Data Sources and Gateways: Design and Open Specification

A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions

Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain's requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario's requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios' nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism's efficiency.

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

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 CrowdHEALTH, 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.

Classification Techniques for Cardio-Vascular Diseases Using Supervised Machine Learning

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.

Advanced Interoperability Techniques: Structure Mapping Service in CrowdHEALTH Project

INTRODUCTION

Due to healthcare information systems interoperability issues, health-related events may be missed. Numerous techniques based on medical standards and technologies have been recommended to address this challenge. Nevertheless, these techniques do not follow a holistic approach.

AIM

The purpose of the paper is to present the structure mapping service which is developed in the context of the initial design of the interoperability solution of the CrowdHEALTH project.

METHODS

In the CrowdHEALTH project, subcomponents responsible for providing the appropriate interoperability solution have been created with first the rule engine for the implementation of the business logic. Another subcomponent responsible for creating and managing the knowledge related to the link that exists between information structures, or mappings between them, is the Structure Mapping Service. The envisioned approach for structure mapping is based on ontology alignment. Besides, the Terminology Service is responsible for providing a set of operations on medical terminologies used for the coding of medical knowledge, which fills the information structures.

RESULTS

Within the development of the Structure Mapping component, the Ontology alignment process will be automated. Two phases exist when using the structure mapping service: the training phase and the run-time phase. In the training phase ontologies are created and are aligned, and data maps between the use case (source) data sets and the HHR Manager classes (target) are formed. Afterward, these data maps can be stored in the knowledge base. In the runtime phase, queries can be made to the structure mapping component by the data converter. The structure mapping controller can then access the Knowledge Base to retrieve the appropriate mapping and transformations and provide these as a response to the query.

CONCLUSIONS

The initial design of the advanced interoperability techniques is described with emphasis put to Structural Mapping service which can be easily deployed and seamlessly integrated into healthcare settings. This design will be assessed and further enriched during the project life circle.