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 retrospective view on the promise on machine translation for Bahasa Melayu-English

Research and development activities for machine translation systems from English language to others are more progressive than vice versa. It has been more than 30 years since the machine translation was introduced and yet a Malay language or Bahasa Melayu (BM) to English machine translation engine is not available. Consequently, many translation systems have been developed for the world's top 10 languages in terms of native speakers, but none for BM, although the language is used by more than 200 million speakers around the world. This paper attempts to seek possible reasons as why such situation occurs. A summative overview to show progress, challenges as well as future works on MT is presented. Issues faced by researchers and system developers in modeling and developing a machine translation engine are also discussed. The study of the previous translation systems (from other languages to English) reveals that the accuracy level can be achieved up to 85 %. The figure suggests that the translation system is not reliable if it is to be utilized in a serious translation activity. The most prominent difficulties are the complexity of grammar rules and ambiguity problems of the source language. Thus, we hypothesize that the inclusion of ‘semantic’ property in the translation rules may produce a better quality BM-English MT engine

Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization

Proton Magnetic Resonance Spectroscopy (1H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a real application context. Systems based on models built with bad quality data are likely to underperform in their decision support tasks. In this study, we propose a system to filter out such bad quality data. It is based on convex Non-Negative Matrix Factorization models, used as a dimensionality reduction procedure, and on the use of several classifiers to discriminate between good and bad quality data.Peer ReviewedPostprint (author's final draft

Dennis Littky, the Educational Activist: Can His Model Revamp the Public Educational System?

When an individual observes a classroom of today, he will see many elements that are recognizable to anyone who attended school during the last one hundred years, students working from textbooks, repetitive worksheets, and rows of desks holding students completing tasks directed by the teacher. Even though societal and technological advancements are increasing rapidly, our school system has stayed stagnant. What this means for students is the lack of individuality, teachers’ non acceptance of personal interests, lack of personal voice, and in many cases, a non relationship between teacher and student beyond the classroom assignment (Castleman & Littky, 2007)

Mean field game model of corruption

A simple model of corruption that takes into account the effect of the interaction of a large number of agents by both rational decision making and myopic behavior is developed. Its stationary version turns out to be a rare example of an exactly solvable model of mean-field-game type. The results show clearly how the presence of interaction (including social norms) influences the spread of corruption

Barriers to Preoperative Teaching in a Culturally Diverse Healthcare Environment

The role of the professional nurse is integral in educating and ensuring that patients understand essential components of their plan of care. This is especially true for patients who are to undergo surgical interventions; evidence has demonstrated that preoperative education provided to patients is linked with positive patient outcomes and a decrease in post-operative complications (Blackstone, Garrett, & Hasselkus, 2011). This qualitative study investigated the barriers that nurses experience in providing preoperative education to diverse patients in a multicultural healthcare environment. Ten registered nurses at a private community hospital in the San Francisco Bay Area participated in an hour long one-on-one semi-structured interview over the course of six months to explore knowledge that nurses identify as important to teach patients before surgery, what they actually teach, and the barriers they experience in the delivery of this information. These interviews were coded using qualitative research software, and revealed challenges relating to language barriers, mistrust of translation services, and the perceived restrictions of time. The barriers resulted in sub-optimal delivery of preoperative information. Although nurses wanted to provide the best care they could, the barriers posed significant challenges. Consequently, nurses experienced moral distress under circumstances in which they are aware of the quality of the information they provide. The phenomenon of satisficing was identified as a coping strategy to the routine nursing practice of preoperative education

The Existence Value of Peat Swamp Forest in Peninsular Malaysia

Forests form the dominant natural ecosystem in Malaysia. About 55% of Malaysian land area is forested and endows a rich diversity of flora and fauna. Peat swamp forests constitute a significant component of forest and account for about 75% of the country’s total wetlands. Many peat swamp forests have already been converted to new land uses including palm oil plantations, agriculture and housing. The south-east Pahang peat swamp forest (SEPPSF), located at Pahang state is the largest peat swamp forest cover in Peninsular Malaysia and is believed to be the mainland Asia’s largest and intact peat swamp forest. It harbours unique flora and fauna, provides benefits and services of national interest and supports the livelihood of the aborigines (Orang Asli) communities. Many of the benefits and services from peat swamp forests are unpriced and this can lead to faulty land use decision making. Non market valuation can provide important information on the value of many currently unpriced items and enable decision makers to consider the opportunity costs of proposed land use changes. Total economic value (TEV), which includes use and non-use values, is a complex method to determine the estimated total benefits for a tropical forest. This study reports on a contingent valuation study of existence value (non-use value) of the SEPPSF. The economic value is based on the mean maximum willingness to pay of the households in Kuantan (the capital city of Pahang state) to conserve the forest.SEPPSF, economic value, CVM, Agricultural and Food Policy, Consumer/Household Economics, Environmental Economics and Policy, Farm Management, Land Economics/Use,

Lagrangian Coherent Structures: A Climatological Look

A relatively new area at the crossroads of fluid and nonlinear dynamics are objects known as Lagrangian Coherent Structures (LCSs). LCSs are mathematically classified to differentiate parts of fluid flows. They, themselves, are the most influential parts of fluids. These objects have the most influence on the fluids around them and they allow for a sense of hierarchy in an otherwise busy environment of endless variables and trajectories. While all particles of fluids have the same dynamics on an individual basis, areas of fluid are not created equal and to be able to detect which parts will be the most important to look at allows for easier, but just as accurate, prediction of fluid movement. Recent applications include cleanup operations during the BP Deepwater Horizon oil spill, pollutant transfer in oceanic basins, and the analysis of polar storm activity. This thesis explores LCSs from the discrete mathematics to the future climatological impacts using virtual fluid simulations and LCS detection tools to facilitate analysis as well as diving into a case study with real and unapproximated oceanic data in the days following the Fukushima Daiichi nuclear power plant disaster

遠距離介護者は何をしてるのか : 提案の判断と離れて暮らす家族の知識

This study clarifies what long-distance caregivers do. To this end, I conducted a conversation analysis of video data of a care conference in which a long-distance caregiver participated. When a professional caregiver proposes a care plan to a long-distance caregiver, he/she substitutes technical terms with laymen terms for easy understanding, indicating his/her orientation that a long-distance caregiver is not a professional. However, if the caregiver laughs or averts his/her eyes during the discussion, it weakens the proposition. This means that a long-distance caregiver is important for judgment of a proposition. Occasionally, even if a long-distance caregiver and a professional caregiver judge the proposition and reach an agreement, as a basis for judgment, there is a conflict about which of them knows the family better