Using Natural Language Processing Techniques to Provide Personalized Educational Materials for Chronic Disease Patients in China: Development and Assessment of a Knowledge-Based Health Recommender System.

JMIR Med Inform. 2020 Apr 23;8(4):e17642

Authors: Wang Z, Huang H, Cui L, Chen J, An J, Duan H, Ge H, Deng N

Abstract
BACKGROUND: Health education emerged as an important intervention for improving the awareness and self-management abilities of chronic disease patients. The development of information technologies has changed the form of patient educational materials from traditional paper materials to electronic materials. To date, the amount of patient educational materials on the internet is tremendous, with variable quality, which makes it hard to identify the most valuable materials by individuals lacking medical backgrounds.
OBJECTIVE: The aim of this study was to develop a health recommender system to provide appropriate educational materials for chronic disease patients in China and evaluate the effect of this system.
METHODS: A knowledge-based recommender system was implemented using ontology and several natural language processing (NLP) techniques. The development process was divided into 3 stages. In stage 1, an ontology was constructed to describe patient characteristics contained in the data. In stage 2, an algorithm was designed and implemented to generate recommendations based on the ontology. Patient data and educational materials were mapped to the ontology and converted into vectors of the same length, and then recommendations were generated according to similarity between these vectors. In stage 3, the ontology and algorithm were incorporated into an mHealth system for practical use. Keyword extraction algorithms and pretrained word embeddings were used to preprocess educational materials. Three strategies were proposed to improve the performance of keyword extraction. System evaluation was based on a manually assembled test collection for 50 patients and 100 educational documents. Recommendation performance was assessed using the macro precision of top-ranked documents and the overall mean average precision (MAP).
RESULTS: The constructed ontology contained 40 classes, 31 object properties, 67 data properties, and 32 individuals. A total of 80 SWRL rules were defined to implement the semantic logic of mapping patient original data to the ontology vector space. The recommender system was implemented as a separate Web service connected with patients' smartphones. According to the evaluation results, our system can achieve a macro precision up to 0.970 for the top 1 recommendation and an overall MAP score up to 0.628.
CONCLUSIONS: This study demonstrated that a knowledge-based health recommender system has the potential to accurately recommend educational materials to chronic disease patients. Traditional NLP techniques combined with improvement strategies for specific language and domain proved to be effective for improving system performance. One direction for future work is to explore the effect of such systems from the perspective of patients in a practical setting.

PMID: 32324148 [PubMed - as supplied by publisher]

BioHackathon 2015: Semantics of data for life sciences and reproducible research.

F1000Res. 2020;9:136

Authors: Vos RA, Katayama T, Mishima H, Kawano S, Kawashima S, Kim JD, Moriya Y, Tokimatsu T, Yamaguchi A, Yamamoto Y, Wu H, Amstutz P, Antezana E, Aoki NP, Arakawa K, Bolleman JT, Bolton E, Bonnal RJP, Bono H, Burger K, Chiba H, Cohen KB, Deutsch EW, Fernández-Breis JT, Fu G, Fujisawa T, Fukushima A, García A, Goto N, Groza T, Hercus C, Hoehndorf R, Itaya K, Juty N, Kawashima T, Kim JH, Kinjo AR, Kotera M, Kozaki K, Kumagai S, Kushida T, Lütteke T, Matsubara M, Miyamoto J, Mohsen A, Mori H, Naito Y, Nakazato T, Nguyen-Xuan J, Nishida K, Nishida N, Nishide H, Ogishima S, Ohta T, Okuda S, Paten B, Perret JL, Prathipati P, Prins P, Queralt-Rosinach N, Shinmachi D, Suzuki S, Tabata T, Takatsuki T, Taylor K, Thompson M, Uchiyama I, Vieira B, Wei CH, Wilkinson M, Yamada I, Yamanaka R, Yoshitake K, Yoshizawa AC, Dumontier M, Kosaki K, Takagi T

Abstract
We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

PMID: 32308977 [PubMed - in process]

Design, development and validation of a system for automatic help to medical text understanding.

