We present a cognitive-based semantic approach that uses rule-based Natural Language Processing (NLP) in conjunction with a world model and cognitive frames to semantically analyze, understand, and rank digital text in search engines. The goal is to improve the relevance, accuracy, and efficiency of information search. The world model represents things existing in the real world (e.g., subject-related ontologies or classifications essential for understanding the topics to be analyzed) whereas cognitive frames specify possible users’ interactions with the world, including things that people should know or do (e.g., tasks, methods, procedures, cognitive processes) in such interactions. Using a rule-based semantic approach in conjunction with a subject-related world model and task-relevant cognitive frames to understand and evaluate text is innovative approach in search engine technology. It addresses three limitations of the existing approaches: the inadequate measure of the meaningful content in web pages; a poor understanding of users’ intention and tasks in their search and, the irrelevance and inaccuracy of search results. This method has led to the successful implementation of a full-scale semantic search engine in medicine (available at Seenso.com). The method is applicable and adaptable to other disciplines and other types of computer applications.
Artificial intelligence in Health CareMuhammedIyas
This technical seminar presentation provides an overview of artificial intelligence in healthcare. It introduces artificial intelligence and how it is classified. It also discusses how AI technologies like machine learning, machine vision, and natural language processing are being used in healthcare for applications such as disease prediction, drug manufacturing, treatment decision-making, and surgery. The presentation highlights advantages of AI in healthcare like more accurate disease identification, lower treatment costs, and reduced errors. It also notes challenges around training, adoption, regulations, and security.
Bio IT World 2019 - AI For Healthcare - Simon Taylor, LucidworksLucidworks
1) An AI system implemented at Johns Hopkins Hospital helped optimize hospital operations and bed assignment. It allowed beds to be assigned 30% faster.
2) This reduced the need to keep surgery patients in recovery rooms longer than necessary by 80% and cut wait times for ER patients to receive beds by 20%.
3) The efficiencies also allowed the hospital to accept 60% more transfer patients from other hospitals.
5 Powerful Real World Examples Of How AI Is Being Used In HealthcareBernard Marr
Healthcare can be transformed with the innovation and insights of artificial intelligence and machine learning. From robot-assisted surgery to virtual nursing assistants, diagnosing conditions, facilitating workflow and analyzing images, AI and machines can help improve outcomes for patients and lower costs for providers.
The document discusses the role of artificial intelligence in healthcare. It describes various aspects of AI including machine learning, knowledge engineering, robotics, and machine perception. It notes that AI has great potential to improve healthcare by helping address issues like workforce shortages and rising patient needs as populations age. However, successfully integrating AI into healthcare systems faces challenges like overcoming technical and regulatory limitations, addressing ethical concerns, and ensuring AI is used to augment rather than replace human professionals. Overall, the document presents an overview of AI in healthcare, its opportunities and challenges.
Artificial intelligence is disrupting healthcare in several ways:
- AI is improving disease prediction, customized medicine development, and other areas of human biology.
- The growth of AI in healthcare is driven by factors like increased funding, demand for precision medicine, and cost reductions, allowing for more accurate and early disease diagnosis.
- However, some end users are reluctant to adopt AI healthcare technologies due to lack of trust and potential risks, though AI also offers opportunities to improve outcomes for patients and in emerging markets.
The document discusses how artificial intelligence can help address challenges posed by infectious diseases. It describes how AI uses past disease data to predict outbreaks, and how algorithms created from behavioral and epidemiological data can help target prevention efforts. The document also outlines several successes of AI in predicting disease outbreaks like dengue fever in advance. Overall, the document advocates that AI has great potential to help monitor infectious diseases and facilitate more proactive public health responses if its tools are developed and applied effectively.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
This document summarizes a workshop presentation on AI in healthcare. It begins by discussing the hype around AI and how it has not yet delivered many results. It then outlines some challenges to using AI in healthcare like a lack of understanding of what AI can do, poor implementation strategies, and a shortage of trained workforce. The objectives of the workshop are then stated as understanding AI's real potential and how to invest wisely. Various AI technologies like machine learning, natural language processing, and voice technology are described. Key requirements for successful AI include understanding its limitations and developing a strategy to bring real value.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Artificial intelligence in Health CareMuhammedIyas
This technical seminar presentation provides an overview of artificial intelligence in healthcare. It introduces artificial intelligence and how it is classified. It also discusses how AI technologies like machine learning, machine vision, and natural language processing are being used in healthcare for applications such as disease prediction, drug manufacturing, treatment decision-making, and surgery. The presentation highlights advantages of AI in healthcare like more accurate disease identification, lower treatment costs, and reduced errors. It also notes challenges around training, adoption, regulations, and security.
