Precision medicine is a rapidly evolving approach to healthcare that uses patient-specific data to tailor medical treatment and therapies to an individual’s unique needs.
Leveraging Data Analysis for Advancements in Healthcare and Medical Research.pdfSoumodeep Nanee Kundu
Data analysis in healthcare encompasses a wide range of applications, all geared toward improving patient care and well-being. It begins with the collection of diverse healthcare data, which includes electronic health records, medical imaging, genomic data, wearable device data, and more. These data sources provide a rich tapestry of information that can be analysed to unlock valuable insights and drive healthcare advancements.
One of the primary areas where data analysis is a game-changer is in clinical decision-making. Through the utilization of data-driven algorithms, healthcare professionals are empowered to make informed decisions regarding patient diagnosis, treatment plans, and prognosis. Clinical Decision Support Systems (CDSS), powered by data analysis, provide real-time guidance based on evidence-based medical knowledge, assisting physicians in choosing the most appropriate treatments and interventions. This not only enhances patient care but also reduces medical errors and ensures that treatment decisions are aligned with the most current medical research.
Data analysis is also instrumental in early disease identification and monitoring. Machine learning models, for example, can predict the onset of diseases like diabetes, Alzheimer's, and cardiovascular conditions by analysing patient data. This early detection capability enables healthcare providers to intervene proactively, potentially preventing or mitigating the severity of these conditions. This aspect of data analysis significantly contributes to the shift from reactive to proactive healthcare, improving patient outcomes and reducing healthcare costs.
Epidemiology and public health are areas where data analysis plays a vital role. The analysis of healthcare data is essential for tracking and predicting disease outbreaks, which is especially critical in the context of infectious diseases and bioterrorism preparedness. Real-time analysis of health data can offer early warning signs of emerging epidemics, allowing authorities to take timely preventive measures and allocate resources efficiently.
APPLICATION OF DATA SCIENCE IN HEALTHCAREAnnaAntony16
About the application of data science in healthcare. Healthcare is an essential field that touches on people's lives in many ways, and it has been revolutionized by data science over the years. Data science has enabled healthcare providers to better understand patients' needs, identify the root causes of diseases, and design effective treatment plans.
In the realm of healthcare, data is a critical asset that holds the potential to revolutionise patient care, enhance treatment outcomes, and streamline healthcare operations. One of the most valuable resources in this data-driven landscape is healthcare datasets. These datasets encompass a wide range of information, from patient medical records and clinical trial data to health insurance claims and public health statistics.
Healthcare datasets serve as the foundation for evidence-based medicine, enabling researchers and healthcare professionals to analyse trends, identify patterns, and make informed decisions. By delving into these datasets, medical researchers can uncover new insights into disease progression, treatment efficacy, and patient outcomes. This knowledge is crucial for developing more effective therapies, improving diagnostic accuracy, and tailoring treatment plans to individual patients' needs.
Moreover, healthcare datasets play a pivotal role in public health initiatives. By examining data on disease incidence, vaccination rates, and health behaviours, public health officials can design targeted interventions, allocate resources more efficiently, and monitor the impact of public health policies. This data-driven approach helps in controlling the spread of infectious diseases, promoting healthy lifestyles, and ultimately reducing the burden of illness on society.
The integration of healthcare datasets with advanced analytics and machine learning technologies opens up even more possibilities. Predictive models built on these datasets can forecast disease outbreaks, identify high-risk patient populations, and optimise resource allocation in healthcare facilities. These predictive insights are invaluable for proactive healthcare management and ensuring that patients receive timely and appropriate care.
However, the effective use of healthcare datasets is not without challenges. Issues related to data privacy, security, and interoperability need to be addressed to ensure that sensitive patient information is protected and that data from different sources can be integrated seamlessly. Additionally, the quality and completeness of data are crucial for drawing accurate conclusions, necessitating rigorous data management and validation practices.
In conclusion, healthcare datasets are a vital resource that holds immense potential for advancing medical research, improving patient care, and enhancing public health outcomes. As technology continues to evolve, the ability to harness the power of these datasets will become increasingly important in shaping the future of healthcare.
