Predicting occupational injury causal factors using text-based analytics : A systematic review

Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research

Big Data Risk Assessment the 21st Century approach to safety science

Safety Science has been developed over time with notable models in the early 20th Century such as Heinrich’s iceberg model and the Swiss cheese model. Common techniques such fault tree and event tree analyses, HAZOP analysis and bow-ties construction are widely used within industry. These techniques are based on the concept that failures of a system can be caused by deviations or individual faults within a system, combinations of latent failures, or even where each part of a complex system is operating within normal bounds but a combined effect creates a hazardous situation. In this era of Big Data, systems are becoming increasingly complex, producing such a large quantity of data related to safety that cannot be meaningfully analysed by humans to make decisions or uncover complex trends that may indicate the presence of hazards. More subtle and automated techniques for mining these data are required to provide a better understanding of our systems and the environment within which they operate, and insights to hazards that may not otherwise be identified. Big Data Risk Analysis (BDRA) is a suite of techniques being researched to identify the use of non-traditional techniques from big data sources to predict safety risk. This paper describes early trials of BDRA that have been conducted on railway signal information and text-based reports of railway safety near misses and the ongoing research that is looking at combining various data sources to uncover obscured trends that cannot be identified by considering each source individually. The paper also discusses how visual analytics may be a key tool in analysing Big Data to support knowledge elicitation and decision-making, as well as providing information in a form that can be readily interpreted by a variety of audiences

Модифікований метод автоматизації прийняття управлінських рішень на основі інтелектуального аналізу даних

