Cochrane's Linked Data Project: How it Can Advance our Understanding of Surrogate Endpoints.

J Law Med Ethics. 2019 09;47(3):374-380

Authors: Mavergames C, Beecher D, Becker LA, Last A, Ali A

Abstract
Cochrane has developed a linked data infrastructure to make the evidence and data from its rich repositories more discoverable to facilitate evidence-based health decision-making. These annotated resources can enhance the study and understanding of biomarkers and surrogate endpoints.

PMID: 31560633 [PubMed - indexed for MEDLINE]

Model-Driven Decision Making in Multiple Sclerosis Research: Existing Works and Latest Trends.

Patterns (N Y). 2020 Nov 13;1(8):100121

Authors: Alshamrani R, Althbiti A, Alshamrani Y, Alkomah F, Ma X

Abstract
Multiple sclerosis (MS) is a neurological disorder that strikes the central nervous system. Due to the complexity of this disease, healthcare sectors are increasingly in need of shared clinical decision-making tools to provide practitioners with insightful knowledge and information about MS. These tools ought to be comprehensible by both technical and non-technical healthcare audiences. To aid this cause, this literature review analyzes the state-of-the-art decision support systems (DSSs) in MS research with a special focus on model-driven decision-making processes. The review clusters common methodologies used to support the decision-making process in classifying, diagnosing, predicting, and treating MS. This work observes that the majority of the investigated DSSs rely on knowledge-based and machine learning (ML) approaches, so the utilization of ontology and ML in the MS domain is observed to extend the scope of this review. Finally, this review summarizes the state-of-the-art DSSs, discusses the methods that have commonalities, and addresses the future work of applying DSS technologies in the MS field.

PMID: 33294867 [PubMed]

Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy.

Entropy (Basel). 2020 Mar 30;22(4):

Authors: Koltcov S, Ignatenko V, Boukhers Z, Staab S

Abstract
Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models-Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)-we, first of all, show that the minimum of Renyi entropy coincides with the "true" number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.

PMID: 33286169 [PubMed]

Biomedica. 2020 Dec 2;40(4):702-721. doi: 10.7705/biomedica.5182.

ABSTRACT

Introduction: As an initiative to improve the quality of health care, the trend in biomedical research focused on health disparities and sex has increased. Objective: To carry out a characterization of the scientific evidence on health disparity defined as the gap between the distribution of health and the possible gender bias for access to medical services. Materials and methods: We conducted a simultaneous search of two fundamental descriptors in the scientific literature in the Medline PubMed database: healthcare disparities and sexism. Subsequently, a main semantic network was built and some structural subunits (communities) were identified for the analysis of information organization patterns. We used open-source software: Cytoscape to analyze and visualize the semantic network, and MapEquation for community detection, as well as an ad hoc code available in a public access repository. Results: The core network corpus showed that the terms on heart disease were the most common among the descriptors of medical conditions. Patterns of information related to public policies, health services, social determinants, and risk factors were identified from the structural subunits, but with a certain tendency to remain indirectly connected to the nodes of medical conditions. Conclusions: Scientific evidence indicates that gender disparity does matter for the care quality in many diseases, especially those related to the circulatory system. However, there is still a gap between the medical and social factors that give rise to possible disparities by sex.

PMID:33275349 | PMC:PMC7808772 | DOI:10.7705/biomedica.5182

HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images.

Med Image Anal. 2020 Oct 29;68:101890

Authors: van Rijthoven M, Balkenhol M, Siliņa K, van der Laak J, Ciompi F

Abstract
We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentric patches at multiple resolutions with different fields of view, feed different branches of HookNet, and intermediate representations are combined via a hooking mechanism. We describe a framework to design and train HookNet for achieving high-resolution semantic segmentation and introduce constraints to guarantee pixel-wise alignment in feature maps during hooking. We show the advantages of using HookNet in two histopathology image segmentation tasks where tissue type prediction accuracy strongly depends on contextual information, namely (1) multi-class tissue segmentation in breast cancer and, (2) segmentation of tertiary lymphoid structures and germinal centers in lung cancer. We show the superiority of HookNet when compared with single-resolution U-Net models working at different resolutions as well as with a recently published multi-resolution model for histopathology image segmentation. We have made HookNet publicly available by releasing the source code1 as well as in the form of web-based applications2,3 based on the grand-challenge.org platform.

