Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation.

Advances in computer science in combination with the next-generation sequencing have introduced a new era in biology, enabling advanced state-of-the-art analysis of complex biological data. Bioinformatics is evolving as a union field between computer Science and biology, enabling the representation, storage, management, analysis and exploration of many types of data with a plethora of machine learning algorithms and computing tools. In this study, we used machine learning algorithms to detect differentially expressed genes between different types of cancer and showing the existence overlap to final results from RNA-sequencing analysis. The datasets were obtained from the National Center for Biotechnology Information resource. Specifically, dataset GSE68086 which corresponds to PMID:200,068,086. This dataset consists of 171 blood platelet samples collected from patients with six different tumors and healthy individuals. All steps for RNA-sequencing analysis (preprocessing, read alignment, transcriptome reconstruction, expression quantification and differential expression analysis) were followed. Machine Learning- based Random Forest and Gradient Boosting algorithms were applied to predict significant genes. The Rstudio statistical tool was used for the analysis.

TarBase-v9.0 extends experimentally supported miRNA-gene interactions to cell-types and virally encoded miRNAs.

TarBase is a reference database dedicated to produce, curate and deliver high quality experimentally-supported microRNA (miRNA) targets on protein-coding transcripts. In its latest version (v9.0, https://dianalab.e-ce.uth.gr/tarbasev9), it pushes the envelope by introducing virally-encoded miRNAs, interactions leading to target-directed miRNA degradation (TDMD) events and the largest collection of miRNA-gene interactions to date in a plethora of experimental settings, tissues and cell-types. It catalogues ∼6 million entries, comprising ∼2 million unique miRNA-gene pairs, supported by 37 experimental (high- and low-yield) protocols in 172 tissues and cell-types. Interactions are annotated with rich metadata including information on genes/transcripts, miRNAs, samples, experimental contexts and publications, while millions of miRNA-binding locations are also provided at cell-type resolution. A completely re-designed interface with state-of-the-art web technologies, incorporates more features, and allows flexible and ingenious use. The new interface provides the capability to design sophisticated queries with numerous filtering criteria including cell lines, experimental conditions, cell types, experimental methods, species and/or tissues of interest. Additionally, a plethora of fine-tuning capacities have been integrated to the platform, offering the refinement of the returned interactions based on miRNA confidence and expression levels, while boundless local retrieval of the offered interactions and metadata is enabled.

Analysing sentiment change detection of Covid-19 tweets.

The Covid-19 pandemic made a significant impact on society, including the widespread implementation of lockdowns to prevent the spread of the virus. This measure led to a decrease in face-to-face social interactions and, as an equivalent, an increase in the use of social media platforms, such as Twitter. As part of Industry 4.0, sentiment analysis can be exploited to study public attitudes toward future pandemics and sociopolitical situations in general. This work presents an analysis framework by applying a combination of natural language processing techniques and machine learning algorithms to classify the sentiment of each tweet as positive, or negative. Through extensive experimentation, we expose the ideal model for this task and, subsequently, utilize sentiment predictions to perform time series analysis over the course of the pandemic. In addition, a change point detection algorithm was applied in order to identify the turning points in public attitudes toward the pandemic, which were validated by cross-referencing the news report at that particular period of time. Finally, we study the relationship between sentiment trends on social media and, news coverage of the pandemic, providing insights into the public's perception of the pandemic and its influence on the news.

A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project.

The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP). Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-022-00171-1.

Chios Hospital Information System Assessment.

A very important aspect for organizations that provide healthcare services is to have fully functional and successful information systems. A successful hospital information system can contribute to high quality healthcare services provided to the patients of the hospital. In this paper, is presented the evaluation of the information system of Chios Hospital, "Skylitsio". The survey was conducted using a questionnaire which consists demographic questions and questions that measure the factors of the DeLone & McLean success model. The participants of the survey were 71 users of the clinical information system. Cronbach's alpha reliability test, descriptive statistics, and further data analyses to investigate the relations between the factors of the DeLone & McLean success model were performed. Based on the results, the users of the information system are satisfied with it, as well as they find the system useful and easy to use. The average value of the "information quality" is 3.78 out of 5, the "system quality" is 3.61, the "service quality" is 3.45, the "use" is 3.83, the "user satisfaction" is 3.46, and the "user benefit" is 3.76. The research concludes with a validation of the DeLone & McLean success model and it seems that the information system of the General Hospital of Chios is successful based on the users' opinions.

Temporal trends in pulmonary embolism prevalence in Greece during 2013-2017.

BACKGROUND: Pulmonary embolism (PE) epidemiological data about the disease prevalence in the general population are unclear. The present study aims to investigate the prevalence of PE in Greece and the associated temporal trends for the years 2013-2017. METHODS: Data on medical prescriptions for PE in the years 2013-2017 were provided by the Greek National Health Service Organization (EOPYY). Data on age, gender, specialty of the prescribing physician and prescription unit were provided as well. RESULTS: The total number of medical prescriptions for PE for the study period was 101,426. Of the total prescriptions, 51% were issued by the Public Sector and 48% by the Private Sector. In 2013 the prevalence of PE was 5.43 cases per 100,000 citizens and increased constantly until 2017 with 23.79 cases per 100,000 population. Prevalence was higher in all years studied in the age group of 70-80 years. For the year 2017, we observed 69.35 cases per 100,000 population for subjects 70-80 years, followed by the ages 80-90 (60.58/100,000) and 60-70 years (56.47 /100,000). Females displayed higher PE prevalence than males and higher increasing trend. CONCLUSION: PE prevalence has an increasing trend throughout the years 2013-2017 while prevalence in females is higher than males and displays a higher increasing trend. Our results may be used to appropriately organize nationwide health care campaigns aiming at the diagnosis, treatment and prevention of PE.

Citizens' Opinions About a Digital Health Insurance Record.

An Electronic Health Insurance Record (EHIR) could give all the information needed to the insured citizens, informing them about the history of benefits and the health expenses. The aim of this work is to evaluate a Digital Health Insurance Record system as well as to explore the benefits of using this system, both for society and for each citizen individually. A quantitative survey was carried out using a questionnaire shared among 180 people in Greece in 2019. The questionnaire consisted of 25 closed-ended questions, 3 of which related to demographics and the remaining 22 related to the use and benefits of use of the EHIR system. Most of all people who took part in this study believe that EHIR can contribute positively giving both social benefits and benefits for the patients. An important finding of the study is the concern expressed by respondents about the security of the system in the management of sensitive personal data. Based on citizens' opinions a Digital Health Insurance Record can provide a lot of benefits to citizens and to the society as well as to the national health insurance system.

How to Extract and Explore Big Data for Fraud Detection in the Healthcare Sector: The EOPYY Case Study.

Big Data technologies can contribute to medical fraud detection. The aim of this paper is to present the methodological approach of the Hellenic National Organization for the Provision of Health Services (EOPYY) in data analysis to detect financial or medical fraud. To analyze the data for fraud detection, a selection of prescription data from the year 2018 were examined. The Local Correlation Integral algorithm was applied to detect any outliers on the dataset. The results revealed that 7 out of 879 cases could be characterized as outliers. These outliers must be further investigated to determine if they have been associated with fraud. According to the results of this study, this outliers' detection approach can support and help the fraud detection process conducted by the auditing services in Healthcare sector.

Modelling and Evaluation of Policies.

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 CrowdHEALTH 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.

Health in All Policy Making Utilizing Big Data.

INTRODUCTION: Health in all Policies (HiAP) is a valuable method for effective Healthcare policy development. Big data analysis can be useful to both individuals and clinicians so that the full potential of big data is employed. AIM: The present paper deals with Health in All Policies, and how the use of Big Data can lead and support the development of new policies. METHODS: To this end, in the context of the CrowdHEALTH project, data from heterogeneous sources will be exploited and the Policy Development Toolkit (PDT) model will be used. In order to facilitate new insights to healthcare by exploiting all available data sources. RESULTS: In the case study that is being proposed, the NOHS Story Board (inpatient and outpatient health care) utilizing data from reimbursement of disease-related groups (DRGs), as well as medical costs for outpatient data, will be analyzed by the PDT. CONCLUSION: PDT seems promising as an efficient decision support system for policymakers to align with HiAP as it offers Causal Analysis by calculating the total cost (expenses) per ICD-10, Forecasting Information by measuring the clinical effectiveness of reimbursement cost per medical condition, per gender and per age for outpatient healthcare, and Risk Stratification by investigating Screening Parameters, Indexes (Indicators) and other factors related to healthcare management. Thus, PDT could also support HiAP by helping policymakers to tailor various policies according to their needs, such as reduction of healthcare cost, improvement of clinical effectiveness and restriction of fraud.

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

INTRODUCTION: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. AIM: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. METHODS: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. RESULTS: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. CONCLUSIONS: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources.

Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification.

This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only image-level, semantic labels instead of detailed, and pixel-level annotations. This makes it a cost-effective approach for the analysis of large videoendoscopy repositories. Other advantages of the proposed methodology include its capability to suggest possible locations of GI anomalies within the video frames, and its generality, in the sense that abnormal frame detection is based on automatically derived image features. It is implemented in three phases: 1) it classifies the video frames into abnormal or normal using a weakly supervised convolutional neural network (WCNN) architecture; 2) detects salient points from deeper WCNN layers, using a deep saliency detection algorithm; and 3) localizes GI anomalies using an iterative cluster unification (ICU) algorithm. ICU is based on a pointwise cross-feature-map (PCFM) descriptor extracted locally from the detected salient points using information derived from the WCNN. Results, from extensive experimentation using publicly available collections of gastrointestinal endoscopy video frames, are presented. The data sets used include a variety of GI anomalies. Both anomaly detection and localization performance achieved, in terms of the area under receiver operating characteristic (AUC), were >80%. The highest AUC for anomaly detection was obtained on conventional gastroscopy images, reaching 96%, and the highest AUC for anomaly localization was obtained on wireless capsule endoscopy images, reaching 88%.

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions.

Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence.

OBJECTIVE: The paper aims at improving the prediction of superficial bladder recurrence. To this end, feedforward neural networks (FNNs) and a feature selection method based on unsupervised clustering, were employed. MATERIAL AND METHODS: A retrospective prognostic study of 127 patients diagnosed with superficial urinary bladder cancer was performed. Images from biopsies were digitized and cell nuclei features were extracted. To design FNN classifiers, different training methods and architectures were investigated. The unsupervised k-windows (UKW) and the fuzzy c-means clustering algorithms were applied on the feature set to identify the most informative feature subsets. RESULTS: UKW managed to reduce the dimensionality of the feature space significantly, and yielded prediction rates 87.95% and 91.41%, for non-recurrent and recurrent cases, respectively. The prediction rates achieved with the reduced feature set were marginally lower compared to the ones attained with the complete feature set. The training algorithm that exhibited the best performance in all cases was the adaptive on-line backpropagation algorithm. CONCLUSIONS: FNNs can contribute to the accurate prognosis of bladder cancer recurrence. The proposed feature selection method can remove redundant information without a significant loss in predictive accuracy, and thereby render the prognostic model less complex, more robust, and hence suitable for clinical use.

Interrelations between Adsorption Energies and Local Isotherms, Local Monolayer Capacities, and Energy Distribution Functions, as Determined for Heterogeneous Surfaces by Inverse Gas Chromatography.

Physicochemical parameters for adsorption of gases at the submonolayer regions of heterogeneous solid surfaces are measured experimentally as a function of time, and then interrelated as local isotherms θ against adsorption energy varepsilon, fractional changes of adsorption sites f(varepsilon)/c*(max) against varepsilon, θ against f(varepsilon)/c*(max), and distribution functions θ f(varepsilon)/c*(max) over adsorption energy values varepsilon, without using at all the well-known integral equation Theta(p, T)=integral(infinity)(0)θ(p, T, varepsilon)f(varepsilon)dvarepsilon and assumptions concerning the pair f(varepsilon) and θ(p, T, varepsilon). The method uses only chromatographic experimental data obtained by the inverse gas chromatography technique known as reversed-flow gas chromatography. It has been applied to the adsorption of cis-2-butene and trans-2-butene onto particles of Penteli marble at temperatures of 302, 314, 323, and 333 K. The results obtained are comparable with those calculated on the basis of the well-known integral equation. Copyright 2001 Academic Press.