Navigating Healthcare's Seas: Unraveling the Power of Data Mining in HealthcareThe Lifesciences Magazine
Here are 5 Applications of Data Mining in Healthcare: 1. Clinical Decision Support Systems (CDSS) 2. Disease Surveillance and Outbreak Prediction 3. Fraud Detection and Prevention 4. Personalized Medicine 5. Predictive Analytics for Patient Outcomes
How Genomics & Data analysis are intertwined each other (1).pdfNusrat Gulbarga
Genomics and data analysis are closely linked because genomics generates vast amounts of data, which requires sophisticated computational and analytical tools to process and interpret. Genomics involves sequencing, assembling, and annotating the genome, which produces large datasets that require bioinformatics and computational analysis. Data analysis techniques such as machine learning, statistical analysis, and data visualization are critical for interpreting genomic data, identifying patterns, and making meaningful conclusions. In turn, genomic data analysis helps to advance our understanding of genetics, biology, and disease, leading to new discoveries and advances in medicine, agriculture, and other fields. Without data analysis, genomic research would be limited in its ability to extract insights from the vast amounts of genomic data that are generated. Genomics and data analysis are intertwined because genomics generates vast amounts of data that require advanced computational and statistical methods to interpret and analyze. Genomics is the study of an organism's entire genetic makeup, including DNA sequences, gene expression patterns, and epigenetic modifications. With the advent of high-throughput sequencing technologies, genomics has generated an enormous amount of data that requires sophisticated computational tools to analyze and interpret.
Data analysis plays a crucial role in genomics because it helps to identify genetic variations and their functional significance, understand gene expression patterns, and predict the effects of genetic modifications. Sophisticated statistical methods and machine learning algorithms are used to analyze genomic data and identify patterns, associations, and correlations. Data analysis also plays a critical role in personalized medicine, where genomic data is used to identify individualized treatments for patients based on their genetic makeup. Overall, genomics and data analysis are intertwined because they complement each other and are both essential for understanding the complexities of the genetic code and its effects on health and disease. Genomics and data analysis are intertwined because genomics is the study of the entire genetic material of an organism, and data analysis is necessary to interpret and make sense of the vast amount of genomic data generated. Genomics involves sequencing, assembling, and analyzing DNA, RNA, and protein sequences. The resulting data are massive, complex, and require advanced computational tools and techniques to be analyzed effectively. Data analysis helps to identify genes, regulatory elements, and mutations that are responsible for specific traits or diseases. It also helps to compare genomic sequences across different species and populations. Without data analysis, it would be impossible to extract useful information from the vast amount of genomic data produced by sequencing technologies.
Just as a corporation can improve its products and services by analyzing data and using relevant data points to steer future action, the healthcare industry can leverage big data in future decisions to the improvement of personalized medicine. But healthcare can use big data on more fronts than most industries, especially considering transition of healthcare into precision medicine. Visit: https://www.rockwestsolutions.com/sensors/medical-devices/
Leveraging Data Analysis for Advancements in Healthcare and Medical Research.pdfSoumodeep Nanee Kundu
Data analysis in healthcare encompasses a wide range of applications, all geared toward improving patient care and well-being. It begins with the collection of diverse healthcare data, which includes electronic health records, medical imaging, genomic data, wearable device data, and more. These data sources provide a rich tapestry of information that can be analysed to unlock valuable insights and drive healthcare advancements.
One of the primary areas where data analysis is a game-changer is in clinical decision-making. Through the utilization of data-driven algorithms, healthcare professionals are empowered to make informed decisions regarding patient diagnosis, treatment plans, and prognosis. Clinical Decision Support Systems (CDSS), powered by data analysis, provide real-time guidance based on evidence-based medical knowledge, assisting physicians in choosing the most appropriate treatments and interventions. This not only enhances patient care but also reduces medical errors and ensures that treatment decisions are aligned with the most current medical research.
Data analysis is also instrumental in early disease identification and monitoring. Machine learning models, for example, can predict the onset of diseases like diabetes, Alzheimer's, and cardiovascular conditions by analysing patient data. This early detection capability enables healthcare providers to intervene proactively, potentially preventing or mitigating the severity of these conditions. This aspect of data analysis significantly contributes to the shift from reactive to proactive healthcare, improving patient outcomes and reducing healthcare costs.
Epidemiology and public health are areas where data analysis plays a vital role. The analysis of healthcare data is essential for tracking and predicting disease outbreaks, which is especially critical in the context of infectious diseases and bioterrorism preparedness. Real-time analysis of health data can offer early warning signs of emerging epidemics, allowing authorities to take timely preventive measures and allocate resources efficiently.
APPLICATION OF DATA SCIENCE IN HEALTHCAREAnnaAntony16
About the application of data science in healthcare. Healthcare is an essential field that touches on people's lives in many ways, and it has been revolutionized by data science over the years. Data science has enabled healthcare providers to better understand patients' needs, identify the root causes of diseases, and design effective treatment plans.
