This document discusses explainable artificial intelligence (XAI) and its importance in healthcare. It explores how XAI can enhance transparency and trust in AI systems used for clinical decision-making. Various XAI techniques like rule-based models, decision trees and model-agnostic methods are described. The document also examines the legal and ethical considerations of applying XAI in healthcare and discusses challenges and future directions.
This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.
China's strategic plan for Artificial Intelligence mayank batavia
Here's a comprehensive presentation on everything you need to know about China's action plan for Artificial Intelligence (AI). The presentation covers goals, steps, stages, desired outcomes and more on China's AI gameplan. The presentation shows how China intends to leverage AI to transform its own economy and businesses the world over. You can read the detailed blog here. http://almostism.com/chinese-government-three-year-action-plan-on-artificial-intelligence/
Credit card plays a very vital role in todays economy and the usage of credit cards has dramatically increased. Credit card has become one of the most common method of payment for both online and offline as well as for regular purchases of a common man. It is very necessary to distinguish fraudulent credit card transactions by the credit card organizations so their clients are not charged for the purchases that they didn’t make. Despite the fact that using credit card gives huge benefits when used responsibly carefully and however significant credit and financial damages could be caused by fraudulent activities as well. Numerous methods have been proposed to stop these fraudulent activities. The project illustrates the model of a dataset to predict fraud transactions using machine learning. The model then detects if it is a fraudulent or a genuine transaction. The model also analyses and pre processes the dataset along with deployment of multiple anomaly detection using algorithms such as Local forest outlier and Isolation forest. Nikitha Pradeep | Dr. A Rengarajan "Credit Card Fraud Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41289.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep
Algorithmic Bias: Challenges and Opportunities for AI in HealthcareGregory Nelson
Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation, we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.
China's strategic plan for Artificial Intelligence mayank batavia
Here's a comprehensive presentation on everything you need to know about China's action plan for Artificial Intelligence (AI). The presentation covers goals, steps, stages, desired outcomes and more on China's AI gameplan. The presentation shows how China intends to leverage AI to transform its own economy and businesses the world over. You can read the detailed blog here. http://almostism.com/chinese-government-three-year-action-plan-on-artificial-intelligence/
Credit card plays a very vital role in todays economy and the usage of credit cards has dramatically increased. Credit card has become one of the most common method of payment for both online and offline as well as for regular purchases of a common man. It is very necessary to distinguish fraudulent credit card transactions by the credit card organizations so their clients are not charged for the purchases that they didn’t make. Despite the fact that using credit card gives huge benefits when used responsibly carefully and however significant credit and financial damages could be caused by fraudulent activities as well. Numerous methods have been proposed to stop these fraudulent activities. The project illustrates the model of a dataset to predict fraud transactions using machine learning. The model then detects if it is a fraudulent or a genuine transaction. The model also analyses and pre processes the dataset along with deployment of multiple anomaly detection using algorithms such as Local forest outlier and Isolation forest. Nikitha Pradeep | Dr. A Rengarajan "Credit Card Fraud Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41289.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep
Algorithmic Bias: Challenges and Opportunities for AI in HealthcareGregory Nelson
Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation, we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
Artificial intelligence (AI) is already transforming healthcare. It's an invaluable tool, capable of storing and processing vast amounts of data almost simultaneously. AI allows for rapid and accurate diagnosis, early detection, advanced research and much more.
Customer churn classification using machine learning techniquesSindhujanDhayalan
Advanced data mining project on classifying customer churn by
using machine learning algorithms such as random forest,
C5.0, Decision tree, KNN, ANN, and SVM. CRISP-DM approach was followed for developing the project. Accuracy rate, Error rate, Precision, Recall, F1 and ROC curve was generated using R programming and the efficient model was found comparing these values.
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.
following topics are discussed inside the PPT:
Introduction
Objective
Motivation
Literature Survey
Some Key Features of Disease
Plan of Action
Methodology Adopted
Data Collection
Steps to be Performed
Functional Architecture
Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
Cybersecurity and Information Assurance - Cloud ComputingJoseph Pindar
Research paper from 2011 covering the role of Cybersecurity and Information Assurance in enterprise strategy and operational effectiveness.
Idea in Brief:
1. Cybersecurity and Information Assurance is mainly a cost of doing business with the unique opportunity to create significant value by enabling the enterprise to enter markets and use technology that competitors fear.
