Data Science Methodologies Research Articles

Development of Predictions through Machine Learning for Sars-Cov-2 Forecasting in Peru

The SARS-COV-2 virus of the coronavirus family was identified in 2019. This is a type of virus that infects humans and some animals, in Peru it has seriously affected everyone, causing so many deaths, which has resulted in that people be tested to rule out contagion, using laboratory methods recommended by the government of the country. Therefore, the data science methodology was used with this research, where its objective is to predict what types of people are contaminated during SARS-COV-2 by the regions of Peru, identified through laboratory methods, therefore, the ”data bank” was taken by PNDA, the CSV file was used for that study, apart from the fact that it comes from the INS and the CDC of the MINSA. In which, machine learning was developed with the decision tree algorithm and then began coding, in such a way that the distribution called Anaconda was used where it is encoded in Python language, together with that distribution, Jupyter Notebook was used which is a client-server application. The results generated by this research prove that it was possible to identify the types of individuals by SARS-COV-2. These results can help prevention entities against SARS-COV-2 to apply the corresponding preventive measures in a more focused way.

Data science and productivity: A bibliometric review of data science applications and approaches in productivity evaluations

This paper provides a comprehensive review of the applications of data science techniques and methodologies in productivity. The paper is structured as a combination of a bibliometric analysis and an empirical review. In the bibliometric analysis, the sources, authorship, and documents are reviewed and discussed. Visualisation aids, including summative tables and figures, are incorporated. In the empirical review, the corpus of 533 articles identified are reviewed based on the application areas of data science approaches and the primary methodology of the papers, and the selected most impactful and relevant papers in each methodological category are discussed in detail. The objective of this paper is to provide an overview of the current predominant trends and patterns in data science and productivity, explore how the interplay has been manifested, and provide an outlook on future research orientations.

Distance geometry and data science

Data are often represented as graphs. Many common tasks in data science are based on distances between entities. While some data science methodologies natively take graphs as their input, there are many more that take their input in vectorial form. In this survey, we discuss the fundamental problem of mapping graphs to vectors, and its relation with mathematical programming. We discuss applications, solution methods, dimensional reduction techniques, and some of their limits. We then present an application of some of these ideas to neural networks, showing that distance geometry techniques can give competitive performance with respect to more traditional graph-to-vector mappings.

National projects and government programmes: functional algorithm for evaluating and modelling using the Data Science methodology

Programme and target planning procedures in Russia have a lot of shortcomings, related to the selection of priority goals, establishment of criteria for evaluating the effectiveness of target programmes, as well as achievement of goals, development of a system of performance indicators, and so on. In addition, the problem of the lack of a high-quality theoretical and legislative framework for the transition to budget expenditures planning in accordance with the principles of result-oriented budgeting remains urgent. The purpose of this paper is to develop a functional fuzzy computing algorithm for modelling the evaluation of government programmes using neural networks. As a part of this work, we obtained stable results in the form of creating a neural network that can analyze government projects using a multi-criteria method, taking into account the root-mean-square error, with an accuracy of up to 95%. The analysis criteria cover all effective areas for predicting the correct use of the government projects by implementing them in the government systems.

Disease Ecology of Rickettsial Species: A Data Science Approach.

We present an approach to assess the disease ecology of rickettsial species by investigating open databases and by using data science methodologies. First, we explored the epidemiological trend and changes of human rickettsial disease epidemics over the years and compared this trend with knowledge on emerging rickettsial diseases given by published reviews. Second, we investigated the global diversity of rickettsial species recorded in humans, domestic animals and wild mammals, using the Enhanced Infectious Disease Database (EID2) and employing a network analysis approach to represent and quantify transmission ecology of rickettsial species among their carriers, arthropod vectors or mammal reservoirs and humans. Our results confirmed previous studies that emphasized the increasing incidence in rickettsial diseases at the onset of 1970. Using the Global Infectious Diseases and Epidemiology Online Network (GIDEON) database, it was even possible to date the start of this increase of global outbreaks in rickettsial diseases in 1971. Network analysis showed the importance of domestic animals and peridomestic mammals in sharing rickettsial diseases with humans and other wild animals, acting as important hubs or connectors for rickettsial transmission.

