This document describes a proposed mobile application called "ChooseLyf" that aims to provide efficient and effective healthcare assistance. The application includes features like a symptom checker, medication reminder, chatbot, and machine learning models to detect diseases like diabetes, heart disease, and Parkinson's disease. It analyzes user inputs and recommends treatments. The application is designed to make healthcare more accessible and convenient by offering these services on mobile devices.
apidays LIVE Singapore 2022_Analytics in Healthcare.pptxapidays
apidays LIVE Singapore 2022: Digitising at scale with APIs
April 20 & 21, 2022
Analytics in Healthcare: combating diabetes with data
Dr Nashya Haider, Founder & Director at Innotech Consultants
------------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Deep dive into the API industry with our reports:
https://www.apidays.global/industry-reports/
Subscribe to our global newsletter:
https://apidays.typeform.com/to/i1MPEW
AI Regulation Is Coming to Life Sciences: Three Steps to Take NowCognizant
To maximize the value of artificial intelligence and machine learning for patients, healthcare providers together with life sciences enterprises must gear up to meet the continually evolving regulatory landscape.
Presenter: Student
Institution: Grantham University
Date: July 2, 2020
ADOPTING TECHNOLOGY IN HEALTHCARE MANAGEMENT AND THE EFFECTS ON PATIENT OUTCOME
In this presentation you will be exposed to the following:
Problem statement and its current scope
Literature review
Strategic plan, who will benefit and what will the healthcare environment looks like once resolved
Recommendations/ limitations
References
CONTENTS OF THIS PRESENTATION
TABLE OF CONTENTS
PROBLEM STATEMENT
Current scope
LITERATURE REVIEW
Theoretical framework in which the problem exists
01
03
02
04
05
STRATEGIC PLAN
Implementation and benefits
RECOMMENDATION FOR FUTURE STUDY/LIMITATIONS
Social and political barriers to implementation
REFERENCES
Over 20 references with vast majority within five years.
COMMENTS
PROBLEM STATEMENT
01
Current Scope
Problem statement
Many healthcare professionals are less adoptive to technology advances, they are not up to date with new medical discoveries, performance measurements and decrease coordination with each other (Seblega 2010). These deficiencies resulted in the analysis of challenges that exists with technology adoption to include, costs, interoperability, outdated technology, difficulty in use of technology and complicated asset tracking and implementation.
Who are affected?
Practicioners, managers, employees, investors, patients and the economy on a whole
Demographics
Analysis done on the two selected countries of Nigeria and the United States both concluded that technology adoption in healthcare is linked to usefulness and ease of use of technology.
01 CONTINUES
History of problem
Discussion about the use of computers began in 1960s.
The possibility of electronic health records (EHR), were examined in 1991.
When did the problem appeared?
Since the discussion to use technology to enhance medical care
01 CONTINUES
CURRENT SCOPE
The challenges in health technology adoption is significant because despite the evolution of the society, the importance of these tools for modern technology to improve quality care outcomes and other elaborate benefits that are associated with it is limited (underutilized or low) because of factors to include financial concerns, poor infrastructure, low technical expertise and resistance from healthcare professionals (Zayyad 2018).
01 CONTINUES
What is currently being done?
The resistance experienced by both health professionals and patients soon decrease even because of the Coronavirus pandemic. This pandemic is a push factor towards medical technology adoption. Wicklund (2020), explained that the future of healthcare is now reshaped. The increase in the use of telemedicine is seen across the world as it helps in deciding which patients are to be seen in the hospital or elsewhere. This is believed that in order to prevent the spread of the virus patients must be isolated. In addition, there are technologies used to deal with Coronavirus namely symptom trackers, Chat.
AI in Healthcare Innovative use cases and applications.pdfmahaffeycheryld
AI in healthcare revolutionizes patient care by enhancing diagnostics, personalizing treatments, and improving operational efficiency. Machine learning algorithms analyze vast medical data to predict diseases, optimize treatment plans, and facilitate early intervention. AI-powered tools like chatbots and virtual assistants streamline administrative tasks, while advanced imaging analysis improves accuracy in detecting conditions like cancer. Integrating AI in healthcare not only accelerates research and drug development but also ensures better patient outcomes and reduced healthcare costs. Embrace the future of medicine with AI, driving innovation and transforming the healthcare landscape for a healthier, smarter tomorrow.
https://www.leewayhertz.com/ai-use-cases-in-healthcare/
Wilhide, Peeples, & Anthony Kouyate (2015) Evidence-Based mHealth Chronic Dis...Robin Anthony Kouyate, PhD
This document discusses the development of a framework for designing evidence-based mobile health (mHealth) apps to support chronic disease management. The framework was developed over two years through an iterative process of applying the framework to design mHealth apps for different diseases. The final framework includes 7 domains to guide app development: 3 strategic domains to identify value drivers, outcomes, and program objectives, 3 intervention domains to design clinical and behavioral interventions, and 1 domain focused on app features and content. The framework is intended to facilitate the systematic development of scalable, replicable mHealth interventions that can be evaluated for their effectiveness.
