This document discusses using fuzzy clustering techniques with big data to diagnose diseases, specifically focusing on diabetes. It first provides background on big data in healthcare and challenges in managing large, diverse clinical datasets. It then discusses fuzzy logic and how it can help handle uncertainty in clinical data. The proposed approach uses fuzzy subtractive clustering on a clinical diabetes database to create a compact fuzzy model and increase prediction accuracy for diagnosing diabetes. The outcomes indicate this integrated method can effectively diagnose diabetes from clinical big data.
Digital health summit - Baylor Scott & White innovation panelNick van Terheyden
Overview of the status and need for Digital Health delivered at the Baylor Scott and White Digital Health Summit focusing on innovation and the risks and rewards and the innovation process
Medical System and Artificial Intelligence: How AI assists hospital-dependent...AI Publications
The main objective of this study is to examine how artificial intelligence assists hospital-dependent patients and explore the role of artificial intelligence in the medical system. Hospital-dependent patients have become common in current society due to the elderly with multiple chronic conditions and the COVID-19 infection patient. Thus, it is undeniable that the medical field is lacking healthcare workers. However, in a globalized world, artificial intelligence, the field of science and engineering technology that makes intelligent machines perform given tasks, is chosen to be used as a tool for assisting hospital-dependent patients and collecting databases from the patients. Nevertheless, the paper will cover the use of artificial intelligence in the medical system, hospital-dependent patients as well as provide both positive and negative aspects and the comparison of using artificial intelligence instead of human intelligence. To conclude, we detail how artificial intelligence can take part in the medical system, assist hospital-dependent patients and study further the future of artificial intelligence in the medical system.
Outline
Value Based Healthcare System – How it is seen today
Healthcare Challenge & IoT as a Solution
IoT – Big Data Structure
Recent Trends in IoT Big Data Analytics
Challenges & Our Future
In-depth Knowledge of
What causes the most premature death?
Distribution of Disease burden from 1990 - 2020
Challenges in Healthcare
Future Healthcare
IoT Machine Talking to Machine
Prediction of IoT Usage
About PEPGRA HEALTHCARE,
A leading healthcare communication firm with years of excellence serving clients with a dedicated team of Medical, Regulatory and Scientific writers specialized in all therapeutic areas.
Contact us at :
UK: +44-1143520021
US/Canada: +1-972-502-9262
India: +91-8754446690
info@pepgra.com
www.pepgra.com
Artificial Intelligence (AI) has revolutionized in information technology.AI is a subfield of computer science that includes the creation of intelligent machines and software that work and react like human beings. AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising. But “Do any traffic lights use Artificial Intelligence??”, I thought a lot of this when waiting in a red light. This paper gives an overview of Artificial Intelligence and its applications used in human life. This will explore the current use of Artificial Intelligence technologies in Network Intrusion for protecting computer and communication networks from intruders, in the medical area-medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it, in the computer games, and in Advertising. Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. This paper gives an introduction to a self-learning system based on RBF neural network and how the system can simulate the traffic police’s experience. This paper is focusing on how to evaluate the effect of the control with the changing of the traffic and adjust the signal with the different techniques of Artificial Intelligence.
Digital health summit - Baylor Scott & White innovation panelNick van Terheyden
Overview of the status and need for Digital Health delivered at the Baylor Scott and White Digital Health Summit focusing on innovation and the risks and rewards and the innovation process
Medical System and Artificial Intelligence: How AI assists hospital-dependent...AI Publications
The main objective of this study is to examine how artificial intelligence assists hospital-dependent patients and explore the role of artificial intelligence in the medical system. Hospital-dependent patients have become common in current society due to the elderly with multiple chronic conditions and the COVID-19 infection patient. Thus, it is undeniable that the medical field is lacking healthcare workers. However, in a globalized world, artificial intelligence, the field of science and engineering technology that makes intelligent machines perform given tasks, is chosen to be used as a tool for assisting hospital-dependent patients and collecting databases from the patients. Nevertheless, the paper will cover the use of artificial intelligence in the medical system, hospital-dependent patients as well as provide both positive and negative aspects and the comparison of using artificial intelligence instead of human intelligence. To conclude, we detail how artificial intelligence can take part in the medical system, assist hospital-dependent patients and study further the future of artificial intelligence in the medical system.
Outline
Value Based Healthcare System – How it is seen today
Healthcare Challenge & IoT as a Solution
IoT – Big Data Structure
Recent Trends in IoT Big Data Analytics
Challenges & Our Future
In-depth Knowledge of
What causes the most premature death?
Distribution of Disease burden from 1990 - 2020
Challenges in Healthcare
Future Healthcare
IoT Machine Talking to Machine
Prediction of IoT Usage
About PEPGRA HEALTHCARE,
A leading healthcare communication firm with years of excellence serving clients with a dedicated team of Medical, Regulatory and Scientific writers specialized in all therapeutic areas.
Contact us at :
UK: +44-1143520021
US/Canada: +1-972-502-9262
India: +91-8754446690
info@pepgra.com
www.pepgra.com
Artificial Intelligence (AI) has revolutionized in information technology.AI is a subfield of computer science that includes the creation of intelligent machines and software that work and react like human beings. AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising. But “Do any traffic lights use Artificial Intelligence??”, I thought a lot of this when waiting in a red light. This paper gives an overview of Artificial Intelligence and its applications used in human life. This will explore the current use of Artificial Intelligence technologies in Network Intrusion for protecting computer and communication networks from intruders, in the medical area-medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it, in the computer games, and in Advertising. Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. This paper gives an introduction to a self-learning system based on RBF neural network and how the system can simulate the traffic police’s experience. This paper is focusing on how to evaluate the effect of the control with the changing of the traffic and adjust the signal with the different techniques of Artificial Intelligence.