Bio IT World 2019 - AI For Healthcare - Simon Taylor, LucidworksLucidworks
1) An AI system implemented at Johns Hopkins Hospital helped optimize hospital operations and bed assignment. It allowed beds to be assigned 30% faster.
2) This reduced the need to keep surgery patients in recovery rooms longer than necessary by 80% and cut wait times for ER patients to receive beds by 20%.
3) The efficiencies also allowed the hospital to accept 60% more transfer patients from other hospitals.
5 Powerful Real World Examples Of How AI Is Being Used In HealthcareBernard Marr
Healthcare can be transformed with the innovation and insights of artificial intelligence and machine learning. From robot-assisted surgery to virtual nursing assistants, diagnosing conditions, facilitating workflow and analyzing images, AI and machines can help improve outcomes for patients and lower costs for providers.
The document discusses the role of artificial intelligence in healthcare. It describes various aspects of AI including machine learning, knowledge engineering, robotics, and machine perception. It notes that AI has great potential to improve healthcare by helping address issues like workforce shortages and rising patient needs as populations age. However, successfully integrating AI into healthcare systems faces challenges like overcoming technical and regulatory limitations, addressing ethical concerns, and ensuring AI is used to augment rather than replace human professionals. Overall, the document presents an overview of AI in healthcare, its opportunities and challenges.
Artificial intelligence is disrupting healthcare in several ways:
- AI is improving disease prediction, customized medicine development, and other areas of human biology.
- The growth of AI in healthcare is driven by factors like increased funding, demand for precision medicine, and cost reductions, allowing for more accurate and early disease diagnosis.
- However, some end users are reluctant to adopt AI healthcare technologies due to lack of trust and potential risks, though AI also offers opportunities to improve outcomes for patients and in emerging markets.
The document discusses how artificial intelligence can help address challenges posed by infectious diseases. It describes how AI uses past disease data to predict outbreaks, and how algorithms created from behavioral and epidemiological data can help target prevention efforts. The document also outlines several successes of AI in predicting disease outbreaks like dengue fever in advance. Overall, the document advocates that AI has great potential to help monitor infectious diseases and facilitate more proactive public health responses if its tools are developed and applied effectively.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
This document summarizes a workshop presentation on AI in healthcare. It begins by discussing the hype around AI and how it has not yet delivered many results. It then outlines some challenges to using AI in healthcare like a lack of understanding of what AI can do, poor implementation strategies, and a shortage of trained workforce. The objectives of the workshop are then stated as understanding AI's real potential and how to invest wisely. Various AI technologies like machine learning, natural language processing, and voice technology are described. Key requirements for successful AI include understanding its limitations and developing a strategy to bring real value.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Artificial intelligence is being used in many areas of health and medicine to improve outcomes. AI can help detect diseases like cancer more accurately and at earlier stages. It is also used to analyze medical images and has been shown to spot abnormalities with over 90% accuracy. AI systems are also being developed to customize treatment plans for individuals based on their specific medical histories and characteristics. As more data becomes available through technologies like genomics and wearable devices, AI will play a larger role in precision medicine by developing highly personalized prevention and treatment strategies.
Role of artificial intelligence in health carePrachi Gupta
Artificial intelligence has many applications in healthcare, including improving disease diagnosis through analysis of medical imaging and other patient data, aiding radiologists in detecting abnormalities, and enabling constant remote patient monitoring. The use of AI is expected to lower medical costs through greater accuracy and better predictive analysis. It is being applied to issues like managing the coronavirus outbreak through monitoring patients and regulating hospital visitor flow. Going forward, AI may help predict where virus outbreaks are likely to occur.