IRJET- Mobile Assisted Remote Healthcare ServiceIRJET Journal
This document describes a mobile application that provides remote healthcare services using data mining techniques. The application allows users to input symptoms and will then predict potential diseases, provide first aid recommendations, locate nearby hospitals, and book appointments with doctors. It is intended to help patients, health workers, and those in remote areas access healthcare services. The application protects patient privacy and includes access controls for data. It uses a knowledge-based model stored as a graph database to make diagnoses and recommendations by identifying relationships between diseases, symptoms, and information.
Artificial intelligence can help improve pharmacovigilance in three key ways:
1) AI can automate repetitive manual tasks like data entry of adverse event reports to improve efficiency.
2) Machine learning algorithms can be trained on large databases of adverse drug reaction reports to help identify new safety signals.
3) AI tools show promise in extracting clinically relevant information from individual case safety reports without human review, reducing the time spent on case processing. However, challenges remain around data quality, costs, and ensuring AI augmentation rather than replacement of human experts.
This document describes a proposed mobile application called "ChooseLyf" that aims to provide efficient and effective healthcare assistance. The application includes features like a symptom checker, medication reminder, chatbot, and machine learning models to detect diseases like diabetes, heart disease, and Parkinson's disease. It analyzes user inputs and recommends treatments. The application is designed to make healthcare more accessible and convenient by offering these services on mobile devices.
apidays LIVE Singapore 2022_Analytics in Healthcare.pptxapidays
apidays LIVE Singapore 2022: Digitising at scale with APIs
April 20 & 21, 2022
Analytics in Healthcare: combating diabetes with data
Dr Nashya Haider, Founder & Director at Innotech Consultants
------------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Deep dive into the API industry with our reports:
https://www.apidays.global/industry-reports/
Subscribe to our global newsletter:
https://apidays.typeform.com/to/i1MPEW
AI Regulation Is Coming to Life Sciences: Three Steps to Take NowCognizant
To maximize the value of artificial intelligence and machine learning for patients, healthcare providers together with life sciences enterprises must gear up to meet the continually evolving regulatory landscape.
Presenter: Student
Institution: Grantham University
Date: July 2, 2020
ADOPTING TECHNOLOGY IN HEALTHCARE MANAGEMENT AND THE EFFECTS ON PATIENT OUTCOME
In this presentation you will be exposed to the following:
Problem statement and its current scope
Literature review
Strategic plan, who will benefit and what will the healthcare environment looks like once resolved
Recommendations/ limitations
References
CONTENTS OF THIS PRESENTATION
TABLE OF CONTENTS
PROBLEM STATEMENT
Current scope
LITERATURE REVIEW
Theoretical framework in which the problem exists
01
03
02
04
05
STRATEGIC PLAN
Implementation and benefits
RECOMMENDATION FOR FUTURE STUDY/LIMITATIONS
Social and political barriers to implementation
REFERENCES
Over 20 references with vast majority within five years.
COMMENTS
PROBLEM STATEMENT
01
Current Scope
Problem statement
Many healthcare professionals are less adoptive to technology advances, they are not up to date with new medical discoveries, performance measurements and decrease coordination with each other (Seblega 2010). These deficiencies resulted in the analysis of challenges that exists with technology adoption to include, costs, interoperability, outdated technology, difficulty in use of technology and complicated asset tracking and implementation.
Who are affected?
Practicioners, managers, employees, investors, patients and the economy on a whole
Demographics
Analysis done on the two selected countries of Nigeria and the United States both concluded that technology adoption in healthcare is linked to usefulness and ease of use of technology.
01 CONTINUES
History of problem
Discussion about the use of computers began in 1960s.
The possibility of electronic health records (EHR), were examined in 1991.
When did the problem appeared?
Since the discussion to use technology to enhance medical care
01 CONTINUES
CURRENT SCOPE
The challenges in health technology adoption is significant because despite the evolution of the society, the importance of these tools for modern technology to improve quality care outcomes and other elaborate benefits that are associated with it is limited (underutilized or low) because of factors to include financial concerns, poor infrastructure, low technical expertise and resistance from healthcare professionals (Zayyad 2018).
01 CONTINUES
What is currently being done?
The resistance experienced by both health professionals and patients soon decrease even because of the Coronavirus pandemic. This pandemic is a push factor towards medical technology adoption. Wicklund (2020), explained that the future of healthcare is now reshaped. The increase in the use of telemedicine is seen across the world as it helps in deciding which patients are to be seen in the hospital or elsewhere. This is believed that in order to prevent the spread of the virus patients must be isolated. In addition, there are technologies used to deal with Coronavirus namely symptom trackers, Chat.
