1,986 research outputs found

    Mining social media data for biomedical signals and health-related behavior

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    Social media data has been increasingly used to study biomedical and health-related phenomena. From cohort level discussions of a condition to planetary level analyses of sentiment, social media has provided scientists with unprecedented amounts of data to study human behavior and response associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance, sentiment analysis especially for mental health, and other areas. We also discuss a variety of innovative uses of social media data for health-related applications and important limitations in social media data access and use.Comment: To appear in the Annual Review of Biomedical Data Scienc

    Utilizing Consumer Health Posts for Pharmacovigilance: Identifying Underlying Factors Associated with Patients’ Attitudes Towards Antidepressants

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    Non-adherence to antidepressants is a major obstacle to antidepressants therapeutic benefits, resulting in increased risk of relapse, emergency visits, and significant burden on individuals and the healthcare system. Several studies showed that non-adherence is weakly associated with personal and clinical variables, but strongly associated with patients’ beliefs and attitudes towards medications. The traditional methods for identifying the key dimensions of patients’ attitudes towards antidepressants are associated with some methodological limitations, such as concern about confidentiality of personal information. In this study, attempts have been made to address the limitations by utilizing patients’ self report experiences in online healthcare forums to identify underlying factors affecting patients attitudes towards antidepressants. The data source of the study was a healthcare forum called “askapatients.com”. 892 patients’ reviews were randomly collected from the forum for the four most commonly prescribed antidepressants including Sertraline (Zoloft) and Escitalopram (Lexapro) from SSRI class, and Venlafaxine (Effexor) and duloxetine (Cymbalta) from SNRI class. Methodology of this study is composed of two main phases: I) generating structured data from unstructured patients’ drug reviews and testing hypotheses concerning attitude, II) identification and normalization of Adverse Drug Reactions (ADRs), Withdrawal Symptoms (WDs) and Drug Indications (DIs) from the posts, and mapping them to both The UMLS and SNOMED CT concepts. Phase II also includes testing the association between ADRs and attitude. The result of the first phase of this study showed that “experience of adverse drug reactions”, “perceived distress received from ADRs”, “lack of knowledge about medication’s mechanism”, “withdrawal experience”, “duration of usage”, and “drug effectiveness” are strongly associated with patients attitudes. However, demographic variables including “age” and “gender” are not associated with attitude. Analysis of the data in second phase of the study showed that from 6,534 identified entities, 73% are ADRs, 12% are WDs, and 15 % are drug indications. In addition, psychological and cognitive expressions have higher variability than physiological expressions. All three types of entities were mapped to 811 UMLS and SNOMED CT concepts. Testing the association between ADRs and attitude showed that from twenty-one physiological ADRs specified in the ASEC questionnaire, “dry mouth”, “increased appetite”, “disorientation”, “yawning”, “weight gain”, and “problem with sexual dysfunction” are associated with attitude. A set of psychological and cognitive ADRs, such as “emotional indifference” and “memory problem were also tested that showed significance association between these types of ADRs and attitude. The findings of this study have important implications for designing clinical interventions aiming to improve patients\u27 adherence towards antidepressants. In addition, the dataset generated in this study has significant implications for improving performance of text-mining algorithms aiming to identify health related information from consumer health posts. Moreover, the dataset can be used for generating and testing hypotheses related to ADRs associated with psychiatric mediations, and identifying factors associated with discontinuation of antidepressants. The dataset and guidelines of this study are available at https://sites.google.com/view/pharmacovigilanceinpsychiatry/hom

