Health Information Text Characteristics

Millions of people search online for medical text, but these texts are often too complicated to understand. Readability evaluations are mostly based on surface metrics such as character or words counts and sentence syntax, but content is ignored. We compared four types of documents, easy and difficult WebMD documents, patient blogs, and patient educational material, for surface and content-based metrics. The documents differed significantly in reading grade levels and vocabulary used. WebMD pages with high readability also used terminology that was more consumer-friendly. Moreover, difficult documents are harder to understand due to their grammar and word choice and because they discuss more difficult topics. This indicates that we can simplify many documents by focusing on word choice in addition to sentence structure, however, for difficult documents this may be insufficient

Dynamic Generation of a Table of Contents with Consumer-Friendly Labels

Consumers increasingly look to the Internet for health information, but available resources are too difficult for the majority to understand. Interactive tables of contents (TOC) can help consumers access health information by providing an easy to understand structure. Using natural language processing and the Unified Medical Language System (UMLS), we have automatically generated TOCs for consumer health information. The TOC are categorized according to consumer-friendly labels for the UMLS semantic types and semantic groups. Categorizing phrases by semantic types is significantly more correct and relevant. Greater correctness and relevance was achieved with documents that are difficult to read than with those at an easier reading level. Pruning TOCs to use categories that consumers favor further increases relevancy and correctness while reducing structural complexity

A Classifier to Evaluate Language Specificity in Medical Documents

Consumer health information written by health care professionals is often inaccessible to the consumers it is written for. Traditional readability formulas examine syntactic features like sentence length and number of syllables, ignoring the target audience\u27s grasp of the words themselves. The use of specialized vocabulary disrupts the understanding of patients with low reading skills, causing a decrease in comprehension. A naive Bayes classifier for three levels of increasing medical terminology specificity (consumer/patient, novice health learner, medical professional) was created with a lexicon generated from a representative medical corpus. Ninety-six percent accuracy in classification was attained. The classifier was then applied to existing consumer health web pages. We found that only 4% of pages were classified at a layperson level, regardless of the Flesch reading ease scores, while the remaining pages were at the level of medical professionals. This indicates that consumer health web pages are not using appropriate language for their target audience

An Automated Method to Enrich and Expand Consumer Health Vocabularies Using GloVe Word Embeddings

Clear language makes communication easier between any two parties. However, a layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical jargon, which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this dissertation, we present an automatic method to enrich existing concepts in a medical ontology with additional laymen terms and also to expand the number of concepts in the ontology that do not have associated laymen terms. Our work has the benefit of being applicable to vocabularies in any domain. Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. We improve these vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. By performing iterative feedback using GloVe’s candidate terms, we can boost the number of word occurrences in the co-occurrence matrix allowing our approach to work with a smaller training corpus. Our novel algorithms and GloVe were evaluated using two laymen datasets from the National Library of Medicine (NLM), the Open-Access and Collaborative Consumer Health Vocabulary (OAC CHV) and the MedlinePlus Healthcare Vocabulary. For our first goal, enriching concepts, the results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Our best algorithm enhanced the corpus with synonyms from WordNet, outperformed GloVe with an F-score relative improvement of 25%. For our second goal, expanding the number of concepts with related laymen’s terms, our synonym-enhanced GloVe outperformed GloVe with a relative F-score relative improvement of 63%. The results of the system were in general promising and can be applied not only to enrich and expand laymen vocabularies for medicine but any ontology for a domain, given an appropriate corpus for the domain. Our approach is applicable to narrow domains that may not have the huge training corpora typically used with word embedding approaches. In essence, by incorporating an external source of linguistic information, WordNet, and expanding the training corpus, we are getting more out of our training corpus. Our system can help building an application for patients where they can read their physician\u27s letters more understandably and clearly. Moreover, the output of this system can be used to improve the results of healthcare search engines, entity recognition systems, and many others

A Study for Building a System of Consumer Vocabulary for Health Information

Objectives: The purposes of this study were to identify the difference between consumer vocabulary and medical vocabulary in terms of health information; to understand the features of consumer vocabulary; and to contribute by building a system that is able to link consumer vocabulary with medical vocabulary. Methods: Data collection was conducted using articles in the knowledge corner of a portal web-site. A total of 43,304 health-related terms (total terms extracted) were collected as objects of this study and these terms were analyzed for their mapping rate and frequency of use (the repeated number of a term). Results: The rate of mapping between the consumer vocabulary for health information and the medical vocabulary was not high. However, the number of unmapped terms was decreased by linking terms having similar forms to preferred terms and by extending synonyms. Conclusion: Linking with preferred terms and extending synonyms are, thus, required to increase the mapping rate between consumer vocabulary for health information and medical vocabulary, and the terms that consumers use are essential to further be researched in order to understand their morphology and features; hence, increasing consumer accessibility to the medical field.OAIID:oai:osos.snu.ac.kr:snu2009-01/102/0000028528/2SEQ:2PERF_CD:SNU2009-01EVAL_ITEM_CD:102USER_ID:0000028528ADJUST_YN:NEMP_ID:A076124DEPT_CD:811CITE_RATE:0FILENAME:건강정보 소비자 용어 시스템 구축을 위한 기반연구.pdfDEPT_NM:간호학과EMAIL:[email protected]_YN:NCONFIRM:

The feasibility of using virtual prototyping technologies for product evaluation

With the continuous development in computer and communications technology the use of computer aided design in design processes is becoming more commonplace. A wide range of virtual prototyping technologies are currently in development, some of which are commercially viable for use within a product design process. These virtual prototyping technologies range from graphics tablets to haptic devices. With the compression of design cycles the feasibility of using these technologies for product evaluation is becoming an ever more important consideration. This thesis begins by presenting the findings of a comprehensive literature review defining product design with a focus on product evaluation and a discussion of current virtual prototyping technologies. From the literature review it was clear that user involvement in the product evaluation process is critical. The literature review was followed by a series of interconnected studies starting with an investigation into design consultancies' access and use of prototyping technologies and their evaluation methods. Although design consultancies are already using photo-realistic renderings, animations and sometimes 3600 view CAD models for their virtual product evaluations, current virtual prototyping hardware and software is often unsatisfactory for their needs. Some emergent technologies such as haptic interfaces are currently not commonly used in industry. This study was followed by an investigation into users' psychological acceptance and physiological discomfort when using a variety of virtual prototyping tools for product evaluation compared with using physical prototypes, ranging from on-screen photo-realistic renderings to 3D 3600 view models developed using a range of design software. The third study then went on to explore the feasibility of using these virtual prototyping tools and the effect on product preference when compared to using physical prototypes. The forth study looked at the designer's requirements for current and future virtual prototyping tools, design tools and evaluation methods. In the final chapters of the thesis the relative strengths and weaknesses of these technologies were re-evaluated and a definitive set of user requirements based on the documentary evidence of the previous studies was produced. This was followed by the development of a speculative series of scenarios for the next generation of virtual prototyping technologies ranging from improvements to existing technologies through to blue sky concepts. These scenarios were then evaluated by designers and consumers to produce documentary evidence and recommendations for preferred and suitable combinations of virtual prototyping technologies. Such hardware and software will require a user interface that is intuitive, simple, easy to use and suitable for both the designers who create the virtual prototypes and the consumers who evaluate them

Building an ontology of cardio-vascular diseases for concept-based information retrieval

International audienc

Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations

BACKGROUND: Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients\u27 notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. OBJECTIVE: We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. METHODS: First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians\u27 agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. RESULTS: Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen\u27s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P \u3c .001). Rich learning features contributed to FOCUS\u27s performance substantially. CONCLUSIONS: FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.