AI in Healthcare Innovative use cases and applications.pdfmahaffeycheryld
AI in healthcare revolutionizes patient care by enhancing diagnostics, personalizing treatments, and improving operational efficiency. Machine learning algorithms analyze vast medical data to predict diseases, optimize treatment plans, and facilitate early intervention. AI-powered tools like chatbots and virtual assistants streamline administrative tasks, while advanced imaging analysis improves accuracy in detecting conditions like cancer. Integrating AI in healthcare not only accelerates research and drug development but also ensures better patient outcomes and reduced healthcare costs. Embrace the future of medicine with AI, driving innovation and transforming the healthcare landscape for a healthier, smarter tomorrow.
https://www.leewayhertz.com/ai-use-cases-in-healthcare/
Wilhide, Peeples, & Anthony Kouyate (2015) Evidence-Based mHealth Chronic Dis...Robin Anthony Kouyate, PhD
This document discusses the development of a framework for designing evidence-based mobile health (mHealth) apps to support chronic disease management. The framework was developed over two years through an iterative process of applying the framework to design mHealth apps for different diseases. The final framework includes 7 domains to guide app development: 3 strategic domains to identify value drivers, outcomes, and program objectives, 3 intervention domains to design clinical and behavioral interventions, and 1 domain focused on app features and content. The framework is intended to facilitate the systematic development of scalable, replicable mHealth interventions that can be evaluated for their effectiveness.
IRJET- Mobile Assisted Remote Healthcare ServiceIRJET Journal
This document describes a mobile application that provides remote healthcare services using data mining techniques. The application allows users to input symptoms and will then predict potential diseases, provide first aid recommendations, locate nearby hospitals, and book appointments with doctors. It is intended to help patients, health workers, and those in remote areas access healthcare services. The application protects patient privacy and includes access controls for data. It uses a knowledge-based model stored as a graph database to make diagnoses and recommendations by identifying relationships between diseases, symptoms, and information.
Artificial intelligence can help improve pharmacovigilance in three key ways:
1) AI can automate repetitive manual tasks like data entry of adverse event reports to improve efficiency.
2) Machine learning algorithms can be trained on large databases of adverse drug reaction reports to help identify new safety signals.
3) AI tools show promise in extracting clinically relevant information from individual case safety reports without human review, reducing the time spent on case processing. However, challenges remain around data quality, costs, and ensuring AI augmentation rather than replacement of human experts.
1) The document discusses how technologies like machine learning, artificial intelligence, big data analytics, and natural language processing can be used to better engage patients and improve their healthcare experiences.
2) These technologies allow for more personalized care by understanding individual patients' needs and preferences. They also enable remote monitoring and participation in clinical trials to reduce barriers to care.
3) Challenges include overcoming risk-averse tendencies within healthcare and building patient trust, but these technologies overall have the potential to enhance patient safety, outcomes, and satisfaction if implemented effectively.
Implementation of Artificial Intelligence Health Technologies & HTA.pptxMarina Ibrahim
This document discusses the implementation of artificial intelligence health technologies and health technology assessment. It defines AI and HTA, describes how AI can help address some HTA challenges and outlines five dimensions to consider for AI health technologies. Applications of AI in healthcare are explained and the technological, clinical, human, professional, economic, and ethical challenges of AI are outlined. The benefits and limitations of AI are also summarized. A case study on an AI-based decision support system for multiple sclerosis is presented and the document concludes that evaluations of AI must address its role in transforming health systems.
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.
This document summarizes an industrial training report on developing a Smart Reminder App. The report acknowledges those who provided guidance and support for the project. It then provides background on the company where the training took place, Agile Softech Pvt. Ltd., which develops customized software solutions. The report abstract introduces the Smart Reminder App, which allows patients to set medication alarms and search for doctors. Finally, the document discusses system analysis conducted for the app, including identifying user needs, feasibility analysis, and technical requirements.
Medic - Artificially Intelligent System for Healthcare Services ...IRJET Journal
This document describes an artificially intelligent system called Medic that aims to provide healthcare services using artificial intelligence technologies. Medic uses natural language processing, fuzzy logic, deep learning and a knowledge base to diagnose diseases from patients' descriptions of their symptoms. It can also recommend medical tests and prescriptions. The system architecture includes interfaces for patients and doctors, a central database, and image recognition and decision making modules. Convolutional neural networks are used for image-based disease identification. The goal of Medic is to make healthcare more accessible and affordable by providing services remotely using artificial intelligence.
BPI 601 – Assignment #4 – Process Analysis and Design - EPC ModeliVannaSchrader3
BPI 601 – Assignment #4 – Process Analysis and Design - EPC Modeling
Using ARIS Business Architect you are to create the TO-BE EPC Process model representing the pre-registration process for Process University as detailed below. Indicate the roles for each activity.
