Italian Age of Acquisition Norms for a Large Set of Words (ItAoA).

Front Psychol. 2019;10:278

Authors: Montefinese M, Vinson D, Vigliocco G, Ambrosini E

Abstract
Age of acquisition (AoA) is an important psycholinguistic variable that affects the performance of healthy individuals and patients in a large variety of cognitive tasks. For this reason, it becomes more and more compelling to collect new AoA norms for a large set of stimuli in order to allow better control and manipulation of AoA in future research. An important motivation of the present study is to extend previous Italian norms by collecting AoA ratings for a much larger range of Italian words for which concreteness and semantic-affective norms are now available thus ensuring greater coverage of words varying along these dimensions. In the present study, we collected AoA ratings for 1,957 Italian content words (adjectives, nouns, and verbs), by asking healthy adult participants to estimate the age at which they thought they had learned the word in a Web survey procedure. First, we found high split-half correlation within our sample, suggesting strong internal reliability. Second, our data indicate that the ratings collected in this study are as valid and reliable as those collected in previous studies for Italian across different age populations (adult and children) and other languages. Finally, we analyzed the relation between AoA ratings and other lexical-semantic variables (e.g., word frequency, imageability, valence, arousal) and showed that these correlations were generally consistent with the correlations reported in other normative studies for Italian and other languages. Therefore, our new AoA norms are a valuable source of information for future research in the Italian language. The full database is available at the Open Science Framework (osf.io/3trg2).

PMID: 30814969 [PubMed]

Fundamental Visual Concept Learning from Correlated Images and Text.

IEEE Trans Image Process. 2019 Feb 18;:

Authors: Du Y, Wang H, Cui Y, Huang X

Abstract
Heterogeneous web media consists of many visual concepts, such as objects, scenes and activities, that cannot be semantically decomposed. The task of learning fundamental visual concepts (FVCs) plays an important role in automatically understanding the elements that compose all visual media, as well as in applications of retrieval, annotation, etc. In this paper, we formulate the problem of FVC learning and propose an approach to this problem called neighboring concept distributing (NCD). Our approach models all data using a concept graph, which considers the visual patches in images as nodes and generates the inter-image edges between visual patches in different images and the intra-image edges between visual patches in the same image. The NCD approach distributes semantic information from images to visual patches based on measurements over the concept graph, including fitness, distinctiveness, smoothness and sparseness, without any pre-trained concept detectors or classifiers. We analyze the learnability of the proposed approach and find that, under some conditions, all concepts can be correctly learned with an arbitrarily high probability as the size of the data increases.We demonstrate the performance of the NCD approach using three public datasets. Experimental results show that our approach outperforms state-of-the-art approaches when learning visual concepts from correlated media.

PMID: 30794173 [PubMed - as supplied by publisher]

Semantic Queries Expedite MedDRA Terms Selection Thanks to a Dedicated User Interface: A Pilot Study on Five Medical Conditions.

Front Pharmacol. 2019;10:50

Authors: Souvignet J, Declerck G, Trombert-Paviot B, Asfari H, Jaulent MC, Bousquet C