    Using Big Data Analytics and Statistical Methods for Improving Drug Safety

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    This dissertation includes three studies, all focusing on utilizing Big Data and statistical methods for improving one of the most important aspects of health care, namely drug safety. In these studies we develop data analytics methodologies to inspect, clean, and model data with the aim of fulfilling the three main goals of drug safety; detection, understanding, and prediction of adverse drug effects.In the first study, we develop a methodology by combining both analytics and statistical methods with the aim of detecting associations between drugs and adverse events through historical patients' records. Particularly we show applicability of the developed methodology by focusing on investigating potential confounding role of common diabetes drugs on developing acute renal failure in diabetic patients. While traditional methods of signal detection mostly consider one drug and one adverse event at a time for investigation, our proposed methodology takes into account the effect of drug-drug interactions by identifying groups of drugs frequently prescribed together.In the second study, two independent methodologies are developed to investigate the role of prescription sequence factor on the likelihood of developing adverse events. In fact, this study focuses on using data analytics for understanding drug-event associations. Our analyses on the historical medication records of a group of diabetic patients using the proposed approaches revealed that the sequence in which the drugs are prescribed, and administered, significantly do matter in the development of adverse events associated with those drugs.The third study uses a chronological approach to develop a network of approved drugs and their known adverse events. It then utilizes a set of network metrics, both similarity- and centrality-based, to build and train machine learning predictive models and predict the likely adverse events for the newly discovered drugs before their approval and introduction to the market. For this purpose, data of known drug-event associations from a large biomedical publication database (i.e., PubMed) is employed to construct the network. The results indicate significant improvements in terms of accuracy of prediction of drug-evet associations compared with similar approaches

    Front-Line Physicians' Satisfaction with Information Systems in Hospitals

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    Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

    Constitution of the market through social media: Dialogical co-production of medicine in a virtual health community organization

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    This research explores new systems of marketing, and new roles and relationships of organizations and consumers developing in healthcare as a result of transformations occurring in technology, consumer/marketer value systems, forms of discourse and institutional roles. Inspired by observations from a Medicine 2.0 community organization, which turn social networking into a business phenomenon – PatientsLikeMe (PLM) – I explore how such systems develop and function and the institutionalizations that reconstitute roles and maintain relationships among actors in these systems through netnographic research. That is, (1) why and how patients in PLM participate in the social co-production of medical knowledge and experience, and (2) how the ‘community’ organizes roles and relations, and institutionalize ‘sharing’ in healthcare where privacy dominates relations. Findings articulate a dialogical approach to organizing roles and relations with the dilution of provisioning in this co-mediated market system, which reflects collaborative, connective and communal relations built on dialogues among diverse healthcare actors. From a theoretical vantage point, Foucauldian notions of biopower and govern-mentality are reconsidered in order to articulate why and how such a system may be attracting healthcare actors and maintain their interest and sharing in this community

    An enhanced concept based approach medical information retrieval to address readability, vocabulary and presentation issues

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    Querying of health information retrieval for health advice has now become a general and notable task performed by individuals on the Internet. However, the failure of the existing approaches to integrate program modules that would address the information needs of all categories of end-users remains. This study focused on proposing an improved framework and designing an enhanced concept based approach (ECBA) for medical information retrieval that would better address readability, vocabulary mismatched and presentation issues by generating medical discharge documents and medical search queries results in both medical expert and layman’s forms. Three special program modules were designed and integrated in the enhanced concept based approach namely: medical terms control module, vocabulary controlled module and readability module to specifically address the information needs of both medical experts and laymen end-users. Eight benched marked datasets namely: Medline, UMLS, MeSH, Metamap, Metathesaurus, Diagnosia 7, Khresmoi Project 6 and Genetic Home Reference were used in validating the systems performance. Additionally, the ECBA was compared using three existing approaches such as concept based approach (CBA), query likelihood model (QLM) and latent semantic indexing (LSI). The evaluation was conducted using the performance and statistical metrics: P@40, NDCG@40, MAP, Analysis of Variance (ANOVA) and Turkey HSD Tests. The outcome of the final experimental results obtained shows that, the ECBA consistently obtained above 93% accuracy rate results on Medline, UMLS and MeSH Datasets, 92% on Metamap, Metathesaurus and Diagnosia 7 datasets and 91% on Khresmoi Project 6 and Genetic Home Reference datasets. Also, the statistical analysis performance results obtained by each of the four approaches: ECBA, CBA, QLM and LSI shows that, there is a significant difference among their Mean Scores, hence, the null hypothesis of no significant difference was rejected

    Information retrieval and text mining technologies for chemistry

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    Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

    Usability analysis of contending electronic health record systems

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    In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

    High-Performance Modelling and Simulation for Big Data Applications

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    This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

    Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

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    Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.Comment: 24 Pages, 8 Tables, 6 Figures; Accepted by PLoS One ; One of the two mentioned Datasets in the manuscript has Closed Access. We will make it public after PLoS One produces the manuscrip
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