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.
Artificial intelligence in field of pharmacyKaustav Dey
AI is a program designed to produce outcome in a manner similar to human intelligence,logic and reasoning.This can be used in field of Pharmacy for betterment of humankind, to save lives,money and time
This document discusses the application of machine learning in healthcare. It begins with an introduction of the author and their background in machine learning engineering. It then discusses the UN Sustainable Development Goals around health and highlights non-communicable and infectious diseases as areas machine learning could help address. The document outlines how machine learning can help expand medical knowledge, disseminate information, enable personalized medicine, and increase patient engagement. It also discusses best practices for business understanding, data modeling, and feature engineering when applying machine learning in healthcare.
Artificial intelligence (AI) is an area of computer science that creates intelligent machines that work like humans. Some key activities of AI include speech recognition, learning, planning, and problem solving. John McCarthy is considered the founder of AI. AI has many applications in healthcare, including virtual assistants for unsupervised and supervised learning as well as reinforcement learning. It also has physical applications through medical devices and robots for surgery and care delivery. AI provides benefits like reducing errors, speeding decisions, and assisting humans without emotions or breaks. However, it also has disadvantages like high costs, potential job loss, and an inability to think creatively or feel empathy.
AI in Health Care: How to Implement Medical Imaging using Machine Learning?Skyl.ai
The document discusses implementing machine learning and computer vision for medical imaging applications. It begins with an overview of machine learning and computer vision in healthcare and challenges like processing large data volumes and reducing human errors. Then, it demonstrates using Skyl.ai's machine learning platform to detect pneumonia from chest X-rays. Key advantages of the platform include fast and collaborative data labeling, model training and monitoring, and both cloud and on-premise deployment options. The presentation concludes by inviting attendees to try a free trial of Skyl.ai.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
The document discusses how cognitive AI can augment doctors and clinicians by helping them address various challenges. It describes how doctors are challenged by the large volume of disparate data from various sources, keeping up with the constantly increasing research literature, selecting personalized treatment plans, and generating novel insights. Researchers face challenges such as exploring connections across domains and generating new insights. The document then introduces IBM Watson and describes how its capabilities such as natural language processing, machine learning, and visual recognition can help extract insights from vast amounts of data and published literature to help doctors understand patient conditions, formulate treatment options, and select personalized plans. It asserts that cognitive AI can engage patients, improve outcomes, and control costs in healthcare.
Healthcare AI Data & Ethics - a 2030 visionAlex Vasey
This document discusses three key gaps that must be addressed to realize the full potential of intelligent health powered by advances in artificial intelligence and patient data:
1) Organizational and technical barriers prevent effective data sharing between healthcare providers due to data being siloed in different systems and formats.
2) Lack of public trust and an inadequate regulatory framework that promotes privacy and security while enabling more access and use of patient data for research.
3) Absence of clear rules or frameworks governing the ethical and social implications of growing AI use in healthcare, such as ensuring AI systems are fair, reliable, private and transparent.
The document provides recommendations in each of these areas to overcome these gaps and advance responsible innovation
Artificial intelligence can help improve healthcare in several ways:
1. It can help doctors make more accurate diagnoses by analyzing large amounts of medical data.
2. AI is already being used in areas like radiology to identify diseases in medical images.
3. It shows promise in personalized treatment recommendations by analyzing individual patient data.
4. In the future, AI may be able to perform some medical tasks like surgery more precisely than humans.
Medical research is published with tremendous speed, making it nearly impossible for a doctor to keep up. Artificial Intelligence could be the answer. The growing amounts of available data enables the use of artificial intelligence in health care, as well as the increasingly sophisticated machine learning algorithms. Yet relatively little of these methods are used in health care.
Ai idea to implementation : Use cases in Healthcare Swathi Young
AI and machine learning are transformative technologies that have the potential to disrupt status quo, enhance innovation, and reduce operational costs in organizations. This presentation provides a high level overview of the important steps to consider when implementing an AI system along with use cases in the healthcare sector.