Int J Med Inform. 2020 Mar 04;138:104109

Authors: Alfano M, Lenzitti B, Lo Bosco G, Muriana C, Piazza T, Vizzini G

Abstract
OBJECTIVE: The paper presents a web-based application, SIMPLE, that facilitates medical text comprehension by identifying the health-related terms of a medical text and providing the corresponding consumer terms and explanations.
BACKGROUND: The comprehension of a medical text is often a difficult task for laypeople because it requires semantic abilities that can differ from a person to another, depending on his/her health-literacy level. Some systems have been developed for facilitating the comprehension of medical texts through text simplification, either syntactical or lexical. The ones dealing with lexical simplification usually replace the original text and do not provide additional information. We have developed a system that provides the consumer terms alongside the original medical terms and also adds consumer explanations. Moreover, differently from other solutions, our system works with multiple languages.
METHODS: We have developed the SIMPLE application that is able to automatically: 1) identify medical terms in a medical text by using medical vocabularies; 2) translate the medical terms into consumer terms through medical-consumer thesauri; 3) provide term explanations by using health-consumer dictionaries. SIMPLE can be used as a standalone web application or can it be embedded into common health platforms for real time identification and explanation of medical terms. At present, it works with English and Italian texts but it can be easily extended to other languages. We have run subjective tests with both medical experts and non-experts as well as objective tests to verify the effectiveness of SIMPLE and its simplicity of use.
RESULTS: Non-experts found SIMPLE easy to use and responsive. The big majority of respondents confirmed they were helped by SIMPLE in understanding medical texts and declared their willingness to continue using SIMPLE and to recommend it to other people. The subjective tests, conducted with medical experts on a set of Italian radiology reports, showed an agreement between SIMPLE and the experts, on the highlighted medical terms, that ranges between 74.05 % and 81.16 % as well as an agreement of around 60 % on the consumer term translation. The objective tests showed that the consumer terms, provided by SIMPLE, are, on average, eighteen times more familiar than the relative medical terms so proving, once more, the effectiveness of SIMPLE in simplifying the medical terms.
CONCLUSIONS: The performed tests demonstrate the effectiveness of SIMPLE, its simplicity of use and the willingness of people in continuing with its use. SIMPLE provides, with a good agreement level, the same information that medical experts would provide. Finally, the consumer terms are 'objectively' more familiar than the related technical terms and as a consequence, much easier to understand.

PMID: 32305022 [PubMed - as supplied by publisher]

Engaging with big data: Occupational therapy needs to recognise the potential of using linked data to support evidence-based practice.

Aust Occup Ther J. 2019 02;66(1):3-4

Authors: Cordier R, Ferrante A

PMID: 30714629 [PubMed - indexed for MEDLINE]

Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm.

Sensors (Basel). 2020 Apr 06;20(7):

Authors: Xue X, Chen J

Abstract
Semantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems' inter-operability, which requires that the sensor ontologies themselves are inter-operable. Therefore, it is necessary to match the sensor ontologies by establishing the meaningful links between semantically related sensor information. Since the Swarm Intelligent Algorithm (SIA) represents a good methodology for addressing the ontology matching problem, we investigate a popular SIA, that is, the Firefly Algorithm (FA), to optimize the ontology alignment. To save the memory consumption and better trade off the algorithm's exploitation and exploration, in this work, we propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA), which combines the compact encoding mechanism with the co-Evolutionary mechanism. Our proposal utilizes the Gray code to encode the solutions, two compact operators to respectively implement the exploiting strategy and exploring strategy, and two Probability Vectors (PVs) to represent the swarms that respectively focuses on the exploitation and exploration. Through the communications between two swarms in each generation, CcFA is able to efficiently improve the searching efficiency when addressing the sensor ontology matching problem. The experiment utilizes the Conference track and three pairs of real sensor ontologies to test our proposal's performance. The statistical results show that CcFA based ontology matching technique can effectively match the sensor ontologies and other general ontologies in the domain of organizing conferences.

PMID: 32268547 [PubMed - in process]

The Innovative and Sustainable Use of Dental Panoramic Radiographs for the Detection of Osteoporosis.

