Big data and machine learning in health

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

The 3rd edition of the International Conference on Big Data and Machine Learning (BML’22) is a major gathering of researchers, engineers, and practitioners within the fields of Big Data, Machine Learning, and Artificial Intelligence, from both theoretical and practical perspectives. We strongly encourage contributions that describe real-world problems, interdisciplinary research, and experimental and/or theoretical studies that yield new insights to advance Big Data and Machine Learning. Submitted papers should be original and relevant to any of the areas listed below. All accepted papers will be presented at the conference by one of the authors and published in the proceedings with an ISBN. The acceptance will be based on their quality, relevance, and originality. Presentation options include both oral and poster sessions. We have planned special sessions for case studies and commercial presentations, as well as tutorials focusing on technical or scientific matters. Companies interested in presenting their products or methodologies, as well as researchers wishing to demonstrate a demo or present a tutorial are encouraged to contact the conference secretariat. We cordially invite all speakers and participants to present their research and/or experiences at BML’22concerning the subjects listed below. While each of the main topic areas is

The 3rd edition of the International Conference on Big Data and Machine Learning (BML’22) is a major gathering of researchers, engineers, and practitioners within the fields of Big Data, Machine Learning, and Artificial Intelligence, from both theoretical and practical perspectives. We strongly encourage contributions that describe real-world problems, interdisciplinary research, and experimental and/or theoretical studies that yield new insights to advance Big Data and Machine Learning. Submitted papers should be original and relevant to any of the areas listed below. All accepted papers will be presented at the conference by one of the authors and published in the proceedings with an ISBN. The acceptance will be based on their quality, relevance, and originality. Presentation options include both oral and poster sessions. We have planned special sessions for case studies and commercial presentations, as well as tutorials focusing on technical or scientific matters. Companies interested in presenting their products or methodologies, as well as researchers wishing to demonstrate a demo or present a tutorial are encouraged to contact the conference secretariat. We cordially invite all speakers and participants to present their research and/or experiences at BML’22concerning the subjects listed below. While each

According to popular belief, big data and machine learning provide a wholly novel approach to science that has the potential to revolutionise scientific progress and will ultimately lead to the ‘end of theory’. Proponents of this view argue that advanced algorithms are able to mine vast amounts of data relating to a given problem without any prior knowledge and that we do not need to concern ourselves with causality, as correlation is sufficient for handling complex issues. Consequently, the human contribution to scientific progress is deemed to be non-essential and replaceable. We, however, following the position most commonly represented in the philosophy of science, argue that the need for human expertise remains. Based on an analysis of big data and machine learning methods in two case studies—skin cancer detection and protein folding—we show that expert knowledge is essential and inherent in the application of these methods. Drawing on this analysis, we establish a classification of the different kinds of expert knowledge that are involved in the application of big data and machine learning in scientific contexts. We address the ramifications of a human-driven expert knowledge approach to big data and machine learning for scientific practice and the discussion about the role of theory. Finally, we show that the ways in which big data and machine learning both influence and are influenced by scientific methodology involve continuous conceptual shifts rather than a rigid paradigm change.

Background/Objectives: The increasing application of artificial intelligence (AI) and machine learning (ML) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI and ML in health and medicine. Methods: We used the Scopus database for searching and extracted articles published between 2000 and 2024. Then, we generated information about productivity, citations, collaboration, most impactful research topics, emerging research topics, and author keywords using Microsoft Excel 365 and VOSviewer software (version 1.6.20). Results: We retrieved a total of 22,113 research articles, with a notable surge in research activity in recent years. Core journals were Scientific Reports and IEEE Access, and core institutions included Harvard Medical School and the Ministry of Education of the People's Republic of China, while core countries comprised the United States, China, India, the United Kingdom, and Saudi Arabia. Citation trends indicated substantial growth and recognition of AI's and ML impact on health and medicine. Frequent author keywords identified key research hotspots, including specific diseases like Alzheimer's disease, Parkinson's diseases, COVID-19, and diabetes. The author keyword analysis identified "deep learning", "convolutional neural network", and "classification" as dominant research themes. Conclusions: AI's transformative potential in AI and ML in health and medicine holds promise for improving global health outcomes.

