Semantic-Enhanced Query Expansion System for Retrieving Medical Image Notes

Most current image retrieval methods require constructing semantic metadata for representing image content. To manually create semantic metadata for medical images is time-consuming, yet it is a crucial component for query expansion. We proposed a new method for searching medical image notes that uses semantic metadata to improve query expansion and leverages a knowledge model developed specifically for the medical image domain to create relevant metadata. We used a syntactic parser and the Unified Medical Language System to analyze the corpus and store text information as semantic metadata in a knowledge model. Our new method has an interactive interface that allows users to provide relevance feedback and construct new queries more efficiently. Sixteen medical professionals evaluated the query expansion module, and each evaluator had prior experience searching for medical images. When using the initial query as the baseline standard, expanded queries achieved a performance boost of 22.6% in terms of the relevance score on first ten results (P-value<0.05). When using Google as another baseline, our system performed 24.6% better in terms of relevance score on the first ten results (P-value<0.05). Overall, 75% of the evaluators said the semantic-enhanced query expansion workflow is logical, easy to follow, and comfortable to use. In addition, 62% of the evaluators preferred using our system instead of Google. Evaluators who were positive about our system found the knowledge map-based visualization of candidate medical search terms helpful in refining cases from the initial search results.     

A Day in the Life of PubMed: Analysis of a Typical Day’s Query Log

Objective

To characterize PubMed usage over a typical day and compare it to previous studies of user behavior on Web search engines.

Design

We performed a lexical and semantic analysis of 2,689,166 queries issued on PubMed over 24 consecutive hours on a typical day.

Measurements

We measured the number of queries, number of distinct users, queries per user, terms per query, common terms, Boolean operator use, common phrases, result set size, MeSH categories, used semantic measurements to group queries into sessions, and studied the addition and removal of terms from consecutive queries to gauge search strategies.

Results

The size of the result sets from a sample of queries showed a bimodal distribution, with peaks at approximately 3 and 100 results, suggesting that a large group of queries was tightly focused and another was broad. Like Web search engine sessions, most PubMed sessions consisted of a single query. However, PubMed queries contained more terms.

Conclusion

PubMed’s usage profile should be considered when educating users, building user interfaces, and developing future biomedical information retrieval systems.     

Automated medical literature retrieval

Background

The constantly growing publication rate of medical research articles puts increasing pressure on medical specialists who need to be aware of the recent developments in their field. The currently used literature retrieval systems allow researchers to find specific papers; however the search task is still repetitive and time-consuming.

Aim

In this paper we describe a system that retrieves medical publications by automatically generating queries based on data from an electronic patient record. This allows the doctor to focus on medical issues and provide an improved service to the patient, with higher confidence that it is underpinned by current research.

Method

Our research prototype automatically generates query terms based on the patient record and adds weight factors for each term. Currently the patient’s age is taken into account with a fuzzy logic derived weight, and terms describing blood-related anomalies are derived from recent blood test results. Conditionally selected homonyms are used for query expansion.The query retrieves matching records from a local index of PubMed publications and displays results in descending relevance for the given patient. Recent publications are clearly highlighted for instant recognition by the researcher.

Results

Nine medical specialists from the Royal Adelaide Hospital evaluated the system and submitted pre-trial and post-trial questionnaires. Throughout the study we received positive feedback as doctors felt the support provided by the prototype was useful, and which they would like to use in their daily routine.

Conclusion

By supporting the time-consuming task of query formulation and iterative modification as well as by presenting the search results in order of relevance for the specific patient, literature retrieval becomes part of the daily workflow of busy professionals.     

