A Modelling Framework for Evidence-Based Public Health Policy Making

It is widely recognised that the process of public health policy making (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, and supported by data analytics and decision-making tools tailored to it. This is because the management of health conditions and their consequences at a public health policy making level can benefit from such type of analysis of heterogeneous data, including health care devices usage, physiological, cognitive, clinical and medication, personal, behavioural, lifestyle data, occupational and environmental data. In this paper we present a novel approach to public health policy making in a form of an ontology, and an integrated platform for realising this approach. Our solution is model-driven and makes use of big data analytics technology. More specifically, it is based on public health policy decision making (PHPDM) models that steer the public health policy decision making process by defining the data that need to be collected, the ways in which they should be analysed in order to produce the evidence useful for public health policymaking, how this evidence may support or contradict various policy interventions (actions), and the stakeholders involved in the decision-making process. The resulted web-based platform has been implemented using Hadoop, Spark and HBASE, developed in the context of a research programme on public health policy making for the management of hearing loss called EVOTION, funded by the Horizon 2020

Mining balance disorders' data for the development of diagnostic decision support systems

In this work we present the methodology for the development of the EMBalance diagnostic Decision Support System (DSS) for balance disorders. Medical data from patients with balance disorders have been analysed using data mining techniques for the development of the diagnostic DSS. The proposed methodology uses various data, ranging from demographic characteristics to clinical examination, auditory and vestibular tests, in order to provide an accurate diagnosis. The system aims to provide decision support for general practitioners (GPs) and experts in the diagnosis of balance disorders as well as to provide recommendations for the appropriate information and data to be requested at each step of the diagnostic process. Detailed results are provided for the diagnosis of 12 balance disorders, both for GPs and experts. Overall, the reported accuracy ranges from 59.3 to 89.8% for GPs and from 74.3 to 92.1% for experts

Fatty Acid Composition of Developing Sea Buckthorn (Hippophae rhamnoides L.) Berry and the Transcriptome of the Mature Seed

Background: Sea buckthorn (Hippophae rhamnoides L.) is a hardy, fruit-producing plant known historically for its medicinal and nutraceutical properties. The most recognized product of sea buckthorn is its fruit oil, composed of seed oil that is rich in essential fatty acids, linoleic (18:2\u3c9-6) and \u3b1-linolenic (18:3\u3c9-3) acids, and pulp oil that contains high levels of monounsaturated palmitoleic acid (16:1\u3c9-7). Sea buckthorn is fast gaining popularity as a source of functional food and nutraceuticals, but currently has few genomic resources; therefore, we explored the fatty acid composition of Canadian-grown cultivars (ssp. mongolica) and the sea buckthorn seed transcriptome using the 454 GS FLX sequencing technology. Results: GC-MS profiling of fatty acids in seeds and pulp of berries indicated that the seed oil contained linoleic and \u3b1-linolenic acids at 33-36% and 30-36%, respectively, while the pulp oil contained palmitoleic acid at 32-42%. 454 sequencing of sea buckthorn cDNA collections from mature seeds yielded 500,392 sequence reads, which identified 89,141 putative unigenes represented by 37,482 contigs and 51,659 singletons. Functional annotation by Gene Ontology and computational prediction of metabolic pathways indicated that primary metabolism (protein>nucleic acid>carbohydrate>lipid) and fatty acid and lipid biosynthesis pathways were highly represented categories. Sea buckthorn sequences related to fatty acid biosynthesis genes in Arabidopsis were identified, and a subset of these was examined for transcript expression at four developing stages of the berry. Conclusion: This study provides the first comprehensive genomic resources represented by expressed sequences for sea buckthorn, and demonstrates that the seed oil of Canadian-grown sea buckthorn cultivars contains high levels of linoleic acid and \u3b1-linolenic acid in a close to 1:1 ratio, which is beneficial for human health. These data provide the foundation for further studies on sea buckthorn oil, the enzymes involved in its biosynthesis, and the genes involved in the general hardiness of sea buckthorn against environmental conditions.Peer reviewed: YesNRC publication: Ye

Evidence based policy making in healthcare using big data analytics

The effective management of various health conditions depends on and requires appropriate public health policies (PHP). Public health policy can affect several aspects of healthcare provision including: (a) prevention and early diagnosis of diseases; (b) early treatment of diagnosed conditions through the provision of appropriate health care devices; (c) longer term treatment of long term disabilities and chronic diseases through systematic checks of the patient’s condition and the provision of other vital rehabilitation related services; (d) protection of people with health care devices from the harmful effects of their living environment; (e) setup of standards, services and technology for promoting and ensuring patients’ participation and inclusion within various settings (e.g., at work, at school/educational establishments, in everyday life). Although there is a need for evidence based public health policy making, there is currently no computerised tool to enable the process. The overall aim of our research is to develop an integrated platform by incorporating a big data analytics (BDA) platform that facilitates the collection and analysis of heterogeneous data related to healthcare services, including health care device usage, physiological, cognitive, medical, personal, occupational, behavioural, lifestyle, environmental and open web data. For the purposes of the development of this integrated platform we are introducing a Public Health Policy Decision Making modeling language that allows the specification of models that are executable by the platform. For the evaluation of the developed platform, we developed a scenario, instantiated the ontology model using Protégé and generated synthetic data. We also ran the scenario using real patient data from EVOTION project. We performed subjective evaluation of the platform as a policy making tool using three questionnaires (one for policy makers, one for clinicians and one for data analysts) and analysed the results. The novelty of this thesis lies not only in the specification of the PHPDM modeling language, as there is no other ontology on public health policy decision making, but also in the development of the BDA engine and the prototype, as there is no other similar policy making platform to date. Some open issues regarding the developed platform include (a) further formalization and addition of new constructs to the developed PHPDM specification language to support the full lifecycle of policy formation processes, (b) the provision of templates, Evidence Based Policy Making in Healthcare using Big Data Analytics guidelines and supportive material (e.g. tooltips in the interface and tutorial videos) to help policy makers specify data analytics workflows and criteria, (c) interoperability with other data analytics tools and existing health data repositories, (d) the provision of the developed platform as a service, (e) the implementation of more data mining and statistical analysis algorithms and (f) the development of a decision support system that will enable the platform to not only support the execution of big data analytics, but to also directly support the policy making process

Mining balance disorders' data for the development of diagnostic decision support systems

In this work we present the methodology for the development of the EMBalance diagnostic Decision Support System (DSS) for balance disorders. Medical data from patients with balance disorders have been analysed using data mining techniques for the development of the diagnostic DSS. The proposed methodology uses various data, ranging from demographic characteristics to clinical examination, auditory and vestibular tests, in order to provide an accurate diagnosis. The system aims to provide decision support for general practitioners (GPs) and experts in the diagnosis of balance disorders as well as to provide recommendations for the appropriate information and data to be requested at each step of the diagnostic process. Detailed results are provided for the diagnosis of 12 balance disorders, both for GPs and experts. Overall, the reported accuracy ranges from 59.3 to 89.8% for GPs and from 74.3 to 92.1% for experts. © 2016 Elsevier Lt