Literature Watch
An in silico model of the effects of vitamin D3 on mycobacterium infected macrophage.
An in silico model of the effects of vitamin D3 on mycobacterium infected macrophage.
Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:1443-1446
Authors: Gough M, May E
Abstract
Mycobacterium tuberculosis is a global health concern, causing over one million deaths a year. Alveolar macrophages, as the primary host cell of this intracellular bacterium, play an important role in the course of disease. Vitamin D3 is known to have a potent effect on macrophage behavior during infection, modulating the production of pro- and anti-inflammatory cytokines and immune effector molecules. In a vitamin D3 deficient host, the immune systems response to infection is greatly impaired. We used a quantitative systems biology approach to model the intracellular effects of vitamin D3 and compared our simulation output to our in vitro model of mycobacterium infection of macrophages in the presence and absence of Vitamin D3. Our in silico model results agreed with the in vitro assay results of interleukin-10, an anti-inflammatory protein whose production is known to be influenced by vitamin D3. This model will provide a platform for further investigation of the effects of vitamin D3 deficiency on host immune response to infection.
PMID: 28268597 [PubMed - in process]
Social media for arthritis-related comparative effectiveness and safety research and the impact of direct-to-consumer advertising.
Social media for arthritis-related comparative effectiveness and safety research and the impact of direct-to-consumer advertising.
Arthritis Res Ther. 2017 Mar 07;19(1):48
Authors: Curtis JR, Chen L, Higginbotham P, Nowell WB, Gal-Levy R, Willig J, Safford M, Coe J, O'Hara K, Sa'adon R
Abstract
BACKGROUND: Social media may complement traditional data sources to answer comparative effectiveness/safety questions after medication licensure.
METHODS: The Treato platform was used to analyze all publicly available social media data including Facebook, blogs, and discussion boards for posts mentioning inflammatory arthritis (e.g. rheumatoid, psoriatic). Safety events were self-reported by patients and mapped to medical ontologies, resolving synonyms. Disease and symptom-related treatment indications were manually redacted. The units of analysis were unique terms in posts. Pre-specified conditions (e.g. herpes zoster (HZ)) were selected based upon safety signals from clinical trials and reported as pairwise odds ratios (ORs); drugs were compared with Fisher's exact test. Empirically identified events were analyzed using disproportionality analysis and reported as relative reporting ratios (RRRs). The accuracy of a natural language processing (NLP) classifier to identify cases of shingles associated with arthritis medications was assessed.
RESULTS: As of October 2015, there were 785,656 arthritis-related posts. Posts were predominantly US posts (75%) from patient authors (87%) under 40 years of age (61%). For HZ posts (n = 1815), ORs were significantly increased with tofacitinib versus other rheumatoid arthritis therapies. ORs for mentions of perforated bowel (n = 13) were higher with tocilizumab versus other therapies. RRRs associated with tofacitinib were highest in conditions related to baldness and hair regrowth, infections and cancer. The NLP classifier had a positive predictive value of 91% to identify HZ. There was a threefold increase in posts following television direct-to-consumer advertisement (p = 0.04); posts expressing medication safety concerns were significantly more frequent than favorable posts.
CONCLUSION: Social media is a challenging yet promising data source that may complement traditional approaches for comparative effectiveness research for new medications.
PMID: 28270190 [PubMed - in process]
Controlling testing volume for respiratory viruses using machine learning and text mining.
Controlling testing volume for respiratory viruses using machine learning and text mining.
AMIA Annu Symp Proc. 2016;2016:1910-1919
Authors: Mai MV, Krauthammer M
Abstract
Viral testing for pediatric inpatients with respiratory symptoms is common, with considerable associated charges. In an attempt to reduce testing volumes, we studied whether data available at the time of admission could aid in identifying children with low likelihood of having a particular viral origin of their symptoms, and thus safely forgo broad viral testing. We collected clinical data for 1,685 pediatric inpatients receiving respiratory virus testing from 2010-2012. Machine-learning on the data allowed us to construct pre-test models predicting whether a patient would test positive for a particular virus. Text mining improved the predictions for one viral test. Cost-sensitive models optimized for test sensitivity showed reasonable test specificities and an ability to reduce test volume by up to 46% for single viral tests. We conclude that diverse forms of data in the electronic medical record can be used productively to build models that help physicians reduce testing volumes.
PMID: 28269950 [PubMed - in process]
Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.
Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.