Process Description:
Currently (AS-IS), the Graduate pre-registration process is as follows for Process University: – the goal is to have students select appropriate classes for the next semester as efficiently as possible without jeopardizing the business rules and prerequisite rules of the University. These rules are as follows:
· A student is not allowed to preregister until they have been officially accepted into the program.
· If a student has an outstanding bill with the university they will not be able to preregister.
· The Student is allowed to meet with a graduate adviser to advise course selection (even if they have an outstanding bill). (it is the student’s choice to do so)
· Students must adhere to the prerequisites of classes – therefore each course selected must be checked to see if the student has completed the required prerequisites of each course pre-selected. Courses will not be approved until course prerequisites are satisfied. In rare cases the student may get approval from administration if they do not have the appropriate prerequisites.
· Once a course is approved, a seat in that course is assigned to the student if there are still seats available (if course is not closed) and the course is added to the roster of the student.
· If a course is closed, the student may request a meeting with a graduate adviser to see if the student is eligible to be added into the course.
· If a course is closed and the student does not wish to take further action, or the student was denied further action; the student is asked to select another course.
· If the student opts not to complete the preregistration, the registrar (administration) and the student’s adviser are notified.
(TO-BE) Process University will be implementing on-line pre-registration in the near future. Design and model the TO-BE process with this in mind, however all the business rules listed above still must be maintained in addition to the new rule below.
· A student is prompted and asked if they would like to register for another course, once the student has indicated they do not want to register for any more courses OR they have registered for the maximum number of courses (which is four), the system will print a list of courses they registered for.
Requirements:
1. Using the EPC modeling methodology, design the TO-BE process model for the scenario described above. ** You MUST have at least one loop in this process
Running head: Artificial Intelligence & Robotics used in the 21st century
1
Artificial Intelligence & Robotics used in the 21st century
2
Artificial Intelligence & Robotics used in the 21st century
Christopher Slaton_CS698_IP2.doc
Colorado Technical University
T ...
Integrating openIMIS in the Undergraduate and Postgraduate Medical CurriculumIris Thiele Isip-Tan
The document discusses integrating the openIMIS health insurance management system into medical education curricula. It provides examples of:
- Courses that teach openIMIS skills and how it relates to clinical practice and health insurance processes
- Expectations that students will be able to use openIMIS, describe its functions, and uphold ethics in medical informatics
- Ways openIMIS data could inform research, health technology decisions, and provider payment models under universal health coverage
The document suggests openIMIS training should not just focus on software navigation, but also use scenarios to discuss privacy, ethics and how openIMIS data relates to broader health issues.
Designing medical devices that meet the needs of patients, healthcare providers, and the broader healthcare system requires a thorough understanding of human factors and usability principles. In this presentation, we'll explore the application of these principles to medical device design.
The document summarizes eHealth services being developed by European projects OASIS and REMOTE to improve quality of life for elderly people. The services include (1) monitoring health conditions of elderly people at home and on the move, (2) defining and personalizing health profiles, and (3) providing health coaching, remote monitoring, alerts and assistance applications. Stakeholders like elderly users, relatives, and caregivers are involved. The services use biomedical sensors and are being validated in pilots across Europe cities.
This document summarizes a research paper on developing a cloud-based health prediction system. The system allows users to enter their health issues and details like weight and height online. It then provides an accurate health prediction by matching the user's data to an analysis database. The cloud-based system is designed to be user-friendly and accessible from anywhere at any time. It aims to help users identify potential health problems early without visiting a doctor. The system architecture uses HTML, CSS, JavaScript, PHP and a MySQL database. It flows user data through registration, selecting health details, and logout for security.
Ligia Alexandra Gaspar - bachelor thesis in collaboration with OAMK Digital Patient project I developed a prototype of a health application that contains 3D human body modeling and is inspired from doctor Peter D'Adamo's work on nutrition and blood types
The document discusses findings from a study on consumer and expert attitudes toward artificial intelligence (AI) and its intersection with longevity. The study surveyed consumers on their knowledge and views of implementing AI in various areas of life, such as life/work/care, financial planning, community/infrastructure, healthcare/caregiving, and the workplace. It also surveyed experts for their perspectives. Key findings include: consumers see benefits of AI as outweighing risks but also have uncertainties; perceptions of AI's benefits and risks vary across demographic groups and application areas; and experts see great potential for AI to enhance health outcomes and reduce human errors in medicine.
Mental Health Chatbot System by Using Machine LearningIRJET Journal
This document discusses the development of a mental health chatbot system using machine learning. The proposed chatbot would provide mental health services through a chat feature, voice input options in multiple languages, and a mood recommendation tool. Natural language processing and neural networks would be used to train the chatbot to understand language and respond appropriately. The goal is to make affordable and accessible mental healthcare available to more people.
Collective intelligence in healthcare can help address system challenges.
The Health Consensus system gathers professionals' input using different methods to reach consensus on issues like assessing health plans, selecting quality indicators, and training.
Participants perceive the process as efficient and that their involvement provides value to useful and relevant contributions.