Int J Environ Res Public Health. 2020 Apr 03;17(7):

Authors: Yeung AWK, Mozos I

Abstract
This bibliometric study evaluated the scientific impact of papers dealing with osteoporosis detected by dental panoramic radiographs by performing citation analysis and cited reference analysis. Retrospective data was extracted from the Web of Science Core Collection database and imported into VOSviewer, CRExplorer, and CitNetExplorer for analyzing semantic contents, cited references, and temporal citation network. The 280 relevant papers identified were cited 4874 times, having an h-index of 38 and 17.4 citations per paper. The top five major contributing countries were Japan (n = 54, 19.3%), USA (n = 43, 15.4%), Brazil (n = 38, 13.6%), Turkey (n = 38, 13.6%), and the UK (n = 32, 11.4%). Citation per paper correlated with publication count among the authors and institutions. Mandibular cortical width was the most frequently used and most cited measurement index. References published during the 1970s and 1980s have built the foundation for the development of research that investigates the potential associations between osteoporosis and radiographic measurements on panoramic radiographs. Osteoporosis detection by dental panoramic radiographs is a perennially investigated research topic with global contributions. Panoramic radiographs are considered early detection and screening tools for osteoporosis by worldwide research.

PMID: 32260243 [PubMed - in process]

Dynamically loading IFC models on a web browser based on spatial semantic partitioning.

Vis Comput Ind Biomed Art. 2019 Jun 03;2(1):4

Authors: Lu HL, Wu JX, Liu YS, Wang WQ

Abstract
Industry foundation classes (IFC) is an open and neutral data format specification for building information modeling (BIM) that plays a crucial role in facilitating interoperability. With increases in web-based BIM applications, there is an urgent need for fast loading large IFC models on a web browser. However, the task of fully loading large IFC models typically consumes a large amount of memory of a web browser or even crashes the browser, and this significantly limits further BIM applications. In order to address the issue, a method is proposed for dynamically loading IFC models based on spatial semantic partitioning (SSP). First, the spatial semantic structure of an input IFC model is partitioned via the extraction of story information and establishing a component space index table on the server. Subsequently, based on user interaction, only the model data that a user is interested in is transmitted, loaded, and displayed on the client. The presented method is implemented via Web Graphics Library, and this enables large IFC models to be fast loaded on the web browser without requiring any plug-ins. When compared with conventional methods that load all IFC model data for display purposes, the proposed method significantly reduces memory consumption in a web browser, thereby allowing the loading of large IFC models. When compared with the existing method of spatial partitioning for 3D data, the proposed SSP entirely uses semantic information in the IFC file itself, and thereby provides a better interactive experience for users.

PMID: 32240404 [PubMed - as supplied by publisher]

Big Trouble in Big Datasets: The Problem with Causality.

Eur J Vasc Endovasc Surg. 2020 03;59(3):446

Authors: Twine CP

PMID: 31740283 [PubMed - indexed for MEDLINE]

A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome.

Epilepsy Behav. 2020 Mar 27;106:107021

Authors: Chiang KL, Huang CY, Hsieh LP, Chang KP

Abstract
PURPOSE: The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiology-based approach. We developed a propositional artificial intelligence (AI) system based on the above concepts to support physicians in the diagnosis of epilepsy.
METHODS: We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsy-related knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic electroencephalography (EEG) findings, treatments, etc. We used the Ontology Web Language with Description Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship between these ontologies.
RESULTS: Dravet syndrome was taken as an illustration for epilepsy syndromes implementation. We designed an interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drool's execution, we successfully demonstrate the process of differential diagnosis.
CONCLUSION: We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis.

PMID: 32224446 [PubMed - as supplied by publisher]

Drug vector representation: a tool for drug similarity analysis.

Mol Genet Genomics. 2020 Mar 28;:

Authors: Lin L, Wan L, He H, Liu W

Abstract
DrugMatrix is a valuable toxicogenomic dataset, which provides in vivo transcriptome data corresponding to hundreds of chemical drugs. However, the relationships between drugs and how those drugs affect the biological process are still unknown. The high dimensionality of the microarray data hinders its application. The aims of this study are to (1) represent the transcriptome data by lower-dimensional vectors, (2) compare drug similarity, (3) represent drug combinations by adding vectors and (4) infer drug mechanism of action (MoA) and genotoxicity features. We borrowed the latent semantic analysis (LSA) technique from natural language processing to represent treatments (drugs with multiple concentrations and time points) by dense vectors, each dimension of which is an orthogonal biological feature. The gProfiler enrichment tool was used for the 100-dimensional vector feature annotation. The similarity between treatments vectors was calculated by the cosine function. Adding vectors may represent drug combinations, treatment times or treatment doses that are not presented in the original data. Drug-drug interaction pairs had a higher similarity than random drug pairs in the hepatocyte data. The vector features helped to reveal the MoA. Differential feature expression was also implicated for genotoxic and non-genotoxic carcinogens. An easy-to-use Web tool was developed by Shiny Web application framework for the exploration of treatment similarities and drug combinations (https://bioinformatics.fafu.edu.cn/drugmatrix/). We represented treatments by vectors and provided a tool that is useful for hypothesis generation in toxicogenomic, such as drug similarity, drug repurposing, combination therapy and MoA.