Artificial intelligence has great potential to revolutionize healthcare. It can help predict ICU transfers and hospital readmissions by identifying at-risk patients from their medical data. AI is also used in medical testing through new methods like bloodless blood testing using smartphone ECGs. It improves clinical workflows by reducing physician burnout through tools like vein finders. AI helps prevent infections by monitoring patients for early signs of sepsis or other healthcare-acquired infections. During the COVID-19 pandemic, AI has assisted with tracking and forecasting outbreaks, diagnosing patients, processing health claims, and developing new drugs to treat the virus.
Patients are about to see a new doctor: artificial intelligence by EntefyEntefy
The health care industry has already seen advanced artificial intelligent systems make an impact in areas like medical diagnosis and patient care. But the long-term big-picture importance of AI in medicine may be something else entirely: a potential fix for the intractable problem of too few doctors and nurses worldwide. And as part of that, a solution to health care’s public enemy number one—paperwork.
Entefy curated a presentation based on our article about the impact of artificial intelligence in medical care. These slides provide a snapshot of how AI is at use in medical care today, the advances and limits of current AI systems, and AI’s potential in patient care. The presentation contains useful data and analysis for anyone interested in the intersection of AI and medical care.
For additional analysis and links to our background sources, read “Patients are about to see a new doctor: artificial intelligence" on our blog at https://blog.entefy.com/view/298/Patients-are-about-to-see-a-new-doctor-artificial-intelligence.
Artificial intelligence is being used in healthcare in several ways: to detect diabetic retinopathy from retinal images, enable low-dose CT scans with improved image quality, and analyze chest CT scans and patient data to rapidly detect COVID-19. Startups are also applying AI to portable retinal imaging devices and AI-powered robots are being used to screen for COVID-19 in hospitals. Going forward, AI systems across hospitals will share aggregated clinical data to continuously learn and identify new medical patterns that can improve diagnosis and treatment.
This document discusses how artificial intelligence is being used in healthcare for more accurate and faster diagnosis of medical conditions. It explains that AI can assist doctors in diagnosis or even make diagnoses independently using machine learning. The technology is being implemented in hospitals using diagnostic AI that can offer suggestions to doctors. While initial costs are high, AI is expected to save billions and greatly increase the efficiency of diagnosis. It predicts that AI will be widely used in healthcare by 2025 to benefit patients through reduced costs, more accessible care, and better outcomes.
Artificial intelligence in orthopaedicsSaswata Datta
This document discusses the history and applications of artificial intelligence in orthopaedics. It begins with definitions of AI and provides examples of early AI pioneers. It then outlines the current and potential future uses of AI in orthopaedics, including for analyzing medical images, assisting with surgical navigation and procedures, and evaluating treatments. While AI may replace some tasks, the document argues that AI will likely change and enhance the role of orthopaedic surgeons rather than replace them entirely. It closes by acknowledging challenges with AI and calling for maintaining the important doctor-patient relationship.
How artificial intelligence ai assist in medicine, an example of diffrent dev...Shazia Iqbal
The document discusses the use of artificial intelligence in medicine. It provides examples of how AI is being used through devices like robots for transporting medical supplies, telepresence physicians for remote examinations, and AI assistants for neurosurgery and dermatology. The document also discusses the advantages of AI in medicine as well as challenges and ethical issues, such as responsibility for mistakes, job loss concerns, and data privacy. It concludes that AI has promising potential to improve healthcare but policies are needed to address ethical and financial issues.
Healthcare AI will undoubtedly become one of the fastest growing industries in the industry. Although the medical and health artificial intelligence industry was valued at US$ 600 million in 2014 , it is expected to reach a staggering US$ 150 billion by 2026. There are countless AI applications in the healthcare industry, let’s look at some outstanding ones.
Panel: FROM SMALL TO BIG TO RICH DATA: Dealing with new sources of data in Biomedicine Precision and Participatory Medicine
Fernando J. Martin-Sanchez, Professor and Chair of Health Informatics at Melbourne Medical School, discusses new sources of data in biomedicine including small, big, and rich data. He describes how small data connects people with meaningful insights from big data to be understandable for everyday tasks. Martin-Sanchez also discusses precision medicine, participatory health, and how convergence between the two can help integrate multiple data sources including genomics, the exposome, and digital health to improve disease prevention and treatment outcomes.