KnowledgeLink: Impact of Context-Sensitive Information Retrieval on Clinicians' Information Needs

ObjectiveInfobuttons are message-based content search and retrieval functions embedded within other applications that dynamically return information relevant to the clinical task at hand. The objective of this study was to determine whether infobuttons effectively answer providers' questions about medications or affect patient care decisions.DesignThe authors implemented and evaluated a medication infobutton application called KnowledgeLink. Health care providers at 18 outpatient clinics were randomized to one of two versions of KnowledgeLink, one that linked to information from Micromedex (Thomson Micromedex, Greenwood Village, Co) and the other to material from SkolarMD (Wolters Kluwer Health, Palo Alto, CA).MeasurementsData were collected about the frequency of use and demographics of users, patients, and drugs that were queried. Users were periodically surveyed with short questionnaires and then with a more extensive survey at the end of one year.ResultsDuring the first year, KnowledgeLink was used 7,972 times by 359 users to look up information about 1,723 medications for 4,961 patients. Clinicians used KnowledgeLink twice a month on average, and during an average of 1.2% of patient encounters. KnowledgeLink was used by a wide variety of medical staff, not just physicians and nurse practitioners. The frequency of usage and the questions asked varied with user role (primary care physician, specialist physician, nurse practitioner). Although the median KnowledgeLink session was brief (21 seconds), KnowledgeLink answered users' queries 84% of the time, and altered patient care decisions 15% of the time. Users rated KnowledgeLink favorably on multiple scales, recommended extending KnowledgeLink to other content domains, and suggested enhancing the interface to allow refinement of the query and selection of the target resource.ConclusionAn infobutton can satisfy information needs about medications. Although used infrequently and for brief sessions, KnowledgeLink was positively received, answered most users' questions, and had a significant impact on medical decision making. The next steps would be to broaden the domains that KnowledgeLink covers to more specifically tailor results to the user type, to provide options when queries are not immediately answered, and to implement KnowledgeLink within other electronic clinical applications.     

A Document Clustering and Ranking System for Exploring MEDLINE Citations

ObjectiveA major problem faced in biomedical informatics involves how best to present information retrieval results. When a single query retrieves many results, simply showing them as a long list often provides poor overview. With a goal of presenting users with reduced sets of relevant citations, this study developed an approach that retrieved and organized MEDLINE citations into different topical groups and prioritized important citations in each group.DesignA text mining system framework for automatic document clustering and ranking organized MEDLINE citations following simple PubMed queries. The system grouped the retrieved citations, ranked the citations in each cluster, and generated a set of keywords and MeSH terms to describe the common theme of each cluster.MeasurementsSeveral possible ranking functions were compared, including citation count per year (CCPY), citation count (CC), and journal impact factor (JIF). We evaluated this framework by identifying as “important” those articles selected by the Surgical Oncology Society.ResultsOur results showed that CCPY outperforms CC and JIF, i.e., CCPY better ranked important articles than did the others. Furthermore, our text clustering and knowledge extraction strategy grouped the retrieval results into informative clusters as revealed by the keywords and MeSH terms extracted from the documents in each cluster.ConclusionsThe text mining system studied effectively integrated text clustering, text summarization, and text ranking and organized MEDLINE retrieval results into different topical groups.     

基于中医药领域本体的信息检索模型研究

针对传统基于关键词匹配的中医药信息检索存在查全率和查准率低下的缺陷,将本体与潜在语义索引相结合,提出一种基于中医药领域本体的语义信息检索模型.该模型基于本体概念扩展树构建相应的查询扩展方法和语义向量空间模型,将用户查询和文档集映射到同一潜在语义空间,通过计算查询向量与文档之间的相似度返回检索结果.着重阐述了该模型的体系结构、实现过程和关键技术,并对其实用性进行论证.     

Refining Boolean queries to identify relevant studies for systematic review updates

ObjectiveSystematic reviews are important in health care but are expensive to produce and maintain. The authors explore the use of automated transformations of Boolean queries to improve the identification of relevant studies for updates to systematic reviews.Materials and MethodsA set of query transformations, including operator substitution, query expansion, and query reduction, were used to iteratively modify the Boolean query used for the original systematic review. The most effective transformation at each stage is identified using information about the studies included and excluded from the original review. A dataset consisting of 22 systematic reviews was used for evaluation. Updated queries were evaluated using the included and excluded studies from the updated version of the review. Recall and precision were used as evaluation measures.ResultsThe updated queries were more effective than the ones used for the original review, in terms of both precision and recall. The overall number of documents retrieved was reduced by more than half, while the number of relevant documents found increased by 10.3%.ConclusionsIdentification of relevant studies for updates to systematic reviews can be carried out more effectively by using information about the included and excluded studies from the original review to produce improved Boolean queries. These updated queries reduce the overall number of documents retrieved while also increasing the number of relevant documents identified, thereby representing a considerable reduction in effort required by systematic reviewers.     