PMID: 33260110 [PubMed - as supplied by publisher]

Effects of physical exercise on executive function in cognitively healthy older adults: A systematic review and meta-analysis of randomized controlled trials: Physical exercise for executive function.

Int J Nurs Stud. 2020 Oct 24;114:103810

Authors: Xiong J, Ye M, Wang L, Zheng G

Abstract
OBJECTIVE: To assess the effect of physical exercise interventions on executive function in cognitively healthy adults aged 60 years and older.
METHODS: Four electronic databases, the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Web of Science and Embase, were comprehensively searched from their inception to November 25, 2019. Randomized controlled trials (RCTs) examining the effect of physical exercise on executive function in cognitively healthy older adults were included.
RESULTS: Twenty-five eligible trials with fair methodological quality were identified. Compared to a no-exercise intervention, physical exercise had positive effect on working memory (Hedge's g=0.127, p<0.01, I2= 0%), cognitive flexibility (Hedge's g=0.511; p=0.007, I2=89.08%), and inhibitory control (Hedge's g=0.136; p=0.001, I2=0%) in cognitively healthy older adults. The moderator analysis indicated that more than 13 weeks of aerobic exercise significantly improved working memory and cognitive flexibility, and intervention lasting more than 26 weeks significantly improved inhibition; mind-body exercise significantly improved working memory. No significant effect on planning or semantic verbal fluency (SVF) was found.
CONCLUSION: Regular physical exercise training, especially aerobic exercise and mind-body exercise, had positive benefit for improving working memory, cognitive flexibility and inhibitory control of executive function in cognively healthy older adults. Further well-designed RCTs should focus on the impact of specific exercise forms with a standardized exercise scheme on executive function in cognitively healthy older adults.

PMID: 33248291 [PubMed - as supplied by publisher]

Detection of Suicidality Among Opioid Users on Reddit: Machine Learning-Based Approach.

J Med Internet Res. 2020 Nov 27;22(11):e15293

Authors: Yao H, Rashidian S, Dong X, Duanmu H, Rosenthal RN, Wang F

Abstract
BACKGROUND: In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns.
OBJECTIVE: This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic.
METHODS: Reddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data.
RESULTS: Classification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments.
CONCLUSIONS: Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target.

PMID: 33245287 [PubMed - as supplied by publisher]

Multiscale Cross-Domain Thermochemical Knowledge-Graph.

J Chem Inf Model. 2020 Nov 26;:

Authors: Mosbach S, Menon A, Farazi F, Krdzavac N, Zhou X, Akroyd J, Kraft M

Abstract
In this paper, we develop a set of software agents which improve a knowledge-graph containing thermodynamic data of chemical species by means of quantum chemical calculations and error-canceling balanced reactions. The knowledge-graph represents species-associated information by making use of the principles of linked data, as employed in the Semantic Web, where concepts correspond to vertices and relationships between the concepts correspond to edges of the graph. We implement this representation by means of ontologies, which formalize the definition of concepts and their relationships, as a critical step to achieve interoperability between heterogeneous data formats and software. The agents, which conduct quantum chemical calculations and derive the estimates of standard enthalpies of formation, update the knowledge-graph with newly obtained results, improving data values, and adding nodes and connections between them. A key distinguishing feature of our approach is that it extends an existing, general-purpose knowledge-graph, called J-Park Simulator (http://theworldavatar.com), and its ecosystem of autonomous agents, thus enabling seamless cross-domain applications in wider contexts. To this end, we demonstrate how quantum calculations can directly affect the atmospheric dispersion of pollutants in an industrial emission use-case.

PMID: 33242243 [PubMed - as supplied by publisher]

Visualization Environment for Federated Knowledge Graphs: Development of an Interactive Biomedical Query Language and Web Application Interface.