This document discusses the use of artificial intelligence and machine learning techniques for chronic disease detection and management. It provides background on chronic diseases and their impact globally. It then discusses how machine learning algorithms can be used to analyze medical data from electronic health records to predict chronic diseases and suggest treatments. Various studies that have developed models using techniques like decision trees, neural networks, and random forests to detect diseases like cancer, kidney disease and diabetes are summarized. The ability of artificial intelligence to help diagnose chronic diseases earlier and improve healthcare management is also mentioned.
2016 IBM Interconnect - medical devices transformationElizabeth Koumpan
Emerging technologies such as Internet of Things, 3D Printing are driving the creation of new business models and forcing the Industry for transformation. The product centric model where the Industry main objective was to develop the device, is moving to software and services model, with the focus on Big Data & Analytics, Integration and Cloud.
The maturation of technologies such as social, mobile, analytics, cloud, 3D printing, bio- and nanotechnology are rapidly shifting the competitive landscape. These emerging technologies create an environment that is connected and open, simple and intelligent, fast and scalable. Organizations must embrace disruptive technologies to drive innovation
Developing a World Leading Technology Enabled Health Programme of ResearchMaged N. Kamel Boulos
The document discusses developing a world-leading technology-enabled health research program by linking research to the real world. It notes current issues like the "mHealth app glut" and declining user interest due to a supply-demand mismatch. The proposed solution is to establish a partnership that brings together stakeholders from academia, healthcare providers, digital health industry, and the public. This partnership would use agile design methods, early and continuous user involvement, and evaluation approaches suited to digital interventions to develop solutions that meet real-world needs and ensure user acceptance. The goal is sustainable digital health programs through full engagement of stakeholders throughout the product lifecycle.
Survey On Machine Learning Based Patient Monitoring Algorithm Using Oxygen Sa...IRJET Journal
This document describes a study on developing a machine learning-based patient monitoring algorithm using oxygen saturation (SpO2) levels. The proposed system would continuously monitor a patient's temperature and SpO2 using IoT devices and sensors. It would then construct a machine learning model to predict patient severity and regularly upload the data to a private server. This would allow doctors to remotely monitor patients' conditions without them needing to stay in the hospital. The system aims to reduce risks to patients' lives and limit healthcare worker exposure by enabling early detection and monitoring of health issues.
Unleashing the Power of Data: Enhancing Physician Outreach through Machine Le...IRJET Journal
This document discusses how machine learning can enhance physician outreach efforts. It begins by outlining limitations of traditional outreach methods like mailings and calls. The document then explores how machine learning techniques like predictive modeling, recommender systems, natural language processing, and real-time analytics can optimize physician targeting, personalize communication, automate processes, and assess effectiveness. However, it notes challenges like data quality issues, privacy concerns, and physician resistance must be addressed. Case studies demonstrate benefits of customization, targeted communication, and increased referrals through machine learning-powered outreach.
Investigating Assisting Mental Health Condition using Sentiment Analysis thro...IRJET Journal
This document presents a proposed Android application to assist those with mental health conditions using sentiment analysis through natural language processing (NLP). The application would allow users to sign up, take personality tests, track their mental state by writing daily thoughts in a diary with machine learning used to predict mood, chat with other users and mental health professionals, and have one-on-one video conversations with professionals. The goal is to provide accessible remote mental health support for all, as seeking treatment remains stigmatized in some societies and remote areas lack facilities. The proposed system uses technologies like NLP, machine learning, chatbots and video calls to analyze language and facilitate communication within the application.
This document summarizes an invited talk given by Dr. Aladdin Ayesh on artificial intelligence topics. The talk covered definitions of AI, major AI fields like machine learning, planning, natural language processing and computer vision. It also discussed applications of AI such as intelligent interfaces, personalization, smart services and analytics. Throughout the talk, examples and potential future directions were provided for different AI topics.
Más contenido relacionado
Similar a User-Centric AI Analytics for Chronic Health Conditions Management
1) The document discusses how technologies like machine learning, artificial intelligence, big data analytics, and natural language processing can be used to better engage patients and improve their healthcare experiences.
2) These technologies allow for more personalized care by understanding individual patients' needs and preferences. They also enable remote monitoring and participation in clinical trials to reduce barriers to care.
3) Challenges include overcoming risk-averse tendencies within healthcare and building patient trust, but these technologies overall have the potential to enhance patient safety, outcomes, and satisfaction if implemented effectively.
Implementation of Artificial Intelligence Health Technologies & HTA.pptxMarina Ibrahim
This document discusses the implementation of artificial intelligence health technologies and health technology assessment. It defines AI and HTA, describes how AI can help address some HTA challenges and outlines five dimensions to consider for AI health technologies. Applications of AI in healthcare are explained and the technological, clinical, human, professional, economic, and ethical challenges of AI are outlined. The benefits and limitations of AI are also summarized. A case study on an AI-based decision support system for multiple sclerosis is presented and the document concludes that evaluations of AI must address its role in transforming health systems.