Abstract
Background: Searching into the MedDRA terminology is usually limited to a hierarchical search, and/or a string search. Our objective was to compare user performances when using a new kind of user interface enabling semantic queries versus classical methods, and evaluating term selection improvement in MedDRA. Methods: We implemented a forms-based web interface: OntoADR Query Tools (OQT). It relies on OntoADR, a formal resource describing MedDRA terms using SNOMED CT concepts and corresponding semantic relations, enabling terminological reasoning. We then compared time spent on five examples of medical conditions using OQT or the MedDRA web-based browser (MWB), and precision and recall of the term selection. Results: OntoADR Query Tools allows the user to search in MedDRA: One may enter search criteria by selecting one semantic property from a dropdown list and one or more SNOMED CT concepts related to the range of the chosen property. The user is assisted in building his query: he can add criteria and combine them. Then, the interface displays the set of MedDRA terms matching the query. Meanwhile, on average, the time spent on OQT (about 4 min 30 s) is significantly lower (-35%; p < 0.001) than time spent on MWB (about 7 min). The results of the System Usability Scale (SUS) gave a score of 62.19 for OQT (rated as good). We also demonstrated increased precision (+27%; p = 0.01) and recall (+34%; p = 0.02). Computed "performance" (correct terms found per minute) is more than three times better with OQT than with MWB. Discussion: This pilot study establishes the feasibility of our approach based on our initial assumption: performing MedDRA queries on the five selected medical conditions, using terminological reasoning, expedites term selection, and improves search capabilities for pharmacovigilance end users. Evaluation with a larger number of users and medical conditions are required in order to establish if OQT is appropriate for the needs of different user profiles, and to check if conclusions can be extended to other kinds of medical conditions. The application is currently limited by the non-exhaustive coverage of MedDRA by OntoADR, but nevertheless shows good performance which encourages continuing in the same direction.

PMID: 30792654 [PubMed]

A Semantic-Enabled Platform for Realizing an Interoperable Web of Things.

Sensors (Basel). 2019 Feb 19;19(4):

Authors: Lanza J, Sánchez L, Gómez D, Santana JR, Sotres P

Abstract
Nowadays, the Internet of Things (IoT) ecosystem is experiencing a lack of interoperability across the multiple competing platforms that are available. Consequently, service providers can only access vertical data silos that imply high costs and jeopardize their solutions market potential. It is necessary to transform the current situation with competing non-interoperable IoT platforms into a common ecosystem enabling the emergence of cross-platform, cross-standard, and cross-domain IoT services and applications. This paper presents a platform that has been implemented for realizing this vision. It leverages semantic web technologies to address the two key challenges in expanding the IoT beyond product silos into web-scale open ecosystems: data interoperability and resources identification and discovery. The paper provides extensive description of the proposed solution and its implementation details. Regarding the implementation details, it is important to highlight that the platform described in this paper is currently supporting the federation of eleven IoT deployments (from heterogeneous application domains) with over 10,000 IoT devices overall which produce hundreds of thousands of observations per day.

PMID: 30791498 [PubMed - in process]

Cultural influences on perception of disability and disabled people: a comparison of opinions from students in the United Kingdom (UK) Pakistan (PAK) about a generic wheelchair using a semantic differential scale.

Disabil Rehabil Assist Technol. 2019 Feb 19;:1-13

Authors: Asghar S, Edward Torrens G, Harland R

Abstract
Assistive Technology (AT) product use occurs within a socio-cultural setting. The growth internationally in the AT product market suggests that designers need to be aware of the influences that diverse cultures may have on the societal perception of an AT product through its semantic attributes. The study aimed to evaluate the visual interaction with an AT product by young adults from Pakistan, a collectivist society, and the United Kingdom (UK), an individualist society. A paper-based questionnaire survey was carried out with 281 first-year undergraduate students from the UK and Pakistan to evaluate their perception towards the visual representation of a generic conventional wheelchair image. A semantics differential (SD) scale method was used involving a seven-point bipolar SD scale incorporating sixteen pairs of adjectives defining functional, meaning, and usability attributes of the product. The mean (M) and standard deviation (sd) values were obtained for each pair of adjectives and compared between both groups by employing appropriate parametric tests. The results show that having a diverse cultural background did not appear to have overtly influenced the meanings ascribed to the generic manual wheelchair, which was unexpected. The University 'Internationalist' environment may have influenced the results. Some minor but critical differences were found for some pairs of adjectives (bulky-compact, heavy-light), having p-value less than .05 (p < .05) that related to previous experience of wheelchairs and/or their use. Further studies are planned to investigate and validate outcomes with other student and non-student groups. Implications for Rehabilitation The semantic attributes of assistive technologies highlight a number of aspects that have implications for those involved in Assistive Technology (AT) product development, manufacturing and marketing. • For online sales, the AT products rely on the web page image to communicate the purpose and attributes of the product. There are limited explorations related to the semantic/communicative attributes of AT product presented in images, as perceived by individuals from diverse cultural backgrounds. • The knowledge towards semantic meaning ascribed to the AT product is important to investigate to provide a perspective that goes beyond practical functions of the AT product towards the communicative function. • Information of comprehending semantics and significance of the AT product from a social (non-users) viewpoint may benefits manufacturers in the development of AT products that best meet the societal needs, preferences and expectations. • A model of best practice, with a focus on semantic manipulation will offer Industrial Designers (ID) internationally with the suitable process and tools to reframe perceptions of disability and enhance acceptance of AT products not only for users, but also for the society around them.