A Holistic Approach for Measuring Quality of Life in “La Condesa” Neighbourhood in Mexico City

This paper presents our approach for computing an index of quality of life1 (QoL) through a data science methodology considering quantitative and qualitative measures. Our methodology seeks to help to promote the maximisation of a holistic2 return of investment that we propose and name elasticity of quality of life (E-QoL). E-QoL models and calibrates variables associated to social, economic, cultural, psychological, health perspectives to find an “optimum” composite benefit including economic and “wellbeing” aspects. Our notion of return of investment expressed by the E-QoL is holistic because it considers different objective and subjective perspectives to provide a multi-facet understanding of QoL and the way it can impact the perception of citizens about their wellbeing (e.g., happiness, self-fulfilment, satisfaction). Wellbeing is an immaterial perception difficult to measure. Yet, supported by studies on the impact of quality of life on wellbeing perception, our claim is that studying measurable variables and combining them in a Mathematical model it can be possible to analyse and understand wellbeing subjective perception. Studying wellbeing subjective perception under different objective and subjective views to provide a holistic study of QoL. Thus, the model would help us to experiment and explore different perspectives of citizens’ wellbeing perception and how changes in the urban environment impact it. This work has chosen the historical neighbourhood La Condesa located 4 Km. from the Historical Downtown Area of Mexico City as case study where governmental programs have been designed to promote the construction of social lodging and in consequence bring inhabitants, leisure activities and business life to central territories. It seems that the growth in economy, population increase in the neighbourhood has not been compatible with citizens QoL and wellbeing perception and has not led to the increase of the quality of life index observed in the neighbourhood.

Mining Big Data in Education: Affordances and Challenges

The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education.

New digital laboratories of experimental knowledge production: Artificial intelligence and education research

Education data scientists, learning engineers and precision education specialists are new experts in knowledge production in educational research. By bringing together data science methodologies and advanced artificial intelligence (AI) systems with disciplinary expertise from the psychological, biological and brain sciences, they are building a new field of AI-based learning science. This article presents an examination of how education research is being remade as an experimental data-intensive science. AI is combining with learning science in new ‘digital laboratories’ where ownership over data, and power and authority over educational knowledge production, are being redistributed to research assemblages of computational machines and scientific expertise.

Exploring the Efficiency of Various Supervised Machine Learning Techniques to Predict the Heart Disease using Risk Factors

Data Science in healthcare is a innovative and capable for industry implementing the data science applications. Data analytics is recent science in to discover the medical data set to explore and discover the disease. It’s a beginning attempt to identify the disease with the help of large amount of medical dataset. Using this data science methodology, it makes the user to find their disease without the help of health care centres. Healthcare and data science are often linked through finances as the industry attempts to reduce its expenses with the help of large amounts of data. Data science and medicine are rapidly developing, and it is important that they advance together. Health care information is very effective in the society. In a human life day to day heart disease had increased. Based on the heart disease to monitor different factors in human body to analyse and prevent the heart disease. To classify the factors using the machine learning algorithms and to predict the disease is major part. Major part of involves machine level based supervised learning algorithm such as SVM, Naviebayes, Decision Trees and Random forest.

The use of data science to analyse physiology of oxygen delivery in the extracorporeal circulation

BackgroundRecent scientific reports have brought into light a new concept of goal-directed perfusion (GDP) that aims to recreate physiological conditions in which the risk of end-organ malperfusion is minimalized. The aim of our study was to analyse patients’ interim physiology while on cardiopulmonary bypass based on the haemodynamic and tissue oxygen delivery measurements. We also aimed to create a universal formula that may help in further implementation of the GDP concept.MethodsWe retrospectively analysed patients operated on at the Wroclaw University Hospital between June 2017 and December 2018. Since our observations provided an extensive amount of data, including the patients’ demographics, surgery details and the perfusion-related data, the Data Science methodology was applied.ResultsA total of 272 (mean age 62.5 ± 12.4, 74% male) cardiac surgery patients were included in the study. To study the relationship between haemodynamic and tissue oxygen parameters, the data for three different values of DO2i (280 ml/min/m2, 330 ml/min/m2 and 380 ml/min/m2), were evaluated. Each set of those lines showed a descending function of CI in Hb concentration for the set DO2i.ConclusionsModern calculation tools make it possible to create a common data platform from a very large database. Using that methodology we created models of haemodynamic compounds describing tissue oxygen delivery. The obtained unique patterns may both allow the adaptation of the flow in relation to the patient’s unique morphology that changes in time and contribute to wider and safer implementation of perfusion strategy which has been tailored to every patient’s individual needs.