Applied Artificial Intelligence & How it's Transforming Life SciencesKumaraguru Veerasamy
In this SlideShare, we cover an overview history of artificial intelligence (AI), before exploring its applications in healthcare, biotechnology & pharmaceuticals. The slides will also cover the market outlook of AI, and how big pharmaceutical companies are investing in the technology. In addition, there are a couple of case studies on applied AI, namely in genomics and liquid biopsy (glycoproteomics).
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
Standardization and wider use of Electronic Health records (EHR) creates opportunities for
better understanding patterns of illness and care within and across medical systems. In the healthcare
systems, hidden event signatures allow taking decision for patient’s diagnosis, prognosis, and
management. Temporal history of event codes embedded in patients' records, investigates frequently
occurring sequences of event codes across patients. There is a framework that enables the
representation, retrieval, and mining of high order latent event structure and relationships within
single and multiple event sequences. There is a wealth of hidden information present in the large
databases. Different data mining techniques can be used for retrieving data. A classifier approach for
detection of diabetes is presented in this paper and shows how Naive Bayes can be used for
classification purpose. In this system, medical data is categories into five categories namely low,
average, high and very high and critical, treatment is given as per the predicted category. The system
will predict the class label of unknown sample. Hence two basic functions namely classification
(training) and prediction (testing) will be performed. An algorithm and database used affects the
accuracy of the system. It can answer complex queries for diagnosing diabetes disease and thus assist
healthcare practitioners to make intelligent clinical decisions which traditional decision support
systems cannot.Over the last decade, so many information visualization techniques have been
developed to support the exploration of large data sets. There are various interactive visual data
mining tools available for visual data analysis. It is possible to perform clinical assessment for visual
interactive knowledge discovery in large electronic health record databases. In this paper, we
proposed that it is possible to develop a tool for data visualization for interactive knowledge
discovery.
Artificial intelligence to fight against covid19saritamathania
Artificial intelligence (AI) and machine learning are playing a significant role in understanding and addressing the crisis caused by COVID-19. The technology mimic human intelligence and ingest great volumes of data to quickly chart patterns and identify insights.
One example is when BenevolentAI, a global leader in the development and application of artificial intelligence for drug discovery, took just few days to find that Baricitinib (a drug currently approved for rheumatoid arthritis, owned by Eli Lilly) is a strongest candidate and can be a potential treatment for COVID-19 patients.
This accelerated the clinical trials of #Baricitinib and Eli Lilly (a giant American Pharmaceutical company) has already commenced phase III clinical trials of Baricitinib to treat COVID-19.
Few more names include Deepmind, ImmunoPrecise, Insilico, healx, Imperial College, Tech Mahindra, and Deargen. Some Indian companies include NIRAMAI, Staqu, Qure.AI, Tech Mahindra, and DiyCam.
Will healthcare be delivered by george jetson in the futureNick van Terheyden
Gartner ranked Dell the #1 worldwide IT services provider in healthcare in 2014. Dell sees global disruptions in healthcare delivery and continues to invest in strategies to address these rapid changes. They are actively enhancing development, implementation and adoption of novel technologies, services, and applications that will revolutionize information-driven care, resulting in improved patient outcomes and overall cost savings worldwide. Dr. Nick is responsible for providing strategic insight and will discuss some Dell’s strategies to achieve an IT environment that is interconnected, efficient and patient-focused.
Towards a successful implementation of game mechanics (gamification) in e-hea...Maged N. Kamel Boulos
Cite as: Kamel Boulos MN. Towards a successful implementation of game mechanics (gamification) in e-health interventions (updated: 09//2015). In: Baptista TM, Kamel Boulos MN, Rodrigues FM, Rocha A. E-Health, psychology and medicine: the future of a close cooperation (invited symposium). In: Proceedings of the 14th European Congress of Psychology, Milan, Italy, 7-10 July 2015. URL: http://www.ecp2015.it/scientific-program/invited-symposia/ - WebCite cache: http://www.webcitation.org/6YIHINbi0
Artificial Intelligence and Machine Learning are transforming the work of human labor. Healthcare professionals will see their work transformed and augmented with this technology, but the manner in which these changes will occur is nuanced. In this presentation, I will explore the manner in which the labor of healthcare will be transformed, review evidence to support this prediction, and remark on the changes already underway.
Advances in information and communication technologies have led to the emergence of Internet of Things
(IoT). In the modern health care environment, the usage of IoT technologies brings convenience to physicians and
patients since they are applied to various medical areas (such as real-time monitoring, patient information and healthcare
management). The body sensor network (BSN) technology is one of the core technologies of IoT developments in
healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor
nodes
https://maison-workshop.com/prof-amit-sheth/
Video: https://youtu.be/pRUXTuxm3as
Keynote at the 7th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2021) – Special Edition on Responsible, August 21, 2021 and is co-located with the 15th International Joint Conference on Artificial Intelligence (IJCAI 2021). Also ASONAM 2021.