JMIR Med Inform. 2020 Nov 23;8(11):e17964

Authors: Cox S, Ahalt SC, Balhoff J, Bizon C, Fecho K, Kebede Y, Morton K, Tropsha A, Wang P, Xu H

Abstract
BACKGROUND: Efforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results.
OBJECTIVE: We aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs.
METHODS: We developed a biomedical query language and web application interphase-termed as Translator Query Language (TranQL)-to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph.
RESULTS: We developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as "gene," "chemical substance," and "disease," that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated.
CONCLUSIONS: TranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice.

PMID: 33226347 [PubMed - as supplied by publisher]

Disengagement from mental health treatment and re-offending in those with psychosis: a multi-state model of linked data.

Soc Psychiatry Psychiatr Epidemiol. 2020 Dec;55(12):1639-1648

Authors: Hwang YIJ, Albalawi O, Adily A, Hudson M, Wand H, Kariminia A, O'Driscoll C, Allnutt S, Grant L, Sara G, Ogloff J, Greenberg DM, Butler T

Abstract
BACKGROUND AND AIMS: Individuals with psychosis are over-represented in the criminal justice system and, as a group, are at elevated risk of re-offending. Recent studies have observed an association between increased contacts with mental health services and reduced re-offending, as well as reduced risk of re-offending in those who are ordered to mental health treatment rather than punitive sanctions. In furthering this work, this study examines the effect of disengagement from mental health treatment on probability of re-offence in offenders with psychosis over time.
METHODS: Data linkage was conducted with judicial, health and mortality datasets from New South Wales, Australia (2001-2015). The study population included 4960 offenders with psychosis who received non-custodial sentences and engaged with community-based mental health treatment. Risk factors for leaving treatment and/or reconviction were examined using multivariate cox regression. Further, a multi-state model was used to observe the probabilities associated with individuals moving between three states: conviction, disengagement from mental health treatment and subsequent re-conviction.
RESULTS: A threefold increase was observed in the risk of re-offending for those who disengaged from treatment compared to those who did not (aHR = 2.76, 95% CI 1.65-4.62, p < 0.001). The median time until re-offence was 195 days, with the majority (67%) being convicted within one year of leaving treatment. A higher risk of leaving treatment was found for those born outside of Australia, with substance-related psychosis, and a history of violent offence.
CONCLUSIONS: The findings argue for an emphasis on continued engagement with mental health services following release for offenders with psychosis and identify subgroups within this population for whom concentrated efforts regarding treatment retention should be targeted.

PMID: 32390094 [PubMed - indexed for MEDLINE]

Association of Vascular Endothelial Growth Factor Subtypes with Melanoma Patients' Characteristics and Survival: A Semantic Connectivity Map Analysis.

Acta Derm Venereol. 2020 Jan 07;100(1):adv00019

Authors: Cazzaniga S, Wiedmer C, Frangež Ž, Shafighi M, Beltraminelli H, Weber B, Simon D, Naldi L, Simon HU, Hunger RE, Seyed Jafari SM

PMID: 31742647 [PubMed - indexed for MEDLINE]

West J Emerg Med. 2020 Oct 27;21(6):141-145. doi: 10.5811/westjem.2020.8.48305.

ABSTRACT

INTRODUCTION: The American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results.

METHODS: Using the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A "group match" was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate.

RESULTS: Of the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a "group match" had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs.

CONCLUSION: Excluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.

PMID:33207159 | PMC:PMC7673880 | DOI:10.5811/westjem.2020.8.48305

PCLiON: An Ontology for Data Standardization and Sharing of Prostate Cancer Associated Lifestyles.