PMID: 30776927 [PubMed - as supplied by publisher]

Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy.

Lab Invest. 2019 Feb 15;:

Authors: Signaevsky M, Prastawa M, Farrell K, Tabish N, Baldwin E, Han N, Iida MA, Koll J, Bryce C, Purohit D, Haroutunian V, McKee AC, Stein TD, White CL, Walker J, Richardson TE, Hanson R, Donovan MJ, Cordon-Cardo C, Zeineh J, Fernandez G, Crary JF

Abstract
Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessments. Recently, powerful machine learning-based approaches have emerged, allowing the recognition and quantification of pathological changes from digital images. Here, we applied deep learning to the neuropathological assessment of NFT in postmortem human brain tissue to develop a classifier capable of recognizing and quantifying tau burden. The histopathological material was derived from 22 autopsy brains from patients with tauopathies. We used a custom web-based informatics platform integrated with an in-house information management system to manage whole slide images (WSI) and human expert annotations as ground truth. We utilized fully annotated regions to train a deep learning fully convolutional neural network (FCN) implemented in PyTorch against the human expert annotations. We found that the deep learning framework is capable of identifying and quantifying NFT with a range of staining intensities and diverse morphologies. With our FCN model, we achieved high precision and recall in naive WSI semantic segmentation, correctly identifying tangle objects using a SegNet model trained for 200 epochs. Our FCN is efficient and well suited for the practical application of WSIs with average processing times of 45 min per WSI per GPU, enabling reliable and reproducible large-scale detection of tangles. We measured performance on test data of 50 pre-annotated regions on eight naive WSI across various tauopathies, resulting in the recall, precision, and an F1 score of 0.92, 0.72, and 0.81, respectively. Machine learning is a useful tool for complex pathological assessment of AD and other tauopathies. Using deep learning classifiers, we have the potential to integrate cell- and region-specific annotations with clinical, genetic, and molecular data, providing unbiased data for clinicopathological correlations that will enhance our knowledge of the neurodegeneration.

PMID: 30770886 [PubMed - as supplied by publisher]

An Ontology Approach for Knowledge Representation of ECG Data.

Stud Health Technol Inform. 2019;257:520-525

Authors: Zouri M, Zouri N, Ferworn A

Abstract
The number of features that can be extracted from ECG signals has increased with the advancement in signal processing techniques. At the same time, there is an increase in research efforts to support efficient and effective analysis and interpretation of these signals. In this paper, we propose the use of ontology for knowledge representation and discovery of ECG data. Given the lack of a widely acceptable standards, the use of ontology can support the establishment of common understanding of the kind of knowledge that can be extracted from the ECG data and shared among various heterogeneous systems. The proposed ontology is both platform and application independent. Furthermore, it is possible to enrich the proposed ontology with new knowledge that may not explicitly be expressed in the data.

PMID: 30741250 [PubMed - in process]

The role of stress in drug addiction. An integrative review.