AI is the science and engineering of creating intelligent machines and software. It draws from fields like computer science, biology, psychology and linguistics. The goal is to develop systems that can perform tasks normally requiring human intelligence, like visual perception, decision making and language translation. Some key applications of AI include machine learning, expert systems, natural language processing and computer vision. As AI systems continue advancing, they are becoming better than humans at certain tasks like playing strategic games.
Artificial intelligence is being used in many areas of health and medicine to improve outcomes. AI can help detect diseases like cancer more accurately and at earlier stages. It is also used to analyze medical images and has been shown to spot abnormalities with over 90% accuracy. AI systems are also being developed to customize treatment plans for individuals based on their specific medical histories and characteristics. As more data becomes available through technologies like genomics and wearable devices, AI will play a larger role in precision medicine by developing highly personalized prevention and treatment strategies.
Role of artificial intelligence in health carePrachi Gupta
Artificial intelligence has many applications in healthcare, including improving disease diagnosis through analysis of medical imaging and other patient data, aiding radiologists in detecting abnormalities, and enabling constant remote patient monitoring. The use of AI is expected to lower medical costs through greater accuracy and better predictive analysis. It is being applied to issues like managing the coronavirus outbreak through monitoring patients and regulating hospital visitor flow. Going forward, AI may help predict where virus outbreaks are likely to occur.
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.
Artificial intelligence in field of pharmacyKaustav Dey
AI is a program designed to produce outcome in a manner similar to human intelligence,logic and reasoning.This can be used in field of Pharmacy for betterment of humankind, to save lives,money and time
This document discusses the application of machine learning in healthcare. It begins with an introduction of the author and their background in machine learning engineering. It then discusses the UN Sustainable Development Goals around health and highlights non-communicable and infectious diseases as areas machine learning could help address. The document outlines how machine learning can help expand medical knowledge, disseminate information, enable personalized medicine, and increase patient engagement. It also discusses best practices for business understanding, data modeling, and feature engineering when applying machine learning in healthcare.
Artificial intelligence (AI) is an area of computer science that creates intelligent machines that work like humans. Some key activities of AI include speech recognition, learning, planning, and problem solving. John McCarthy is considered the founder of AI. AI has many applications in healthcare, including virtual assistants for unsupervised and supervised learning as well as reinforcement learning. It also has physical applications through medical devices and robots for surgery and care delivery. AI provides benefits like reducing errors, speeding decisions, and assisting humans without emotions or breaks. However, it also has disadvantages like high costs, potential job loss, and an inability to think creatively or feel empathy.
AI in Health Care: How to Implement Medical Imaging using Machine Learning?Skyl.ai
The document discusses implementing machine learning and computer vision for medical imaging applications. It begins with an overview of machine learning and computer vision in healthcare and challenges like processing large data volumes and reducing human errors. Then, it demonstrates using Skyl.ai's machine learning platform to detect pneumonia from chest X-rays. Key advantages of the platform include fast and collaborative data labeling, model training and monitoring, and both cloud and on-premise deployment options. The presentation concludes by inviting attendees to try a free trial of Skyl.ai.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
The document discusses how cognitive AI can augment doctors and clinicians by helping them address various challenges. It describes how doctors are challenged by the large volume of disparate data from various sources, keeping up with the constantly increasing research literature, selecting personalized treatment plans, and generating novel insights. Researchers face challenges such as exploring connections across domains and generating new insights. The document then introduces IBM Watson and describes how its capabilities such as natural language processing, machine learning, and visual recognition can help extract insights from vast amounts of data and published literature to help doctors understand patient conditions, formulate treatment options, and select personalized plans. It asserts that cognitive AI can engage patients, improve outcomes, and control costs in healthcare.
Healthcare AI Data & Ethics - a 2030 visionAlex Vasey
This document discusses three key gaps that must be addressed to realize the full potential of intelligent health powered by advances in artificial intelligence and patient data:
1) Organizational and technical barriers prevent effective data sharing between healthcare providers due to data being siloed in different systems and formats.
2) Lack of public trust and an inadequate regulatory framework that promotes privacy and security while enabling more access and use of patient data for research.