With the increasing legalization of medical and recreational use of substances, more research is needed to understand the association between mental health and user behavior related to drug consumption. Specifically, drug overdose and substance use-related mental health issues have become two major topics that have been widely discussed on social media platforms. Big social media data has the potential to provide deeper insights about these associations to public health analysts for making policy decisions. Multiple national population surveys have found that about half of those who experience a mental health illness during their lives will also experience a substance use disorder and vice versa. The communications related to addiction and mental health are complex to process and understand given their language and contextual characteristics. Surface-level data analysis alone is not sufficient to understand the complex nature of relationships among the addiction and mental health context. Moreover, dark web vendors have been using social media as a new marketplace for drugs. Social media users also discuss the novel drugs emerging in dark web marketplaces and associated side effects/health conditions. These communications get complex when researchers try to annotate them or link them to a specific mental health entity. Considering the significant sensitivity of such communications and to protect user privacy on social media, a potential solution requires reliable algorithms for modeling such communications. We demonstrate the value of incorporating domain-specific knowledge in natural language understanding to identify the relationship between mental health and drug addiction. We discuss end-to-end knowledge-infused deep learning frameworks that leverage the pre-trained language representation model and domain-specific declarative knowledge source to extract entities and their relationships jointly. Our model is further tailored to focus on the entities mentioned in the sentence where ontology is used to locate the target entity’s position. We also demonstrate the capabilities of inclusion of the knowledge-aware representation in association with language models that can extract the Drug-Mental health condition associations.
Acknowledgments: Usha Lokala, Raminta Daniulaityte, Francois Lamy, Manas Gaur, Jyotishman Pathak, and collaborators on NIDA/NIH and NSF funded projects on Addiction and Mental Health.
http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression
A Case Analysis on Involvement of Big Data during Natural Disaster and Pandem...YogeshIJTSRD
Big data is an upcoming technology and requires utmost care for an efficient and smooth implementation of the technology. In case of healthcare the most challenging part of big data is the privacy, data security, handling large volume of medical imaging data and data leakage. It can be useful to this sector when big data is made structured, relevant, smart and accessible and the managers should give importance to the strategic and business value of big data technology rather than only concentrating at the technological aspect of the implementation. The use of big data in natural disasters and pandemics helps to understand and make better decision with fast processing of the data that are collected through various sources such as social media, sensors and other internet activities. This paper tries to focus on effective involvement of Big Data in natural disaster and pandemic and also identify the current and future use of Big Data in health care sector. The paper identifies the critical aspects which are used for Big data implementation and describe ways to handle the challenges related to it. Mr. Bibin Mathew | Dr. Swati John "A Case Analysis on Involvement of Big Data during Natural Disaster and Pandemics and its Uses in the Health Care Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45049.pdf Paper URL: https://www.ijtsrd.com/management/other/45049/a-case-analysis-on-involvement-of-big-data-during-natural-disaster-and-pandemics-and-its-uses-in-the-health-care-sector/mr-bibin-mathew
A BIG DATA REVOLUTION IN HEALTH CARE SECTOR: OPPORTUNITIES, CHALLENGES AND TE...ijistjournal
Health care sector grows tremendously in last few decades. The health care sector has generated huge amounts of data that has huge volume, enormous velocity and vast variety. Also it comes from a variety of new sources as hospitals are now tend to implemented electronic health record (EHR) systems. These sources have strained the existing capabilities of existing conventional relational database management systems. In such scenario, Big data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information.
This paper basically studies the impact of implementing the big data solutions on the healthcare sector, the potential opportunities, challenges and available platform and tools to implement Big data analytics in health care sector.
Applied Artificial Intelligence & How it's Transforming Life SciencesKumaraguru Veerasamy
In this SlideShare, we cover an overview history of artificial intelligence (AI), before exploring its applications in healthcare, biotechnology & pharmaceuticals. The slides will also cover the market outlook of AI, and how big pharmaceutical companies are investing in the technology. In addition, there are a couple of case studies on applied AI, namely in genomics and liquid biopsy (glycoproteomics).
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
Standardization and wider use of Electronic Health records (EHR) creates opportunities for
better understanding patterns of illness and care within and across medical systems. In the healthcare
systems, hidden event signatures allow taking decision for patient’s diagnosis, prognosis, and
management. Temporal history of event codes embedded in patients' records, investigates frequently
occurring sequences of event codes across patients. There is a framework that enables the
representation, retrieval, and mining of high order latent event structure and relationships within
single and multiple event sequences. There is a wealth of hidden information present in the large
databases. Different data mining techniques can be used for retrieving data. A classifier approach for
detection of diabetes is presented in this paper and shows how Naive Bayes can be used for
classification purpose. In this system, medical data is categories into five categories namely low,
average, high and very high and critical, treatment is given as per the predicted category. The system
will predict the class label of unknown sample. Hence two basic functions namely classification
(training) and prediction (testing) will be performed. An algorithm and database used affects the
accuracy of the system. It can answer complex queries for diagnosing diabetes disease and thus assist
healthcare practitioners to make intelligent clinical decisions which traditional decision support
systems cannot.Over the last decade, so many information visualization techniques have been
developed to support the exploration of large data sets. There are various interactive visual data
mining tools available for visual data analysis. It is possible to perform clinical assessment for visual
interactive knowledge discovery in large electronic health record databases. In this paper, we
proposed that it is possible to develop a tool for data visualization for interactive knowledge
discovery.
Artificial intelligence to fight against covid19saritamathania
Artificial intelligence (AI) and machine learning are playing a significant role in understanding and addressing the crisis caused by COVID-19. The technology mimic human intelligence and ingest great volumes of data to quickly chart patterns and identify insights.
One example is when BenevolentAI, a global leader in the development and application of artificial intelligence for drug discovery, took just few days to find that Baricitinib (a drug currently approved for rheumatoid arthritis, owned by Eli Lilly) is a strongest candidate and can be a potential treatment for COVID-19 patients.