Physiol Behav. 2019 Jan 31;:

Authors: Ruisoto P, Contador I

Abstract
BACKGROUND: The high prevalence and burden to society of drug abuse and addiction is undisputed. However, its conceptualisation as a brain disease is controversial, and available interventions insufficient. Research on the role of stress in drug addiction may bridge positions and develop more effective interventions.
AIM: The aim of this paper is to integrate the most influential literature to date on the role of stress in drug addiction.
METHODS: A literature search was conducted of the core collections of Web of Science and Semantic Scholar on the topic of stress and addiction from a neurobiological perspective in humans. The most frequently cited articles and related references published in the last decade were finally redrafted into a narrative review based on 130 full-text articles.
RESULTS AND DISCUSSION: First, a brief overview of the neurobiology of stress and drug addiction is provided. Then, the role of stress in drug addiction is described. Stress is conceptualised as a major source of allostatic load, which result in progressive long-term changes in the brain, leading to a drug-prone state characterized by craving and increased risk of relapse. The effects of stress on drug addiction are mainly mediated by the action of corticotropin-releasing factor and other stress hormones, which weaken the hippocampus and prefrontal cortex and strengthen the amygdala, leading to a negative emotional state, craving and lack of executive control, increasing the risk of relapse. Both, drugs and stress result in an allostatic overload responsible for neuroadaptations involved in most of the key features of addiction: reward anticipation/craving, negative affect, and impaired executive functions, involved in three stages of addiction and relapse.
CONCLUSION: This review elucidates the crucial role of stress in drug addiction and highlights the need to incorporate the social context where brain-behaviour relationships unfold into the current model of addition.

PMID: 30711532 [PubMed - as supplied by publisher]

VEO-Engine: Interfacing and Reasoning with an Emotion Ontology for Device Visual Expression.

HCI Int 2018 Posters Ext Abstr (2018). 2018 Jul;851:349-355

Authors: Amith M, Lin R, Liang C, Gong Y, Tao C

Abstract
In order for machines to understand or express emotion to users, the specific emotions must be formally defined and the software coded to how those emotions are to be expressed. This is particularly important if devices or computer-based tools are utilized in clinical settings, which may be stressful for patients and where emotions can dominate their decision making. We have reported our development and feasibility results of an ontology, Visualized Emotion Ontology (VEO), that links abstract visualizations that express specific emotions. Here, we used VEO with the VEO-Engine, a software API package that interfaces with the VEO. The VEO-Engine was developed in Java using Apache Jena and OWL-API. The software package was tested on a Raspberry Pi machine with a small touchscreen display that linked each visualization to an emotion. The VEO-Engine stores input parameters of emotional situations and valences to reason and interpret users' emotions using the ontology-based reasoner. With this software, devices can interfaced wirelessly, so smart devices with visual displays can interact with the ontology. By means of the VEO-Engine, we show the portability and usability of the VEO in human-computer interaction.

PMID: 30701263 [PubMed]

Complexity in disease management: A linked data analysis of multimorbidity in Aboriginal and non-Aboriginal patients hospitalised with atherothrombotic disease in Western Australia.