3) Absence of clear rules or frameworks governing the ethical and social implications of growing AI use in healthcare, such as ensuring AI systems are fair, reliable, private and transparent.
The document provides recommendations in each of these areas to overcome these gaps and advance responsible innovation
Artificial intelligence can help improve healthcare in several ways:
1. It can help doctors make more accurate diagnoses by analyzing large amounts of medical data.
2. AI is already being used in areas like radiology to identify diseases in medical images.
3. It shows promise in personalized treatment recommendations by analyzing individual patient data.
4. In the future, AI may be able to perform some medical tasks like surgery more precisely than humans.
Medical research is published with tremendous speed, making it nearly impossible for a doctor to keep up. Artificial Intelligence could be the answer. The growing amounts of available data enables the use of artificial intelligence in health care, as well as the increasingly sophisticated machine learning algorithms. Yet relatively little of these methods are used in health care.
Ai idea to implementation : Use cases in Healthcare Swathi Young
AI and machine learning are transformative technologies that have the potential to disrupt status quo, enhance innovation, and reduce operational costs in organizations. This presentation provides a high level overview of the important steps to consider when implementing an AI system along with use cases in the healthcare sector.
Artificial intelligence has great potential to revolutionize healthcare. It can help predict ICU transfers and hospital readmissions by identifying at-risk patients from their medical data. AI is also used in medical testing through new methods like bloodless blood testing using smartphone ECGs. It improves clinical workflows by reducing physician burnout through tools like vein finders. AI helps prevent infections by monitoring patients for early signs of sepsis or other healthcare-acquired infections. During the COVID-19 pandemic, AI has assisted with tracking and forecasting outbreaks, diagnosing patients, processing health claims, and developing new drugs to treat the virus.
Patients are about to see a new doctor: artificial intelligence by EntefyEntefy
The health care industry has already seen advanced artificial intelligent systems make an impact in areas like medical diagnosis and patient care. But the long-term big-picture importance of AI in medicine may be something else entirely: a potential fix for the intractable problem of too few doctors and nurses worldwide. And as part of that, a solution to health care’s public enemy number one—paperwork.
Entefy curated a presentation based on our article about the impact of artificial intelligence in medical care. These slides provide a snapshot of how AI is at use in medical care today, the advances and limits of current AI systems, and AI’s potential in patient care. The presentation contains useful data and analysis for anyone interested in the intersection of AI and medical care.
For additional analysis and links to our background sources, read “Patients are about to see a new doctor: artificial intelligence" on our blog at https://blog.entefy.com/view/298/Patients-are-about-to-see-a-new-doctor-artificial-intelligence.
Artificial intelligence is being used in healthcare in several ways: to detect diabetic retinopathy from retinal images, enable low-dose CT scans with improved image quality, and analyze chest CT scans and patient data to rapidly detect COVID-19. Startups are also applying AI to portable retinal imaging devices and AI-powered robots are being used to screen for COVID-19 in hospitals. Going forward, AI systems across hospitals will share aggregated clinical data to continuously learn and identify new medical patterns that can improve diagnosis and treatment.
This document discusses how artificial intelligence is being used in healthcare for more accurate and faster diagnosis of medical conditions. It explains that AI can assist doctors in diagnosis or even make diagnoses independently using machine learning. The technology is being implemented in hospitals using diagnostic AI that can offer suggestions to doctors. While initial costs are high, AI is expected to save billions and greatly increase the efficiency of diagnosis. It predicts that AI will be widely used in healthcare by 2025 to benefit patients through reduced costs, more accessible care, and better outcomes.
Artificial intelligence in orthopaedicsSaswata Datta
This document discusses the history and applications of artificial intelligence in orthopaedics. It begins with definitions of AI and provides examples of early AI pioneers. It then outlines the current and potential future uses of AI in orthopaedics, including for analyzing medical images, assisting with surgical navigation and procedures, and evaluating treatments. While AI may replace some tasks, the document argues that AI will likely change and enhance the role of orthopaedic surgeons rather than replace them entirely. It closes by acknowledging challenges with AI and calling for maintaining the important doctor-patient relationship.