Int J Med Inform. 2020 Nov 07;145:104332

Authors: Chen Y, Yu C, Liu X, Xi T, Xu G, Sun Y, Zhu F, Shen B

Abstract
BACKGROUND: Researches on Lifestyle medicine (LM) have emerged in recent years to garner wide attention. Prostate cancer (PCa) could be prevented and treated by positive lifestyles, but the association between lifestyles and PCa is always personalized.
OBJECTIVES: In order to solve the heterogeneity and diversity of different data types related to PCa, establish a standardized lifestyle ontology, promote the exchange and sharing of disease lifestyle knowledge, and support text mining and knowledge discovery.
METHODS: The overall construction of PCLiON was created in accordance with the principles and methodology of ontology construction. Following the principles of evidence-based medicine, we screened and integrated the lifestyles and their related attributes. Protégé was used to construct and validate the semantic framework. All annotations in PCLiON were based on SNOMED CT, NCI Thesaurus, the Cochrane Library and FooDB, etc. HTML5 and ASP.NET was used to develop the independent Web page platform and corresponding intelligent terminal application. The PCLiON also uploaded to the National Center for Biomedical Ontology BioPortal.
RESULTS: PCLiON integrates 397 lifestyles and lifestyle-related factors associated with PCa, and is the first of its kind for a specific disease. It contains 320 attribute annotations and 11 object attributes. The logical relationship and completeness meet the ontology requirements. Qualitative analysis was carried out for 329 terms in PCLiON, including factors which are protective, risk or associated but functional unclear, etc. PCLiON is publicly available both at http://pcaontology.net/PCaLifeStyleDefault.aspx and https://bioportal.bioontology.org/ontologies/PCALION.
CONCLUSIONS: Through the bilingual online platforms, complex lifestyle research data can be transformed into standardized, reliable and responsive knowledge, which can promote the shared-decision making (SDM) on lifestyle intervention and assist patients in lifestyle self-management toward the goal of PCa targeted prevention.

PMID: 33186790 [PubMed - as supplied by publisher]

Int J Med Inform. 2021 Jan;145:104305. doi: 10.1016/j.ijmedinf.2020.104305. Epub 2020 Oct 21.

ABSTRACT

PURPOSE: To develop and internally validate an illness burden index among Medicare beneficiaries before or after a cancer diagnosis.

METHODS: Data source: SEER-CAHPS, linking Surveillance, Epidemiology, and End Results (SEER) cancer registry, Medicare enrollment and claims, and Medicare Consumer Assessment of Healthcare Providers and Systems (Medicare CAHPS) survey data providing self-reported sociodemographic, health, and functional status information. To generate a score for everyone in the dataset, we tabulated 4 groups within each annual subsample (2007-2013): 1) Medicare Advantage (MA) beneficiaries or 2) Medicare fee-for-service (FFS) beneficiaries, surveyed before cancer diagnosis; 3) MA beneficiaries or 4) Medicare FFS beneficiaries surveyed after diagnosis. Random survival forests (RSFs) predicted 12-month all-cause mortality and drew predictor variables (mean per subsample = 44) from 8 domains: sociodemographic, cancer-specific, health status, chronic conditions, healthcare utilization, activity limitations, proxy, and location-based factors. Roughly two-thirds of the sample was held out for algorithm training. Error rates based on the validation ("out-of-bag," OOB) samples reflected the correctly classified percentage. Illness burden scores represented predicted cumulative mortality hazard.

RESULTS: The sample included 116,735 Medicare beneficiaries with cancer, of whom 73 % were surveyed after their cancer diagnosis; overall mean mortality rate in the 12 months after survey response was 6%. SEER-CAHPS Illness Burden Index (SCIBI) scores were positively skewed (median range: 0.29 [MA, pre-diagnosis] to 2.85 [FFS, post-diagnosis]; mean range: 2.08 [MA, pre-diagnosis] to 4.88 [MA, post-diagnosis]). The highest decile of the distribution had a 51 % mortality rate (range: 29-71 %); the bottom decile had a 1% mortality rate (range: 0-2 %). The error rate was 20 % overall (range: 9% [among FFS enrollees surveyed after diagnosis] to 36 % [MA enrollees surveyed before diagnosis]).

CONCLUSIONS: This new morbidity measure for Medicare beneficiaries with cancer may be useful to future SEER-CAHPS users who wish to adjust for comorbidity.

PMID:33188949 | PMC:PMC7736519 | DOI:10.1016/j.ijmedinf.2020.104305

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review.

J Med Syst. 2020 Nov 09;44(12):205

Authors: Castillo-Sánchez G, Marques G, Dorronzoro E, Rivera-Romero O, Franco-Martín M, De la Torre-Díez I

Abstract
According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O'Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python.