semCDI: A Query Formulation for Semantic Data Integration in caBIG

Objectives

To develop mechanisms to formulate queries over the semantic representation of cancer-related data services available through the cancer Biomedical Informatics Grid (caBIG).

Design

The semCDI query formulation uses a view of caBIG semantic concepts, metadata, and data as an ontology, and defines a methodology to specify queries using the SPARQL query language, extended with Horn rules. semCDI enables the joining of data that represent different concepts through associations modeled as object properties, and the merging of data representing the same concept in different sources through Common Data Elements (CDE) modeled as datatype properties, using Horn rules to specify additional semantics indicating conditions for merging data.

Validation

In order to validate this formulation, a prototype has been constructed, and two queries have been executed against currently available caBIG data services.

Discussion

The semCDI query formulation uses the rich semantic metadata available in caBIG to build queries and integrate data from multiple sources. Its promise will be further enhanced as more data services are registered in caBIG, and as more linkages can be achieved between the knowledge contained within caBIG''s NCI Thesaurus and the data contained in the Data Services.

Conclusion

semCDI provides a formulation for the creation of queries on the semantic representation of caBIG. This constitutes the foundation to build a semantic data integration system for more efficient and effective querying and exploratory searching of cancer-related data.     

A Support System for Content-Based Medical Image Retrieval in Object Oriented Databases

This work describes both the concepts used in an Object Manager for storage of medical images as one more data type associated to objects, and a support system developed to offer this kind of tool to medical application developers. The purpose of this work is to support the retrieval of images through queries based on the graphical contents of the stored images. The usual approach uses icons and textual attributes stored with the images to specify the queries. This work uses a novel modeling technique to define the “image data type,” by means of which it is possible to decide, before the query itself, the key data of each image that must be extracted from the image when it is stored in the database, so the search can be accelerated when queries are issued. This approach enables building of expansible systems, where new image processing algorithms can be added easily, using its syntactic representation stored through an Image Meta-schema into the application database schema. This work shows how such a system has been implemented, and also provides a query language used to refer and execute these algorithms from inside the database management system.     

A frequency-based technique to improve the spelling suggestion rank in medical queries

ObjectiveThere is an abundance of health-related information online, and millions of consumers search for such information. Spell checking is of crucial importance in returning pertinent results, so the authors propose a technique for increasing the effectiveness of spell-checking tools used for health-related information retrieval.DesignA sample of incorrectly spelled medical terms was submitted to two different spell-checking tools, and the resulting suggestions, derived under two different dictionary configurations, were re-sorted according to how frequently each term appeared in log data from a medical search engine.MeasurementsUnivariable analysis was carried out to assess the effect of each factor (spell-checking tool, dictionary type, re-sort, or no re-sort) on the probability of success. The factors that were statistically significant in the univariable analysis were then used in multivariable analysis to evaluate the independent effect of each of the factors.ResultsThe re-sorted suggestions proved to be significantly more accurate than the original list returned by the spell-checking tool. The odds of finding the correct suggestion in the number one rank were increased by 63% after re-sorting using the authors' method. This effect was independent of both the dictionary and the spell-checking tools that were used.ConclusionUsing knowledge about the frequency of a given word's occurrence in the medical domain can significantly improve spelling correction for medical queries.     

