Generation of realistic synthetic validation healthcare datasets using generative adversarial networks

Background: Assurance of digital health interventions involves, amongst others, clinical validation, which requires large datasets to test the application in realistic clinical scenarios. Development of such datasets is time consuming and challenging in terms of maintaining patient anonymity and consent. Objective: The development of synthetic datasets that maintain the statistical properties of the real datasets. Method: An artificial neural network based, generative adversarial network was implemented and trained, using numerical and categorical variables, including ICD-9 codes from the MIMIC III dataset, to produce a synthetic dataset. Results: The synthetic dataset, exhibits a correlation matrix highly similar to the real dataset, good Jaccard similarity and passing the KS test. Conclusions: The proof of concept was successful with the approach being promising for further work

How to assess and prepare health systems in low- and middle-income countries for integration of services: a systematic review

Despite growing support for integration of frontline services, a lack of information about the pre-conditions necessary to integrate such services hampers the ability of policy makers and implementers to assess how feasible or worthwhile integration may be, especially in low- and middle-income countries (LMICs). We adopted a modified systematic review with aspects of realist review, including quantitative and qualitative studies that incorporated assessment of health system preparedness for and capacity to implement integrated services. We searched Medline via Ovid, Web of Science and the Cochrane library using terms adapted from Dudley and Garner’s systematic review on integration in LMICs. From an initial list of 10 550 articles, 206 were selected for full-text review by two reviewers who independently reviewed articles and inductively extracted and synthesized themes related to health system preparedness. We identified five ‘context’ related categories and four health system ‘capability’ themes. The contextual enabling and constraining factors for frontline service integration were: (1) the organizational framework of frontline services, (2) health care worker preparedness, (3) community and client preparedness, (4) upstream logistics and (5) policy and governance issues. The intersecting health system capabilities identified were the need for: (1) sufficiently functional frontline health services, (2) sufficiently trained and motivated health care workers, (3) availability of technical tools and equipment suitable to facilitate integrated frontline services and (4) appropriately devolved authority and decision-making processes to enable frontline managers and staff to adapt integration to local circumstances. Moving beyond claims that integration is defined differently by different programs and thus unsuitable for comparison, this review demonstrates that synthesis is possible. It presents a common set of contextual factors and health system capabilities necessary for successful service integration which may be considered indicators of preparedness and could form the basis for an ‘integration preparedness tool’

The Visiting Art: A Creative Approach to Rehabilitation and Reintegration of the Aging Prison Population

This research explores the potential benefits of art therapy with aging inmates, with a primary focus on Canadian prisons. This bibliographical compilation asks the question: “How can art therapy interventions support the needs of the aging prison population?” The collection, analysis, and synthesis of the relevant literature suggest that correctional facilities are struggling to recognize and respond to the physical and mental challenges experienced by the growing population of older inmates. Chronic stress, trauma, loneliness, boredom, depression, chronic pain, substance abuse, suicidal ideations, and dementia are some of the issues that can be addressed creatively in art therapy. The ultimate purpose of this theoretical research is to synthesize a body of work with the most recent data available in the area of forensic and geriatric art therapy, from which interventions and programming could be developed. Specific attention is given to potential therapeutic goals, emerging discussion and artmaking themes, and suitable art materials. The paper also compares preferences for individual versus group sessions, along with directive versus nondirective approaches. Cultural considerations and potential countertransference implications are addressed. Lastly, research gaps and topics in need of further exploration are considered

Data Analysis Methods for Software Systems

Using statistics, econometrics, machine learning, and functional data analysis methods, we evaluate the consequences of the lockdown during the COVID-19 pandemics for wage inequality and unemployment. We deduce that these two indicators mostly reacted to the first lockdown from March till June 2020. Also, analysing wage inequality, we conduct analysis separately for males and females and different age groups.We noticed that young females were affected mostly by the lockdown.Nevertheless, all the groups reacted to the lockdown at some level

A method for machine learning generation of realistic synthetic datasets for validating healthcare applications

Digital health applications can improve quality and effectiveness of healthcare, by offering a number of new tools to users, which are often considered a medical device. Assuring their safe operation requires, amongst others, clinical validation, needing large datasets to test them in realistic clinical scenarios. Access to datasets is challenging, due to patient privacy concerns. Development of synthetic datasets is seen as a potential alternative. The objective of the paper is the development of a method for the generation of realistic synthetic datasets, statistically equivalent to real clinical datasets, and demonstrate that the Generative Adversarial Network (GAN) based approach is fit for purpose. A generative adversarial network was implemented and trained, in a series of six experiments, using numerical and categorical variables, including ICD-9 and laboratory codes, from three clinically relevant datasets. A number of contextual steps provided the success criteria for the synthetic dataset. A synthetic dataset that exhibits very similar statistical characteristics with the real dataset was generated. Pairwise association of variables is very similar. A high degree of Jaccard similarity and a successful K-S test further support this. The proof of concept of generating realistic synthetic datasets was successful, with the approach showing promise for further work