PLoS One. 2018;13(8):e0201496

Authors: Hussain MA, Katzenellenbogen JM, Sanfilippo FM, Murray K, Thompson SC

Abstract
BACKGROUND: Hospitalisation for atherothrombotic disease (ATD) is expected to rise in coming decades. However, increasingly, associated comorbidities impose challenges in managing patients and deciding appropriate secondary prevention. We investigated the prevalence and pattern of multimorbidity (presence of two or more chronic conditions) in Aboriginal and non-Aboriginal Western Australian residents with ATDs.
METHODS AND FINDINGS: We used population-based de-identified linked administrative health data from 1 January 2000 to 30 June 2014 to identify a cohort of patients aged 25-59 years admitted to Western Australian hospitals with a discharge diagnosis of ATD. The prevalence of common chronic diseases in these patients was estimated and the patterns of comorbidities and multimorbidities empirically explored using two different approaches: identification of the most commonly occurring pairs and triplets of comorbid diseases, and through latent class analysis (LCA). Half of the cohort had multimorbidity, although this was much higher in Aboriginal people (Aboriginal: 79.2% vs. non-Aboriginal: 39.3%). Only a quarter were without any documented comorbidities. Hypertension, diabetes, alcohol abuse disorders and acid peptic diseases were the leading comorbidities in the major comorbid combinations across both Aboriginal and non-Aboriginal cohorts. The LCA identified four and six distinct clinically meaningful classes of multimorbidity for Aboriginal and non-Aboriginal patients, respectively. Out of the six groups in non-Aboriginal patients, four were similar to the groups identified in Aboriginal patients. The largest proportion of patients (33% in Aboriginal and 66% in non-Aboriginal) was assigned to the "minimally diseased" (or relatively healthy) group, with most patients having less than two conditions. Other groups showed variability in degree and pattern of multimorbidity.
CONCLUSION: Multimorbidity is common in ATD patients and the comorbidities tend to interact and cluster together. Physicians need to consider these in their clinical practice. Different treatment and secondary prevention strategies are likely to be useful for management in these cluster groups.

PMID: 30106971 [PubMed - indexed for MEDLINE]

Optimising the use of linked administrative data for infectious diseases research in Australia.

Public Health Res Pract. 2018 Jun 14;28(2):

Authors: Moore HC, Blyth CC

Abstract
Infectious diseases remain a major cause of morbidity in Australia. A wealth of data exists in administrative datasets, which are linked through established data-linkage infrastructure in most Australian states and territories. These linkages can support robust studies to investigate the burden of disease, the relative contribution of various aetiological agents to disease, and the effectiveness of population-based prevention policies - research that is critical to the success of current and future vaccination programs. At a recent symposium in Perth, epidemiologists, clinicians and policy makers in the infectious diseases field discussed the various benefits of, and barriers to, data-linkage research, with a focus on respiratory infection research. A number of issues and recommendations emerged. The demand for data-linkage projects is starting to outweigh the capabilities of exisiting data-linkage infrastructure. There is a need to further streamline processes relating to data access, increase data sharing and conduct nationally collaborative projects. Concerns about data security and sharing across jurisdictional borders can be addressed through multiple safe data solutions. Researchers need to do more to ensure that the benefits of linking datasets to answer policy-relevant questions are being realised for the benefit of community groups, government authorities, funding bodies and policy makers. Increased collaboration and engagement across all sectors can optimise the use of linked data to help reduce the burden of infectious diseases.

PMID: 29925082 [PubMed - indexed for MEDLINE]

queryMed: Semantic Web functions for linking pharmacological and medical knowledge to data.

Bioinformatics. 2019 Jan 18;:

Authors: Rivault Y, Dameron O, Le Meur N

Abstract
Summary: In public health research and more precisely in the reuse of electronic health data, selecting patients, identifying specific events and interpreting results typically requires biomedical knowledge. The queryMed R package aims to facilitate the integration of medical and pharmacological knowledge stored in formats compliant with the Linked Data paradigm (e.g. OWL ontologies and RDF datasets) into the R statistical programming environment. We show how it allowed us to identify all the drugs prescribed for critical limb ischemia (CLI) and also to detect one contraindicated prescription for one patient by linking a medical database of 1003 CLI patients to ontologies.
Availability: queryMed is readily usable for medical data mappings and enrichment. Sources, R vignettes and test data are available on GitHub (https://github.com/yannrivault/queryMed) and are archived on Zenodo (https://doi.org/10.5281/zenodo.1323481).

PMID: 30657867 [PubMed - as supplied by publisher]

Unsupervised Low-Dimensional Vector Representations for Words, Phrases and Text that are Transparent, Scalable, and produce Similarity Metrics that are not Redundant with Neural Embeddings.