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.
This document summarizes an industrial training report on developing a Smart Reminder App. The report acknowledges those who provided guidance and support for the project. It then provides background on the company where the training took place, Agile Softech Pvt. Ltd., which develops customized software solutions. The report abstract introduces the Smart Reminder App, which allows patients to set medication alarms and search for doctors. Finally, the document discusses system analysis conducted for the app, including identifying user needs, feasibility analysis, and technical requirements.
Medic - Artificially Intelligent System for Healthcare Services ...IRJET Journal
This document describes an artificially intelligent system called Medic that aims to provide healthcare services using artificial intelligence technologies. Medic uses natural language processing, fuzzy logic, deep learning and a knowledge base to diagnose diseases from patients' descriptions of their symptoms. It can also recommend medical tests and prescriptions. The system architecture includes interfaces for patients and doctors, a central database, and image recognition and decision making modules. Convolutional neural networks are used for image-based disease identification. The goal of Medic is to make healthcare more accessible and affordable by providing services remotely using artificial intelligence.
BPI 601 – Assignment #4 – Process Analysis and Design - EPC ModeliVannaSchrader3
BPI 601 – Assignment #4 – Process Analysis and Design - EPC Modeling
Using ARIS Business Architect you are to create the TO-BE EPC Process model representing the pre-registration process for Process University as detailed below. Indicate the roles for each activity.
Process Description:
Currently (AS-IS), the Graduate pre-registration process is as follows for Process University: – the goal is to have students select appropriate classes for the next semester as efficiently as possible without jeopardizing the business rules and prerequisite rules of the University. These rules are as follows:
· A student is not allowed to preregister until they have been officially accepted into the program.
· If a student has an outstanding bill with the university they will not be able to preregister.
· The Student is allowed to meet with a graduate adviser to advise course selection (even if they have an outstanding bill). (it is the student’s choice to do so)
· Students must adhere to the prerequisites of classes – therefore each course selected must be checked to see if the student has completed the required prerequisites of each course pre-selected. Courses will not be approved until course prerequisites are satisfied. In rare cases the student may get approval from administration if they do not have the appropriate prerequisites.
· Once a course is approved, a seat in that course is assigned to the student if there are still seats available (if course is not closed) and the course is added to the roster of the student.
· If a course is closed, the student may request a meeting with a graduate adviser to see if the student is eligible to be added into the course.
· If a course is closed and the student does not wish to take further action, or the student was denied further action; the student is asked to select another course.
· If the student opts not to complete the preregistration, the registrar (administration) and the student’s adviser are notified.
(TO-BE) Process University will be implementing on-line pre-registration in the near future. Design and model the TO-BE process with this in mind, however all the business rules listed above still must be maintained in addition to the new rule below.
· A student is prompted and asked if they would like to register for another course, once the student has indicated they do not want to register for any more courses OR they have registered for the maximum number of courses (which is four), the system will print a list of courses they registered for.
Requirements:
1. Using the EPC modeling methodology, design the TO-BE process model for the scenario described above. ** You MUST have at least one loop in this process
Running head: Artificial Intelligence & Robotics used in the 21st century
1
Artificial Intelligence & Robotics used in the 21st century
2
Artificial Intelligence & Robotics used in the 21st century
Christopher Slaton_CS698_IP2.doc
Colorado Technical University
T ...
Integrating openIMIS in the Undergraduate and Postgraduate Medical CurriculumIris Thiele Isip-Tan
The document discusses integrating the openIMIS health insurance management system into medical education curricula. It provides examples of:
- Courses that teach openIMIS skills and how it relates to clinical practice and health insurance processes
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User-Centric AI Analytics for Chronic Health Conditions Management
1. User-Centric AI Analytics for Chronic Health
Conditions Management
Professor Aladdin Ayesh
School of Natural and Computing Sciences
University of Aberdeen
Aberdeen, UK
aladdin.ayesh@abdn.ac.uk
https://orcid.org/0000-0002-5883-6113
Keynote talk at IEEE Conference on Intelligent Methods,
Systems, and Applications (IMSA), Cairo, Egypt,
July 2023
2. User-Centric AI Analytics for Chronic Health Conditions Management
Outline
Introduction
Chronic Health Conditions
Nutrition Related
Physiological Related
Mental Related
Managing Chronic Conditions
In educational context
Ageing population and smart cities context
Personalising AI Models
What is next?
Conclusion
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3. User-Centric AI Analytics for Chronic Health Conditions Management
Introduction
Introduction
The use of AI analytics in health applications has seen a rapid
growth in recent years. This is helped by advances in two areas:
▶ advances in sensor technologies including the wide spread use
of wearable devices, e.g. smart watches, which enable
continuous data gathering with minimum disruption to daily
life.