AMIA Annu Symp Proc. 2016;2016:1880-1889
Authors: Kuo TT, Rao P, Maehara C, Doan S, Chaparro JD, Day ME, Farcas C, Ohno-Machado L, Hsu CN
Abstract
Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and diverse concepts, such as data elements (i.e., important concepts related to a certain medical condition), a plausible solution is to combine various NLP tools into an ensemble to improve extraction performance. However, it is unclear to what extent ensembles of popular NLP tools improve the extraction of numerous and diverse concepts. Therefore, we built an NLP ensemble pipeline to synergize the strength of popular NLP tools using seven ensemble methods, and to quantify the improvement in performance achieved by ensembles in the extraction of data elements for three very different cohorts. Evaluation results show that the pipeline can improve the performance of NLP tools, but there is high variability depending on the cohort.
PMID: 28269947 [PubMed - in process]
Investigating Longitudinal Tobacco Use Information from Social History and Clinical Notes in the Electronic Health Record.
Investigating Longitudinal Tobacco Use Information from Social History and Clinical Notes in the Electronic Health Record.
AMIA Annu Symp Proc. 2016;2016:1209-1218
Authors: Wang Y, Chen ES, Pakhomov S, Lindemann E, Melton GB
Abstract
The electronic health record (EHR) provides an opportunity for improved use of clinical documentation including leveraging tobacco use information by clinicians and researchers. In this study, we investigated the content, consistency, and completeness of tobacco use data from structured and unstructured sources in the EHR. A natural language process (NLP) pipeline was utilized to extract details about tobacco use from clinical notes and free-text tobacco use comments within the social history module of an EHR system. We analyzed the consistency of tobacco use information within clinical notes, comments, and available structured fields for tobacco use. Our results indicate that structured fields for tobacco use alone may not be able to provide complete tobacco use information. While there was better consistency for some elements (e.g., status and type), inconsistencies were found particularly for temporal information. Further work is needed to improve tobacco use information integration from different parts of the EHR.
PMID: 28269918 [PubMed - in process]
Towards Comprehensive Clinical Abbreviation Disambiguation Using Machine-Labeled Training Data.
Towards Comprehensive Clinical Abbreviation Disambiguation Using Machine-Labeled Training Data.
AMIA Annu Symp Proc. 2016;2016:560-569
Authors: Finley GP, Pakhomov SV, McEwan R, Melton GB
Abstract
Abbreviation disambiguation in clinical texts is a problem handled well by fully supervised machine learning methods. Acquiring training data, however, is expensive and would be impractical for large numbers of abbreviations in specialized corpora. An alternative is a semi-supervised approach, in which training data are automatically generated by substituting long forms in natural text with their corresponding abbreviations. Most prior implementations of this method either focus on very few abbreviations or do not test on real-world data. We present a realistic use case by testing several semi-supervised classification algorithms on a large hand-annotated medical record of occurrences of 74 ambiguous abbreviations. Despite notable differences between training and test corpora, classifiers achieve up to 90% accuracy. Our tests demonstrate that semi-supervised abbreviation disambiguation is a viable and extensible option for medical NLP systems.
PMID: 28269852 [PubMed - in process]
Automated Detection of Privacy Sensitive Conditions in C-CDAs: Security Labeling Services at the Department of Veterans Affairs.
Automated Detection of Privacy Sensitive Conditions in C-CDAs: Security Labeling Services at the Department of Veterans Affairs.
AMIA Annu Symp Proc. 2016;2016:332-341
Authors: Bouhaddou O, Davis M, Donahue M, Mallia A, Griffin S, Teal J, Nebeker J
Abstract
Care coordination across healthcare organizations depends upon health information exchange. Various policies and laws govern permissible exchange, particularly when the information includes privacy sensitive conditions. The Department of Veterans Affairs (VA) privacy policy has required either blanket consent or manual sensitivity review prior to exchanging any health information. The VA experience has been an expensive, administratively demanding burden on staffand Veterans alike, particularly for patients without privacy sensitive conditions. Until recently, automatic sensitivity determination has not been feasible. This paper proposes a policy-driven algorithmic approach (Security Labeling Service or SLS) to health information exchange that automatically detects the presence or absence of specific privacy sensitive conditions and then, to only require a Veteran signed consent for release when actually present. The SLS was applied successfully to a sample of real patient Consolidated-Clinical Document Architecture(C-CDA) documents. The SLS identified standard terminology codes by both parsing structured entries and analyzing textual information using Natural Language Processing (NLP).
PMID: 28269828 [PubMed - in process]
Visualizing patient journals by combining vital signs monitoring and natural language processing.