This accelerated the clinical trials of #Baricitinib and Eli Lilly (a giant American Pharmaceutical company) has already commenced phase III clinical trials of Baricitinib to treat COVID-19.
Few more names include Deepmind, ImmunoPrecise, Insilico, healx, Imperial College, Tech Mahindra, and Deargen. Some Indian companies include NIRAMAI, Staqu, Qure.AI, Tech Mahindra, and DiyCam.
Will healthcare be delivered by george jetson in the futureNick van Terheyden
Gartner ranked Dell the #1 worldwide IT services provider in healthcare in 2014. Dell sees global disruptions in healthcare delivery and continues to invest in strategies to address these rapid changes. They are actively enhancing development, implementation and adoption of novel technologies, services, and applications that will revolutionize information-driven care, resulting in improved patient outcomes and overall cost savings worldwide. Dr. Nick is responsible for providing strategic insight and will discuss some Dell’s strategies to achieve an IT environment that is interconnected, efficient and patient-focused.
Towards a successful implementation of game mechanics (gamification) in e-hea...Maged N. Kamel Boulos
Cite as: Kamel Boulos MN. Towards a successful implementation of game mechanics (gamification) in e-health interventions (updated: 09//2015). In: Baptista TM, Kamel Boulos MN, Rodrigues FM, Rocha A. E-Health, psychology and medicine: the future of a close cooperation (invited symposium). In: Proceedings of the 14th European Congress of Psychology, Milan, Italy, 7-10 July 2015. URL: http://www.ecp2015.it/scientific-program/invited-symposia/ - WebCite cache: http://www.webcitation.org/6YIHINbi0
Artificial Intelligence and Machine Learning are transforming the work of human labor. Healthcare professionals will see their work transformed and augmented with this technology, but the manner in which these changes will occur is nuanced. In this presentation, I will explore the manner in which the labor of healthcare will be transformed, review evidence to support this prediction, and remark on the changes already underway.
Advances in information and communication technologies have led to the emergence of Internet of Things
(IoT). In the modern health care environment, the usage of IoT technologies brings convenience to physicians and
patients since they are applied to various medical areas (such as real-time monitoring, patient information and healthcare
management). The body sensor network (BSN) technology is one of the core technologies of IoT developments in
healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor
nodes
https://maison-workshop.com/prof-amit-sheth/
Video: https://youtu.be/pRUXTuxm3as
Keynote at the 7th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2021) – Special Edition on Responsible, August 21, 2021 and is co-located with the 15th International Joint Conference on Artificial Intelligence (IJCAI 2021). Also ASONAM 2021.
With the increasing legalization of medical and recreational use of substances, more research is needed to understand the association between mental health and user behavior related to drug consumption. Specifically, drug overdose and substance use-related mental health issues have become two major topics that have been widely discussed on social media platforms. Big social media data has the potential to provide deeper insights about these associations to public health analysts for making policy decisions. Multiple national population surveys have found that about half of those who experience a mental health illness during their lives will also experience a substance use disorder and vice versa. The communications related to addiction and mental health are complex to process and understand given their language and contextual characteristics. Surface-level data analysis alone is not sufficient to understand the complex nature of relationships among the addiction and mental health context. Moreover, dark web vendors have been using social media as a new marketplace for drugs. Social media users also discuss the novel drugs emerging in dark web marketplaces and associated side effects/health conditions. These communications get complex when researchers try to annotate them or link them to a specific mental health entity. Considering the significant sensitivity of such communications and to protect user privacy on social media, a potential solution requires reliable algorithms for modeling such communications. We demonstrate the value of incorporating domain-specific knowledge in natural language understanding to identify the relationship between mental health and drug addiction. We discuss end-to-end knowledge-infused deep learning frameworks that leverage the pre-trained language representation model and domain-specific declarative knowledge source to extract entities and their relationships jointly. Our model is further tailored to focus on the entities mentioned in the sentence where ontology is used to locate the target entity’s position. We also demonstrate the capabilities of inclusion of the knowledge-aware representation in association with language models that can extract the Drug-Mental health condition associations.
Acknowledgments: Usha Lokala, Raminta Daniulaityte, Francois Lamy, Manas Gaur, Jyotishman Pathak, and collaborators on NIDA/NIH and NSF funded projects on Addiction and Mental Health.
http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression
A Case Analysis on Involvement of Big Data during Natural Disaster and Pandem...YogeshIJTSRD
Big data is an upcoming technology and requires utmost care for an efficient and smooth implementation of the technology. In case of healthcare the most challenging part of big data is the privacy, data security, handling large volume of medical imaging data and data leakage. It can be useful to this sector when big data is made structured, relevant, smart and accessible and the managers should give importance to the strategic and business value of big data technology rather than only concentrating at the technological aspect of the implementation. The use of big data in natural disasters and pandemics helps to understand and make better decision with fast processing of the data that are collected through various sources such as social media, sensors and other internet activities. This paper tries to focus on effective involvement of Big Data in natural disaster and pandemic and also identify the current and future use of Big Data in health care sector. The paper identifies the critical aspects which are used for Big data implementation and describe ways to handle the challenges related to it. Mr. Bibin Mathew | Dr. Swati John "A Case Analysis on Involvement of Big Data during Natural Disaster and Pandemics and its Uses in the Health Care Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45049.pdf Paper URL: https://www.ijtsrd.com/management/other/45049/a-case-analysis-on-involvement-of-big-data-during-natural-disaster-and-pandemics-and-its-uses-in-the-health-care-sector/mr-bibin-mathew
A BIG DATA REVOLUTION IN HEALTH CARE SECTOR: OPPORTUNITIES, CHALLENGES AND TE...ijistjournal
Health care sector grows tremendously in last few decades. The health care sector has generated huge amounts of data that has huge volume, enormous velocity and vast variety. Also it comes from a variety of new sources as hospitals are now tend to implemented electronic health record (EHR) systems. These sources have strained the existing capabilities of existing conventional relational database management systems. In such scenario, Big data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information.