▶ advances in data analytic algorithms, e.g. deep learning, and
high performance computers that allowed the processing of
large datasets at speed that was not imaginable a decade ago.
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4. User-Centric AI Analytics for Chronic Health Conditions Management
Introduction
Introduction
In this talk, we look at AI analytics use in managing chronic health
conditions with examples from my team projects.
Focus on the challenges in managing these conditions due to the
variations in individual circumstances.
These variations directed the research into user-centric approach
leading to variety of research questions. In the following sections,
we explore examples from our research work in this area.
Conclude with what, in our opinion, to be the next steps and
some remaining open research questions.
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5. User-Centric AI Analytics for Chronic Health Conditions Management
Chronic Health Conditions
Chronic Health Conditions
Chronic health conditions can cover a wide range of diseases and
disorders, which share some common characteristics:
▶ improvement in treatments and health care turned these
diseases from terminal or seriously impairing to manageable
conditions.
▶ require active management plans to control their effects and
to maintain quality of life for the sufferer.
▶ their impact and re-occurring manifestation may differ from
person to person depending on several factors including
personal and environmental.
▶ have great psychological and emotional impact on their
sufferers with a wide spectrum of variations.
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6. User-Centric AI Analytics for Chronic Health Conditions Management
Chronic Health Conditions
Nutrition Related
Nutrition Related
There is a growing host of applications aimed towards managing
nutrition related chronic health conditions, e.g. Diabetes Type 2.
However, many of these applications lack:
▶ Sufficient nutritional research limiting their scientific
validation for long term effectiveness.
▶ comprehensive user models for personalisation, applications
are often limited to preference models if any.
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7. User-Centric AI Analytics for Chronic Health Conditions Management
Chronic Health Conditions
Nutrition Related
Nutrition Related
A key challenge in this area is capturing the right amount and type
of data from users and environment with minimum disruption to
daily life.
Techniques we have been exploring combine the use of
gamification, wearable sensors, and self-reporting, in addition to
nutritional dictionaries. The current work we are doing in this area
is still at early stages and follows multiple streams.
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8. User-Centric AI Analytics for Chronic Health Conditions Management
Chronic Health Conditions
Physiological Related
Physiological Related
Other physiological health conditions may require more user-centric
approaches to fuse multi-modal data and provide explanations [1].
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9. User-Centric AI Analytics for Chronic Health Conditions Management
Chronic Health Conditions
Mental Related
Mental Related
There is a growing recognition of the potential impact of AI
technologies on mental health [2, 3, 4] and well-being [5]. If this
impact is well managed, it has a huge potential in addressing a
large number of mental health issues and improving quality of life
for millions on a daily basis.
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10. User-Centric AI Analytics for Chronic Health Conditions Management
Managing Chronic Conditions
Managing Chronic Conditions
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11. User-Centric AI Analytics for Chronic Health Conditions Management
Managing Chronic Conditions
In educational context
In educational context
Educational systems have been in use for years and became
essential requirement during the COVID-19 pandemic.
Their wide spread use gives us the opportunities to address variety
of conditions and AI algorithms that can be generalised beyond the
original purpose of complementing educational systems
For example:
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12. User-Centric AI Analytics for Chronic Health Conditions Management
Managing Chronic Conditions
In educational context
In educational context
Stress
Emotional stress is a common condition impact technology users.
Some acute forms can be observed during the use of educational
systems [2] which enabled us to provide adaptive mechanisms by
which the system responds to the user needs and reduce the
temporary stress. Our approach combined quantitative and
qualitative data analytics informing a rule-based system to perform
the adaptation.
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13. User-Centric AI Analytics for Chronic Health Conditions Management
Managing Chronic Conditions
In educational context
In educational context
Dyslexia
Dyslexia is another condition that often impact student
performance both in class rooms and while using online
educational systems [3]. For this condition, a more sophisticated
optical sensor system, i.e. eye-gazing goggles, were necessary to
assess student’s motivation while using online educational systems.
The eye tracking data was combined with other sensory readings,
namely EEG [6], to develop a framework that enables personalised
services in the educational context.
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14. User-Centric AI Analytics for Chronic Health Conditions Management
Managing Chronic Conditions
Ageing population and smart cities context
Ageing population and smart cities context
Another context for chronic health conditions management is
assistive technologies that are becoming ever more necessary with
ageing populations and the emergent of smart cities. Two relevant
areas, which are currently focal topics are empathetic technologies
and neural interfaces. There are multiple IEEE standards working
groups developing standards and recommended practices in these
two areas, e.g. [5] and [1].
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15. User-Centric AI Analytics for Chronic Health Conditions Management
Personalising AI Models
Personalising AI Models
We adopted the principle of personalisation in exploring and
developing AI models [4, 7] and not just the systems developed or
enabled by AI.