Until now, much of the work on machine learning and health has focused on processes inside the hospital or clinic. However, this represents only a narrow set of tasks and challenges related to health; there is greater potential for impact by leveraging machine learning in health tasks more broadly. In this Perspective we aim to highlight potential opportunities and challenges for machine learning within a holistic view of health and its influences. To do so, we build on research in population and public health that focuses on the mechanisms between different cultural, social and environmental factors and their effect on the health of individuals and communities. We present a brief introduction to research in these fields, data sources and types of tasks, and use these to identify settings where machine learning is relevant and can contribute to new knowledge. Given the key foci of health equity and disparities within public and population health, we juxtapose these topics with the machine learning subfield of algorithmic fairness to highlight specific opportunities where machine learning, public and population health may synergize to achieve health equity. Algorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public health can extend beyond clinical practice. While working with general health data comes with its own challenges, most notably ensuring algorithmic fairness in the face of existing health disparities, the area provides new kinds of data and questions for the machine learning community.

The concept of precision agriculture (PA) refers to the observation, impact assessment, and timely strategic responses to fine-scale variations in the causal components of agricultural production. Big data and machine learning (ML) play integral roles in this paradigm by providing detailed insights into various farming processes. In PA, data from remote sensing platforms and weather forecasts are collected and analysed to monitor crop health and soil conditions, enabling farmers to make informed decisions. Examples of ML techniques and potential sources of big data are discussed in this chapter. Furthermore, three case studies examine the potential of big data and ML models within the PA cropping systems. The three case studies illustrated the practical integration of ML in processing this data, leading to enhanced decision-making. With the advancement of data collection techniques and machine learning methods, agricultural decision-making and overall productivity can be improved significantly.

Recently, big data and machine learning have become increasingly important in various environments, including health care, industry, government, scientific research, business organizations, social networking, and natural resource management. In particular, they apply to the field of health care and make it possible to process and analyze many data that humans or traditional computer tools cannot do simultaneously. Big data and machine learning are having a significant influence on the health care business, and their analyses may help the whole industry. The opportunities they will create will serve as a springboard for the future of the health care business. This will lead to a paradigm shift, shifting the industry's focus from disease-based diagnosis to patient-based diagnosis. This article will discuss big data technology, including its definition, characteristics, technology, relationship, and influence on the healthcare industry. The concept of machine learning technology, as well as its influence on healthcare, will be also explored.

The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible. Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the incentive for the authors to complete a series of three textbooks entitled Learning in Medicine Part and Three, Springer Heidelberg Germany, 2012-2013, describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details. For this reason we produced a 100 page cookbook, entitled Machine Learning in Medicine - Cookbook One, with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled Machine Learning in Medicine - Cookbook Two is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care. Similarly to Learning in Medicine - Cookbook the current work will describe stepwise analyses of over twenty machine learning methods, that are, likewise, based on the three major machine learning methodologies:Cluster methodologies (Chaps. 1-3)Linear methodologies (Chaps. 4-11)Rules methodologies (Chaps. 12-20)In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8. We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing general purposes, main scientific questions and conclusions are given in place. Finally, we will demonstrate that modern machine learning performs sometimes better than traditional statistics does. learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.

<p>Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in machine learning, main technologies in Big Data, and some applications of machine learning in Big Data. Challenges of machine learning applications in Big Data are discussed. Some new methods and technology progress of machine learning in Big Data are also presented.</p>

The chapter is intended to introduce Big Data and machine learning concepts to the reader while highlighting the importance of including green practices to make these technologies more effective. The topics and concepts covered by this chapter are listed below: The Big Data generation, acquisition, storage and the analysis techniques. The challenges, the current state-of-the-art and the future of Big Data. The machine learning concepts and the correlation between Big Data and machine learning. Green Big Data technology and its benefits and the current scenario of green computing. The chapter intends to emphasize on the need for green computing and helps the readers gain a perspective on the green practices that can revolutionize the technology world as well as our planet.

The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results.