Visualizing patient journals by combining vital signs monitoring and natural language processing.
Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:2529-2532
Authors: Vilic A, Petersen JA, Hoppe K, Sorensen HB
Abstract
This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admission, which is assessed by electronically monitoring vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on the existing patient journal to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see drastic changes in the patients' health, and thereby enabling staff to see where in the journal critical events have taken place.
PMID: 28268838 [PubMed - in process]
S2NI: a mobile platform for nutrition monitoring from spoken data.
S2NI: a mobile platform for nutrition monitoring from spoken data.
Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:1991-1994
Authors: Hezarjaribi N, Reynolds CA, Miller DT, Chaytor N, Ghasemzadeh H
Abstract
Diet and physical activity are important lifestyle and behavioral factors in self-management and prevention of many chronic diseases. Mobile sensors such as accelerometers have been used in the past to objectively measure physical activity or detect eating time. Diet monitoring, however, still relies on self-recorded data by end users where individuals use mobile devices for recording nutrition intake by either entering text or taking images. Such approaches have shown low adherence in technology adoption and achieve only moderate accuracy. In this paper, we propose development and validation of Speech-to-Nutrient-Information (S2NI), a comprehensive nutrition monitoring system that combines speech processing, natural language processing, and text mining in a unified platform to extract nutrient information such as calorie intake from spoken data. After converting the voice data to text, we identify food name and portion size information within the text. We then develop a tiered matching algorithm to search the food name in our nutrition database and to accurately compute calorie intake. Due to its pervasive nature and ease of use, S2NI enables users to report their diet routine more frequently and at anytime through their smartphone. We evaluate S2NI using real data collected with 10 participants. Our experimental results show that S2NI achieves 80.6% accuracy in computing calorie intake.
PMID: 28268720 [PubMed - in process]
NIDA Translational Avant-Garde Award for Development of Medication to Treat Substance Use Disorders (UG3/UH3)
Alliance of Glycobiologists for Cancer Research: Translational Tumor Glycomics Laboratories (U01)
Alliance of Glycobiologists for Cancer Research: Biological Tumor Glycomics Laboratories (U01)
"cystic fibrosis"; +9 new citations
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Value added medicines: what value repurposed medicines might bring to society?
Value added medicines: what value repurposed medicines might bring to society?
J Mark Access Health Policy. 2017;5(1):1264717
Authors: Toumi M, Rémuzat C
Abstract
Background & objectives: Despite the wide interest surrounding drug repurposing, no common terminology has been yet agreed for these products and their full potential value is not always recognised and rewarded, creating a disincentive for further development. The objectives of the present study were to assess from a wide perspective which value drug repurposing might bring to society, but also to identify key obstacles for adoption of these medicines and to discuss policy recommendations. Methods: A preliminary comprehensive search was conducted to assess how the concept of drug repurposing was described in the literature. Following completion of the literature review, a primary research was conducted to get perspective of various stakeholders across EU member states on drug repurposing (healthcare professionals, regulatory authorities and Health Technology Assessment (HTA) bodies/payers, patients, and representatives of the pharmaceutical industry developing medicines in this field). Ad hoc literature review was performed to illustrate, when appropriate, statements of the various stakeholders. Results: Various nomenclatures have been used to describe the concept of drug repurposing in the literature, with more or less broad definitions either based on outcomes, processes, or being a mix of both. In this context, Medicines for Europe (http://www.medicinesforeurope.com/value-added-medicines/) established one single terminology for these medicines, known as value added medicines, defined as 'medicines based on known molecules that address healthcare needs and deliver relevant improvements for patients, healthcare professionals and/or payers'. Stakeholder interviews highlighted three main potential benefits for value added medicines: (1) to address a number of medicine-related healthcare inefficiencies related to irrational use of medicines, non-availability of appropriate treatment options, shortage of mature products, geographical inequity in medicine access; (2) to improve healthcare system efficiency; and (3) to contribute to sustainability of healthcare systems through economic advantages. Current HTA framework, generic stigma, and pricing rules, such as internal reference pricing or tendering processes in place in some countries, were reported as the current key hurdles preventing the full recognition of value added medicines' benefits, discouraging manufacturers from bringing such products to the market. Discussion & conclusions: There is currently a gap between increasing regulatory authority interest in capturing value added medicines' benefits and the resistance of HTA bodies/payers, who tend to ignore this important segment of the pharmaceutical field. This situation calls for policy changes to foster appropriate incentives to enhance value recognition of value added medicines and deliver the expected benefit to society. Policy changes from HTA perspective should include: absence of any legislative barriers preventing companies from pursuing HTA; HTA requirements proportionate to potential reward; HTA decision-making framework taking into account the specific characteristics of value added medicines; eligibility for early HTA dialogues; Policy changes from pricing perspective should encompass: tenders/procurement policies allowing differentiation from generic medicines; eligibility for early entry agreement; non-systematic implementation of external and internal reference pricing policies; recognition of indication-specific pricing. At the same time, the pharmaceutical industry should engage all the stakeholders (patients, healthcare providers, HTA bodies/payers) in early dialogues to identify their expectations and to ensure the developed value added medicines address their needs.