This paper basically studies the impact of implementing the big data solutions on the healthcare sector, the potential opportunities, challenges and available platform and tools to implement Big data analytics in health care sector.
Application of Big Data in Medical Science brings revolution in managing heal...IJEEE
Big Data can be combined with new technology to bring about positive conversion in the health care segment. A technology aimed at making Big Data analytics a certainty will act as a key element in transforming the way the health care industry operates today. The study and analysis of Big Data can be used for tracking and managing population health care effectively and efficiently. In ten years, eighty percent of the work people do in medicine will be replaced by technology. And medicine will not look anything like what it does today. Healthcare will change enormously as it becomes a data-driven industry. But the magnitude of the data, the speed at which it’s growing and the threat it could pose to individual privacy mean mastering "big data" is one of biomedicine's most pressing challenges. Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. This also plays a vital role in delivering preventive care. Health care will change a great deal as it becomes a data- driven industry. But the size of the data, the speed at which it’s growing and the threat it could cause to individual privacy mean mastering it is one of biomedicine's most critical challenges. In this research paper we will discuss problems faced by big data, obstacles in using big data in the health industry, how big Data analytics can take health care to a new level by enhancing the overall quality of patient care.
The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Benefits of Big Data in Health Care A Revolutionijtsrd
Lifespan of a normal human is increasing with the world population and it produces new challenge in health care. big data change the method of data management ,leverage data and analyzing data.with the help of big data we can reduces the costs of treatment, reducing medication and provide better treatment with predictive analytics. Health related data collected from various sources like electronic health record EHR ,medical imaging system, genomic sequencing, pay of records, pharmaceutical research , and medical devices, etc. are refers to as big data in healthcare. Dr. Ritushree Narayan ""Benefits of Big Data in Health Care: A Revolution"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22974.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22974/benefits-of-big-data-in-health-care-a-revolution/dr-ritushree-narayan
Big Data Analytics for Smart Health CareEshan Bhuiyan
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers.
As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions.
Our approach to data analysis, data use, and data management are evolving across all sectors as a result of big data. And one sector where it may be effectively deployed is Cloud Tech Trends in healthcare, where it can help people avoid dangerous illnesses, lower the total cost of treatment, and anticipate disease outbreaks.
Big Data, CEP and IoT : Redefining Holistic Healthcare Information Systems an...Tauseef Naquishbandi
Healthcare industry has been a significant area for innovative application of various technologies over decades. Being an area of social relevance governmental spending on healthcare have always been on the rise over the years. Event Processing (CEP) has been in use for many years for situational awareness and response generation. Computing technologies have played an important role in improvising several aspects of healthcare. Recently emergent technology paradigms of Big Data, Internet of Things (IoT) and Complex Event Processing (CEP) have the potential not only to deal with pain areas of healthcare domain but also to redefine healthcare offerings. This paper aims to lay the groundwork for a healthcare system which builds upon integration of Big Data, CEP and IoT.
Al-Khouri, A.M. (2014) "Privacy in the Age of Big Data: Exploring the Role of Modern Identity Management Systems". World Journal of Social Science, Vol. 1, No. 1, pp. 37-47.
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Data file handling has been effectively used in the program.
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Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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Vol. 1, Issue. 3, June – 2015 ISSN (Online): 2394-8310
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Integration of Fuzzy Clustering Technique with
Big Data for Disease Diagnosis
1
Dr.S.Sapna, 2
Dr. Pravin Kumar
1
Assistant Professor, Department of Computer Science, Bharathidasan College of Arts & Science, Erode, INDIA
2
Assistant Professor, Department of ECE, KSR College of Engineering, Tiruchengode, INDIA
Abstract—This paper presents an integrative approach to
predict the diabetic disease from clinical big data. The clinical
database is generally redundant, incomplete, vague and
unpredictable. A fuzzy technique is integrated to handle
vagueness in clinical big data. A compact fuzzy model is created
using subtractive clustering. The main aim of proposed approach
is to increase the prediction accuracy. The outcomes indicate that
the proposed method can be used effectively in healthcare to
diagnose the diabetic disease.
Keywords—big data, clinical, fuzzy, hidden knowledge,
prediction, subtractive clustering.
I. INTRODUCTION
The main aim of clinical informatics is to take in real
world medical data from all stages of human being to help
improve our understanding of treatment and medical practice.
The volume of clinical data is likely to increase drastically in
the forthcoming years. Health informatics tools include
amongst others computers, clinical guidelines, formal medical
terminologies, and information and communication systems
[10,9]. Healthcare reimbursement models are changing;
meaningful use and pay for performance are emerging as
critical new factors in today’s healthcare environment.
Although profit is not and should not be a primary motivator,
it is vitally important for healthcare organizations to acquire
the available tools, infrastructure, and techniques to leverage
big data effectively [8,16]. The field of Health Informatics is
on the crossover of its most exciting period to date, entering a
new era where technology is starting to handle Big Data,
bringing about unlimited potential for information growth
[11,6]. The large volume of data in health care exceeded our
human ability for diagnosing disease without powerful tools.
