Xinxin Zhu’s research while affiliated with Yale-New Haven Hospital and other places

Objective: The study sought to describe the literature related to the development of methods for auditing the Unified Medical Language System (UMLS), with particular attention to identifying errors and inconsistencies of attributes of the concepts in the UMLS Metathesaurus. Materials and methods: We applied the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach by searching the MEDLINE database and Google Scholar for studies referencing the UMLS and any of several terms related to auditing, error detection, and quality assurance. A qualitative analysis and summarization of articles that met inclusion criteria were performed. Results: Eighty-three studies were reviewed in detail. We first categorized techniques based on various aspects including concepts, concept names, and synonymy (n = 37), semantic type assignments (n = 36), hierarchical relationships (n = 24), lateral relationships (n = 12), ontology enrichment (n = 8), and ontology alignment (n = 18). We also categorized the methods according to their level of automation (ie, automated systematic, automated heuristic, or manual) and the type of knowledge used (ie, intrinsic or extrinsic knowledge). Conclusions: This study is a comprehensive review of the published methods for auditing the various conceptual aspects of the UMLS. Categorizing the auditing techniques according to the various aspects will enable the curators of the UMLS as well as researchers comprehensive easy access to this wealth of knowledge (eg, for auditing lateral relationships in the UMLS). We also reviewed ontology enrichment and alignment techniques due to their critical use of and impact on the UMLS.

Although controlled biomedical terminologies have been with us for centuries, it is only in the last couple of decades that close attention has been paid to the quality of these terminologies. The result of this attention has been the development of auditing methods that apply formal methods to assessing whether terminologies are complete and accurate. We have performed an extensive literature review to identify published descriptions of these methods and have created a framework for characterizing them. The framework considers manual, systematic and heuristic methods that use knowledge (within or external to the terminology) to measure quality factors of different aspects of the terminology content (terms, semantic classification, and semantic relationships). The quality factors examined included concept orientation, consistency, non-redundancy, soundness and comprehensive coverage. We reviewed 130 studies that were retrieved based on keyword search on publications in PubMed, and present our assessment of how they fit into our framework. We also identify which terminologies have been audited with the methods and provide examples to illustrate each part of the framework.

Objective: To explore approaches for integrating and visualizing time-oriented medication data in narrative and structured formats and to address related issues on handling temporal abstraction, granularity, and uncertainty. The ultimate goal is to improve medication reconciliation by providing clinicians with more accurate medication information in patient care. Methods: An event taxonomy was generated to capture different combinations of clinical and temporal uncertainties. A prototype of a temporal visualization system was implemented using an open source software package called Timeline. Medications were parsed and mapped to the event taxonomy, and then represented in Timelines. Seventy-five medications from narrative discharge summary reports and seventy-nine medications from structured orders were used as data input for temporal visualization. Five physicians served as domain experts and answered ten proof-of-concept survey questions. Results: Overall positive feedback from experts suggested the potential value of the proposed timeline visualization method. Challenges were also identified, and future work will include reconciliation of medications from various sources based on temporal attributes and medication classification.

We present a method that extracts medication information from discharge summaries. The program relies on parsing rules written as a set of regular expressions and on a user-configurable drug lexicon. Our evaluation shows a precision of 94% and recall of 83% in the extraction of medication information. We use a broader definition of medication information than previous studies, including drug names appearing with and without dosage information, misspelled drug names, and contextual information.

To examine recent research work in the development and evaluation of controlled biomedical terminologies - especially, the representation of structured, controlled definitional knowledge about the terms themselves; such terminologies are often referred to as 'ontologies'. A review of the published literature using PubMed, as well as full-text searches of recent Medinfo and American Medical Informatics Association (AMIA) Symposia proceedings, searching for the terms 'ontology' and 'ontologies' and for articles discussing specific, prominent ontological work. We summaries the ontologic aspects of twelve current terminology projects: Galen, the Unified Medical Language System (UMLS), the Medical Entities Dictionary (MED), SNOMED-CT, LOINC, the Foundational Model of Anatomy (FMA), the Gene Ontology (GO), ISO Reference Terminology Model for Nursing Diagnosis, NDF-RT, RxNorm, the NCI Thesaurus, and DOLCE+. We discuss the origins, domain, and ontologic representation of each of these and attempt to summarize the impact that each has had on terminologic work and biomedical applications. We also note the contributions of the Protégé tool to many of these efforts. Terminologic research and development have advanced significantly in the past 20 years, especially since the recent orientation toward controlled biomedical ontologies. This work has had significant impact on the development of terminologies themselves, their acceptance and dissemination as standards, and their use in supporting biomedical information systems.

Information needs studies in clinical settings often face the problem of integrating and analyzing data collected using different study tools. Generally, a coding taxonomy or a model is developed to capture and code the study data. Significant efforts are required to develop a model that not only captures the study data but is also closer to the clinical domain to draw meaningful real world inferences. Further, a study-specific model limits comparative evaluation of studies across different institutions. In this paper, we propose a reference model for representing nursing information needs. We use an iterative and collaborative approach in representing the concepts in the model. The model consists of 33 information (need) concepts, 29 information sources and 7 categories of nursing tasks which were used to code 228 identified information need instances.

An escalating global interest in traditional Chinese acupuncture (TCA) demands easy use and reuse of the medical knowledge associated with TCA points, which in turn require appropriate knowledge representation of TCA. A proper knowledge representation generally involves enumeration of conceptual symbols and arrangement of these symbols into some meaningful structure. This study uses the unified medical language system (UMLS) as the source of conceptual symbols and proposes a terminology model as the structure to arrange terms. The purpose is to systematically capture and represent appropriate TCA knowledge in practice. The initial terminology model is refined in an iterative process. The resulting terminology model captures and represents key aspects of TCA knowledge through properly designed model attributes and relations.