Federated Query Processing over Heterogeneous Data Sources in a Semantic Data Lake

Data provides the basis for emerging scientific and interdisciplinary data-centric applications with the potential of improving the quality of life for citizens. Big Data plays an important role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Open data initiatives have encouraged the publication of Big Data by exploiting the decentralized nature of the Web, allowing for the availability of heterogeneous data generated and maintained by autonomous data providers. Consequently, the growing volume of data consumed by different applications raise the need for effective data integration approaches able to process a large volume of data that is represented in different format, schema and model, which may also include sensitive data, e.g., financial transactions, medical procedures, or personal data. Data Lakes are composed of heterogeneous data sources in their original format, that reduce the overhead of materialized data integration. Query processing over Data Lakes require the semantic description of data collected from heterogeneous data sources. A Data Lake with such semantic annotations is referred to as a Semantic Data Lake. Transforming Big Data into actionable knowledge demands novel and scalable techniques for enabling not only Big Data ingestion and curation to the Semantic Data Lake, but also for efficient large-scale semantic data integration, exploration, and discovery. Federated query processing techniques utilize source descriptions to find relevant data sources and find efficient execution plan that minimize the total execution time and maximize the completeness of answers. Existing federated query processing engines employ a coarse-grained description model where the semantics encoded in data sources are ignored. Such descriptions may lead to the erroneous selection of data sources for a query and unnecessary retrieval of data, affecting thus the performance of query processing engine. In this thesis, we address the problem of federated query processing against heterogeneous data sources in a Semantic Data Lake. First, we tackle the challenge of knowledge representation and propose a novel source description model, RDF Molecule Templates, that describe knowledge available in a Semantic Data Lake. RDF Molecule Templates (RDF-MTs) describes data sources in terms of an abstract description of entities belonging to the same semantic concept. Then, we propose a technique for data source selection and query decomposition, the MULDER approach, and query planning and optimization techniques, Ontario, that exploit the characteristics of heterogeneous data sources described using RDF-MTs and provide a uniform access to heterogeneous data sources. We then address the challenge of enforcing privacy and access control requirements imposed by data providers. We introduce a privacy-aware federated query technique, BOUNCER, able to enforce privacy and access control regulations during query processing over data sources in a Semantic Data Lake. In particular, BOUNCER exploits RDF-MTs based source descriptions in order to express privacy and access control policies as well as their automatic enforcement during source selection, query decomposition, and planning. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over data sources that not only contain the relevant entities to answer a query, but also are regulated by policies that allow for accessing these relevant entities. Finally, we tackle the problem of interest based update propagation and co-evolution of data sources. We present a novel approach for interest-based RDF update propagation that consistently maintains a full or partial replication of large datasets and deal with co-evolution

Comprehensive Ontology Design for Autism Spectrum Disorder

Ontology is a formal explicit description of concepts in the knowledge domain. In recent years, developing ontology in different domains is a hot topic for many researchers, especially in the medical field because of the benefits offered to users. Using ontology allows sharing and reusing domain knowledge in an efficient and explicit way. In particular, ontology in medical field can facilitate the access to query data, precise knowledge, and seamless sharing of electronic medical records (EMR). Thus ontology increases the accuracy of doctor’s diagnostic decision. Autism Spectrum Disorder (ASD) often presents with difficulties in verbal and nonverbal communication, behavior and social interactions. Autism is difficult to define due to the complex heterogeneous disorders in this domain and to the lack of coherent set of knowledge that deals with all aspects of autism. The purpose of this research was to address these shortcomings by developing a comprehensive ASD ontology that formally conceptualizes both domain and operational autism knowledge, unifies autism terminology, and facilitates access to precise autistic information for both general public and expert users, thus enabling better diagnostic and treatment decisions. To build such ontology, we investigated many medical research works in the various areas of autism such as disorders, effects and treatments of ASD. The study was done with the purpose of extracting and gathering information from the most trusted sources such as existing ontologies, standard textbooks, relevant articles and clinical studies. These sources were used to build a semantic map linking key concept classes. Mainly we focused on properties and relationships between these classes to formally describe the autistic domain and operational knowledge and to bring the scattered knowledge into the ontological form. Ontology instantiation for each subclass was based on pilot studies and clinical cases. The system was implemented using Protégé, an ontological framework developed by the Stanford Center for Biomedical Informatics Research at the Stanford University. The ontology was built using the Web Ontology Language (OWL). OWL is a semantic web language designed to indicate the rich and complex knowledge of the domain. Moreover, we developed a basic web query system for the ASD ontology to present the ontology information to different users around the world. The developed system has been evaluated to measure quality of embedded knowledge, ontology correctness and the usability of its web query system