PMID: 32222838 [PubMed - as supplied by publisher]

MLCD: A Unified Software Package for Cancer Diagnosis.

JCO Clin Cancer Inform. 2020 Mar;4:290-298

Authors: Wu W, Li B, Mercan E, Mehta S, Bartlett J, Weaver DL, Elmore JG, Shapiro LG

Abstract
PURPOSE: Machine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health/National Cancer Institute (NIH/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy diagnosis and machine learning algorithms that can improve the quality of both clinical practice and ongoing research.
METHODS: Whole-slide images of 240 well-characterized breast biopsy cases, initially assembled under R01 CA140560, were used for developing the algorithms and training the machine learning models. This software package is based on the methodology developed and published under our recent NIH/NCI-sponsored research grant (R01 CA172343) for finding regions of interest (ROIs) in whole-slide breast biopsy images, for segmenting ROIs into histopathologic tissue types and for using this segmentation in classifiers that can suggest final diagnoses.
RESULT: The package provides an ROI detector for whole-slide images and modules for semantic segmentation into tissue classes and diagnostic classification into 4 classes (benign, atypia, ductal carcinoma in situ, invasive cancer) of the ROIs. It is available through the GitHub repository under the Massachusetts Institute of Technology license and will later be distributed with the Pathology Image Informatics Platform system. A Web page provides instructions for use.
CONCLUSION: Our tools have the potential to provide help to other cancer researchers and, ultimately, to practicing physicians and will motivate future research in this field. This article describes the methodology behind the software development and gives sample outputs to guide those interested in using this package.

PMID: 32216637 [PubMed - as supplied by publisher]

Enabling semantic queries across federated bioinformatics databases.

Database (Oxford). 2019 01 01;2019:

Authors: Sima AC, Mendes de Farias T, Zbinden E, Anisimova M, Gil M, Stockinger H, Stockinger K, Robinson-Rechavi M, Dessimoz C

Abstract
MOTIVATION: Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases.
RESULTS: We introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: (i) Bgee, a gene expression relational database; (ii) Orthologous Matrix (OMA), a Hierarchical Data Format 5 orthology DS; and (iii) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialized RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface.

PMID: 31697362 [PubMed - indexed for MEDLINE]

Deep learning based searching approach for RDF graphs.

PLoS One. 2020;15(3):e0230500

Authors: Soliman H

Abstract
The Internet is a remarkably complex technical system. Its rapid growth has also brought technical issues such as problems to information retrieval. Search engines retrieve requested information based on the provided keywords. Consequently, it is difficult to accurately find the required information without understanding the syntax and semantics of the content. Multiple approaches are proposed to resolve this problem by employing the semantic web and linked data techniques. Such approaches serialize the content using the Resource Description Framework (RDF) and execute the queries using SPARQL to resolve the problem. However, an exact match between RDF content and query structure is required. Although, it improves the keyword-based search; however, it does not provide probabilistic reasoning to find the semantic relationship between the queries and their results. From this perspective, in this paper, we propose a deep learning-based approach for searching RDF graphs. The proposed approach treats document requests as a classification problem. First, we preprocess the RDF graphs to convert them into N-Triples format. Second, bag-of-words (BOW) and word2vec feature modeling techniques are combined for a novel deep representation of RDF graphs. The attention mechanism enables the proposed approach to understand the semantic between RDF graphs. Third, we train a convolutional neural network for the accurate retrieval of RDF graphs using the deep representation. We employ 10-fold cross-validation to evaluate the proposed approach. The results show that the proposed approach is accurate and surpasses the state-of-the-art. The average accuracy, precision, recall, and f-measure are up to 97.12%, 98.17%, 95.56%, and 96.85%, respectively.

PMID: 32203547 [PubMed - as supplied by publisher]

Deep Saliency Hashing for Fine-grained Retrieval.