2. Learn from other disciplines and use existing methodologies to deliver enterprise outcomes.
3. Focus on the end-consumer
Toda mujer en condiciones de embarazo, en el trabajo de parto, en el parto y postparto tendrá derecho a estar acompañada por la persona que ella elija, a ser considerada y tratada como una persona sana, a fin de que psicológiamente se facilite su actuación como progenitora de su propio cuerpo y parto.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
Artificial intelligence (AI) is already transforming healthcare. It's an invaluable tool, capable of storing and processing vast amounts of data almost simultaneously. AI allows for rapid and accurate diagnosis, early detection, advanced research and much more.
Customer churn classification using machine learning techniquesSindhujanDhayalan
Advanced data mining project on classifying customer churn by
using machine learning algorithms such as random forest,
C5.0, Decision tree, KNN, ANN, and SVM. CRISP-DM approach was followed for developing the project. Accuracy rate, Error rate, Precision, Recall, F1 and ROC curve was generated using R programming and the efficient model was found comparing these values.
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.
following topics are discussed inside the PPT:
Introduction
Objective
Motivation
Literature Survey
Some Key Features of Disease
Plan of Action
Methodology Adopted
Data Collection
Steps to be Performed
Functional Architecture
Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
Cybersecurity and Information Assurance - Cloud ComputingJoseph Pindar
Research paper from 2011 covering the role of Cybersecurity and Information Assurance in enterprise strategy and operational effectiveness.
Idea in Brief:
1. Cybersecurity and Information Assurance is mainly a cost of doing business with the unique opportunity to create significant value by enabling the enterprise to enter markets and use technology that competitors fear.
2. Learn from other disciplines and use existing methodologies to deliver enterprise outcomes.
3. Focus on the end-consumer
Toda mujer en condiciones de embarazo, en el trabajo de parto, en el parto y postparto tendrá derecho a estar acompañada por la persona que ella elija, a ser considerada y tratada como una persona sana, a fin de que psicológiamente se facilite su actuación como progenitora de su propio cuerpo y parto.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
List out the challenges of ml ai for delivering clinical impact - PubricaPubrica
Pubrica explores the main challenges and limitations of AI in healthcare and considers the steps required to translate these potentially transformative technologies from research to clinical practice.
Continue Reading: https://bit.ly/3o4hjPT
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
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The Role of Artificial Intelligence in Revolutionizing Healthcare A Comprehen...ijtsrd
A breakthrough era that holds enormous promise for increasing patient care, lowering healthcare costs, and improving overall healthcare outcomes has arrived with the integration of Artificial Intelligence AI in healthcare. This in depth analysis examines the several ways in which AI is transforming healthcare, including diagnosis, treatment, drug research, patient management, and administrative procedures. To lay a strong foundation for understanding AI, machine learning, and deep learning applications in healthcare, the examination begins with clarifying their core principles. It explores how AI might be used to analyze large scale, intricate medical datasets including electronic health records EHRs , medical imaging, and genomes, enabling the early detection of disease, precise diagnosis, and tailored therapy recommendations. Additionally, AI driven technologies like natural language processing NLP have demonstrated considerable potential in extracting important insights from unstructured clinical notes and research literature, supporting clinical decision support and medical research. AI powered robotics and automation have also begun to play crucial roles in rehabilitation and minimally invasive surgery, lowering the invasiveness of operations and speeding up patient recovery. The review emphasizes the efforts that are still being made to create AI driven drug discovery systems that hasten the identification of new treatments and enhance the layouts of clinical trials. By examining trends and patterns in healthcare data, it also examines AIs function in predictive analytics, predicting disease outbreaks, and enhancing population health management. Furthermore, in the context of optimizing healthcare operations and lowering administrative duties, the contribution of AI to administrative tasks such as medical billing, fraud detection, and resource allocation is considered. The review emphasizes the significance of privacy, transparency, and responsible AI deployment while highlighting the ethical and regulatory concerns involved with AI in healthcare. In order to fully realize the potential of AI, it also analyzes potential adoption barriers and the necessity of interdisciplinary cooperation between healthcare experts, data scientists, and legislators. In conclusion, this in depth analysis offers a complete overview of how AI is altering healthcare and provides insights into its present successes and potential in the future. This effort intends to spur innovation, educate stakeholders, and open the door for a more effective, patient centered, and accessible healthcare ecosystem by shedding light on the revolutionary effects of AI on healthcare. Kajal Gohane | Roshini S | Komal Pode "The Role of Artificial Intelligence in Revolutionizing Healthcare: A Comprehensive Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/pa
1Deliverable 3 - Evaluate Research and DataAttempt 2EttaBenton28
1
Deliverable 3 - Evaluate Research and Data
Attempt 2
Jamie Raines
Rasmussen College
HSA5000CBE Section 01CBE Scholarly Research and Writing
Caroline Gulbrandsen
9/1/2022
2
Research Question Evaluation
The Credibility of the Data
The research question integrated into this study is related to how artificial intelligence (AI)
integration in clinical radiology has the potential to disrupt the industry. According to Becker et al.