Data Science in the Chemical Engineering Curriculum

With the increasing availability of large amounts of data, methods that fall under the term data science are becoming important assets for chemical engineers to use. Methods, broadly speaking, are needed to carry out three tasks, namely data management, statistical and machine learning and data visualization. While claims have been made that data science is essentially statistics, consideration of the three tasks previously mentioned make it clear that it is really broader than just statistics alone and furthermore, statistical methods from a data-poor era are likely insufficient. While there have been many successful applications of data science methodologies, there are still many challenges that must be addressed. For example, just because a dataset is large, does not necessarily mean it is meaningful or information rich. From an organizational point of view, a lack of domain knowledge and a lack of a trained workforce among other issues are cited as barriers for the successful implementation of data science within an organization. Many of the methodologies employed in data science are familiar to chemical engineers; however, it is generally the case that not all the methods required to carry out data science projects are covered in an undergraduate chemical engineering program. One option to address this is to adjust the curriculum by modifying existing courses and introducing electives. Other examples include the introduction of a data science minor or a postgraduate certificate or a Master’s program in data science.

Caracterización geográfica de la vivienda en México: un enfoque de Ciencia de Datos

Una tendencia global en los primeros años del siglo XXI ha sido la generación de plataformas y datos abiertos por parte de entidades gubernamentales para la consul- ta pública. Estos datos permiten a los ciudadanos generar nuevas técnicas y meca- nismos para su análisis con la intención de lograr mejoras en el sistema político, social y económico de un país. En esta investigación, proponemos una metodología de Ciencia de Datos centrada en la caracterización geográfica de la vivienda mexi- cana, utilizando datos abiertos del período 2014-2017 y aplicando técnicas de aprendizaje automatizado no supervisado. La aplicación de esta metodología permi- tió descubrir, que son 7 los tipos de viviendas en México, todos ellos distribuidos a lo largo y ancho del territorio mexicano. Los resultados son desplegados en mapas que podrán ser de gran utilidad en ciencia aplicada, para el estudio de fenómenos humanos y geográficos en diferentes dominios del conocimiento, tales como: segu- ridad, transporte, salud, economía, industria, agricultura, entre otras.

Change of Data-Driven Drug Design Trends Through Patent Analysis

The pharmaceutical industry is one of the most research and development (R&D)-intensive industries. This industry has tried many strategies to overcome the limitations of a business model that had a high return and high risk. In recent years, the fourth industrial revolution has affected many industries, causing them to update their traditional production and business strategies to a “data science-based” approach. This data science methodology, based on the largely increased size of the data environment, has actively changed the pharmaceutical industry. Therefore, this study aimed to identify specific characteristics of data science innovation in the pharmaceutical industry through the analysis of patent data from the triadic patent databases from the United States, Japan, and Europe.

Recent advances in hyperspectral imaging for melanoma detection

AbstractSkin cancer is one of the most common types of cancer. Skin cancers are classified as nonmelanoma and melanoma, with the first type being the most frequent and the second type being the most deadly. The key to effective treatment of skin cancer is early detection. With the recent increase of computational power, the number of algorithms to detect and classify skin lesions has increased. The overall verdict on systems based on clinical and dermoscopic images captured with conventional RGB (red, green, and blue) cameras is that they do not outperform dermatologists. Computer‐based systems based on conventional RGB images seem to have reached an upper limit in their performance, while emerging technologies such as hyperspectral and multispectral imaging might possibly improve the results. These types of images can explore spectral regions beyond the human eye capabilities. Feature selection and dimensionality reduction are crucial parts of extracting salient information from this type of data. It is necessary to extend current classification methodologies to use all of the spatiospectral information, and deep learning models should be explored since they are capable of learning robust feature detectors from data. There is a lack of large, high‐quality datasets of hyperspectral skin lesion images, and there is a need for tools that can aid with monitoring the evolution of skin lesions over time. To understand the rich information contained in hyperspectral images, further research using data science and statistical methodologies, such as functional data analysis, scale‐space theory, machine learning, and so on, are essential.This article is categorized under: Applications of Computational Statistics > Health and Medical Data/Informatics

