Disseminating Research Outputs: The CrowdHEALTH Project

Magdalinou, Andriana; Mantas, John; Montandon, Lydia; Weber, Patrick; Gallos, Parisis

doi:10.5455/aim.2019.27.348-354

Cited by 7 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A threshold-based classifier may work well in many applications, but it may be the case that a more complicated system will perform better. It depends on the problem you are dealing with (11)(12)(13)(14)(15)(16)(17).…”

Section: Discussionmentioning

confidence: 99%

Classification Techniques for Cardio- Vascular Diseases Using Supervised Machine Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

Introduction: The World Health Organization has estimated that 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the developed countries are due to cardiovascular diseases. The early prognosis of cardiovascular diseases can aid in making decisions on lifestyle changes in high risk patients. Aim: The aim of this paper is to build and compare classification techniques for cardiovascular diseases. Methods: The dataset contained 4270 patients and 14 attributes and it is available on the UCI data repository. The prediction is a binary outcome (event and no event). Variables of each attribute is a potential risk factor. There are both demographic, behavioral and medical risk factors. The classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD). Results: Different classifiers were tested. The SMOTE technique was used in order to solve the class imbalance. The cross-validation method was used in order to estimate how accurately our predictive models will perform. We evaluate our classifiers by using the following metrics: precision, recall, F1-score, Accuracy, AUC (Area Under Curve). Conclusions: Based on the resluts, the best scores have the Random Forest and Decision Tree classifiers.

show abstract

Section: Discussionmentioning

confidence: 99%

Classification Techniques for Cardio- Vascular Diseases Using Supervised Machine Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Big Data Analytics can start consuming datasets and produce fine-tuned analytical and predictive models. Early detection of treatment protocols outcome and respective occurring costs are such cases (34)(35)(36)(37)(38)(39)(40)(41).…”

Section: Discussionmentioning

confidence: 99%

Evidence-based Public Health Policy Models Development and Evaluation using Big Data Analytics and Web Technologies

Μούτσελος

2020

Med Arch

View full text Add to dashboard Cite

Introduction. According to WHO, "health policy refers to decisions, plans, and actions that are undertaken to achieve specific health care goals within a society". Although policymaking is important to be based on scientific evidence, in many countries, evidence-informed decision-making remains the exception rather than the rule. Aim: This work presents a cloud-based Decision Support System for public health decision-making. Methods: In Crowd-HEALTH, the concept of a Public Health Policy (PHP) is directly connected with one or more Key Performance Indexes (KPIs). The design and technical details of the system implementations are reported, along with use case scenarios. Results: The Policy Development Toolkit presents a unique interface and point of reference for policymakers, allowing them to create policy models and obtain analytical results for evidence-based decisions and evaluations. Conclusions: The hierarchical structure of the Public Health Policy Model offers versatility in the creation and handling of the policies, resulting in Health Analytics Tools Results Objects which offer quantitative policy support and provide the basis for meta-analytic operations.

show abstract

“…The present work defines what a Public Health Policy (PHP) is in the context of the CrowdHEALTH project (12)(13)(14)(15)(16)(17). It also proposes state-of-the-art best practices of PHPs in general and in NCDs particularly.…”

Section: Discussionmentioning

confidence: 99%

Modelling and Evaluation of Policies

et al. 2020

Self Cite

View full text Add to dashboard Cite

Introduction: NCDs (non-communicable diseases) are considered an important social issue and a financial burden to the health care systems in the EU which can be decreased if cost-effective policies are implemented, along with proactive interventions. The Crowd-HEALTH project recognizes that NCD poses a burden for the healthcare sector and society and aims at focusing on NCDs' public health policies. Aim: The aim of this paper is to present the concept of Public Health Policy (PHP), elaborate on the state-of-the-art of PHPs development, and propose a first approach to the modeling and evaluation of PHPs used in a toolkit that is going to support decision making, the Policy Development Toolkit (PDT). Methods: The policy creation module is a part of the PDT aiming to integrate the results of the rest of the health analytics and policy components. It is the module that selects, filters, and aggregates all relevant information to help policy-makers with the decision making process. The policies creation component is connected to the visualization component to provide the final users with data visualization on different PHPs, including outcomes from data-driven models, such as risk stratification, clinical pathways mining, forecasting or causal analysis models, outcomes from cost-benefit analysis, and suggestions and recommendations from the results of different measured KPIs, using data from the Holistic Health Records (HHRs). Results: In the context of CrowdHEALTH project, PHP can be defined as the decisions taken for actions by those responsible in the public sector that covers a set of actions or inactions that affect a group of public and private actors of the health care system. In the CrowdHEALTH project, the Policy Development Toolkit works as the main interface between the final users and the whole system in the CrowdHEALTH platform. The three components related to policy creation are: (i) the policy modeling component, (ii) the population identification component and (iii) the policy evaluation component. In policy evaluation, KPIs are used as measurable indicators to help prevent ambiguity problems in the interpretation of the model and the structure. Conclusions: This initial Policy creation component design might be modified during the project life circle according to the concept complexity.

show abstract

Disseminating Research Outputs: The CrowdHEALTH Project

Cited by 7 publications

References 21 publications

Classification Techniques for Cardio- Vascular Diseases Using Supervised Machine Learning

Classification Techniques for Cardio- Vascular Diseases Using Supervised Machine Learning

Evidence-based Public Health Policy Models Development and Evaluation using Big Data Analytics and Web Technologies

Modelling and Evaluation of Policies

Contact Info

Product

Resources

About