(2022), AI is strongly connected to the operations performed in clinical radiology for better test
results. The technology allows machines to achieve human-level performance while detecting
tumors during radiology tests. There are suitable improvements performed in the AI industry using
research that structures technical operations by machines to validate the Integration of AI
algorithms for patient care. The credibility of the data in the article is reliable since the authors
researched how healthcare professionals who have used artificial intelligence have promoted better
health management. The European Society of Radiology has accredited all the authors due to their
degrees and advanced educational levels (Becker et al., 2022). In the other article, the authors also
integrate credibility since workers have experience in hospitals and are at a university level of
education (Mulryan et al., 2022). Most research participants agreed that AI integration in radiology
information technology (IT) departments has promoted accuracy and reduced excess time for
setting up systems.
The next article focused on the use of AI for medical imaging, whereby it is clear that the
demand for AI is constantly progressing. According to Mulryan et al. (2022), the advancements in
AI have been adverse, causing some radiologists to develop a negative attitude towards the
industry that could potentially eliminate human jobs. AI has been found to simulate human brain
capacity, which does not get received well by professionals in healthcare settings. This indicates
more operations can get performed to validate AI operations since they are needed for system
management. The article's data was collected from journalists, radiologists, commercial
Robert Neuteboom
hold advanced degrees in a relevant field - is this what you mean?
Robert Neuteboom
Robert Neuteboom
presented as credible
Robert Neuteboom
Robert Neuteboom
Robert Neuteboom
As I mentioned in my comment on your previous submission, you need to use these terms separately in your evaluation. Saying that credibility is reliable does not describe either of these terms nor does it offer examples. Your claim about conducting research of existing studies would fall under credibility. You might also talk about the authors' credentials, employment, and affiliations -- anything that demonstrates trustworthiness. Reliability, conversely, has to do with consistency across items, time, and researchers, inter-rater alignment, and replicability ...
Artificial intelligence in healthcare revolutionizing personalized healthcare...Fit Focus Hub
Embark on a groundbreaking journey into the future of healthcare, where Artificial Intelligence (AI) is reshaping the landscape and ushering in a new era of personalized medicine tailored to the unique needs of each individual patient.
Explore the transformative power of AI as it becomes the catalyst for a healthcare revolution that goes beyond one-size-fits-all approaches.
In this illuminating exploration, we delve into how AI technologies are spearheading a paradigm shift in the delivery of healthcare services, putting patients at the center of attention.
Witness how machine learning algorithms analyze vast datasets, encompassing genetic information, medical histories, lifestyle choices, and environmental factors, to unlock insights that guide healthcare providers in crafting precise and personalized treatment plans.
Discover the pivotal role of AI in early disease detection, where predictive analytics and data-driven algorithms contribute to proactive interventions.
By identifying subtle patterns and potential risk factors, AI empowers healthcare professionals to intervene at the earliest stages, often before symptoms manifest, leading to more effective and targeted treatment strategies.
Explore the integration of wearable devices and IoT technologies, allowing for continuous patient monitoring beyond the confines of traditional healthcare settings.
AI-driven remote monitoring ensures real-time data analysis, enabling healthcare providers to make informed decisions and adjustments to individual care plans, promoting a proactive and patient-centric approach to healthcare.
Witness the acceleration of drug discovery and development through AI, as sophisticated algorithms analyze vast datasets to identify potential therapeutic targets and streamline the research and development process.
The result is a more efficient and tailored approach to pharmaceuticals, reducing trial-and-error methods and enhancing treatment outcomes.
Through captivating case studies and real-world examples, gain insights into how AI is optimizing resource allocation, improving patient engagement, and fostering a collaborative ecosystem between healthcare providers and patients.
Embrace the future of healthcare, where the marriage of human expertise and AI-driven insights paves the way for a more personalized, precise, and effective approach to individualized patient care.
Join us on this journey through the transformative impact of Artificial Intelligence in Healthcare, where the promise of personalized medicine becomes a reality, and each patient's unique characteristics guide the way towards a healthier and more tailored future.
Generative AI in Health Care a scoping review and a persoanl experience.Vaikunthan Rajaratnam
A scoping review of the literature, its impact and challenges in healthcare, and a personal experience of its application in practice, teaching, and research.
