied Spark-MCA to the 201403 US edition of SNOMED CT to exhaustively compute all the formal concepts and sub concept relationships in about 2 hours with 96 processors using an Amazon Web Service cluster. We found a total of 799,868 formal concepts, within which 500,583 are not contained in the 201403 release. We compared these concepts with the cumulative addition of 22,687 concepts from the 5 "delta" files from the 201403 release to the 201609 release. 3,231 matches were found between those suggested by FCA and those from cumulative concept addition by the SNOMED CT Editorial Panel. This result provides encouraging evidence that our approach could be useful for enhancing the semantic completeness of SNOMED CT.