Bat-Inspired Algorithm Based Query Expansion for Medical Web Information Retrieval

With the increasing amount of medical data available on the Web, looking for health information has become one of the most widely searched topics on the Internet. Patients and people of several backgrounds are now using Web search engines to acquire medical information, including information about a specific disease, medical treatment or professional advice. Nonetheless, due to a lack of medical knowledge, many laypeople have difficulties in forming appropriate queries to articulate their inquiries, which deem their search queries to be imprecise due the use of unclear keywords. The use of these ambiguous and vague queries to describe the patients’ needs has resulted in a failure of Web search engines to retrieve accurate and relevant information. One of the most natural and promising method to overcome this drawback is Query Expansion. In this paper, an original approach based on Bat Algorithm is proposed to improve the retrieval effectiveness of query expansion in medical field. In contrast to the existing literature, the proposed approach uses Bat Algorithm to find the best expanded query among a set of expanded query candidates, while maintaining low computational complexity. Moreover, this new approach allows the determination of the length of the expanded query empirically. Numerical results on MEDLINE, the on-line medical information database, show that the proposed approach is more effective and efficient compared to the baseline.     

面向中药新药研发的语义搜索系统

为解决中药新药研发中的信息集成和检索问题,设计并实现了语义搜索系统TCMSearch。为实现分布式、异构数据库的语义集成和一致性访问,提出语义视图,来定义关系型数据库与领域本体之间的模式映射。该系统根据关系型数据库的语义视图,将用户提出的语义查询重写为结构查询语言（SQL）查询,再分派给各个关系型数据库,最终将查询结果进行语义封装。它还基于本体构建文本内容的语义索引,从而实现了基于概念的内容检索。这些本体驱动的方法,使该系统与关键词搜索系统相比,具有更高的查准率与查全率。该系统已成功部署,它基于一个大型中药领域本体,通过Web方式为中药领域专家提供智能搜索服务。     

A Comparative Evaluation of Full-text,Concept-based,and Context-sensitive Search

Objectives

Study comparatively (1) concept-based search, using documents pre-indexed by a conceptual hierarchy; (2) context-sensitive search, using structured, labeled documents; and (3) traditional full-text search. Hypotheses were: (1) more contexts lead to better retrieval accuracy; and (2) adding concept-based search to the other searches would improve upon their baseline performances.

Design

Use our Vaidurya architecture, for search and retrieval evaluation, of structured documents classified by a conceptual hierarchy, on a clinical guidelines test collection.

Measurements

Precision computed at different levels of recall to assess the contribution of the retrieval methods. Comparisons of precisions done with recall set at 0.5, using t-tests.

Results

Performance increased monotonically with the number of query context elements. Adding context-sensitive elements, mean improvement was 11.1% at recall 0.5. With three contexts, mean query precision was 42% ± 17% (95% confidence interval [CI], 31% to 53%); with two contexts, 32% ± 13% (95% CI, 27% to 38%); and one context, 20% ± 9% (95% CI, 15% to 24%). Adding context-based queries to full-text queries monotonically improved precision beyond the 0.4 level of recall. Mean improvement was 4.5% at recall 0.5. Adding concept-based search to full-text search improved precision to 19.4% at recall 0.5.

Conclusions

The study demonstrated usefulness of concept-based and context-sensitive queries for enhancing the precision of retrieval from a digital library of semi-structured clinical guideline documents. Concept-based searches outperformed free-text queries, especially when baseline precision was low. In general, the more ontological elements used in the query, the greater the resulting precision.     

Query Health: standards-based,cross-platform population health surveillance

Objective

Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects.

Materials and methods

Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language.

Results

We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed.

Discussions

This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC''s Data Access Framework initiative.

Conclusions

Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites.     

