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Papers by Raghunandan Alugubelli

Research paper thumbnail of Exploratory Study of Artificial Intelligence in Healthcare

The rise of artificial intelligence has brought a positive shift in the sector by providing accur... more The rise of artificial intelligence has brought a positive shift in the sector by providing accurate data-driven decisions. The data from large systems is used for the early detection of chronic illnesses. These illnesses include cancer, diabetes, and cardiovascular diseases, etc. Existing technology is limited in terms of medical diagnosis etc. With the advent of ML/AI in the healthcare system, we expect to see much automation in clinical decision-making. We illustrate popular machine learning algorithms, their applications followed by methodology. This research will focus on the impact of Artificial Intelligence applications on the healthcare sector, its history, challenges, and concerns in the medical field. BACKGROUND INFORMATION Fostering trust in AI systems is a tremendous obstacle to bringing the most transformative AI technologies into reality, such as large-scale integration of machine intelligence in medicine. The challenge is to implement guiding ethical principles and aspirations and make the responsible practice of AI accessible, reproducible, and achievable for all who engage with the AI system. Meeting this challenge is critical to ensuring that medical professionals are prepared to correctly leverage AI in their practice and, ultimately, save lives. This research will concentrate on the influence AI applications have on the healthcare sector, its need, and its history in the medical field. Artificial intelligence models will assist doctors in various applications like patient care and administrative operations. (2011, March) Plant, R. et. al. According to the National Academies of Science, Engineering-diagnostic mistakes lead to roughly 10% of patient fatalities and 6 to 17% of hospital problems. It's crucial to remember that diagnostic errors aren't always caused by poor physician performance. Diagnostic mistakes, according to experts, are caused by a number of causes, including: • Collaboration and integration of health information technology are inefficient (Health IT) • Communication breakdowns between physicians, patients, and their families • A healthcare work system that is designed to be insufficiently supportive of diagnostic procedures. LITERATURE REVIEW Machine learning is being increasingly and frequently utilized in the healthcare field in various ways, like automating medical billing, clinical decision support, and establishing clinical care standards. Friedman, C., & Elhadad, N. (2014) et al. There are several significant applications of machine learning and healthcare ideas in medicine. The first medical machine learning system to diagnose acute toxicities in patients getting radiation treatment for head and neck malignancies has been created by researchers. In radiology, deep learning in healthcare automatically detects complicated patterns and assists radiologists in making informed judgments when analyzing pictures such as traditional radiography, CT, MRI, PET scans, and radiology reports. Machine learning-based automated detection and diagnostic systems have been demonstrated to perform as well as an expert radiologist. Google is creating a machine learning platform to identify breast cancer from images. These are only a handful of the numerous applications of machine learning in healthcare Jack Jr, C(2013) et.al. Natural Language Processing Nearly 80% of the information kept or "locked" in electronic health record systems is unstructured healthcare data for machine learning. These are papers or text files, not data components that could not previously be evaluated without a human viewing the information. Unfortunately, human language, often known as "natural language," is extremely complicated, lacks consistency, and contains many ambiguities, jargon, and vagueness. Therefore, machine learning in healthcare frequently uses natural language processing (NLP) tools to transform these papers into more usable and analyzable data.

Research paper thumbnail of Visualization for Data Analytics and Data Science

Journal of Emerging Technologies and Innovative Research, 2018

We are living in a world that generates large amounts of data daily. Corporations are making data... more We are living in a world that generates large amounts of data daily. Corporations are making datadriven decisions basing on their data and data visualization is an essential part of it. Organizations store essential data for a better competitive position and save it in future and decision-making processes. We have various tools available in the market-some of them are Sisense, Tableau desktop, Whatagraph, and Hubspot. These tools assist in making data presentation easy. This paper focuses on the importance of data visualization, primary tools, and software for data visualization, and theoretical architectural framework for data visualization. Finally, the article will focus on the critical challenges faced by data visualization and steps for mitigation.