J Biomed Inform. 2019 Jan 14;:103096

Authors: Smalheiser NR, Cohen AM, Bonifield G

Abstract
Neural embeddings are a popular set of methods for representing words, phrases or text as a low dimensional vector (typically 50-500 dimensions). However, it is difficult to interpret these dimensions in a meaningful manner, and creating neural embeddings requires extensive training and tuning of multiple parameters and hyperparameters. We present here a simple unsupervised method for representing words, phrases or text as a low dimensional vector, in which the meaning and relative importance of dimensions is transparent to inspection. We have created a near-comprehensive vector representation of words, and selected bigrams, trigrams and abbreviations, using the set of titles and abstracts in PubMed as a corpus. This vector is used to create several novel implicit word-word and text-text similarity metrics. The implicit word-word similarity metrics correlate well with human judgement of word pair similarity and relatedness, and outperform or equal all other reported methods on a variety of biomedical benchmarks, including several implementations of neural embeddings trained on PubMed corpora. Our implicit word-word metrics capture different aspects of word-word relatedness than word2vec-based metrics and are only partially correlated (rho = 0.5-0.8 depending on task and corpus). The vector representations of words, bigrams, trigrams, abbreviations, and PubMed title+abstracts are all publicly available from http://arrowsmith.psych.uic.edu/arrowsmith_uic/word_similarity_metrics.html for release under CC-BY-NC license. Several public web query interfaces are also available at the same site, including one which allows the user to specify a given word and view its most closely related terms according to direct co-occurrence as well as different implicit similarity metrics.

PMID: 30654030 [PubMed - as supplied by publisher]

Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives.

Comput Biol Med. 2018 03 01;94:1-10

Authors: Sabra S, Mahmood Malik K, Alobaidi M

Abstract
Venous thromboembolism (VTE) is the third most common cardiovascular disorder. It affects people of both genders at ages as young as 20 years. The increased number of VTE cases with a high fatality rate of 25% at first occurrence makes preventive measures essential. Clinical narratives are a rich source of knowledge and should be included in the diagnosis and treatment processes, as they may contain critical information on risk factors. It is very important to make such narrative blocks of information usable for searching, health analytics, and decision-making. This paper proposes a Semantic Extraction and Sentiment Assessment of Risk Factors (SESARF) framework. Unlike traditional machine-learning approaches, SESARF, which consists of two main algorithms, namely, ExtractRiskFactor and FindSeverity, prepares a feature vector as the input to a support vector machine (SVM) classifier to make a diagnosis. SESARF matches and maps the concepts of VTE risk factors and finds adjectives and adverbs that reflect their levels of severity. SESARF uses a semantic- and sentiment-based approach to analyze clinical narratives of electronic health records (EHR) and then predict a diagnosis of VTE. We use a dataset of 150 clinical narratives, 80% of which are used to train our prediction classifier support vector machine, with the remaining 20% used for testing. Semantic extraction and sentiment analysis results yielded precisions of 81% and 70%, respectively. Using a support vector machine, prediction of patients with VTE yielded precision and recall values of 54.5% and 85.7%, respectively.

PMID: 29353160 [PubMed - indexed for MEDLINE]

Fuzzy Ontology and LSTM-Based Text Mining: A Transportation Network Monitoring System for Assisting Travel.

Sensors (Basel). 2019 Jan 09;19(2):

Authors: Ali F, El-Sappagh S, Kwak D

Abstract
Intelligent Transportation Systems (ITSs) utilize a sensor network-based system to gather and interpret traffic information. In addition, mobility users utilize mobile applications to collect transport information for safe traveling. However, these types of information are not sufficient to examine all aspects of the transportation networks. Therefore, both ITSs and mobility users need a smart approach and social media data, which can help ITSs examine transport services, support traffic and control management, and help mobility users travel safely. People utilize social networks to share their thoughts and opinions regarding transportation, which are useful for ITSs and travelers. However, user-generated text on social media is short in length, unstructured, and covers a broad range of dynamic topics. The application of recent Machine Learning (ML) approach is inefficient for extracting relevant features from unstructured data, detecting word polarity of features, and classifying the sentiment of features correctly. In addition, ML classifiers consistently miss the semantic feature of the word meaning. A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach. The proposed fuzzy ontology describes semantic knowledge about entities and features and their relation in the transportation domain. Fuzzy ontology and smart methodology are developed in Web Ontology Language and Java, respectively. By utilizing word embedding with fuzzy ontology as a representation of text, Bi-LSTM shows satisfactory improvement in both the extraction of features and the classification of the unstructured text of social media.

