Exploring patient information needs in type 2 diabetes: A cross sectional study of questions.

This study set out to analyze questions about type 2 diabetes mellitus (T2DM) from patients and the public. The aim was to better understand people's information needs by starting with what they do not know, discovered through their own questions, rather than starting with what we know about T2DM and subsequently finding ways to communicate that information to people affected by or at risk of the disease. One hundred and sixty-four questions were collected from 120 patients attending outpatient diabetes clinics and 300 questions from 100 members of the public through the Amazon Mechanical Turk crowdsourcing platform. Twenty-three general and diabetes-specific topics and five phases of disease progression were identified; these were used to manually categorize the questions. Analyses were performed to determine which topics, if any, were significant predictors of a question's being asked by a patient or the public, and similarly for questions from a woman or a man. Further analysis identified the individual topics that were assigned significantly more often to the crowdsourced or clinic questions. These were Causes (CI: [-0.07, -0.03], p < .001), Risk Factors ([-0.08, -0.03], p < .001), Prevention ([-0.06, -0.02], p < .001), Diagnosis ([-0.05, -0.02], p < .001), and Distribution of a Disease in a Population ([-0.05,-0.01], p = .0016) for the crowdsourced questions and Treatment ([0.03, 0.01], p = .0019), Disease Complications ([0.02, 0.07], p < .001), and Psychosocial ([0.05, 0.1], p < .001) for the clinic questions. No highly significant gender-specific topics emerged in our study, but questions about Weight were more likely to come from women and Psychosocial questions from men. There were significantly more crowdsourced questions about the time Prior to any Diagnosis ([(-0.11, -0.04], p = .0013) and significantly more clinic questions about Health Maintenance and Prevention after diagnosis ([0.07. 0.17], p < .001). A descriptive analysis pointed to the value provided by the specificity of questions, their potential to disclose emotions behind questions, and the as-yet unrecognized information needs they can reveal. Large-scale collection of questions from patients across the spectrum of T2DM progression and from the public-a significant percentage of whom are likely to be as yet undiagnosed-is expected to yield further valuable insights.

Soliciting and Responding to Patients' Questions about Diabetes Through Online Sources.

When patients cannot get answers from health professionals or retain the information given, increasingly they search online for answers, with limited success. Researchers from the United States, Ireland, and the United Kingdom explored this problem for patients with type 2 diabetes mellitus (T2DM). In 2014, patients attending an outpatient clinic (UK) were asked to submit questions about diabetes. Ten questions judged representative of different types of patient concerns were selected by the researchers and submitted to search engines within trusted and vetted websites in the United States, Ireland, and the United Kingdom. Two researchers independently assessed if answers could be found in the three top-ranked documents returned at each website. The 2014 search was repeated in June, 2016, examining the two top-ranked documents returned. One hundred and sixty-four questions were collected from 120 patients during 12 outpatient clinics. Most patients had T2DM (95%). Most questions were about diabetes (N = 155) with the remainder related to clinic operation (N = 9). Of the questions on diabetes, 152 were about T2DM. The 2014 assessment found no adequate answers to the questions in 90 documents (10 questions, 3 websites, 3 top documents). In the 2016 assessment, 1 document out of 60 (10 questions, 3 websites, 2 top documents) provided an adequate answer relating to 1 of the 10 questions. Available online sources of information do not provide answers to questions from patients with diabetes. Our results highlight the urgent need to develop novel ways of providing answers to patient questions about T2DM.

A questions-based investigation of consumer mental-health information.

Despite the wealth of mental-health information available online to consumers, research has shown that the mental-health information needs of consumers are not being met. This study contributes to that research by soliciting consumer questions directly, categorizing them, analyzing their form, and assessing the extent to which they can be answered from a trusted and vetted source of online information, namely the website of the US National Institute of Mental Health (NIMH). As an alternative to surveys and analyses of online activity, this study shows how consumer questions provide new insight into what consumers do not know and how they express their information needs. The study crowdsourced 100 consumer questions through Amazon Inc.'s Mechanical Turk. Categorization of the questions shows broad agreement with earlier studies in terms of the content of consumer questions. It also suggests that consumers' grasp of mental health issues may be low compared to other health topics. The majority of the questions (74%) were simple in form, with the remainder being multi-part, multifaceted or narrative. Even simple-form questions could, however, have complex interpretations. Fifty four questions were submitted to the search box at the NIMH website. For 32 questions, no answer could be found in the top one to three documents returned. Inadequacies in the search and retrieval technology deployed at websites account for some of the failure to find answers. The nature of consumer questions in mental health also plays a role. A question that has a false presupposition is less likely to have an answer in trusted and vetted sources of information. Consumer questions are also expressed with a degree of specificity that makes the retrieval of relevant information difficult. The significance of this study is that it shows what an analysis of consumer mental-health questions can tell us about consumer information needs and it provides new insight into the difficulties facing consumers looking for answers to their questions in online resources.

Structural similarities between brain and linguistic data provide evidence of semantic relations in the brain.

This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model.

Mining experimental evidence of molecular function claims from the literature.

MOTIVATION: The rate at which gene-related findings appear in the scientific literature makes it difficult if not impossible for biomedical scientists to keep fully informed and up to date. The importance of these findings argues for the development of automated methods that can find, extract and summarize this information. This article reports on methods for determining the molecular function claims that are being made in a scientific article, specifically those that are backed by experimental evidence. RESULTS: The most significant result is that for molecular function claims based on direct assays, our methods achieved recall of 70.7% and precision of 65.7%. Furthermore, our methods correctly identified in the text 44.6% of the specific molecular function claims backed up by direct assays, but with a precision of only 0.92%, a disappointing outcome that led to an examination of the different kinds of errors. These results were based on an analysis of 1823 articles from the literature of Saccharomyces cerevisiae (budding yeast). AVAILABILITY: The annotation files for S.cerevisiae are available from ftp://genome-ftp.stanford.edu/pub/yeast/data_download/literature_curation/gene_association.sgd.gz. The draft protocol vocabulary is available by request from the first author.