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16. User-Centric AI Analytics for Chronic Health Conditions Management
Personalising AI Models
Personalising AI Models
So when machine learning and analytic algorithms are developed
and trained,
⋆ more personal factors would have been taken into consideration ⋆
=⇒ user-centric and individualised learned models
=⇒ produce more relatable results from a user perspective than
focusing on abstract metrics.
We found that the need for such personalised AI models is even
more evident in the context of chronic health conditions.
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17. User-Centric AI Analytics for Chronic Health Conditions Management
Personalising AI Models
Personalising AI Models
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18. User-Centric AI Analytics for Chronic Health Conditions Management
What is next?
What is next?
Integrated Framework
▶ Developing an integrated framework for mental and physical
health monitoring.
▶ Combining the various sensors already used in existing
wearable and other domestic devices.
▶ User specific customisable framework.
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19. User-Centric AI Analytics for Chronic Health Conditions Management
What is next?
What is next?
Evaluation Methods
▶ Investigating better metrics and evaluation methods in
evaluating machine learning and analytic algorithms especially
for personalised AI models and User-Centric AI applications.
▶ Integrating aspects of explainable and responsible AI to be
embedded in practical applications to provide a better
oversight of algorithmic AI.
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20. User-Centric AI Analytics for Chronic Health Conditions Management
Conclusion
Conclusion
In this talk, we have presented briefly:
▶ the personalisation requirements and potential benefits of AI
technologies in managing chronic health conditions.
▶ the case for more personalised AI models to take into
consideration the fine differences in personal and
environmental factors
We conclude with some still open research questions and our
current and immediate future research plans in this area.
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21. User-Centric AI Analytics for Chronic Health Conditions Management
Conclusion
Open Research Questions
Still open questions:
▶ explainable AI from deep learning and feature selection
algorithms to allow processing of big data and multimodal
sensors
▶ responsible AI including privacy and well-being impact
▶ machine learning algorithms metrics and evaluation methods
especially for personalised AI models.
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22. User-Centric AI Analytics for Chronic Health Conditions Management
Conclusion
Current Research Plans
Our current research plans in this area is to focus on
▶ Nutrition related, e.g. diabetes and obesity
▶ Alzheimer’s
Our research plans for the near future is to focus on
▶ Well-being and mental conditions
▶ Cardiovascular, e.g. heart conditions, blood pressure, etc.
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23. User-Centric AI Analytics for Chronic Health Conditions Management
Conclusion
Selected References
D. Zapala, A. Hossaini, M. Kianpour, G. Sahonero-Alvarez, and A. Ayesh, “A functional bci model by the p2731 working group:
psychology,” Brain-Computer Interfaces, vol. 0, no. 0, pp. 1–10, 2021.
Y. M. Lim, A. Ayesh, and M. Stacey, “Continuous stress monitoring under varied demands using unobtrusive devices,”
International Journal of Human-Computer Interaction, vol. 36, no. 4, pp. 326–340, 2020.
R. Wang, L. Chen, I. Solheim, T. Schulz, and A. Ayesh, “Conceptual motivation modeling for students with dyslexia for enhanced
assistive learning,” in Proceedings of the 2017 ACM Workshop on Intelligent Interfaces for Ubiquitous and Smart Learning,
SmartLearn ’17, (New York, NY, USA), pp. 11–18, ACM, 2017.
A. Ayesh, M. Arevalillo-Herráez, and P. Arnau-González, “Class discovery from semi-structured eeg data for affective computing
and personalisation,” (Oxford, UK), pp. 96–101, IEEE, 2017.
D. Schiff, A. Ayesh, L. Musikanski, and J. C. Havens, “Ieee 7010: A new standard for assessing the well-being implications of
artificial intelligence,” in IEEE SMC2020 Special Session on Human Well-Being in the Context of Autonomous and Intelligent
Systems, 2020.
R. Wang, L. Chen, and A. Ayesh, “Multimodal motivation modelling and computing towards motivationally intelligent e-learning
systems,” CCF Transactions on Pervasive Computing and Interaction, 2022.
A. Ayesh, M. Arevalillo-Herráez, and F. J. Ferri, “Towards psychologically based personalised modelling of emotions using
associative classifiers,” International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), vol. 10, no. 2, pp. 52–64,
2016.
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24. User-Centric AI Analytics for Chronic Health Conditions Management
Conclusion
Q & A
Acknowledgement
▶ My thanks to the IMAS conference organizing chairs and
committees for giving me this opportunity to share my work
with everyone here today and for their hard work in making
this event a success.
▶ My thanks also go to The IEEE Computer Society
Distinguished Visitors Program (DVP) for providing the
platform to connect researchers and speakers worldwide.
▶ Finally my thanks and gratitude to all of you for attending
and for your interest.
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25. User-Centric AI Analytics for Chronic Health Conditions Management
Conclusion
Q & A
Any Questions?
Contact
Please do not hesitate in contacting me:
aladdin.ayesh@abdn.ac.uk
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