PMID: 28265347 [PubMed - in process]
Loss of BRCA1 in the cells of origin of ovarian cancer induces glycolysis: A window of opportunity for ovarian cancer chemoprevention.
Loss of BRCA1 in the cells of origin of ovarian cancer induces glycolysis: A window of opportunity for ovarian cancer chemoprevention.
Cancer Prev Res (Phila). 2017 Mar 06;:
Authors: Chiyoda T, Hart PC, Eckert MA, McGregor SM, Lastra RR, Hamamoto R, Nakamura Y, Yamada SD, Olopade OI, Lengyel E, Romero IL
Abstract
Mutations in the breast cancer susceptibility gene 1 (BRCA1) are associated with an increased risk of developing epithelial ovarian cancer. However, beyond the role of BRCA1 in DNA repair, little is known about other mechanisms by which BRCA1 impairment promotes carcinogenesis. Given that altered metabolism is now recognized as important in the initiation and progression of cancer, we asked whether loss of BRCA1 changes metabolism in the cells of origin of ovarian cancer. The findings show that silencing BRCA1 in ovarian surface epithelial and fallopian tube cells increased glycolysis. Furthermore, when these cells were transfected with plasmids carrying deleterious BRCA1 mutations (5382insC or the P1749R), there was an increase in hexokinase-2 (HK2), a key glycolytic enzyme. This effect was mediated by MYC and the signal transducer and activator of transcription 3 (STAT3). To target the metabolic phenotype induced by loss of BRCA1, a drug repurposing approach was used and aspirin was identified as an agent that counteracted the increase in HK2 and the increase in glycolysis induced by BRCA1 impairment. Evidence from this study indicates that the tumor suppressor functions of BRCA1 extend beyond DNA repair to include metabolic endpoints and identifies aspirin as an ovarian cancer chemopreventive agent capable of reversing the metabolic derangements caused by loss of BRCA1.
PMID: 28264838 [PubMed - as supplied by publisher]
Eventration of the Right Hemidiaphragm with Resultant Right Atrial Compression-A Rare Finding.
Eventration of the Right Hemidiaphragm with Resultant Right Atrial Compression-A Rare Finding.
Echocardiography. 2016 Sep;33(9):1432-3
Authors: Lau GT, To AC
PMID: 27247197 [PubMed - indexed for MEDLINE]
Attention Should be Drawn to Rare Diseases and Interpretation of Sequence Variants.
Attention Should be Drawn to Rare Diseases and Interpretation of Sequence Variants.
Chin Med J (Engl). 2016 May 05;129(9):1009-10
Authors: Tang BS
PMID: 27098782 [PubMed - indexed for MEDLINE]
Can the EVIDEM Framework Tackle Issues Raised by Evaluating Treatments for Rare Diseases: Analysis of Issues and Policies, and Context-Specific Adaptation.
Can the EVIDEM Framework Tackle Issues Raised by Evaluating Treatments for Rare Diseases: Analysis of Issues and Policies, and Context-Specific Adaptation.
Pharmacoeconomics. 2016 Mar;34(3):285-301
Authors: Wagner M, Khoury H, Willet J, Rindress D, Goetghebeur M
Abstract
BACKGROUND: The multiplicity of issues, including uncertainty and ethical dilemmas, and policies involved in appraising interventions for rare diseases suggests that multicriteria decision analysis (MCDA) based on a holistic definition of value is uniquely suited for this purpose. The objective of this study was to analyze and further develop a comprehensive MCDA framework (EVIDEM) to address rare disease issues and policies, while maintaining its applicability across disease areas.
METHODS: Specific issues and policies for rare diseases were identified through literature review. Ethical and methodological foundations of the EVIDEM framework v3.0 were systematically analyzed from the perspective of these issues, and policies and modifications of the framework were performed accordingly to ensure their integration.