Research paper thumbnail of Data Mining and Analytics Framework for Healthcare

International Journal of Creative Research Thoughts, 2018

Healthcare organizations rely on data more than ever, making data collection and processing vital... more Healthcare organizations rely on data more than ever, making data collection and processing vital for any organization (Demirkan, 2013). The advancement of technology every day has led to more and more data to a level where it has become hard for an organization to manage using the current technology and approaches (AOCNP, 2015). These facts have led to big data in almost every organization that deals with data and consumers. Therefore, for any organization to meet the present and future demands of an organization, such as

Research paper thumbnail of Exploratory Study of Artificial Intelligence in Healthcare

INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT], 2016

The rise of artificial intelligence has brought a positive shift in the sector by providing accur... more The rise of artificial intelligence has brought a positive shift in the sector by providing accurate data-driven decisions. The data from large systems is used for the early detection of chronic illnesses. These illnesses include cancer, diabetes, and cardiovascular diseases, etc. Existing technology is limited in terms of medical diagnosis etc. With the advent of ML/AI in the healthcare system, we expect to see much automation in clinical decision-making. We illustrate popular machine learning algorithms, their applications followed by methodology. This research will focus on the impact of Artificial Intelligence applications on the healthcare sector, its history, challenges, and concerns in the medical field.

Research paper thumbnail of Exploratory Study of Artificial Intelligence in Healthcare

The rise of artificial intelligence has brought a positive shift in the sector by providing accur... more The rise of artificial intelligence has brought a positive shift in the sector by providing accurate data-driven decisions. The data from large systems is used for the early detection of chronic illnesses. These illnesses include cancer, diabetes, and cardiovascular diseases, etc. Existing technology is limited in terms of medical diagnosis etc. With the advent of ML/AI in the healthcare system, we expect to see much automation in clinical decision-making. We illustrate popular machine learning algorithms, their applications followed by methodology. This research will focus on the impact of Artificial Intelligence applications on the healthcare sector, its history, challenges, and concerns in the medical field. BACKGROUND INFORMATION Fostering trust in AI systems is a tremendous obstacle to bringing the most transformative AI technologies into reality, such as large-scale integration of machine intelligence in medicine. The challenge is to implement guiding ethical principles and aspirations and make the responsible practice of AI accessible, reproducible, and achievable for all who engage with the AI system. Meeting this challenge is critical to ensuring that medical professionals are prepared to correctly leverage AI in their practice and, ultimately, save lives. This research will concentrate on the influence AI applications have on the healthcare sector, its need, and its history in the medical field. Artificial intelligence models will assist doctors in various applications like patient care and administrative operations. (2011, March) Plant, R. et. al. According to the National Academies of Science, Engineering-diagnostic mistakes lead to roughly 10% of patient fatalities and 6 to 17% of hospital problems. It's crucial to remember that diagnostic errors aren't always caused by poor physician performance. Diagnostic mistakes, according to experts, are caused by a number of causes, including: • Collaboration and integration of health information technology are inefficient (Health IT) • Communication breakdowns between physicians, patients, and their families • A healthcare work system that is designed to be insufficiently supportive of diagnostic procedures. LITERATURE REVIEW Machine learning is being increasingly and frequently utilized in the healthcare field in various ways, like automating medical billing, clinical decision support, and establishing clinical care standards. Friedman, C., & Elhadad, N. (2014) et al. There are several significant applications of machine learning and healthcare ideas in medicine. The first medical machine learning system to diagnose acute toxicities in patients getting radiation treatment for head and neck malignancies has been created by researchers. In radiology, deep learning in healthcare automatically detects complicated patterns and assists radiologists in making informed judgments when analyzing pictures such as traditional radiography, CT, MRI, PET scans, and radiology reports. Machine learning-based automated detection and diagnostic systems have been demonstrated to perform as well as an expert radiologist. Google is creating a machine learning platform to identify breast cancer from images. These are only a handful of the numerous applications of machine learning in healthcare Jack Jr, C(2013) et.al. Natural Language Processing Nearly 80% of the information kept or "locked" in electronic health record systems is unstructured healthcare data for machine learning. These are papers or text files, not data components that could not previously be evaluated without a human viewing the information. Unfortunately, human language, often known as "natural language," is extremely complicated, lacks consistency, and contains many ambiguities, jargon, and vagueness. Therefore, machine learning in healthcare frequently uses natural language processing (NLP) tools to transform these papers into more usable and analyzable data.