Big Data analytics and Fuzzy logic techniques, helps to better
understand the objectives of diagnosing and treating patients
in need of healthcare.
Big data in healthcare refers to electronic health data sets
so large and complex that they are difficult (or impossible) to
manage with traditional software and/or hardware; nor can
they be easily managed with traditional or common data
management tools and methods. Big data in healthcare is
overwhelming not only because of its volume but also because
of the diversity of data types and the speed at which it must be
managed as it includes clinical data and clinical decision
support systems [12]. Fuzzy logic system is used for solving a
wide range of problems in different application involving
large volume of data. It also allows us to introduce the
learning and adaptation capabilities. The fuzzy set framework
has been used in several different process of diagnosis of
disease. Fuzzy logic is a computational paradigmthat provides
a mathematical tool for dealing with the uncertainty and the
imprecision typical of human reasoning [14].
The clinical data are usually uncertain and vague to arrive
at a conclusion. So, in this paper fuzzy subtractive clustering
technique is proposed to diagnose the diabetic disease from
the clinical diabetic data. This integrative approach provides
flexible information capability for medical practitioners in
handling ambiguous situations.
II. BIG DATA
Big Data is data whose scale, diversity, and complexity
require novel architecture, methods, procedures, and analytics
to manage it and extract value and hidden knowledge from it.
The greatest challenges is to deal with large dataset with high
amount of dimensionality, together in terms of the number of
features the data has, as well the number of rows of data that
user is dealing with. Big Data requires different approaches
with an aim to solve new problems or existing problems in a
better way.
International Business Machines [3] estimates that every
day user creates 2.5 quintillion bytes of data, so much that 90%
of the data in the world today has been created in the last two
years alone. The big data come from sensors that are used to
gather climate information, posts to social media sites, digital
pictures and videos, purchase transaction records, and cell
phone GPS signals.
Data is a raw unorganized facts, is in and of itself
worthless. Information means potential valuable concepts
based on data. Knowledge is what we understand based upon
information. Wisdom means effective use of knowledge in
decision making. The key enablers for the appearance and
growth of Big-Data are increase in storage capabilities,
increase in processing power and availability of data. There are
huge volumes of data in the world. From the beginning of
recorded time until 2003, User’s created 5 billion gigabytes
2. International Journal of Electrical, Computing Engineering and Communication (IJECC)
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(109) of data. In 2011, the same amount was created every two
days. In 2013, the same amount of data is created every 10
minutes. Prior to 2012 the US was the largest single contributor
to global data. 32 % united states, 13% china, 4% India 19%
Western Europe and 32% of rest of world. In 2020 the
emerging markets are showing the largest increases in data
growth 23% United States, 21% china, 6% India, 15% Western
Europe and 35% rest of world. In 2012 the amount of
information stored worldwide exceeded 2.8 zetabytes (1021).
By 2016 the cumulative size of all the world’s data center’s
excepted to exceed to 16,000 acres which is equivalent in area
to a two lane highway stretching fromTokyo to San Francisco
over 5,000 miles. An estimated 33% of information could be
useful if appropriately tagged and analysed. The amount
actually analysed is only 0.5%. By 2020 the total amount of
data stored is expected to be 50x larger than today. In the
digital universe all that has been created and stored are nearly
half of which is unprotected.
A. V’s of Big Data
The five V’s of big data are volume, velocity, variety and
veracity. Volume means data at rest. Enterprises are packed
with ever-growing data of all types, easily amassing terabytes
(1012) - even petabytes (1015) - of information. Turn 12
terabytes of Tweets created each day into improved product
sentiment analysis. Convert 350 billion annual meter readings
to better predict power consumption. Velocity means data in
motion. Sometimes 2 minutes is too late. For time-sensitive
processes such as catching fraud, big data must be used as it
streams into the enterprise in order to maximize its value.
Scrutinize 5 million trade events created each day to identify
potential fraud. Analyze 500 million daily call detail records
in real-time to predict customer churn faster. The latest is 10
nano seconds delay is too much. Variety means data in many
forms i.e., structured and unstructured data such as text, sensor
data, audio, video, click streams, log files and more. New
insights are found when analyzing these data types together.
Monitor 100’s of live video feeds from surveillance cameras
to target points of interest. Exploit the 80% data growth in
images, video and documents to improve customer
satisfaction. Veracity means data in doubt that is uncertainty
due to data inconsistency and incompleteness, ambiguities,
latency, deception, Model approximations. Value means a
clear understanding of costs and benefits. The challenge is to
find a way to transform raw data into information that has
value, either internally, or for making a business out of it. The
pictorial representation of V’s of Big data is shown below in
the Fig 1.
Big Data is generated by social media and networks (all of
us are generating data), Scientific instruments (collecting all
sorts of data), Mobile devices (tracking all objects all the time),
Sensor technology and networks (measuring all kinds of data).
The progress and innovation is no longer hindered by the
ability to collect data. But, by the ability to manage, analyze,
summarize, visualize, and discover knowledge from the
collected data in a timely manner and in a scalable fashion
would certainly be the challenges of information technology
community [3,13].
Fig. 1 V’s of Big Data
III. DIABETES MELLITUS
Diabetes Mellitus often simply referred to as diabetes is
the coercive effect of insulin on the glucose metabolism.