In the realm of healthcare, data is a critical asset that holds the potential to revolutionise patient care, enhance treatment outcomes, and streamline healthcare operations. One of the most valuable resources in this data-driven landscape is healthcare datasets. These datasets encompass a wide range of information, from patient medical records and clinical trial data to health insurance claims and public health statistics.
Healthcare datasets serve as the foundation for evidence-based medicine, enabling researchers and healthcare professionals to analyse trends, identify patterns, and make informed decisions. By delving into these datasets, medical researchers can uncover new insights into disease progression, treatment efficacy, and patient outcomes. This knowledge is crucial for developing more effective therapies, improving diagnostic accuracy, and tailoring treatment plans to individual patients' needs.
Moreover, healthcare datasets play a pivotal role in public health initiatives. By examining data on disease incidence, vaccination rates, and health behaviours, public health officials can design targeted interventions, allocate resources more efficiently, and monitor the impact of public health policies. This data-driven approach helps in controlling the spread of infectious diseases, promoting healthy lifestyles, and ultimately reducing the burden of illness on society.
The integration of healthcare datasets with advanced analytics and machine learning technologies opens up even more possibilities. Predictive models built on these datasets can forecast disease outbreaks, identify high-risk patient populations, and optimise resource allocation in healthcare facilities. These predictive insights are invaluable for proactive healthcare management and ensuring that patients receive timely and appropriate care.
However, the effective use of healthcare datasets is not without challenges. Issues related to data privacy, security, and interoperability need to be addressed to ensure that sensitive patient information is protected and that data from different sources can be integrated seamlessly. Additionally, the quality and completeness of data are crucial for drawing accurate conclusions, necessitating rigorous data management and validation practices.
In conclusion, healthcare datasets are a vital resource that holds immense potential for advancing medical research, improving patient care, and enhancing public health outcomes. As technology continues to evolve, the ability to harness the power of these datasets will become increasingly important in shaping the future of healthcare.
Navigating Healthcare's Seas: Unraveling the Power of Data Mining in HealthcareThe Lifesciences Magazine
Here are 5 Applications of Data Mining in Healthcare: 1. Clinical Decision Support Systems (CDSS) 2. Disease Surveillance and Outbreak Prediction 3. Fraud Detection and Prevention 4. Personalized Medicine 5. Predictive Analytics for Patient Outcomes
How Genomics & Data analysis are intertwined each other (1).pdfNusrat Gulbarga
Genomics and data analysis are closely linked because genomics generates vast amounts of data, which requires sophisticated computational and analytical tools to process and interpret. Genomics involves sequencing, assembling, and annotating the genome, which produces large datasets that require bioinformatics and computational analysis. Data analysis techniques such as machine learning, statistical analysis, and data visualization are critical for interpreting genomic data, identifying patterns, and making meaningful conclusions. In turn, genomic data analysis helps to advance our understanding of genetics, biology, and disease, leading to new discoveries and advances in medicine, agriculture, and other fields. Without data analysis, genomic research would be limited in its ability to extract insights from the vast amounts of genomic data that are generated. Genomics and data analysis are intertwined because genomics generates vast amounts of data that require advanced computational and statistical methods to interpret and analyze. Genomics is the study of an organism's entire genetic makeup, including DNA sequences, gene expression patterns, and epigenetic modifications. With the advent of high-throughput sequencing technologies, genomics has generated an enormous amount of data that requires sophisticated computational tools to analyze and interpret.
Data analysis plays a crucial role in genomics because it helps to identify genetic variations and their functional significance, understand gene expression patterns, and predict the effects of genetic modifications. Sophisticated statistical methods and machine learning algorithms are used to analyze genomic data and identify patterns, associations, and correlations. Data analysis also plays a critical role in personalized medicine, where genomic data is used to identify individualized treatments for patients based on their genetic makeup. Overall, genomics and data analysis are intertwined because they complement each other and are both essential for understanding the complexities of the genetic code and its effects on health and disease. Genomics and data analysis are intertwined because genomics is the study of the entire genetic material of an organism, and data analysis is necessary to interpret and make sense of the vast amount of genomic data generated. Genomics involves sequencing, assembling, and analyzing DNA, RNA, and protein sequences. The resulting data are massive, complex, and require advanced computational tools and techniques to be analyzed effectively. Data analysis helps to identify genes, regulatory elements, and mutations that are responsible for specific traits or diseases. It also helps to compare genomic sequences across different species and populations. Without data analysis, it would be impossible to extract useful information from the vast amount of genomic data produced by sequencing technologies.