IMPACT OF ARTIFICIAL INTELLIGENCE ON THE AUTOMATION OF DIGITAL HEALTH SYSTEMijseajournal
Automating digital systems in healthcare plays a significant role in transforming the quality-of-care
services delivered to patients across the board. This role is anticipated to be accomplished by the
development and implementation of artificial intelligence in healthcare which has the potential to impact
the provision of healthcare services. This paper sought to investigate the impact of adopting and
implementing artificial intelligence on the automation of digital health systems within the different levels of
healthcare. The general objective of the research study was to investigate the impact of artificial
intelligence in the automation of digital health systems. The specific goals were to understand the concept
of artificial intelligence and how it automates digital strategies, to determine the AI systems that have been
developed and implemented in the healthcare systems, to establish the factors that influence the adoption
of AI in healthcare, and to find out the outcomes of implementing AI in digital health systems. The research
employed the descriptive research design. The study population included healthcare workers,
policymakers, IT specialists, and management teams in the healthcare sector in the State of Kentucky. The
sampling technique for the study was the purposive sampling technique. The study collected data using
semi-structured interviews administered through Google Teams and Zoom. Data analysis was analyzed
using the computer-assisted software for analyzing qualitative data, NVivo. The findings were that AI as a
technological concept has the potential to impact the automation of digital health systems and is key to
automating health services such as the diagnosis and treatment of illnesses and management of claims and
payments. The study recommended that policy supports the application of artificial intelligence in
healthcare, thus enabling the automation of several healthcare services and thus improving the delivery of
care.
Theory of Human Caring on APN Role Student PresentationWeb PageMikeEly930
Theory of Human Caring on APN Role Student Presentation
Web Page
Assignment Prompt
Explore the influence of Jean Watson’s Theory of Human Caring on your future role as an APN. The student will explore the concepts and Caritas processes from the Theory of Human Caring and present how these concepts may impact their future APN role.
Directions:
1. The student will create a PowerPoint and include speaker notes that may be added to the speaker note section on each slide.
2. The presentation should be limited to no more than 10 slides. See suggested slides below.
3. If you are unfamiliar with Dr. Watson's theory see this overview.
A suggested outline for the presentation may include the following slides:
Slide 1 - Introduction to yourself and future planned APN role and practice
Slide 2 - Previous experience with Watson’s Theory of Human Caring
Slide 3 - Core Concepts of the Theory Applicable to the APN role
Slide 4 - Core Concepts of the Theory Applicable to the APN role (as needed)
Slide 5 - Five Carative Factors or Caritas Processes You Plan to Use in the APN Role
Slide 6 - Five Carative Factors or Caritas Processes You Plan to Use in the APN Role (as needed)
Slide 7 - What Does the Theory of Human Caring Mean to You
Slide 8 - APN Implications of Theory of Human Caring
Slide 9 - Summary/Main Points
Slide 10 - Reference
Expectations
· Format: PPT Presentation with Speaker Notes
· Length: 10 Slides, maximum
· Plagiarism free.
· Turnitin receipt.
· Please reply to the two-discussion post below.
· APA Format with intext citation
· Each post must have two scholarly references
· 180-to-200-word count minimal
· Make it sound personal
Keyandra W
Discussion 1
Top of Form
Under the healthcare context, big data (BD) signifies immense volumes of data resulting from the adoption of digital tools that gather patients' data and help direct hospital performance. Globally, healthcare systems are increasingly facing incredible challenges due to disability and the aging population, patients' expectations, and increased technology use. The increasing use of BD can help clinicians meet these goals unprecedentedly. The potential of BD in the medical industry relies on the ability to turn high data volumes into actionable knowledge and detect patterns for decision-maker and precision medicine. The use of BD in healthcare contributes towards ensuring patients' safety in several contexts. Evidence bolsters that EHRs can become a vital tool for communication across healthcare teams and a valuable information hub when implemented well (Pastorino et al., 2019). However, the process towards the use of BD requires interdisciplinary collaboration and adapt performance and design of the systems. Additionally, the proliferating use of big data requires the healthcare teams to build technological infrastructure to invest in human capital and cover and house the massive volumes of medical care data to guide people into the novel frontier of health and wellbeing. The ...
A comprehensive study on disease risk predictions in machine learning IJECEIAES
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
Heath care projects need high level of investment, diverse set of stakeholders, and comply with rigorous federal and state regulations, and standards. In addition, project outcomes have direct impact on safety and well-being of patients. This speech focuses on challenges and opportunities in implementing Health care IT projects. Also discusses strategies to adopt agile methodologies in health care industry. Finally, highlights critical success factors in implementing Healthcare Projects successfully.
Learning Outcomes:
Understand characteristics of Healthcare projects
Learn challenges and opportunities in implementing Healthcare projects
Learn agile adoption strategies in Health IT
Learn and apply Critical Success Factors to improve project success
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers