Evaluation of an electronic prescription platform: Clinicians' feedback on three distinct services aiming to facilitate clinical decision and safer e-prescription.

BACKGROUND: Health Care Professionals (HCPs) are the main end-users of digital clinical tools such as electronic prescription systems. For this reason, it is of high importance to include HCPs throughout the design, development and evaluation of a newly introduced system to ensure its usefulness, as well as confirm that it tends to their needs and can be integrated in their everyday clinical practice. METHODS: In the context of the PrescIT project, an electronic prescription platform with three services was developed (i.e., Prescription Check, Prescription Suggestion, Therapeutic Prescription Monitoring). To allow an iterative process of discovery through user feedback, design and implementation, a two-phase evaluation was carried out, with the participation of HCPs from three hospitals in Northern Greece. The two-phase evaluation included presentations of the platform, followed by think-aloud sessions, individual platform testing and the collection of qualitative as well as quantitative feedback, through standard questionnaires (e.g., SUS, PSSUQ). RESULTS: Twenty one HCPs (8 in the first, 18 in the second phase, and five present in both) participated in the two-phase evaluation. HCPs comprised clinicians varying in their specialty and one pharmacist. Clinicians' feedback during the first evaluation phase already deemed usability as "excellent" (with SUS scores ranging from 75 to 95/100, showing a mean value of 86.6 and SD of 9.2) but also provided additional user requirements, which further shaped and improved the services. In the second evaluation phase, clinicians explored the system's usability, and identified the services' strengths and weaknesses. Clinicians perceived the platform as useful, as it provides information on potential adverse drug reactions, drug-to-drug interactions and suggests medications that are compatible with patients' comorbidities and current medication. CONCLUSIONS: The developed PrescIT platform aims to increase overall safety and effectiveness of healthcare services. Therefore, including clinicians in a two-phase evaluation confirmed that the introduced system is useful, tends to the users' needs, does not create fatigue and can be integrated in their everyday clinical practice to support clinical decision and e-prescribing.

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.

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.

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.

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%.