Just as a corporation can improve its products and services by analyzing data and using relevant data points to steer future action, the healthcare industry can leverage big data in future decisions to the improvement of personalized medicine. But healthcare can use big data on more fronts than most industries, especially considering transition of healthcare into precision medicine. Visit: https://www.rockwestsolutions.com/sensors/medical-devices/
The Power of Data Analytics in Smart HealthcareWerkDone
Data analytics involves the use of various techniques to analyze and interpret large amounts of data to uncover patterns and insights. In healthcare, data analytics can be used improve the delivery of patient care, predict disease outbreaks, and develop personalized treatment plans.
Data Science in Healthcare" by authors Sergio Consoli, Diego Reforgiato Recupero, and Milan Petkovic is an insightful guide that delves into the intersection of data science and healthcare. As a first-year student in Pharmaceutical Management, I found this book to be a valuable resource for understanding how data-driven approaches are transforming the healthcare industry, offering fresh perspectives and practical insights for future professionals like myself.
How Artificial Intelligence is revolutionizing Personalized Medicine.pdfEnterprise Wired
Here is how artificial intelligence is revolutionizing personalized medicine? 1. The Power of Data 2. Genomic Medicine and AI 3. Diagnostic Accuracy 4. Predictive Analytics 5. Drug Discovery and Development 6. Ethical and Regulatory Considerations
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
Machine literacy and artificial intelligence( AI) have become potent tools that are transforming several industries, including healthcare. The integration of AI and machine literacy in healthcare has opened up new possibilities, transubstantiating the way medical professionals diagnose, treat, and watch for cases. These technologies have the eventuality to enhance delicacy, effectiveness, and patient issues while also addressing difficulties in healthcare delivery. From individual and imaging analysis to prophetic analytics and substantiated treatment, AI and machine literacy offer promising advancements. Still, along with the openings come ethical debates, sequestration enterprises, and the need for official fabrics to insure responsible and transparent use of these technologies. In this composition, we will claw into the role of AI and machine literacy in healthcare, exploring their significance, benefits, and impact on colorful aspects of the assiduity.
Data-Driven Precision Medicine: A Must-Have for the Next-Generation of Person...Health Catalyst
Under a precision medicine approach, clinicians, academics, and pharma and biotech researchers and regulators aim to deliver the right drug for the right patient at the right time. Data, however, can present a challenge to precision medicine goals due to gaps in clinical care, research, and drug development when organizations don’t have the ability to capture and report on relevant real-world data. With the right systems to collect and share clinical and molecular data, the healthcare industry can realize the full benefits of precision medicine.
In this rapidly changing landscape, big data is playing a critical role, providing healthcare providers with valuable insights into patient health and enabling the development of more effective and personalized treatment plans.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about transformational advances in patient care, research, and healthcare management. United States is the focus due fact that many academic and research institutions in the country are at the forefront of healthcare data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect, process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more educated decisions, forecast health outcomes, manage population health, customize treatment, optimize workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data intelligence applications raises issues and concerns about data privacy, fairness, transparency, data quality, accountability, fair data access, regulatory compliance, and the balance between automation and human judgment. Emerging themes include AI and machine learning domination, stronger ethical and regulatory frameworks, edge and quantum computing, data democratization, sustainability applications, and developing human-machine collaboration. Data intelligence has an impact that goes beyond healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth. Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine healthcare excellence and extend their influence across sectors.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about
transformational advances in patient care, research, and healthcare management. United States is the
focus due fact that many academic and research institutions in the country are at the forefront of healthcare
data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of
Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and
emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect,
process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more
educated decisions, forecast health outcomes, manage population health, customize treatment, optimize
workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data
intelligence applications raises issues and concerns about data privacy, fairness, transparency, data
quality, accountability, fair data access, regulatory compliance, and the balance between automation and
human judgment. Emerging themes include AI and machine learning domination, stronger ethical and
regulatory frameworks, edge and quantum computing, data democratization, sustainability applications,
and developing human-machine collaboration. Data intelligence has an impact that goes beyond
healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth.
Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine
healthcare excellence and extend their influence across sectors.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about
transformational advances in patient care, research, and healthcare management. United States is the
focus due fact that many academic and research institutions in the country are at the forefront of healthcare
data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of
Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and
emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect,
process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more
educated decisions, forecast health outcomes, manage population health, customize treatment, optimize
workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data
intelligence applications raises issues and concerns about data privacy, fairness, transparency, data
quality, accountability, fair data access, regulatory compliance, and the balance between automation and
human judgment. Emerging themes include AI and machine learning domination, stronger ethical and
regulatory frameworks, edge and quantum computing, data democratization, sustainability applications,
and developing human-machine collaboration. Data intelligence has an impact that goes beyond
healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth.
Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine
healthcare excellence and extend their influence across sectors.
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Please respond to each of the 3 posts with 3 APA sources no older thmaple8qvlisbey
Please respond to each of the 3 posts with 3 APA sources no older than 5 years old. APA format must be exceptional.
Reply 1
Professor,
How can big data impact prescription errors? Be specific and provide examples. Who should be on the team to implement this project and why? Support your work with the literature.
Reply 2
Ruth Niyasimi,
Big Data Risks and Rewards
Big data is defined as the process of collecting, analyzing, and leveraging consumer patient, physical, and clinical data that is too vast or complex to be understood by traditional means of data processing. In healthcare, data is generated from medical records, patient portals, government agencies, research studies, electronic health records, and medical devices. The data generated in healthcare is used to make decisions that will have an impact on patient health outcomes (Raghupathi & Raghupathi, 2014). Healthcare is a critical docket in our society since it is tasked with a duty to prevent, diagnose and treat illnesses and diseases affecting the community. In the past, health information was stored on paper but through advancements in technology, things have significantly changed as patient information is stored on Electronic health records (EHR).
The adoption of big data had significant impacts on customer services and other related issues. According to Raghupathi and Raghupathi (2014), for many years, healthcare has been generating huge volumes of data that was stored in hardcopy. This was a critical step toward improving the quality of healthcare delivery while reducing costs. This huge volume of information is crucial to healthcare because, through digitalization, it has become possible to detect diseases at an early stage and take necessary intervention measures. Secondly, big data enables the ability to enhance continuity, starting when a patient visits a hospital until the last stage of being discharged. For example, the lab tests taken from those patients and other specialized treatments are stored in a way that other departments can access this information in the future preventing duplicate redoing labs and imaging studies (Adibuzzaman et al., 2017). This cuts down costs while improving service delivery.
Although big data has had a tremendous impact on the healthcare systems, it has also created some problems. Firstly, the use of technology such as EHR has resulted in security issues and privacy threats. According to McGonigle and Mastrian (2017), technology has enabled the interoperability of healthcare data. Interoperability means sharing important health data across different organizations while ensuring it is presented understandably to the user. Unauthorized third parties can intersect this information and the Health Insurance Portability and Accountability Act (HIPPA) has shown little concern for patient data breach cases. Another problem is that big data is not static, it requires continuous system updates to ensure that it ...
The United States government’s total public debt has risen recently. The spike is so significant that some experts are expecting a US debt ceiling breach.
Digital Biology: Evolution, Components, Applications, and More | The Lifescie...The Lifesciences Magazine
Applications of Digital Biology: 1. Genomics and Personalized Medicine 2. Drug Discovery and Development 3. Biotechnology and Agriculture 4. Environmental Monitoring
Deep Leg Vein Thrombosis (DVT): Meaning, Causes, Symptoms, Treatment, and Mor...The Lifesciences Magazine
Deep Leg Vein Thrombosis occurs when a blood clot forms in one or more of the deep veins in the legs. These clots can impede blood flow, leading to severe complications.
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The Power of Data Analytics in Smart HealthcareWerkDone
Data analytics involves the use of various techniques to analyze and interpret large amounts of data to uncover patterns and insights. In healthcare, data analytics can be used improve the delivery of patient care, predict disease outbreaks, and develop personalized treatment plans.
Data Science in Healthcare" by authors Sergio Consoli, Diego Reforgiato Recupero, and Milan Petkovic is an insightful guide that delves into the intersection of data science and healthcare. As a first-year student in Pharmaceutical Management, I found this book to be a valuable resource for understanding how data-driven approaches are transforming the healthcare industry, offering fresh perspectives and practical insights for future professionals like myself.