The Use of Data Science to Analyse Physiology of Oxygen Delivery in the Extracorporeal Circulation

Objectives: Recent scientific reports brought into light a new concept of goal-directed perfusion (GDP) that aims to recreate physiological conditions in which the risk of end-organ malperfusion is minimalized. The aim of our study was to analyse patient's interim physiology while on cardiopulmonary bypass based on the haemodynamic and tissue oxygen delivery measurements. We also aimed to create a universal formula that may help in the further implementation of the GDP concept. Methods: We retrospectively analysed patients operated at Wroclaw University Hospital between June 2017 and December 2018. Since our observations provided a huge amount of data, including the patient's demographics, surgery details, and the perfusion-related data, the Data Science methodology was applied. Results: A total of 272 (mean age 62.5±12.4, 74% male) cardiac surgery patients were included in the study. To study the relationship between haemodynamic and tissue oxygen parameters, the data for three different values of DO2i (280 ml/min/m2, 330 ml/min/m2 and 380ml/min/m2), were evaluated. Each set of those lines showed a descending function of CI in Hb concentration for the set DO2i. Conclusions: Data Science is a calculation tool that makes it possible to create a common data platform from a very large database. Using that methodology, we created models of haemodynamic compounds describing tissue oxygen delivery. The obtained unique patterns may both allow the adaptation of the flow in relation to the patient's unique morphology that changes in time and contributes to the wider and safer implementation of tailored to individual patient's needs perfusion strategy. Funding Statement: There was no funding received for this manuscript. Declaration of Interests: The authors stated: None declared. Ethics Approval Statement: The patients’ agreement for treatment and data collection was approved by the local Bioethics Committee.

Dose-Volume Histogram (DVH) Patterns within the Salivary Glands and Clinical Parameters Predict Xerostomia in Head and Neck Cancer (HNC) Patients, from Injury to Recovery

NCI-CTCAE-graded xerostomia is routinely captured in our analytic database demonstrating that its maximum severity and prevalence occurs within 18 mos after RT (injury), with the prevalence decreasing (recovery) where a lower rate of decreasing prevalence can be observed thereafter. Studies have indicated that the spatial distribution of radiation to the salivary glands differentially affects the risk of xerostomia. We sought to characterize the spatial dosimetry to the salivary glands and the oral cavity as it related to the risk of maximum xerostomia injury and its recovery as defined above while controlling for clinical parameters. HNC patients treated from 2007 to 2017 in our institution with CTCAE graded xerostomia available for at least 18 mos of follow-up were evaluated. Patients with moderate to severe xerostomia (CTCAE grade ≥ 2) before RT were excluded. 22 spatial zones were geometrically created from the parotid glands (PG), submandibular glands (SMG) and oral cavity (OC) for each patient. DVH features (D10-D90) for each zone as well as clinical parameters were used to predict xerostomia injury (CTCAE grade ≥ 2) within 18 mos post-RT, and recovery (grade decrease to < 2) after 18 mos of follow-up. A total of 217 HNC patients were identified with 146 developing moderate to severe xerostomia (CTCAE grade ≥ 2) within 18 mos of follow-up. The dataset was randomly split into a training and test dataset at the ratio of 7:3 for each outcome. Ridge logistic regression with 10-fold cross-validation was applied to predict xerostomia injury and recovery respectively in the training datasets. The critical zones associated xerostomia injury was the medial inferior portion of the ipsilateral PG (relative to the primary tumor) and the superior portion of the ipsilateral PG for recovery. The dose – recovery pattern was significantly influenced by the low - dose bath (D80-D90) across the superior portion of ipsilateral PG. The area under the receiver operating characteristic curve (AUC) was 0.78 and 0.74 for xerostomia injury and recovery respectively in validation datasets. The AUC for injury and recovery are 0.72 and 0.70 respectively in test datasets. Our data science methodology demonstrated that different spatial-dose patterns for xerostomia injury vs. recovery and highlighted the strength of this analytic technique along with the importance of assessing xerostomia during follow-up. These observations if validated provide insights into new strategies for RT deintensification. This work was supported by the Radiation Oncology Institute.