Advancing Biomedical Image Retrieval: Development and Analysis of a Test Collection

ObjectiveDevelop and analyze results from an image retrieval test collection.MethodsAfter participating research groups obtained and assessed results from their systems in the image retrieval task of Cross-Language Evaluation Forum, we assessed the results for common themes and trends. In addition to overall performance, results were analyzed on the basis of topic categories (those most amenable to visual, textual, or mixed approaches) and run categories (those employing queries entered by automated or manual means as well as those using visual, textual, or mixed indexing and retrieval methods). We also assessed results on the different topics and compared the impact of duplicate relevance judgments.ResultsA total of 13 research groups participated. Analysis was limited to the best run submitted by each group in each run category. The best results were obtained by systems that combined visual and textual methods. There was substantial variation in performance across topics. Systems employing textual methods were more resilient to visually oriented topics than those using visual methods were to textually oriented topics. The primary performance measure of mean average precision (MAP) was not necessarily associated with other measures, including those possibly more pertinent to real users, such as precision at 10 or 30 images.ConclusionsWe developed a test collection amenable to assessing visual and textual methods for image retrieval. Future work must focus on how varying topic and run types affect retrieval performance. Users’ studies also are necessary to determine the best measures for evaluating the efficacy of image retrieval systems.     

Essie: A Concept-based Search Engine for Structured Biomedical Text

This article describes the algorithms implemented in the Essie search engine that is currently serving several Web sites at the National Library of Medicine. Essie is a phrase-based search engine with term and concept query expansion and probabilistic relevancy ranking. Essie’s design is motivated by an observation that query terms are often conceptually related to terms in a document, without actually occurring in the document text. Essie’s performance was evaluated using data and standard evaluation methods from the 2003 and 2006 Text REtrieval Conference (TREC) Genomics track. Essie was the best-performing search engine in the 2003 TREC Genomics track and achieved results comparable to those of the highest-ranking systems on the 2006 TREC Genomics track task. Essie shows that a judicious combination of exploiting document structure, phrase searching, and concept based query expansion is a useful approach for information retrieval in the biomedical domain.A rapidly increasing amount of biomedical information in electronic form is readily available to researchers, health care providers, and consumers. However, readily available does not mean conveniently accessible. The large volume of literature makes finding specific information ever more difficult. Development of effective search strategies is time consuming, ¹ requires experienced and educated searchers, ² well versed in biomedical terminology, ³ and is beyond the capability of most consumers. ⁴ Essie, a search engine developed and used at the National Library of Medicine, incorporates a number of strategies aimed at alleviating the need for sophisticated user queries. These strategies include a fine-grained tokenization algorithm that preserves punctuation, concept searching utilizing synonymy, and phrase searching based on the user’s query.This article describes related background work, the Essie search system, and the evaluation of that system. The Essie search system is described in detail, including its indexing strategy, query interpretation and expansion, and ranking of search results.     

神经内科临床实习生临床思维能力培养实践

目的 探讨提高神经内科临床医学实习生临床思维能力的方法。方法 选择2017年至2018年临床医学实习生为研究对象,2018年为试验组（98人）,2017年为对照组（95人）。试验组实施临床思维能力培养,对照组采用传统常规的方法培养。实习结束前,采用自制的《临床医学实习生临床思维能力调查问卷》进行调查,评价教学效果。使用SPSS 14.0进行t检验和卡方检验。结果 试验组与对照组均存在临床思维的片面性、表像性、定势性、被动性、懒惰性、简化性、混乱性等问题,试验组存在的临床思维方面问题较对照组明显下降（P<0.001）,其中试验组的临床思维的片面性、表像性、定势性、被动性、懒惰性方面与对照组比较差异有统计学意义（P<0.05）。除语言沟通与表达能力外,试验组的其他临床思维能力自评得分与对照组比较差异都有统计学意义（P<0.05）。试验组的出科考核成绩与对照组比较差异也有统计学意义（P<0.05）。结论 临床医学生在神经内科实习阶段实施临床思维训练,有利于培养学生的临床思维能力,提高人才培养质量。     

The use of relational database commands in retrieval of pedigree information

Genetic research frequently requires retrieval of information about families afflicted with some genetic disease. These queries may involve simple retrieval of information on individuals with particular attributes or finding data from sibships or families who meet some set of criteria. Although the former case may be handled by almost any file management system, the latter case cannot be easily managed since family relationships are necessary to the query. These family relationships are normally stored by means of a pointer system that links the record of each individual with those of his parents. Given such a pointer system, the standard commands of a relational database system can be used to perform such retrievals, thus diminishing the need for special programs to perform such queries.