Research paper thumbnail of Exploratory Study of Artificial Intelligence in Healthcare

The rise of artificial intelligence has brought a positive shift in the sector by providing accur... more The rise of artificial intelligence has brought a positive shift in the sector by providing accurate data-driven decisions. The data from large systems is used for the early detection of chronic illnesses. These illnesses include cancer, diabetes, and cardiovascular diseases, etc. Existing technology is limited in terms of medical diagnosis etc. With the advent of ML/AI in the healthcare system, we expect to see much automation in clinical decision-making. We illustrate popular machine learning algorithms, their applications followed by methodology. This research will focus on the impact of Artificial Intelligence applications on the healthcare sector, its history, challenges, and concerns in the medical field. BACKGROUND INFORMATION Fostering trust in AI systems is a tremendous obstacle to bringing the most transformative AI technologies into reality, such as large-scale integration of machine intelligence in medicine. The challenge is to implement guiding ethical principles and aspirations and make the responsible practice of AI accessible, reproducible, and achievable for all who engage with the AI system. Meeting this challenge is critical to ensuring that medical professionals are prepared to correctly leverage AI in their practice and, ultimately, save lives. This research will concentrate on the influence AI applications have on the healthcare sector, its need, and its history in the medical field. Artificial intelligence models will assist doctors in various applications like patient care and administrative operations. (2011, March) Plant, R. et. al. According to the National Academies of Science, Engineering-diagnostic mistakes lead to roughly 10% of patient fatalities and 6 to 17% of hospital problems. It's crucial to remember that diagnostic errors aren't always caused by poor physician performance. Diagnostic mistakes, according to experts, are caused by a number of causes, including: • Collaboration and integration of health information technology are inefficient (Health IT) • Communication breakdowns between physicians, patients, and their families • A healthcare work system that is designed to be insufficiently supportive of diagnostic procedures. LITERATURE REVIEW Machine learning is being increasingly and frequently utilized in the healthcare field in various ways, like automating medical billing, clinical decision support, and establishing clinical care standards. Friedman, C., & Elhadad, N. (2014) et al. There are several significant applications of machine learning and healthcare ideas in medicine. The first medical machine learning system to diagnose acute toxicities in patients getting radiation treatment for head and neck malignancies has been created by researchers. In radiology, deep learning in healthcare automatically detects complicated patterns and assists radiologists in making informed judgments when analyzing pictures such as traditional radiography, CT, MRI, PET scans, and radiology reports. Machine learning-based automated detection and diagnostic systems have been demonstrated to perform as well as an expert radiologist. Google is creating a machine learning platform to identify breast cancer from images. These are only a handful of the numerous applications of machine learning in healthcare Jack Jr, C(2013) et.al. Natural Language Processing Nearly 80% of the information kept or "locked" in electronic health record systems is unstructured healthcare data for machine learning. These are papers or text files, not data components that could not previously be evaluated without a human viewing the information. Unfortunately, human language, often known as "natural language," is extremely complicated, lacks consistency, and contains many ambiguities, jargon, and vagueness. Therefore, machine learning in healthcare frequently uses natural language processing (NLP) tools to transform these papers into more usable and analyzable data.

Research paper thumbnail of Visualization for Data Analytics and Data Science

Journal of Emerging Technologies and Innovative Research, 2018

We are living in a world that generates large amounts of data daily. Corporations are making data... more We are living in a world that generates large amounts of data daily. Corporations are making datadriven decisions basing on their data and data visualization is an essential part of it. Organizations store essential data for a better competitive position and save it in future and decision-making processes. We have various tools available in the market-some of them are Sisense, Tableau desktop, Whatagraph, and Hubspot. These tools assist in making data presentation easy. This paper focuses on the importance of data visualization, primary tools, and software for data visualization, and theoretical architectural framework for data visualization. Finally, the article will focus on the critical challenges faced by data visualization and steps for mitigation.

