Improving search over Electronic Health Records using UMLS-based query expansion through random walks

ObjectiveMost of the information in Electronic Health Records (EHRs) is represented in free textual form. Practitioners searching EHRs need to phrase their queries carefully, as the record might use synonyms or other related words. In this paper we show that an automatic query expansion method based on the Unified Medicine Language System (UMLS) Metathesaurus improves the results of a robust baseline when searching EHRs.Materials and methodsThe method uses a graph representation of the lexical units, concepts and relations in the UMLS Metathesaurus. It is based on random walks over the graph, which start on the query terms. Random walks are a well-studied discipline in both Web and Knowledge Base datasets.ResultsOur experiments over the TREC Medical Record track show improvements in both the 2011 and 2012 datasets over a strong baseline.DiscussionOur analysis shows that the success of our method is due to the automatic expansion of the query with extra terms, even when they are not directly related in the UMLS Metathesaurus. The terms added in the expansion go beyond simple synonyms, and also add other kinds of topically related terms.ConclusionsExpansion of queries using related terms in the UMLS Metathesaurus beyond synonymy is an effective way to overcome the gap between query and document vocabularies when searching for patient cohorts

Towards a Conceptual Framework for Persistent Use: A Technical Plan to Achieve Semantic Interoperability within Electronic Health Record Systems

Semantic interoperability within the health care sector requires that patient data be fully available and shared without ambiguity across participating health facilities. Ongoing discussions to achieve interoperability within the health care industry continue to emphasize the need for healthcare facilities to successfully adopt and implement Electronic Health Record (EHR) systems. Reluctance by the healthcare industry to implement these EHRs for the purpose of achieving interoperability has led to the proposed research problem where it was determined that there is no existing single data standardization structure that can effectively share and interpret patient data within heterogeneous systems. \ \ The proposed research proposes a master data standardization and translation (MDST) model – XDataRDF -- which incorporates the use of the Resource Description Framework (RDF) that will allow for the seamless exchange of healthcare data among multiple facilities. Using RDF will allow multiple data models and vocabularies to be easily combined and interrelated within a single environment thereby reducing data definition ambiguity.

Improving patient record search: A meta-data based approach

The International Classification of Diseases (ICD) is a type of meta-data found in many Electronic Patient Records. Research to explore the utility of these codes in medical Information Retrieval (IR) applications is new, and many areas of investigation remain, including the question of how reliable the assignment of the codes has been. This paper proposes two uses of the ICD codes in two different contexts of search: Pseudo-Relevance Judgments (PRJ) and Pseudo-Relevance Feedback (PRF). We find that our approach to evaluate the TREC challenge runs using simulated relevance judgments has a positive correlation with the TREC official results, and our proposed technique for performing PRF based on the ICD codes significantly outperforms a traditional PRF approach. The results are found to be consistent over the two years of queries from the TREC medical test collection

A Medical Literature Search System for Identifying Effective Treatments in Precision Medicine

The Precision Medicine Initiative states that treatments for a patient should take into account not only the patient’s disease, but his/her specific genetic variation as well. The vast biomedical literature holds the potential for physicians to identify effective treatment options for a cancer patient. However, the complexity and ambiguity of medical terms can result in vocabulary mismatch between the physician’s query and the literature. The physician’s search intent (finding treatments instead of other types of studies) is difficult to explicitly formulate in a query. Therefore, simple ad hoc retrieval approach will suffer from low recall and precision.In this paper, we propose a new retrieval system that helps physicians identify effective treatments in precision medicine. Given a cancer patient with a specific disease, genetic variation, and demographic information, the system aims to identify biomedical publications that report effective treatments. We approach this goal from two directions. First, we expand the original disease and gene terms using biomedical knowledge bases to improve recall of the initial retrieval. We then improve precision by promoting treatment-related publications to the top using a machine learning reranker trained on 2017 Text Retrieval Conference Precision Medicine (PM) track corpus. Batch evaluation results on 2018 PM track corpus show that the proposed approach effectively improves both recall and precision, achieving performance comparable to the top entries on the leaderboard of 2018 PM track.Master of Science in Information Scienc

Doctor of Philosophy

dissertationElectronic Health Records (EHRs) provide a wealth of information for secondary uses. Methods are developed to improve usefulness of free text query and text processing and demonstrate advantages to using these methods for clinical research, specifically cohort identification and enhancement. Cohort identification is a critical early step in clinical research. Problems may arise when too few patients are identified, or the cohort consists of a nonrepresentative sample. Methods of improving query formation through query expansion are described. Inclusion of free text search in addition to structured data search is investigated to determine the incremental improvement of adding unstructured text search over structured data search alone. Query expansion using topic- and synonym-based expansion improved information retrieval performance. An ensemble method was not successful. The addition of free text search compared to structured data search alone demonstrated increased cohort size in all cases, with dramatic increases in some. Representation of patients in subpopulations that may have been underrepresented otherwise is also shown. We demonstrate clinical impact by showing that a serious clinical condition, scleroderma renal crisis, can be predicted by adding free text search. A novel information extraction algorithm is developed and evaluated (Regular Expression Discovery for Extraction, or REDEx) for cohort enrichment. The REDEx algorithm is demonstrated to accurately extract information from free text clinical iv narratives. Temporal expressions as well as bodyweight-related measures are extracted. Additional patients and additional measurement occurrences are identified using these extracted values that were not identifiable through structured data alone. The REDEx algorithm transfers the burden of machine learning training from annotators to domain experts. We developed automated query expansion methods that greatly improve performance of keyword-based information retrieval. We also developed NLP methods for unstructured data and demonstrate that cohort size can be greatly increased, a more complete population can be identified, and important clinical conditions can be detected that are often missed otherwise. We found a much more complete representation of patients can be obtained. We also developed a novel machine learning algorithm for information extraction, REDEx, that efficiently extracts clinical values from unstructured clinical text, adding additional information and observations over what is available in structured text alone