PMID: 33165729 [PubMed - in process]

Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development.

JMIR Med Inform. 2020 Nov 04;8(11):e18752

Authors: Ammar N, Shaban-Nejad A

Abstract
BACKGROUND: The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies. Classic machine learning and artificial intelligence techniques cannot provide a full scientific understanding of the inner workings of the underlying models. This raises credibility issues due to the lack of transparency and generalizability. Explainable artificial intelligence is an emerging approach for promoting credibility, accountability, and trust in mission-critical areas such as medicine by combining machine-learning approaches with explanatory techniques that explicitly show what the decision criteria are and why (or how) they have been established. Hence, thinking about how machine learning could benefit from knowledge graphs that combine "common sense" knowledge as well as semantic reasoning and causality models is a potential solution to this problem.
OBJECTIVE: In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve mental health surveillance.
METHODS: We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology.
RESULTS: To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children's hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation.
CONCLUSIONS: This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners' ability to provide explanations for the decisions they make.

PMID: 33146623 [PubMed - as supplied by publisher]

Benefits of not smoking during pregnancy for Australian Aboriginal and Torres Strait Islander women and their babies: a retrospective cohort study using linked data.

BMJ Open. 2019 11 21;9(11):e032763

Authors: McInerney C, Ibiebele I, Ford JB, Randall D, Morris JM, Meharg D, Mitchell J, Milat A, Torvaldsen S

Abstract
OBJECTIVES: To provide evidence for targeted smoking cessation policy, the aim of this study was to compare pregnancy outcomes of Aboriginal mothers who reported not smoking during pregnancy with Aboriginal mothers who reported smoking during pregnancy.
DESIGN: Population based retrospective cohort study using linked data.
SETTING: New South Wales, the most populous Australian state.
POPULATION: 18 154 singleton babies born to 13 477 Aboriginal mothers between 2010 and 2014 were identified from routinely collected New South Wales datasets. Aboriginality was determined from birth records and from four linked datasets through an Enhanced Reporting of Aboriginality algorithm.
EXPOSURE: Not smoking at any time during pregnancy.
MAIN OUTCOME MEASURES: Unadjusted and adjusted relative risks (aRR) and 95% CIs from modified Poisson regression were used to examine associations between not smoking during pregnancy and maternal and perinatal outcomes including severe morbidity, inter-hospital transfer, perinatal death, preterm birth and small-for-gestational age. Population attributable fractions (PAFs) were calculated using adjusted relative risks.
RESULTS: Compared with babies born to mothers who smoked during pregnancy, babies born to non-smoking mothers had a lower risk of all adverse perinatal outcomes including perinatal death (aRR=0.58, 95% CI 0.44 to 0.76), preterm birth (aRR=0.58, 95% CI 0.53 to 0.64) and small-for-gestational age (aRR=0.35, 95% CI 0.32 to 0.39). PAFs (%) were 27% for perinatal death, 26% for preterm birth and 48% for small-for-gestational-age. Compared with women who smoked during pregnancy (n=8919), those who did not smoke (n=9235) had a lower risk of being transferred to another hospital (aRR=0.76, 95% CI 0.66 to 0.89).
CONCLUSIONS: Babies born to women who did not smoke during pregnancy had a lower risk of adverse perinatal outcomes. Rates of adverse outcomes among Aboriginal non-smokers were similar to those among the general population. These results quantify the proportion of adverse perinatal outcomes due to smoking and highlight why effective smoking cessation programme are urgently required for this population.

PMID: 31753897 [PubMed - indexed for MEDLINE]

Shouting at each other into the void: A linguistic network analysis of vaccine hesitance and support in online discourse regarding California law SB277.