Research paper thumbnail of Data Mining and Analytics Framework for Healthcare

International Journal of Creative Research Thoughts, 2018

Healthcare organizations rely on data more than ever, making data collection and processing vital... more Healthcare organizations rely on data more than ever, making data collection and processing vital for any organization (Demirkan, 2013). The advancement of technology every day has led to more and more data to a level where it has become hard for an organization to manage using the current technology and approaches (AOCNP, 2015). These facts have led to big data in almost every organization that deals with data and consumers. Therefore, for any organization to meet the present and future demands of an organization, such as

Research paper thumbnail of Exploratory Study of Artificial Intelligence in Healthcare

INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT], 2016

The rise of artificial intelligence has brought a positive shift in the sector by providing accur... more The rise of artificial intelligence has brought a positive shift in the sector by providing accurate data-driven decisions. The data from large systems is used for the early detection of chronic illnesses. These illnesses include cancer, diabetes, and cardiovascular diseases, etc. Existing technology is limited in terms of medical diagnosis etc. With the advent of ML/AI in the healthcare system, we expect to see much automation in clinical decision-making. We illustrate popular machine learning algorithms, their applications followed by methodology. This research will focus on the impact of Artificial Intelligence applications on the healthcare sector, its history, challenges, and concerns in the medical field.

Research paper thumbnail of Exploratory Study of Artificial Intelligence in Healthcare

The rise of artificial intelligence has brought a positive shift in the sector by providing accur... more The rise of artificial intelligence has brought a positive shift in the sector by providing accurate data-driven decisions. The data from large systems is used for the early detection of chronic illnesses. These illnesses include cancer, diabetes, and cardiovascular diseases, etc. Existing technology is limited in terms of medical diagnosis etc. With the advent of ML/AI in the healthcare system, we expect to see much automation in clinical decision-making. We illustrate popular machine learning algorithms, their applications followed by methodology. This research will focus on the impact of Artificial Intelligence applications on the healthcare sector, its history, challenges, and concerns in the medical field. BACKGROUND INFORMATION Fostering trust in AI systems is a tremendous obstacle to bringing the most transformative AI technologies into reality, such as large-scale integration of machine intelligence in medicine. The challenge is to implement guiding ethical principles and aspirations and make the responsible practice of AI accessible, reproducible, and achievable for all who engage with the AI system. Meeting this challenge is critical to ensuring that medical professionals are prepared to correctly leverage AI in their practice and, ultimately, save lives. This research will concentrate on the influence AI applications have on the healthcare sector, its need, and its history in the medical field. Artificial intelligence models will assist doctors in various applications like patient care and administrative operations. (2011, March) Plant, R. et. al. According to the National Academies of Science, Engineering-diagnostic mistakes lead to roughly 10% of patient fatalities and 6 to 17% of hospital problems. It's crucial to remember that diagnostic errors aren't always caused by poor physician performance. Diagnostic mistakes, according to experts, are caused by a number of causes, including: • Collaboration and integration of health information technology are inefficient (Health IT) • Communication breakdowns between physicians, patients, and their families • A healthcare work system that is designed to be insufficiently supportive of diagnostic procedures. LITERATURE REVIEW Machine learning is being increasingly and frequently utilized in the healthcare field in various ways, like automating medical billing, clinical decision support, and establishing clinical care standards. Friedman, C., & Elhadad, N. (2014) et al. There are several significant applications of machine learning and healthcare ideas in medicine. The first medical machine learning system to diagnose acute toxicities in patients getting radiation treatment for head and neck malignancies has been created by researchers. In radiology, deep learning in healthcare automatically detects complicated patterns and assists radiologists in making informed judgments when analyzing pictures such as traditional radiography, CT, MRI, PET scans, and radiology reports. Machine learning-based automated detection and diagnostic systems have been demonstrated to perform as well as an expert radiologist. Google is creating a machine learning platform to identify breast cancer from images. These are only a handful of the numerous applications of machine learning in healthcare Jack Jr, C(2013) et.al. Natural Language Processing Nearly 80% of the information kept or "locked" in electronic health record systems is unstructured healthcare data for machine learning. These are papers or text files, not data components that could not previously be evaluated without a human viewing the information. Unfortunately, human language, often known as "natural language," is extremely complicated, lacks consistency, and contains many ambiguities, jargon, and vagueness. Therefore, machine learning in healthcare frequently uses natural language processing (NLP) tools to transform these papers into more usable and analyzable data.