RESULTS: Analysis showed that the framework integrates ethical dilemmas and issues inherent to appraising interventions for rare diseases but required further integration of specific aspects. Modification thus included the addition of subcriteria to further differentiate disease severity, disease-specific treatment outcomes, and economic consequences of interventions for rare diseases. Scoring scales were further developed to include negative scales for all comparative criteria. A methodology was established to incorporate context-specific population priorities and policies, such as those for rare diseases, into the quantitative part of the framework. This design allows making more explicit trade-offs between competing ethical positions of fairness (prioritization of those who are worst off), the goal of benefiting as many people as possible, the imperative to help, and wise use of knowledge and resources. It also allows addressing variability in institutional policies regarding prioritization of specific disease areas, in addition to existing uncertainty analysis available from EVIDEM.
CONCLUSION: The adapted framework measures value in its widest sense, while being responsive to rare disease issues and policies. It provides an operationalizable platform to integrate values, competing ethical dilemmas, and uncertainty in appraising healthcare interventions.
PMID: 26547306 [PubMed - indexed for MEDLINE]
Exploring biomedical ontology mappings with graph theory methods.
Exploring biomedical ontology mappings with graph theory methods.
PeerJ. 2017;5:e2990
Authors: Kocbek S, Kim JD
Abstract
BACKGROUND: In the era of semantic web, life science ontologies play an important role in tasks such as annotating biological objects, linking relevant data pieces, and verifying data consistency. Understanding ontology structures and overlapping ontologies is essential for tasks such as ontology reuse and development. We present an exploratory study where we examine structure and look for patterns in BioPortal, a comprehensive publicly available repository of live science ontologies.
METHODS: We report an analysis of biomedical ontology mapping data over time. We apply graph theory methods such as Modularity Analysis and Betweenness Centrality to analyse data gathered at five different time points. We identify communities, i.e., sets of overlapping ontologies, and define similar and closest communities. We demonstrate evolution of identified communities over time and identify core ontologies of the closest communities. We use BioPortal project and category data to measure community coherence. We also validate identified communities with their mutual mentions in scientific literature.
RESULTS: With comparing mapping data gathered at five different time points, we identified similar and closest communities of overlapping ontologies, and demonstrated evolution of communities over time. Results showed that anatomy and health ontologies tend to form more isolated communities compared to other categories. We also showed that communities contain all or the majority of ontologies being used in narrower projects. In addition, we identified major changes in mapping data after migration to BioPortal Version 4.
PMID: 28265499 [PubMed - in process]
Assessment of patient perceptions of genomic testing to inform pharmacogenomic implementation.
Assessment of patient perceptions of genomic testing to inform pharmacogenomic implementation.
Pharmacogenet Genomics. 2017 Mar 03;:
Authors: Lee YM, McKillip RP, Borden BA, Klammer CE, Ratain MJ, O'Donnell PH
Abstract
OBJECTIVE: Pharmacogenomics seeks to improve prescribing by reducing drug inefficacy/toxicity. However, views of patients during pharmacogenomic-guided care are largely unknown. We sought to understand the attitudes and perceptions of patients in an institutional implementation project and hypothesized that views would differ on the basis of experience with pharmacogenomic-guided care.
METHODS: Two focus groups were conducted - one group included patients who had previously been subjected to broad pharmacogenomic genotyping with results available to physicians (pharmacogenomic group), whereas the other had not been offered genotyping (traditional care). Five domains were explored: (i) experiences with medications/side effects, (ii) understanding of pharmacogenomics, (iii) impact of pharmacogenomics on relationships with healthcare professionals, (iv) scenarios involving pharmacogenomic-guided prescribing, and (v) responses to pharmacogenomic education materials.
RESULTS: Nine pharmacogenomic and 13 traditional care participants were included. Participants in both groups agreed that pharmacogenomics could inform prescribing and help identify problem prescriptions, but expressed concerns over insurance coverage and employment discrimination. Both groups diverged on who should be permitted to access pharmacogenomic results, with some preferring access only for providers with a longstanding relationship, whereas others argued for open access. Notably, traditional care participants showed greater skepticism about how results might be used. Case scenarios and tested educational materials elicited strong desires on the part of patients for physicians to engage participants when considering pharmacogenomic-based prescribing and to utilize shared decision-making.
CONCLUSION: Participants experiencing pharmacogenomic-guided care were more receptive toward pharmacogenomic information being used than traditional care participants. As key stakeholders in implementation, addressing patients' concerns will be important to successfully facilitate clinical dissemination.
PMID: 28267054 [PubMed - as supplied by publisher]
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