PMID: 30634527 [PubMed - in process]

Assessing HPV vaccination perceptions with online social media in Italy.

Int J Gynecol Cancer. 2019 Jan 10;:

Authors: Angioli R, Casciello M, Lopez S, Plotti F, Minco LD, Frati P, Fineschi V, Panici PB, Scaletta G, Capriglione S, Miranda A, Feole L, Terranova C

Abstract
OBJECTIVE: Because of the widespread availability of the internet and social media, people often collect and disseminate news online making it important to understand the underlying mechanisms to steer promotional strategies in healthcare. The aim of this study is to analyze perceptions regarding the human papillomavirus (HPV) vaccine in Italy.
METHODS: From August 2015 to July 2016, articles, news, posts, and tweets were collected from social networks, posts on forums, blogs, and pictures about HPV. Using other keywords and specific semantic rules, we selected conversations presenting the negative or positive perceptions of HPV. We divided them into subgroups depending on the website, publication date, authors, main theme, and transmission modality.
RESULTS: Most conversations occurred on social networks. Of all the conversations regarding HPV, more than 50% were about vaccination. With regard to conversations exclusively on the HPV vaccine, 47%, 32%, and 21% were positive, negative and neutral, respectively. Only 9% of the conversations mentioned the vaccine trade name and, in these conversations, perception was almost always negative. We observed many peaks in positive conversation trends compared with negative trends. The peaks were related to the web dissemination of particular news regarding HPV vaccination.
CONCLUSIONS: In this study we have shown how mass media influences the diffusion of both negative and positive perceptions about HPV vaccines and suggest better ways to inform people about the importance of HPV vaccination.

PMID: 30630890 [PubMed - as supplied by publisher]

TogoGenome/TogoStanza: modularized Semantic Web genome database.

Database (Oxford). 2019 Jan 01;2019:

Authors: Katayama T, Kawashima S, Okamoto S, Moriya Y, Chiba H, Naito Y, Fujisawa T, Mori H, Takagi T

Abstract
TogoGenome is a genome database that is purely based on the Semantic Web technology, which enables the integration of heterogeneous data and flexible semantic searches. All the information is stored as Resource Description Framework (RDF) data, and the reporting web pages are generated on the fly using SPARQL Protocol and RDF Query Language (SPARQL) queries. TogoGenome provides a semantic-faceted search system by gene functional annotation, taxonomy, phenotypes and environment based on the relevant ontologies. TogoGenome also serves as an interface to conduct semantic comparative genomics by which a user can observe pan-organism or organism-specific genes based on the functional aspect of gene annotations and the combinations of organisms from different taxa. The TogoGenome database exhibits a modularized structure, and each module in the report pages is separately served as TogoStanza, which is a generic framework for rendering an information block as IFRAME/Web Components, which can, unlike several other monolithic databases, also be reused to construct other databases. TogoGenome and TogoStanza have been under development since 2012 and are freely available along with their source codes on the GitHub repositories at https://github.com/togogenome/ and https://github.com/togostanza/, respectively, under the MIT license.

PMID: 30624651 [PubMed - in process]

Investigating the role of interleukin-1 beta and glutamate in inflammatory bowel disease and epilepsy using discovery browsing.