How Artificial Intelligence is revolutionizing Personalized Medicine.pdfEnterprise Wired
Here is how artificial intelligence is revolutionizing personalized medicine? 1. The Power of Data 2. Genomic Medicine and AI 3. Diagnostic Accuracy 4. Predictive Analytics 5. Drug Discovery and Development 6. Ethical and Regulatory Considerations
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
Machine literacy and artificial intelligence( AI) have become potent tools that are transforming several industries, including healthcare. The integration of AI and machine literacy in healthcare has opened up new possibilities, transubstantiating the way medical professionals diagnose, treat, and watch for cases. These technologies have the eventuality to enhance delicacy, effectiveness, and patient issues while also addressing difficulties in healthcare delivery. From individual and imaging analysis to prophetic analytics and substantiated treatment, AI and machine literacy offer promising advancements. Still, along with the openings come ethical debates, sequestration enterprises, and the need for official fabrics to insure responsible and transparent use of these technologies. In this composition, we will claw into the role of AI and machine literacy in healthcare, exploring their significance, benefits, and impact on colorful aspects of the assiduity.
Data-Driven Precision Medicine: A Must-Have for the Next-Generation of Person...Health Catalyst
Under a precision medicine approach, clinicians, academics, and pharma and biotech researchers and regulators aim to deliver the right drug for the right patient at the right time. Data, however, can present a challenge to precision medicine goals due to gaps in clinical care, research, and drug development when organizations don’t have the ability to capture and report on relevant real-world data. With the right systems to collect and share clinical and molecular data, the healthcare industry can realize the full benefits of precision medicine.
In this rapidly changing landscape, big data is playing a critical role, providing healthcare providers with valuable insights into patient health and enabling the development of more effective and personalized treatment plans.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about transformational advances in patient care, research, and healthcare management. United States is the focus due fact that many academic and research institutions in the country are at the forefront of healthcare data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect, process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more educated decisions, forecast health outcomes, manage population health, customize treatment, optimize workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data intelligence applications raises issues and concerns about data privacy, fairness, transparency, data quality, accountability, fair data access, regulatory compliance, and the balance between automation and human judgment. Emerging themes include AI and machine learning domination, stronger ethical and regulatory frameworks, edge and quantum computing, data democratization, sustainability applications, and developing human-machine collaboration. Data intelligence has an impact that goes beyond healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth. Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine healthcare excellence and extend their influence across sectors.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about
transformational advances in patient care, research, and healthcare management. United States is the
focus due fact that many academic and research institutions in the country are at the forefront of healthcare
data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of
Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and
emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect,
process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more
educated decisions, forecast health outcomes, manage population health, customize treatment, optimize
workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data
intelligence applications raises issues and concerns about data privacy, fairness, transparency, data
quality, accountability, fair data access, regulatory compliance, and the balance between automation and
human judgment. Emerging themes include AI and machine learning domination, stronger ethical and
regulatory frameworks, edge and quantum computing, data democratization, sustainability applications,
and developing human-machine collaboration. Data intelligence has an impact that goes beyond
healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth.
Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine
healthcare excellence and extend their influence across sectors.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about
transformational advances in patient care, research, and healthcare management. United States is the
focus due fact that many academic and research institutions in the country are at the forefront of healthcare
data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of
Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and
emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect,
process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more
educated decisions, forecast health outcomes, manage population health, customize treatment, optimize
workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data
intelligence applications raises issues and concerns about data privacy, fairness, transparency, data
quality, accountability, fair data access, regulatory compliance, and the balance between automation and
human judgment. Emerging themes include AI and machine learning domination, stronger ethical and
regulatory frameworks, edge and quantum computing, data democratization, sustainability applications,
and developing human-machine collaboration. Data intelligence has an impact that goes beyond
healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth.
Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine
healthcare excellence and extend their influence across sectors.
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Please respond to each of the 3 posts with 3 APA sources no older thmaple8qvlisbey
Please respond to each of the 3 posts with 3 APA sources no older than 5 years old. APA format must be exceptional.
Reply 1
Professor,
How can big data impact prescription errors? Be specific and provide examples. Who should be on the team to implement this project and why? Support your work with the literature.
Reply 2
Ruth Niyasimi,
Big Data Risks and Rewards
Big data is defined as the process of collecting, analyzing, and leveraging consumer patient, physical, and clinical data that is too vast or complex to be understood by traditional means of data processing. In healthcare, data is generated from medical records, patient portals, government agencies, research studies, electronic health records, and medical devices. The data generated in healthcare is used to make decisions that will have an impact on patient health outcomes (Raghupathi & Raghupathi, 2014). Healthcare is a critical docket in our society since it is tasked with a duty to prevent, diagnose and treat illnesses and diseases affecting the community. In the past, health information was stored on paper but through advancements in technology, things have significantly changed as patient information is stored on Electronic health records (EHR).