How artificial intelligence ai assist in medicine, an example of diffrent dev...Shazia Iqbal
The document discusses the use of artificial intelligence in medicine. It provides examples of how AI is being used through devices like robots for transporting medical supplies, telepresence physicians for remote examinations, and AI assistants for neurosurgery and dermatology. The document also discusses the advantages of AI in medicine as well as challenges and ethical issues, such as responsibility for mistakes, job loss concerns, and data privacy. It concludes that AI has promising potential to improve healthcare but policies are needed to address ethical and financial issues.
Healthcare AI will undoubtedly become one of the fastest growing industries in the industry. Although the medical and health artificial intelligence industry was valued at US$ 600 million in 2014 , it is expected to reach a staggering US$ 150 billion by 2026. There are countless AI applications in the healthcare industry, let’s look at some outstanding ones.
Panel: FROM SMALL TO BIG TO RICH DATA: Dealing with new sources of data in Biomedicine Precision and Participatory Medicine
Fernando J. Martin-Sanchez, Professor and Chair of Health Informatics at Melbourne Medical School, discusses new sources of data in biomedicine including small, big, and rich data. He describes how small data connects people with meaningful insights from big data to be understandable for everyday tasks. Martin-Sanchez also discusses precision medicine, participatory health, and how convergence between the two can help integrate multiple data sources including genomics, the exposome, and digital health to improve disease prevention and treatment outcomes.
AI is the science and engineering of creating intelligent machines and software. It draws from fields like computer science, biology, psychology and linguistics. The goal is to develop systems that can perform tasks normally requiring human intelligence, like visual perception, decision making and language translation. Some key applications of AI include machine learning, expert systems, natural language processing and computer vision. As AI systems continue advancing, they are becoming better than humans at certain tasks like playing strategic games.
This is a brief a brief review of current multi-disciplinary and collaborative projects at Kno.e.sis led by Prof. Amit Sheth. They cover research in big social data, IoT, semantic web, semantic sensor web, health informatics, personalized digital health, social data for social good, smart city, crisis informatics, digital data for material genome initiative, etc. Dec 2015 edition.
ASSESSMENT OF BIOMEDICAL LITERATURE
Components of internal and external validity of controlled clinical trials
Internal validity — extent to which systematic error (bias) is minimized in clinical trials
Selection bias: biased allocation to comparison groups
Performance bias: unequal provision of care apart from treatment under evaluation
Detection bias: biased assessment of outcome
Attrition bias: biased occurrence and handling of deviations from protocol and loss to follow up
Requirements, needs
Planning, direction
Information collection
Information Assessment
- Evaluation for accuracy, correctness, relevance, usefulness
- Source reliability assessment (competency and past behavior based)
- Bias assessment (motivators, interests, funding, objectives)
- Conflicts of interest
- Sources of funding, important business relationships
- Grading of individual items (study, report, analysis, article)
Collation of information
- Exclusion of irrelevant, incorrect, and useless information
-Arrangement of information in a form which enables real-time analysis
- System for rapid retrieval of information
External validity — extent to which results of trials provide a correct basis for generalization to other circumstances
Patients: age, sex, severity of disease and risk factors, comorbidity
Treatment regimens: dosage, timing and route of administration, type of treatment within a class of treatments, concomitant treatments
Settings: level of care (primary to tertiary) and experience and specialization of care provider
Modalities of outcomes: type or definition of outcomes and duration of follow up
1. Clinicians aim to build "digital twins" of cancer patients by simulating treatment options on virtual models using patient data and high-performance computing.
2. Digital twins could enable modeling personalized treatment benefits while incorporating various factors to predict individual health trajectories and outcomes.
3. Barriers include developing multiscale analytics to integrate data and models across scales per patient as well as establishing data platforms and commons to support this analysis.
The Why And How Of Machine Learning And AI: An Implementation Guide For Healt...Health Catalyst
Join Kenneth Kleinberg, Health IT Strategist, and Eric Just, Senior Vice President, Health Catalyst, as they discuss the What, Why, and How of Machine Learning and AI for healthcare leaders.