Актуальність теми. Процес розробки програмного забезпечення починається з формування команди спеціалістів, що будуть залучені в проект. В компаніях досить часто нехтують процесом правильного вибору команди, що в подальшому приводить до низької якості програмних продуктів. Процес вибору учасників команди має бути автоматизованим та прийматися на підставі інформації про співробітників. Програмне забезпечення для автоматизації процесу підбору команди проекту будується на обробці та аналізі текстових документів для подальшого прийняття рішення. В даній магістерській дисертації розглядається вирішення задачі автоматизованого підбору учасників команди проектів з використанням текстових даних, що містять інформацію про професійні та особисті якості співробітників компанії, з метою підвищення точності вибору та швидкодії в порівнянні з існуючими методами. Об’єктом дослідження автоматизація прийняття рішень при підборі учасників команди проекту на основі текстових даних. Предметом дослідження є методи та алгоритми автоматизації прийняття рішень на основі інтелектуального аналізу текстових даних. Мета роботи полягає у розробці ефективного методу автоматизації підтримки прийняття рішень з підбору учасників команди проекту на основі наївної моделі Байєса за критерієм точності та швидкодії отримуваних результатів. Методи дослідження: в роботі використовуються методи теоретичного дослідження: аналіз та синтез. Також застосовувалися емпіричні методи: експеримент, вимірювання та порівняння. Наукова новизна роботи полягає у розробленні модифікованого методу Байєса для автоматизації підтримки прийняття рішень з підбору учасників команди проекту, який на відміну від класичного методу видає рішення за критерієм точності на 10-13% вище та в середньому на 20% швидше. Практична цінність отриманих результатів роботи полягає в тому, що запропонований метод дає змогу підвищити точність в прийнятті рішення з підбору учасників команди проекту. Також в рамках даного дослідження була розроблена автоматизована система підтримки прийняття рішень з підбору співробітників компанії для участі у нових проектах на основі запропонованого модифікованого методу. Апробація роботи. Основні положення і результати роботи були представлені та обговорювались на XІ науковій конференції магістрантів та аспірантів «Прикладна математика та комп’ютинг» ПМК-2018-2 та опубліковані у збірнику тез доповідей. За результатами роботи була написана наукова стаття на тему «Модифікований метод автоматизації прийняття управлінських рішень на основі інтелектуального аналізу даних при створенні команди управління проектами» до наукового міжнародного журналу «Керуючі системи та комп’ютери» 2019 №4. Структура та обсяг роботи. Магістерська дисертація складається з вступу, чотирьох розділів, висновків та додатків. У вступі надано загальну характеристику роботи, виконано оцінку сучасного стану проблеми, обґрунтовано актуальність напрямку досліджень, сформульовано мету і задачі досліджень. У першому розділі описана задача підбору учасників команди для виконання проектів з розроблення програмного забезпечення, розглянуті особливості автоматизованих систем підтримки прийняття рішень, оглянуті існуючі методи інтелектуального аналізу даних для вирішення поставленої задачі. У другому розділі розглянуто принцип роботи наївної моделі Байєса, проаналізовані її переваги та недоліки. Запропонований модифікований метод на основі моделі Байєса для автоматизації підтримки прийняття рішень з підбору учасників команди. У третьому розділі сформовані основні вимоги до автоматизованої системи підтримки прийняття рішень; обґрунтовано вибір засобів, що використовувались при розробці; описана розроблена система, що реалізує модифікований метод автоматизації прийняття рішень з підбору учасників команди проекту. У четвертому розділі визначено критерії оцінки ефективності, які застосовуються до розробленого методу; наведена інформація про дані, що використовувались при аналізі ефективності; проведений аналіз ефективності модифікованого та базового методів автоматизації прийняття рішень з підбору учасників команди. У висновках проаналізовано отримані результати роботи. Робота виконана на 70 аркушах, містить 2 додатки та посилання на список використаних літературних джерел з 30 найменувань. У роботі наведено 10 рисунків та 2 таблиці. Ключові слова: автоматизована система підтримки прийняття рішень, модифікований метод автоматизації прийняття рішень, модель Байєса, підбір учасників команди проекту.Actuality. The process of the software development begins with the formation of a team of specialists who will be involved in the project. Companies often overlook the process of choosing the right team, which leads to low quality of software products in the future. The process of selecting team members must be automated and taken on the basis of employee information. The software for automating the project selection process is based on the processing and analysis of text documents for further decision-making. This master's thesis deals with the problem of automated selection of project team members using text data that contains information about the professional and personal qualities of the company's employees in order to improve the accuracy of the choice and performance compared to existing methods. Object of research is decision-making automation at selection of the project team participants on the basis of text data. Subjects of research are methods and algorithms of decision-making automation on the basis of text data intellectual analysis. Goal of the work is to develop an effective method of automating decision support for selection of project team members based on the Bayesian naive model for the criterion of accuracy and performance of the results. Methods of research include methods of theoretical research: analysis and synthesis. Also there were used empirical methods: experiment, measurement and comparison. Scientific novelty of the work is to develop a modified Bayesian method to automate decision-making support for selection of project team members, which, in contrast to the classical method, gives solutions by accuracy criterion of 10-13% higher and an average of 20% faster. Practical value of the received results of work is that the proposed method allows increase the accuracy in decision making of the selection of project team members. Also, in this research, an automated decision-making support system was developed for selecting company employees for participation in new projects on the basis of the proposed modified method. Approbation. The main provisions and results of the work were presented and discussed at the XI scientific conference of masters and postgraduates "Applied Mathematics and Computer" PMK-2018-2 and published in the proceedings. As a result of the work, a scientific article on the topic "Modified method of decision making automation on the basis of intelligent data analysis when creating a team of project management" was written to the international scientific journal "Control systems and computers" 2019 № 4. Structure and content of the thesis. Master's thesis consists of an introduction, four chapters, conclusions and appendices. The introduction provides a general description of the work, evaluated the current state of the problem, substantiated the relevance of the research direction, formulated the purpose and objectives of the study. The first chapter describes the task of selecting team members to implement software development projects, features of automated decision support systems were considered, the existing methods of intelligent data analysis for solving the problem were examined. The second chapter discusses the principle of the Bayesian naive model, analyzing its advantages and disadvantages. A modified method based on Bayesian model is proposed for automating decision support for selection of team members. In the third section, the main requirements for an automated decision support system are formed; the choice of the means used during development was substantiated; describes a developed system that implements a modified method of decision making automation in selecting project team members. The fourth chapter defines the criteria for assessing the effectiveness of the developed method; provides information on the data used in the analysis of efficiency; the analysis of the efficiency for the modified and basic methods of automated decision making on the selection of team members was conducted. The conclusion contains brief overview of the results obtained in the work. The work is done on 70 pages, contains 2 appendices and reference list of 30 titles. The work contains 10 pictures and 2 tables. Keywords: automated decision support system, modified method of decision making automation, Bayes model, selection of project team members