The adoption of big data had significant impacts on customer services and other related issues. According to Raghupathi and Raghupathi (2014), for many years, healthcare has been generating huge volumes of data that was stored in hardcopy. This was a critical step toward improving the quality of healthcare delivery while reducing costs. This huge volume of information is crucial to healthcare because, through digitalization, it has become possible to detect diseases at an early stage and take necessary intervention measures. Secondly, big data enables the ability to enhance continuity, starting when a patient visits a hospital until the last stage of being discharged. For example, the lab tests taken from those patients and other specialized treatments are stored in a way that other departments can access this information in the future preventing duplicate redoing labs and imaging studies (Adibuzzaman et al., 2017). This cuts down costs while improving service delivery.
Although big data has had a tremendous impact on the healthcare systems, it has also created some problems. Firstly, the use of technology such as EHR has resulted in security issues and privacy threats. According to McGonigle and Mastrian (2017), technology has enabled the interoperability of healthcare data. Interoperability means sharing important health data across different organizations while ensuring it is presented understandably to the user. Unauthorized third parties can intersect this information and the Health Insurance Portability and Accountability Act (HIPPA) has shown little concern for patient data breach cases. Another problem is that big data is not static, it requires continuous system updates to ensure that it ...
The United States government’s total public debt has risen recently. The spike is so significant that some experts are expecting a US debt ceiling breach.
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Deep Leg Vein Thrombosis occurs when a blood clot forms in one or more of the deep veins in the legs. These clots can impede blood flow, leading to severe complications.
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Recent research has demonstrated that a single infusion of engineered T-cells can achieve lasting asthma remission in mice, potentially expanding the indications for cell therapies beyond cancer.
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Cell Division Evolution in Ichthyosporea: Insights from EPFL and EMBL Heidelb...The Lifesciences Magazine
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In this article, we explore some of the most innovative devices for cardiovascular health, including stents and pacemakers with advanced functionalities, minimally invasive heart valves for implantation, and wearable devices for heart rhythm monitoring.
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DL4MicEverywhere offers life scientists easy access to advanced AI for microscopy image analysis, democratizing technology and enhancing biomedical research globally.
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Cashew Allergy: Causes, Symptoms, Preventions, and Diagnosing | The Lifescien...The Lifesciences Magazine
A cashew allergy occurs when the immune system mistakenly identifies proteins in cashews as harmful substances. In response, the body releases histamines and other chemicals, leading to allergic reactions
The Global ECG Patch and Holter Monitor Market was worth USD 1.44 billion in 2023 and is expected to grow to USD 5.04 billion by 2030, at a CAGR of 19.60 percent during the forecast period.
Holter Monitor: A Comprehensive Guide to Continuous Heart Monitoring | The Li...The Lifesciences Magazine
Holter Monitor is a portable device used for continuous monitoring of a person's heart activity, typically over a 24 to 48-hour period. It records the heart's electrical activity, providing valuable insights into irregularities.
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Creutzfeldt-Jakob Disease (CJD) is caused by the abnormal folding of prion proteins in the brain, leading to the formation of insoluble aggregates that damage nerve cells.
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Posterior Cortical Atrophy, often referred to as "PCA," is a neurodegenerative condition characterized by progressive degeneration of the posterior regions of the brain, particularly the occipital, parietal, and temporal lobes.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
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Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
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Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
1. The Role of big data in precision
medicine
Precision medicine is a rapidly evolving approach to healthcare that uses patient-specific data
to tailor medical treatment and therapies to an individual’s unique needs. Big data, or large
amounts of complex and diverse data, plays a crucial role in precision medicine by providing
researchers and healthcare providers with the information they need to develop personalized
treatments for patients. In this essay, we will explore the role of big data in precision medicine,
including how it is collected, analyzed, and used to improve patient outcomes.
Collection of Big Data in Precision Medicine
Big data in precision medicine comes from a variety of sources, including electronic health
records, medical imaging, genomic sequencing, wearable devices, and social determinants of
health. Electronic health records (EHRs) provide a wealth of information about a patient’s
medical history, including past illnesses, surgeries, medications, and laboratory results. Medical
imaging, such as X-rays and MRIs, provides detailed images of a patient’s internal organs and
structures, allowing healthcare providers to identify and diagnose diseases and conditions.
Genomic sequencing provides information about a patient’s DNA, which can be used to identify
genetic mutations that may predispose an individual to certain diseases or conditions.
2. Wearable devices, such as fitness trackers and smartwatches, provide real-time data about a
patient’s activity level, heart rate, and other biometric data. Social determinants of health, such
as income, education level, and living conditions, provide information about the social and
environmental factors that may impact a patient’s health.