J Biomed Semantics. 2018 Dec 27;9(1):25

Authors: Rindflesch TC, Blake CL, Cairelli MJ, Fiszman M, Zeiss CJ, Kilicoglu H

Abstract
BACKGROUND: Structured electronic health records are a rich resource for identifying novel correlations, such as co-morbidities and adverse drug reactions. For drug development and better understanding of biomedical phenomena, such correlations need to be supported by viable hypotheses about the mechanisms involved, which can then form the basis of experimental investigations.
METHODS: In this study, we demonstrate the use of discovery browsing, a literature-based discovery method, to generate plausible hypotheses elucidating correlations identified from structured clinical data. The method is supported by Semantic MEDLINE web application, which pinpoints interesting concepts and relevant MEDLINE citations, which are used to build a coherent hypothesis.
RESULTS: Discovery browsing revealed a plausible explanation for the correlation between epilepsy and inflammatory bowel disease that was found in an earlier population study. The generated hypothesis involves interleukin-1 beta (IL-1 beta) and glutamate, and suggests that IL-1 beta influence on glutamate levels is involved in the etiology of both epilepsy and inflammatory bowel disease.
CONCLUSIONS: The approach presented in this paper can supplement population-based correlation studies by enabling the scientist to identify literature that may justify the novel patterns identified in such studies and can underpin basic biomedical research that can lead to improved treatments and better healthcare outcomes.

PMID: 30587224 [PubMed - in process]

Big Data analysis to improve care for people living with serious illness: The potential to use new emerging technology in palliative care.

Palliat Med. 2018 01;32(1):164-166

Authors: Nwosu AC, Collins B, Mason S

PMID: 28805118 [PubMed - indexed for MEDLINE]

Identifying Principles for the Construction of an Ontology-Based Knowledge Base: A Case Study Approach.

JMIR Med Inform. 2018 Dec 21;6(4):e52

Authors: Jing X, Hardiker NR, Kay S, Gao Y

Abstract
BACKGROUND: Ontologies are key enabling technologies for the Semantic Web. The Web Ontology Language (OWL) is a semantic markup language for publishing and sharing ontologies.
OBJECTIVE: The supply of customizable, computable, and formally represented molecular genetics information and health information, via electronic health record (EHR) interfaces, can play a critical role in achieving precision medicine. In this study, we used cystic fibrosis as an example to build an Ontology-based Knowledge Base prototype on Cystic Fibrobis (OntoKBCF) to supply such information via an EHR prototype. In addition, we elaborate on the construction and representation principles, approaches, applications, and representation challenges that we faced in the construction of OntoKBCF. The principles and approaches can be referenced and applied in constructing other ontology-based domain knowledge bases.
METHODS: First, we defined the scope of OntoKBCF according to possible clinical information needs about cystic fibrosis on both a molecular level and a clinical phenotype level. We then selected the knowledge sources to be represented in OntoKBCF. We utilized top-to-bottom content analysis and bottom-up construction to build OntoKBCF. Protégé-OWL was used to construct OntoKBCF. The construction principles included (1) to use existing basic terms as much as possible; (2) to use intersection and combination in representations; (3) to represent as many different types of facts as possible; and (4) to provide 2-5 examples for each type. HermiT 1.3.8.413 within Protégé-5.1.0 was used to check the consistency of OntoKBCF.
RESULTS: OntoKBCF was constructed successfully, with the inclusion of 408 classes, 35 properties, and 113 equivalent classes. OntoKBCF includes both atomic concepts (such as amino acid) and complex concepts (such as "adolescent female cystic fibrosis patient") and their descriptions. We demonstrated that OntoKBCF could make customizable molecular and health information available automatically and usable via an EHR prototype. The main challenges include the provision of a more comprehensive account of different patient groups as well as the representation of uncertain knowledge, ambiguous concepts, and negative statements and more complicated and detailed molecular mechanisms or pathway information about cystic fibrosis.
CONCLUSIONS: Although cystic fibrosis is just one example, based on the current structure of OntoKBCF, it should be relatively straightforward to extend the prototype to cover different topics. Moreover, the principles underpinning its development could be reused for building alternative human monogenetic diseases knowledge bases.

PMID: 30578220 [PubMed]