The ‘rule of law’ implications of data-driven decision-making: a techno-regulatory perspective

ABSTRACTTechno-regulation is a prominent mechanism for regulating human behaviour. One type of techno-regulation concerns automated decision-making with legal effects. While automated decision-making (ADM) systems in the public domain have traditionally been based on conscious design of decisional norms, increasingly, Data Science methodologies are used to devise these norms. This data-driven approach causes frictions with the underlying principle of public-sector decision-making, namely adherence to the rule of law. In this paper we discuss three major challenges data-driven ADM poses to the Rule Law: law as a normative enterprise, law as a causative enterprise and law as a moral enterprise.

Predicting Mission Alignment and Preventing Mission Drift: Do Revenue Sources Matter?

Activities of nonprofit organizations do not always align with their missions, a managerial problem termed as “mission drift.” Mission drift is difficult to operationalize and quantify; thus, as a critical issue, only a few conceptual pieces or empirical case studies have explored this topic. This paper develops innovative measures to operationalize “mission alignment” using data science methodology, and examines the impact of revenue sources on mission alignment. By using the cosine similarity of text between a mission statement and program description, four measures of mission alignment are devised: the sum cosine similarity, average cosine similarity, weighted sum cosine similarity, and weighted average cosine similarity. Text analysis indicates that a majority of the programs evidence educational purposes, and for-profit business plays an important role in foundations’ projects and funding. The regression analysis shows that personal donation and service revenue can increase mission alignment, while organizational donation and membership dues decrease mission alignment.

METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models

In the last few years, a large number of smart meters have been deployed in buildings to continuously monitor fine-grained energy consumption. Meteorological data deeply impact energy consumption, and an in-depth analysis of collected and correlated data can uncover interesting and actionable insights to improve the overall energy balance of our communities and to enhance people’s awareness of energy wasting. To effectively extract meaningful and interpretable insights from large collections of energy measurements and multi-dimensional meteorological data, innovative data science methodologies should be devised. Research frontiers are addressing self-learning approaches, which allow non-experts to exploit machine learning techniques more easily, and algorithmic transparency of models, hence providing actionable, explicit, declarative knowledge representation. This paper presents METeorological Data Analysis for Thermal Energy CHaracterization (METATECH), a data mining engine based on both exploratory and unsupervised data analytics algorithms, devised to build transparent models correlating weather conditions and energy consumption in buildings. METATECH exploits a joint approach coupling cluster analysis and generalized association rules to allow a deeper yet human-readable understanding of how meteorological data impact heating consumption. First, a partitional clustering algorithm is applied to weather conditions. Then, resulting clusters are characterized by means of generalized association rules, which provide a self-learning explainable model of the most interesting correlations between energy consumption and weather conditions at different granularity levels. The experimental evaluation performed on real datasets demonstrates the effectiveness of the proposed approach in automatically extracting interesting knowledge from data, and provide it transparently to domain experts.

Principles of Automation for Patient Safety in Intensive Care: Learning From Aviation

The transition away from written documentation and analog methods has opened up the possibility of leveraging data science and analytic techniques to improve health care. In the implementation of data science techniques and methodologies, high-acuity patients in the ICU can particularly benefit. The Principles of Automation for Patient Safety in Intensive Care (PASPIC) framework draws on Billings's principles of human-centered aviation (HCA) automation and helps in identifying the advantages, pitfalls, and unintended consequences of automation in health care. Billings's HCA principles are based on the premise that human operators must remain "in command," so that they are continuously informed and actively involved in all aspects of system operations. In addition, automated systems need to be predictable, simple to train, to learn, and to operate, and must be able to monitor the human operators, and every intelligent system element must know the intent of other intelligent system elements. In applying Billings's HCA principles to the ICU setting, PAPSIC has three key characteristics: (1) integration and better interoperability, (2) multidimensional analysis, and (3) enhanced situation awareness. PAPSIC suggests that health care professionals reduce overreliance on automation and implement "cooperative automation" and that vendors reduce mode errors and embrace interoperability. Much can be learned from the aviation industry in automating the ICU. Because it combines "smart" technology with the necessary controls to withstand unintended consequences, PAPSIC could help ensure more informed decision making in the ICU and better patient care.