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This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions.

This conceptual paper highlights the scale and scope of PHI data consumed by deep learning algorithms and their opacity as novel challenges to health data governance.

This paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions.

Health data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security.

This is the first paper focusing on health data governance in relation to AI/machine learning.

The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy.

The study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services.

The proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain.

This study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making.

The purpose of this paper is to show the development of an image processing-based portable equipment for an automatic wear debris analysis. It can analyze both the qualitative and quantitative features of machine wear debris: size, quantity, size distribution, shape, surface texture and material composition via color.

It comprises hardware and software components which can take debris in near real-time from a machine oil sump and process it for features diagnosis. This processing provides the information of the basic features on the user screen which can further be used for machine component health diagnosis.

The developed system has the capacity to replace the existing off-line methods due to its cost effectiveness and simplicity in operation. The system is able to analyze debris basic quantitative and qualitative features greater than 50 micron and less than 300 micron.

Wear debris basic features analysis tool is developed and discussed. The portable and near real-time analysis offered by the discussed work can be more technically effective as compared to the existing off-line and online techniques.

This paper aims to discuss and bring to the attention of researchers and practitioners the data management issues relating to condition‐based maintenance (CBM) optimization.

The common data quality problems encountered in CBM decision analyses are investigated with a view to suggesting methods to resolve these problems. In particular, the approaches for handling missing data in the decision analysis are reviewed.

This paper proposes a data structure for managing the asset‐related maintenance data that support CBM decision analysis. It also presents a procedure for data‐driven CBM optimization comprising the steps of data preparation, model construction and validation, decision‐making, and sensitivity analysis.

Analysis of condition monitoring data using the proportional hazards modeling (PHM) approach has been proved to be successful in optimizing CBM decisions relating to motor transmission equipment, power transformers and manufacturing processes. However, on many occasions, asset managers still make sub‐optimal decisions because of data quality problems. Thus, mathematical models by themselves do not guarantee that correct decisions will be made if the raw data do not have the required quality. This paper examines the significant issues of data management in CBM decision analysis. In particular, the requirements of data captured from two common condition monitoring techniques – namely vibration monitoring and oil analysis – are discussed.

This paper offers advice to asset managers on ways to avoid capturing poor data and the procedure for manipulating imperfect data, so that they can assess equipment conditions and predict failures more accurately. This way, the useful life of physical assets can be extended and the related maintenance costs minimized. It also proposes a research agenda on CBM optimization and associated data management issues.

The purpose of this paper is to presents the current and prospective state of affairs when it comes to the information and communication technologies (ICTs) in condition monitoring (CM) and maintenance, especially for the e-maintenance approach.

The author presents some of the standards for the domain of interest, such as the open system architecture condition-based maintenance. In addition, the e-maintenance approach is gone through as well as such ICTs as, for instance, the emergent web technologies, the service-oriented architecture (SOA), the web services and the Web 2.0 are analysed.

The findings highlight the need for a clearer understanding of the characteristics of different ICTs, such as Web 2.0 technologies, Cloud computing, agent technologies, etc., to be able to use them in an optimal manner for various purposes in the e-maintenance applications. In addition, the standardisation of the emergent ICTs different aspects is shown to be an important factor for the development of different phases of software as well as for its overall acceptance.

The given work presents the current and emergent ICTs for the domain of interest and provides the discussion and various issues connected to these ICTs.

The author provides practical implications of the different ICTs mentioned in the paper, i.e. benefits and possibilities as well as risks when those technologies are implemented for CM and maintenance, especially for the e-maintenance.

The paper provides insight into various current and prospective ICTs for the domain of interest that provides important knowledge for different employees with the objective of a purchase, users of the system, such as technicians, maintenance engineers as well as developers of these systems. Consequently, the paper provides knowledge of different characteristics of the current and prospective technologies, which is important to take into account in order to be able not only to use them in an optimal manner, but also to understand possible constraints if they are used in the system and in applications in the domain.

The purpose of this paper is to check the actual life of lubricating oil.

Present work aims to find the remaining useful life of the lubricant based on study of periodic deterioration of oil. Chronological samples of oil were selected from the dumper of a local open cast mine. The deterioration in oil was studied using Fourier transform infrared (FTIR) spectroscopy.

The data obtained from FTIR spectroscopy was used in vector projection approach and analytical hierarchy process to evaluate the remaining useful life of the lubricating oil.

FTIR spectra were used to study the periodic deterioration of oil. IR radiation with all frequencies in the range was passed through the sample. Radiations at certain frequency, depending upon the molecular structure of compounds in the sample were absorbed and rest was transmitted by the sample. A spectrum representing molecular absorption or transmission was obtained. Transmission spectra have been used in the study. Comparing the percent value of transmission peak of different chronological sample with that of fresh oil was used to represent the periodic degradation in oil.

Leisure centres and health clubs are ideal places for promoting healthy lifestyle. They promote physical exercise and many activities for children, such as swimming, soft play areas, crèche, and team sports. The purpose of this paper is to evaluate the food environment for children in leisure centres and health clubs in London.

In total, 67 venues were visited. All food and drink options were recorded and the proportion of “healthy” options was calculated according to the School Food Trust criteria and Nutrient Profiling Model.

In total, 96 per cent of the venues had vending machines and 51 per cent had onsite restaurants/cafés. According to The School Food Trust criteria, only 13 per cent of vending machine drinks, 77.2 per cent of meals, and 24 per cent of snacks would be allowed in school canteens.

The study revealed that a low proportion of healthy foods and drinks were offered to children in Leisure centres in London. However, the survey was only extended to venues in the capital.

The results of the study suggest that new recommendations such as the Healthy Food Code of Good Practice, omitted leisure centres. The findings presented here could provide scientific evidence for campaigns and interventions aimed at improving the quality and the appropriateness of foods and drinks offered to children.

The paper shows that health campaigns and legislation should target leisure centres and health clubs, in order to improve the food and drinks facilities and promote healthy eating, particularly in light of the upcoming Olympic Games in London 2012.

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.

Increasing health-care costs are a major concern, especially in the USA. The purpose of this paper is to predict the hospital charges of a patient before being admitted. This will help a patient who is getting admitted: “electively” can plan his/her finance. Also, this can be used as a tool by payers (insurance companies) to better forecast the amount that a patient might claim.

This research method involves secondary data collected from New York state’s patient discharges of 2017. A stratified sampling technique is used to sample the data from the population, feature engineering is done on categorical variables. Different regression techniques are being used to predict the target value “total charges.”

Total cost varies linearly with the length of stay. Among all the machine learning algorithms considered, namely, random forest, stochastic gradient descent (SGD) regressor, K nearest neighbors regressor, extreme gradient boosting regressor and gradient boosting regressor, random forest regressor had the best accuracy with R² value 0.7753. “Age group” was the most important predictor among all the features.

This model can be helpful for patients who want to compare the cost at different hospitals and can plan their finances accordingly in case of “elective” admission. Insurance companies can predict how much a patient with a particular medical condition might claim by getting admitted to the hospital.

Health care can be a costly affair if not planned properly. This research gives patients and insurance companies a better prediction of the total cost that they might incur.