Soc Sci Med. 2020 Aug 28;266:113216

Authors: DeDominicis K, Buttenheim AM, Howa AC, Delamater PL, Salmon D, Omer SB, Klein NP

Abstract
In 2015, California passed Senate Bill 277 and became the third state in the United States to ban all nonmedical exemptions from school immunization requirements, effectively prohibiting religious and personal belief exemptions. This attracted grassroots opposition and considerable debate among vaccine hesitant factions online. This mixed-methods study used quantitative linguistic analysis, semantic network analysis, and content analysis techniques to examine 2424 online documents drawn from newspapers, blogs, health websites, government information pages, web forums, personal websites, Facebook groups, among others. The study examined which words and phrases were used most frequently by vaccine skeptics, vaccine defenders, and more neutral media accounts to illuminate how groups with different attitudes towards vaccination discuss and disseminate information about vaccines and vaccine policy online. We proposed an innovative methodology for examining online discourse surrounding vaccine hesitance, as well as for studying the online dissemination of misinformation about vaccines. Our findings highlighted discrepancies in the narratives between what vaccine supporters believe causes vaccine skepticism and the issues that vaccine skeptics actually discuss within their own digital spaces. For example, in these exchanges, the importance of parental rights overshadowed that of children's rights; supporters of vaccines brought up autism in more distinct documents than skeptics do; distrust of government regulators and researchers seemed to unite vaccine skeptics and defenders; and politicians, doctors, and even celebrities often served as proxies in heated exchanges about factual evidence, believability, and the importance of expertise in public discourse.

PMID: 33126093 [PubMed - as supplied by publisher]

The Semantic Data Dictionary - An Approach for Describing and Annotating Data.

Data Intell. 2020;2(4):443-486

Authors: Rashid SM, McCusker JP, Pinheiro P, Bax MP, Santos H, Stingone JA, Das AK, McGuinness DL

Abstract
It is common practice for data providers to include text descriptions for each column when publishing datasets in the form of data dictionaries. While these documents are useful in helping an end-user properly interpret the meaning of a column in a dataset, existing data dictionaries typically are not machine-readable and do not follow a common specification standard. We introduce the Semantic Data Dictionary, a specification that formalizes the assignment of a semantic representation of data, enabling standardization and harmonization across diverse datasets. In this paper, we present our Semantic Data Dictionary work in the context of our work with biomedical data; however, the approach can and has been used in a wide range of domains. The rendition of data in this form helps promote improved discovery, interoperability, reuse, traceability, and reproducibility. We present the associated research and describe how the Semantic Data Dictionary can help address existing limitations in the related literature. We discuss our approach, present an example by annotating portions of the publicly available National Health and Nutrition Examination Survey dataset, present modeling challenges, and describe the use of this approach in sponsored research, including our work on a large NIH-funded exposure and health data portal and in the RPI-IBM collaborative Health Empowerment by Analytics, Learning, and Semantics project. We evaluate this work in comparison with traditional data dictionaries, mapping languages, and data integration tools.

PMID: 33103120 [PubMed]

Cartolabe: A Web-Based Scalable Visualization of Large Document Collections.

IEEE Comput Graph Appl. 2020 Oct 23;PP:

Authors: Caillou P, Renault J, Fekete JD, Letournel AC, Sebag M

Abstract
We describe CARTOLABE, a web-based multi-scale system for visualizing and exploring large textual corpora based on topics, introducing a novel mechanism for the progressive visualization of filtering queries. Initially designed to represent and navigate through scientific publications in different disciplines, CARTOLABE has evolved to become a generic system and accommodate various corpora, ranging from Wikipedia (4.5M entries) to the French National Debate (4.3M entries). CARTOLABE is made of two modules: the first relies on Natural Language Processing methods, converting a corpus and its entities (documents, authors, concepts) into high-dimensional vectors, computing their projection on the 2D plane, and extracting meaningful labels for regions of the plane. The second module is a Web-based visualization, displaying tiles computed from the multidimensional projection of the corpus using the UMAP projection method. This visualization module aims at enabling users with no expertise in visualization and data analysis to get an overview of their corpus, and to interact with it: exploring, querying, filtering, panning and zooming on regions of semantic interest. Three use cases are discussed to illustrate CARTOLABE's versatility and ability to bring large scale textual corpus visualization and exploration to a wide audience.

PMID: 33095705 [PubMed - as supplied by publisher]