IEEE Trans Image Process. 2020 Mar 16;:

Authors: Jin S, Yao H, Sun X, Zhou S, Zhang L, Hua X

Abstract
In recent years, hashing methods have been proved to be effective and efficient for large-scale Web media search. However, the existing general hashing methods have limited discriminative power for describing fine-grained objects that share similar overall appearance but have a subtle difference. To solve this problem, we for the first time introduce the attention mechanism to the learning of fine-grained hashing codes. Specifically, we propose a novel deep hashing model, named deep saliency hashing (DSaH), which automatically mines salient regions and learns semantic-preserving hashing codes simultaneously. DSaH is a two-step end-to-end model consisting of an attention network and a hashing network. Our loss function contains three basic components, including the semantic loss, the saliency loss, and the quantization loss. As the core of DSaH, the saliency loss guides the attention network to mine discriminative regions from pairs of images.We conduct extensive experiments on both fine-grained and general retrieval datasets for performance evaluation. Experimental results on fine-grained datasets, including Oxford Flowers, Stanford Dogs, and CUB Birds demonstrate that our DSaH performs the best for the fine-grained retrieval task and beats the strongest competitor (DTQ) by approximately 10% on both Stanford Dogs and CUB Birds. DSaH is also comparable to several state-of-the-art hashing methods on CIFAR-10 and NUS-WIDE.

PMID: 32191885 [PubMed - as supplied by publisher]

NG-Tax 2.0: A Semantic Framework for High-Throughput Amplicon Analysis.

Front Genet. 2019;10:1366

Authors: Poncheewin W, Hermes GDA, van Dam JCJ, Koehorst JJ, Smidt H, Schaap PJ

Abstract
NG-Tax 2.0 is a semantic framework for FAIR high-throughput analysis and classification of marker gene amplicon sequences including bacterial and archaeal 16S ribosomal RNA (rRNA), eukaryotic 18S rRNA and ribosomal intergenic transcribed spacer sequences. It can directly use single or merged reads, paired-end reads and unmerged paired-end reads from long range fragments as input to generate de novo amplicon sequence variants (ASV). Using the RDF data model, ASV's can be automatically stored in a graph database as objects that link ASV sequences with the full data-wise and element-wise provenance, thereby achieving the level of interoperability required to utilize such data to its full potential. The graph database can be directly queried, allowing for comparative analyses of over thousands of samples and is connected with an interactive Rshiny toolbox for analysis and visualization of (meta) data. Additionally, NG-Tax 2.0 exports an extended BIOM 1.0 (JSON) file as starting point for further analyses by other means. The extended BIOM file contains new attribute types to include information about the command arguments used, the sequences of the ASVs formed, classification confidence scores and is backwards compatible. The performance of NG-Tax 2.0 was compared with DADA2, using the plugin in the QIIME 2 analysis pipeline. Fourteen 16S rRNA gene amplicon mock community samples were obtained from the literature and evaluated. Precision of NG-Tax 2.0 was significantly higher with an average of 0.95 vs 0.58 for QIIME2-DADA2 while recall was comparable with an average of 0.85 and 0.77, respectively. NG-Tax 2.0 is written in Java. The code, the ontology, a Galaxy platform implementation, the analysis toolbox, tutorials and example SPARQL queries are freely available at http://wurssb.gitlab.io/ngtax under the MIT License.

PMID: 32117417 [PubMed]

A Dynamic Dashboarding Application for Fleet Monitoring Using Semantic Web of Things Technologies.

Sensors (Basel). 2020 Feb 20;20(4):

Authors: Hautte SV, Moens P, Herwegen JV, Paepe D, Steenwinckel B, Verstichel S, Ongenae F, Hoecke SV

Abstract
In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications. These, as opposed to fixed-structure dashboard applications, allow users to create visualizations at will and do not have hard-coded sensor bindings. The state-of-the-art in dynamic dashboarding does not cope well with the frequent additions and removals of sensors that must be monitored-these changes must still be configured in the implementation or at runtime by a user. Also, the user is presented with an overload of sensors, aggregations and visualizations to select from, which may sometimes even lead to the creation of dashboard widgets that do not make sense. In this paper, we present a dynamic dashboard that overcomes these problems. Sensors, visualizations and aggregations can be discovered automatically, since they are provided as RESTful Web Things on a Web Thing Model compliant gateway. The gateway also provides semantic annotations of the Web Things, describing what their abilities are. A semantic reasoner can derive visualization suggestions, given the Thing annotations, logic rules and a custom dashboard ontology. The resulting dashboarding application automatically presents the available sensors, visualizations and aggregations that can be used, without requiring sensor configuration, and assists the user in building dashboards that make sense. This way, the user can concentrate on interpreting the sensor data and detecting and solving operational problems early.