Insulin is a hormone central to regulating carbohydrate and fat
metabolism in the body. Insulin is produced from the islets of
langerhans [1,14]. In Latin the word Insula means-"island". Its
concentration has wide spread effect throughout the body.
When control of insulin levels fails, diabetes mellitus will
result. As a consequence, insulin is used medically to treat
some forms of diabetes mellitus. Diabetes type 1 is lack of it
whereas diabetes type 2 is the resistance towards it. Not only
insulin regulates the glucose in the blood but it is also
responsible for lipid metabolism. Insulin Secretion from
beta-cells is principally regulated by plasma glucose levels
[14,15]. Increased uptake of glucose by pancreatic beta-cells
leads to a concomitant increase in metabolism. One must
understand that insulin is offered as medicine only when the
above criteria are broken. Physicians will become familiar
with other aspects of managing the patient with diabetes,
including the importance of postprandial glucose control,
diabetes self-management training etc.
Most of the food that is eaten is converted to glucose, or
sugar which is used for energy. The pancreas secretes insulin
which carries glucose into the cells of our bodies, which in
turn produces energy for the perfect functioning of the body.
When you have diabetes, your body either doesn't make
enough insulin or cannot use its own insulin as well as it
should [5,14]. This causes sugar to build up in your blood
leading to complications like heart disease, stroke, neuropathy,
poor circulation leading to loss of limbs, blindness, kidney
failure, nerve damage and death.
A. General Symptoms of Diabetes
Increased thirst, Increased urination - Weight loss,
Increased appetite – Fatigue, Nausea and/or vomiting -
Blurred vision, Slow-healing infections - Impotence in men.
3. International Journal of Electrical, Computing Engineering and Communication (IJECC)
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B. Types of Diabetes
Type 1: Diabetes also called as Insulin Dependent Diabetes
Mellitus (IDDM), or Juvenile Onset Diabetes Mellitus
commonly seen in children and young adults however, older
patients do present with this form of diabetes on occasion. In
type 1 diabetes, the pancreas undergoes an autoimmune attack
by the body itself therefore; pancreas does not produce the
hormone insulin. The body does not properly metabolize food
resulting in high blood sugar (glucose) and the patient must
rely on insulin shots. Type I disorder appears in people
younger than 35, usually from the ages 10 to 16.
Type II: Diabetes is also called as Non-Insulin Dependent
Diabetes Mellitus (NIDDM) or Adult Onset Diabetes Mellitus.
Patients produce adequate insulin but the body cannot make
use of it as there is a lack of sensitivity to insulin by the cells
of the body. Type II disorder occurs mostly after the age 40.
Gestational Diabetes: Diabetes can occur temporarily during
Pregnancy called as Gestational Diabetes which is due to the
hormonal changes and usually begins in the fifth or sixth
month of pregnancy (between the 24th and 28th weeks). It
usually resolves once the baby is born. 25-50% of women with
eventually develop diabetes later in life, especially in those
who require insulin during pregnancy and those who are
overweight after their delivery.
C. Diagnostic Tests
Urine Test: A urine analysis may be used to look for glucose
and ketones from the breakdown of fat. However, a urine test
alone does not diagnose diabetes. The following blood glucose
tests are used to diagnose diabetes.
Fasting Plasma Glucose Level (FPG): The normal range of
fasting blood glucose is <100 mg/dl. It is done after 8-12
hours of fasting. People with fasting glucose levels from 100-
125 mg/dl are considered to have impaired fasting glucose.
Patients with FPG >126 are consider to have diabetes mellitus.
Post Prandial Plasma Glucose Level (PPG): A blood sugar
test taken after two hours of a meal is known as the post
prandial glucose test or PPG. The normal range for PPG is
<140 mg/dl. People with fasting glucose levels from 140-
200 mg/dl are considered to have impaired glucose tolerance.
Patients with PPG >200 mg/dl are consider to have diabetes
mellitus [14].
IV. CLUSTERING TECHNIQUE
Clustering of numerical data forms the basis of many
classification and system modeling algorithms. The purpose of
clustering is to identify natural groupings of data from a large
data set to produce a concise representation of a system's
behavior. The idea of data grouping, or clustering, is simple in
its nature and is close to the human way of thinking; whenever
there are large amount of data are presented, it usually tend to
summarize the huge number of data into a small number of
groups or categories in order to further facilitate its analysis.
Clustering is a method of unsupervised learning, and a
common technique for statistical data analysis used in many
fields, including machine learning, data mining, pattern
recognition, image analysis, information retrieval, and
bioinformatics [7].
A. Need of Clustering
In the fuzzy logic system if the number of inputs to the
system is increased, then the number of rules increases
exponentially. In predicting diabetes, five inputs and two
outputs are considered. So it is difficult to proceed in the
system and the concept of clustering is followed. In the
clustering method the cluster centers are determined where
each cluster center belongs to one rule in fuzzy logic.
B. Subtractive Clustering
The subtractive clustering method assumes each data point is
a potential cluster center and calculates a measure of the
likelihood that each data point would define the cluster center,
based on the density of surrounding data points. For
subtractive clustering method proposed by [2,4], data points
have to be rescaled to [0, 1] in each dimension. Each data
point )jy,jx(jz is assigned potential jP , according to its
location to all other data points.
n
1j
2
jxix
e*
iP
Where
ar
*
iP is the potential value i data as a cluster center
is the weight between i th data to j th data
x is the data point
is the variables(commonly set 4)
ar is a positive constant called cluster radius
The potential of a data point to be a cluster center is
higher when more data points are closer. The data point with
the highest potential, denoted by *
iP is considered as the first
cluster center )1e,1d(1c . The potential is then recalculated
for all other points excluding the influence of the first cluster
center according to the Equation (3).