The application of machine learning methods to data generated by ubiquitous devices like smartphones presents an opportunity to enhance the quality of health care and diagnostics. Smartphones are ideal for gathering data easily, providing quick feedback on diagnoses, and proposing interventions for health improvement. We reviewed the existing literature to gather studies that have used machine learning models with smartphone-derived data for the prediction and diagnosis of health anomalies. We divided the studies into those that used machine learning models by conducting experiments to retrieve data and predict diseases, and those that used machine learning models on publicly available databases. The details of databases, experiments, and machine learning models are intended to help researchers working in the fields of machine learning and artificial intelligence in the health care domain. Researchers can use the information to design their experiments or determine the databases they could analyze. A comprehensive search of the PubMed and IEEE Xplore databases was conducted, and an in-house keyword screening method was used to filter the articles based on the content of their titles and abstracts. Subsequently, studies related to the 3 areas of voice, skin, and eye were selected and analyzed based on how data for machine learning models were extracted (ie, the use of publicly available databases or through experiments). The machine learning methods used in each study were also noted. A total of 49 studies were identified as being relevant to the topic of interest, and among these studies, there were 31 different databases and 24 different machine learning methods. The results provide a better understanding of how smartphone data are collected for predicting different diseases and what kinds of machine learning methods are used on these data. Similarly, publicly available databases having smartphone-based data that can be used for the diagnosis of various diseases have been presented. Our screening method could be used or improved in future studies, and our findings could be used as a reference to conduct similar studies, experiments, or statistical analyses.

Technology Focus Machine-learning methods have gained tremendous attention in the last decade. These methods are tackling and solving complex problems with impressive results in many different areas. Buzzwords such as “big data” and “deep learning” are now part of our everyday vocabulary. The underlying idea behind machine learning is that computers can identify patterns and learn from data with minimal human intervention. This is not very different from the notion of automatic history matching. Actually, the similarities are more profound: Both machine learning and history-matching methods use the same mathematical tools, such as linear algebra, optimization, Bayesian inference, and Monte Carlo sampling. In fact, I believe that machine learning is a natural research direction to improve our current history-matching methods. At this point, some readers must be thinking about the risks of automated processes. They may very well claim that there are no surrogates for experience, a thorough understanding of the reservoir production mechanisms, and attention to the practical aspects and limitations of the models. After all, if machines can learn, humans can think. I am not trying to say otherwise. My only point is that we cannot ignore the recent developments in machine learning and we must not turn a blind eye to what computer-aided strategies can offer. If used responsibly, they can help us achieve better results in less time. To conclude, I would like to point out that machine-learning methods may also find very interesting applications in data-driven production forecasts. In fact, data-driven methods are not new in the petroleum literature; see, for example, decline-curve analysis. These methods are used currently as complements (and often alternatives) to model-driven approaches. Machine-learning methods have much to offer in this regard, especially in cases with an abundance of data. Recommended additional reading at OnePetro: www.onepetro.org. SPE 191473 An Automatic History-Matching Work Flow for Unconventional Coupling MCMC and Nonintrusive EDFM Methods by Wei Yu, The University of Texas at Austin, et al. SPE 191516 Robust Uncertainty Quantification Through Integration of Distributed Gauss-Newton Optimization With Gaussian Mixture Modeling and Parallelized Sampling Algorithms by Guohua Gao, Shell, et al. SPE 191655 An Efficient Probabilistic Assisted History-Matching Tool Using Gaussian Process Proxy Models: Application to Coalbed Methane Reservoir by Sachin Rana, The Pennsylvania State University, et al.

Machine learning (ML) and large-scale big data are key factors in developing an accurate prediction model for cardiovascular disease (CVD). Although the CVD risk often depends on the race and ethnicity, most previous studies considered only US or European populations for the CVD risk prediction. In this work, to complement previous researches, we analyzed the Korean National Health Insurance Service-National Health Sample Cohort (KNHSC) data and studied the characteristics of ML and big data for predicting the CVD risk. More specifically, we assessed the effectiveness of various ML methods in predicting the 2-year and 10-year risk of CVD such as atrial fibrillation, coronary artery disease, heart failure, and strokes. To develop prediction models, we considered the usual medical examination data, questionnaire survey results, comorbidities, and past medication information available in the KNHSC data. We developed various ML-based prediction models using logistic regression, deep neural networks, random forests, and LightGBM, and validated them using various metrics such as receiver operating characteristic curves, precision-recall curves, sensitivity, specificity, and F1 score. Experimental results showed that all ML models outperformed the baseline method derived from the ACC/AHA guidelines for estimating the 10-year CVD risk, demonstrating the usefulness of ML methods. In addition, in our analysis, whether we included the past medication information as a feature or not, the prediction accuracy of all ML models was comparable to each other. Since the use of medications by the physicians provided important information on the occurrence of diseases, when we included it as a feature, all prediction models achieved a slightly higher prediction accuracy.