Analysis of Big Data in Precision Medicine
Once big data is collected, it must be analyzed to identify patterns and trends that can be used
to develop personalized treatments and therapies for patients. One approach to analyzing big
data in precision medicine is machine learning, a type of artificial intelligence that allows
computers to learn from data and make predictions or decisions based on that data.
Machine learning algorithms can be used to analyze large datasets to identify patterns and
trends that may not be visible to the human eye. For example, machine learning algorithms can
be used to identify genetic mutations that may increase a patient’s risk of developing certain
types of cancer, or to analyze medical imaging data to identify patterns that may indicate a
particular disease or condition.
Another approach to analyzing big data in precision medicine is network analysis, a method
that allows researchers to study the interactions between genes, proteins, and other
biomolecules in the body. Network analysis can be used to identify genetic pathways and
interactions that may be involved in the development of a particular disease or condition. This
information can be used to develop targeted therapies that aim to disrupt these pathways and
prevent or treat the disease.
3. Uses of Big Data in Precision Medicine
Big data is being used in precision medicine in a variety of ways, including drug discovery,
disease diagnosis, treatment selection, and patient monitoring. In drug discovery, big data is
being used to identify new drug targets and to develop more effective and targeted therapies.
For example, machine learning algorithms can be used to identify new drug targets by
analyzing large datasets of genetic and molecular data.
Similarly, network analysis can be used to identify interactions between genes and proteins
that may be involved in disease development, which can help researchers develop targeted
therapies.
In disease diagnosis, big data is being used to improve the accuracy and speed of diagnosis. For
example, medical imaging data can be analyzed using machine learning algorithms to identify
patterns indicating a particular disease or condition. Similarly, genomic sequencing data can be
analyzed to identify genetic mutations that may be associated with a particular disease or
condition.
In treatment selection, big data in precision medicine is being used to develop personalized
treatment plans for patients. For example, genomic sequencing data can be used to identify
genetic mutations that may impact a patient’s response to certain medications. This
information can be used to develop personalized treatment plans tailored to a patient’s unique
needs.
4. In patient monitoring, big data is being used to track patient outcomes and to identify potential
health risks. Wearable devices and other remote monitoring technologies can provide real-time
data about a patient’s health status, allowing healthcare providers to detect changes in health
and to intervene before a more serious problem arises. Similarly, social determinants of health
data can be used to identify patients who may be at higher risk for certain health conditions,
allowing healthcare providers to intervene early and prevent or treat the condition before it
becomes more serious.
Challenges and Limitations of Big Data in Precision Medicine
Despite the many potential benefits of big data in precision medicine, there are also a number
of challenges and limitations that must be addressed in order to fully realize its potential. One
of the biggest challenges is data quality and accuracy. With such large and complex datasets, it
can be difficult to ensure that the data is accurate, complete, and relevant. This can lead to
errors in analysis and incorrect conclusions about patient care.
Another challenge is data privacy and security. With so much sensitive patient data being
collected and analyzed, it is essential to have strong data privacy and security protocols in
place to protect patient confidentiality and to prevent unauthorized access to patient data.
Another limitation of big data in precision medicine is the need for advanced data analysis and
computational tools. Machine learning and network analysis require advanced computational
tools and expertise to analyze large and complex datasets, which can be a barrier for some
healthcare providers and researchers who may not have access to these tools or the necessary
expertise to use them effectively.
5. Finally, there is the issue of data interoperability. With so many different sources of data in
precision medicine, it can be difficult to integrate data from different sources and ensure that
data is standardized and interoperable. This can make it challenging to compare data across
different studies or share data between healthcare providers.
BOTTOM LINE
Big data is playing an increasingly important role in precision medicine, providing researchers
and healthcare providers with the information they need to develop personalized treatments
and therapies for patients. By analyzing large and complex datasets from a variety of sources,
including electronic health records, medical imaging, genomic sequencing, wearable devices,
and social determinants of health, big data is helping to identify new drug targets, improve
disease diagnosis and treatment selection, and monitor patient outcomes.
However, there are also a number of challenges and limitations that must be addressed in
order to fully realize the potential of big data in precision medicine, including data quality and
accuracy, data privacy and security, advanced data analysis and computational tools, and data
interoperability. As precision medicine continues to evolve, it is likely that big data will play an
increasingly important role in improving patient outcomes and advancing our understanding of
disease and healthcare
Also Read: What is Medical Technology?