Combining Text Analysis and Concept Mapping for Conceptual Model Development

The Rio Grande/Río Bravo River (RGB) stretches through Colorado, New Mexico, Texas and Mexico before it reaches the Gulf of Mexico, spanning a politically, socio-economically, and environmentally diverse region. Management decisions in this highly complex coupled human-natural system (CHANS) may lead to unintended outcomes throughout the basin, upstream and downstream. The interactions between the river, the basin’s landscape, and the people that rely on these land and water resources have not been addressed in a whole-basin and spatially explicit modeling approach, which has left a gap in knowledge regarding plans for managing changes in water availability due to climate change. To address this, collaborators (Drs. Paladino and Friedman) conducted a multitude of in-person interviews with water managers, large agricultural water users, and non-governmental actors charged with water management decisions in the RGB. The resulting interviews provided the underlying information used to explore and analyze the social processes that are behind those decisions. A concept map was developed to be used as a tool to visually document the local knowledge on decision making in the RGB and to support the development of a simulation model of the RGB CHANS. Since the RGB basin is large and environmentally and culturally heterogeneous, I tested an approach to reduce the time needed to analyze the interview data and develop the concept map: an automated text analysis, based on a topic model approach. By implementing a topic model on the interviews, I tested whether a topic model had the potential to reduce the time needed for concept map development and/or if the topic model would be able to support the concept mapping process. In this document, I briefly discuss the concept map and its development process, since they form the basis of this research. Then I introduce text analysis and the topic modeling approach specifically, followed by the identification of topics and their relationship to the concept maps. The results from the topic modeling analysis show a large overlap with the topics identified in the context of the concept mapping process. However, the text analysis also identified several topics not covered in the concept map, including (water) rights and regional and local variations. My research displays that while an automated text analysis approach has the potential to support interdisciplinary research on supporting computer simulation model development and parameterization with qualitative information from stakeholder interviews, it also has considerable limitations and is, at this point, not suitable to replace interdisciplinary research efforts

Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

There has been much discussion of the right to explanation in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the black box of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decision-making systems and their rationale in specific cases is a technically challenging problem. Some explanations may offer little meaningful information to data subjects, raising questions around their value. Explanations of automated decisions need not hinge on the general public understanding how algorithmic systems function. Even though such interpretability is of great importance and should be pursued, explanations can, in principle, be offered without opening the black box. Looking at explanations as a means to help a data subject act rather than merely understand, one could gauge the scope and content of explanations according to the specific goal or action they are intended to support. From the perspective of individuals affected by automated decision-making, we propose three aims for explanations: (1) to inform and help the individual understand why a particular decision was reached, (2) to provide grounds to contest the decision if the outcome is undesired, and (3) to understand what would need to change in order to receive a desired result in the future, based on the current decision-making model. We assess how each of these goals finds support in the GDPR. We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims. These counterfactual explanations describe the smallest change to the world that can be made to obtain a desirable outcome, or to arrive at the closest possible world, without needing to explain the internal logic of the system

Autonomous Systems as Legal Agents: Directly by the Recognition of Personhood or Indirectly by the Alchemy of Algorithmic Entities

The clinical manifestations of platelet dense (δ) granule defects are easy bruising, as well as epistaxis and bleeding after delivery, tooth extractions and surgical procedures. The observed symptoms may be explained either by a decreased number of granules or by a defect in the uptake/release of granule contents. We have developed a method to study platelet dense granule storage and release. The uptake of the fluorescent marker, mepacrine, into the platelet dense granule was measured using flow cytometry. The platelet population was identified by the size and binding of a phycoerythrin-conjugated antibody against GPIb. Cells within the discrimination frame were analysed for green (mepacrine) fluorescence. Both resting platelets and platelets previously stimulated with collagen and the thrombin receptor agonist peptide SFLLRN was analysed for mepacrine uptake. By subtracting the value for mepacrine uptake after stimulation from the value for uptake without stimulation for each individual, the platelet dense granule release capacity could be estimated. Whole blood samples from 22 healthy individuals were analysed. Mepacrine incubation without previous stimulation gave mean fluorescence intensity (MFI) values of 83±6 (mean ± 1 SD, range 69–91). The difference in MFI between resting and stimulated platelets was 28±7 (range 17–40). Six members of a family, of whom one had a known δ-storage pool disease, were analysed. The two members (mother and son) who had prolonged bleeding times also had MFI values disparate from the normal population in this analysis. The values of one daughter with mild bleeding problems but a normal bleeding time were in the lower part of the reference interval

Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization's environment and that they appear in different contexts. We use internet information to represent organization's environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time