PMID: 32093134 [PubMed]

Epidemiology of sepsis in cancer patients in Victoria, Australia: a population-based study using linked data.

Aust N Z J Public Health. 2020 Feb;44(1):53-58

Authors: Te Marvelde L, Whitfield A, Shepheard J, Read C, Milne RL, Whitfield K

Abstract
OBJECTIVE: To determine the clinical characteristics, outcomes and longitudinal trends of sepsis occurring in cancer patients.
METHOD: Retrospective study using statewide Victorian Cancer Registry data linked to various administrative datasets.
RESULTS: Among 215,763 incident cancer patients, incidence of sepsis within one year of cancer diagnosis was estimated at 6.4%. The incidence of sepsis was higher in men, younger patients, patients diagnosed with haematological malignancies and those with de novo metastatic disease. Of the 13,316 patients with a first admission with sepsis, 55% had one or more organ failures, 29% required care within an intensive care unit and 13% required mechanical ventilation. Treatments associated with the highest sepsis incidence were stem cell/bone marrow transplant (33%), major surgery (4.4%), chemotherapy (1.1%) and radical radiotherapy (0.6%). The incidence of sepsis with organ failure increased between 2008 and 2015, while 90-day mortality decreased.
CONCLUSIONS: Sepsis in patients with cancer has high mortality and occurs most frequently in the first year after cancer diagnosis. Implications for public health: The number of cancer patients diagnosed with sepsis is expected to increase, causing a substantial burden on patients and the healthcare system.

PMID: 31535416 [PubMed - indexed for MEDLINE]

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services.

Sensors (Basel). 2020 Feb 11;20(4):

Authors: Elsaleh T, Enshaeifar S, Rezvani R, Acton ST, Janeiko V, Bermudez-Edo M

Abstract
With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analyzed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally semantics are heavy to process and not ideal for Internet of things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.

PMID: 32053898 [PubMed - in process]

ClinEpiDB: an open-access clinical epidemiology database resource encouraging online exploration of complex studies.

Gates Open Res. 2019;3:1661

Authors: Ruhamyankaka E, Brunk BP, Dorsey G, Harb OS, Helb DA, Judkins J, Kissinger JC, Lindsay B, Roos DS, San EJ, Stoeckert CJ, Zheng J, Tomko SS

Abstract
The concept of open data has been gaining traction as a mechanism to increase data use, ensure that data are preserved over time, and accelerate discovery. While epidemiology data sets are increasingly deposited in databases and repositories, barriers to access still remain. ClinEpiDB was constructed as an open-access online resource for clinical and epidemiologic studies by leveraging the extensive web toolkit and infrastructure of the Eukaryotic Pathogen Database Resources (EuPathDB; a collection of databases covering 170+ eukaryotic pathogens, relevant related species, and select hosts) combined with a unified semantic web framework. Here we present an intuitive point-and-click website that allows users to visualize and subset data directly in the ClinEpiDB browser and immediately explore potential associations. Supporting study documentation aids contextualization, and data can be downloaded for advanced analyses. By facilitating access and interrogation of high-quality, large-scale data sets, ClinEpiDB aims to spur collaboration and discovery that improves global health.

PMID: 32047873 [PubMed]

eWoT: A Semantic Interoperability Approach for Heterogeneous IoT Ecosystems Based on the Web of Things.

Sensors (Basel). 2020 Feb 04;20(3):

Authors: Cimmino A, Poveda-Villalón M, García-Castro R

Abstract
With the constant growth of Internet of Things (IoT) ecosystems, allowing them to interact transparently has become a major issue for both the research and the software development communities. In this paper we propose a novel approach that builds semantically interoperable ecosystems of IoT devices. The approach provides a SPARQL query-based mechanism to transparently discover and access IoT devices that publish heterogeneous data. The approach was evaluated in order to prove that it provides complete and correct answers without affecting the response time and that it scales linearly in large ecosystems.

PMID: 32033027 [PubMed - in process]