*
KP*
iP*
iP
Where
2
kcrx
e
2
b
r
4
*arbr
… (1)
… (2)
… (3)
… (4)
… (5)
… (6)
4. International Journal of Electrical, Computing Engineering and Communication (IJECC)
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*
iP is the new potential value i-data
*
kP is the potential value data as cluster center
br is a positive constant
c is the cluster center of data
is the weight of I data to cluster center
i
r is the distance between cluster center
is the quash factor
Again, the data point with the highest potential *
kP is
considered to be the next cluster center kc , if
1
*
IP
*
kP
ar
mind
with mind is the minimal distance between 1c and all
previously found cluster centers,the data point is still accepted
as the next cluster center 1c . Further iterations can then be
performed to obtain new cluster centers 2c . If a possible
cluster center does not fulfill the above described conditions, it
is rejected as a cluster center and its potential is set to 0. The
data point with the next highest potential *
KP is selected as the
new possible cluster center and re-tested. The clustering ends
if the following condition is fulfilled by Equation (8).
*
iP*
KP
Where is the reject ratio.
Indicative parameters values for ar , , and *
have
been suggested. Each cluster center is considered as a fuzzy
rule that describes the system behavior of the distance to the
defined cluster centers.
2
k
j
ck
j
x
eik
j
Equation (9) is a common form of subtractive clustering,
hence it needs to create an algorithm to process data
clustering. This paper proposes an algorithm to cluster the
clinical diabetic data to predict the disease effectively.
V. IMPLEMENTATION OF SUBTRACTIVE CLUSTERING FOR
PREDICTION OF DIABETES MELLITUS
The clinical diabetic data were collected from various
diabetic centers at Erode, Tamilnadu. About 1050 diabetic
patients’ data were considered for this prediction and some of
which is shown in Table 1. The subtractive clustering method
is applied to the input-output of the clinical diabetic data to
analyze their performance. The inputs considered are Age,
FPG-Fasting Plasma Glucose, PPG-Post Prandial Plasma
Glucose, G-Gender, P/NP-Pregnant or Non Pregnant and the
outputs are D-Diabetic Status and T1/T2/GD (T1 – Type 1/
T2 – Type 2/GD – Gestational Diabetes) [14]. As five inputs
and two outputs are considered, the number of rules will
increase exponentially, so the subtractive clustering technique
is applied to get the optimal number of rules. By applying
subtractive clustering the number of cluster centers is
obtained, where each cluster center belongs to one rule in
fuzzy logic and also the hidden knowledge from the clinical
database is predicted which aids the physician in decision-
making. This prediction system also helps the user to
anticipate by himself / herself whether he/she is affected with
diabetes or not and also to which type of diabetes he/she
belongs.
TABLE. 1 PRACTICALLY OBSERVED CLINICAL DATABASE OF DIABETES
A. Results and Discussions
The subtractive clustering technique is applied to clinical
diabetic database given in Table 1, and its performance
obtained using Matlab R2007b is shown in Fig 2. After
clustering the data, subtractive clustering produces 9 cluster
centers, where each cluster center belongs to one rule in fuzzy
logic and the root mean square error is found to be 0.9846.
This means if each cluster center is equal to one rule, there are
only nine rules to represent 1050 data as shown in Fig 3.
Fig. 2 Performance of Subtractive Clustering for
Clinical Diabetic Database
INPUTS OUTPUT
S.No. Age FPG
(mg/dl)
PPG
(mg/dl)
G P / NP D T1/ T2 / GD
1 52 95 290 0 0 0.7 0.7
2 51 109 452 1 0 0.91 0.7
. . . . . . . .
1050 43 178 99 0 1 0.68 0.8
… (7)
… (8)
… (9)
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Fig. 3 Fuzzy If-Then Rules
The Fuzzy Inference System (FIS) obtained using
subtractive clustering for clinical diabetic database is shown in
Fig 4. In FIS the input and output attributes are specified. Five
input and two output system are built using FIS for clinical
diabetic database.
Fig. 4 Fuzzy Inference System
Fig. 5 Rule View
Fig. 6 Comparison between Subtractive Output and
Observed Data Output
The rule view obtained using subtractive clustering for
clinical diabetic database is shown in Fig 5. The rule viewer
displays a roadmap of the whole fuzzy inference process. The
system readjusts and computes the new output when the red
line indices are moved around the corresponding input
attributes. The clustering process stops when the maximum
number of iterations is reached. For the implemented practical
diabetic data the clustering process stops at 80th
iteration. The
comparison between subtractive clustering output and observed
data output for D (Diabetes) is shown in Fig 6. FromFig 6, it is
observed that subtractive clustering is close to observed output.
From Fig 5, the hidden knowledge from the clinical database
can be predicted which aids the physician in decision-making.
This prediction system also helps the user to anticipate by
himself / herself whether he/she is affected with diabetes or not
and also predicts to which type of diabetes the he/she belongs.
VI. CONCLUSION
Clinical processes progress over time and all the clinical
actions are indescribable without considering time. Therefore
time is vital to many medical domain problems. For the
collected clinical diabetic data the subtractive clustering
method is applied and its performance is analyzed. Subtractive
clustering technique reduces the number of rules in the rule
base by eliminating the redundant rules thereby reducing the
computational time. The proposed integrated approach helps
the physician and medical experts to diagnosis the diabetic
disease effectively.
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