Attendees will learn:
Practical steps, timeframes and skills as well as real-time data and moving targets associated with the Implementation of ML and AI
How to deal with challenges inherent in ML and AI implementation
What the future holds for ML and AI
Paper presented at the 2012 MLA Quad Chapter meeting in Baltimore, MD, Oct. 13-16. Discusses i2b2 and how it could be used in medical education. And suggests other data if i2b2 not available in your hospital.
Developing the Informatics Workforce for Scotland's Health and Social CareCILIPScotland
1) The document discusses the development needs of Scotland's informatics workforce known as KIND (Knowledge Information and Data) staff based on a 2018-2020 project.
2) It notes the healthcare system is experiencing exponential growth in data, the digital transformation of healthcare, and the impact of COVID-19, requiring KIND staff to adopt new skills and roles to support new models of integrated care.
3) It recommends a networked approach for KIND staff to collaborate across disciplines, integrate with multidisciplinary teams, and utilize new technologies to provide proactive, personalized services through a learning health and care system.
This document provides background information on recommender systems and their application to health recommendations. It first discusses the basics of recommender systems, including collaborative filtering methods that analyze user correlations and content-based methods that compare item attributes. It then covers more advanced techniques like knowledge-based, matrix factorization, and ontology-based methods. The document also addresses challenges like data sparsity and cold starts. Finally, it discusses how recommender systems could be applied to health domains given the increasing use of online health information.
Connected Health & Me - Matic Meglic - Nov 24th 2014ipposi
This document discusses how data sharing is changing healthcare by empowering patients. It outlines a shift from a traditional care model, where patients are passive recipients of care, to one where patients are engaged and empowered through access to their own health data and contextual knowledge. Key drivers of this change include affordable technology, the quantified self-movement, big data, and empowered patients. The document discusses how patient registries and personalized medicine can utilize data to better understand treatment efficacy for similar patients and provide personalized care plans. It also notes challenges around data privacy and the need for guidelines. Overall, the document advocates for empowering patients through access to their own health data while using data and technology to coordinate and improve healthcare.
Dr. Kamran Sartipi has extensive experience in research and innovation across several fields including software engineering, data analytics, information security, and healthcare informatics. He has published over 100 papers and books on topics such as software system analysis, architecture recovery, decision support systems, and security and privacy in distributed systems. Currently, he is leading two large research projects involving intelligent middleware security, user behavior pattern discovery, and knowledge extraction from medical data across multiple data centers.
This document discusses Smart Content and linked data applications at Elsevier. It provides an overview of Elsevier's efforts to develop a linked data repository and infrastructure that connects Elsevier content to external vocabularies and data sources. Examples are given of current Smart Content applications, including linking clinical trial data to drugs and adverse events, linking neuroscience articles to related methods and Wikipedia definitions, and highlighting energy terms in articles. Considerations for planning Smart Content projects are also outlined, such as focusing on use cases, ensuring quality and reliability of resources, planning for ongoing maintenance, and thorough testing.
Research Evaluation And Data Collection MethodsJessica Robles
The document discusses research evaluation and data collection methods used by Healthy People 2020, a US government program that sets national health objectives. It identifies measuring objectives, increasing public awareness of health determinants, providing measurable goals, engaging stakeholders, and identifying research methods as key missions. Healthy People 2020 aims to improve quality and length of life, achieve health equity, create health-promoting environments, and promote well-being across all life stages. One focus area is reducing the disease and economic burden of diabetes and improving quality of life for those with diabetes.
What it takes to build a model for detecting patients that defaults from medi...Olga Zinkevych
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http://dataconf.com.ua/speaker-page/jaya-plmanabhan.php
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- Big data can benefit education by addressing inequities, providing personalized learning based on student profiles, and improving student outcomes through predictive analytics.
- Challenges to big data use include technical issues in handling large datasets, privacy and ethics concerns,
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A Cognitive-Based Semantic Approach to Deep Content Analysis in Search Engines
1. A Cognitive-based Semantic Approach to
Deep Content Analysis in Search Engines
Mei Chen & Michel Décary
Cogilex R&D Inc.,
Montreal, Canada
Paper presented at the IEEE International Conference for Semantic Computing
(ICSC 2018)
Laguna Hills, California, USA
2. The rich information on the Internet reflects not only our
knowledge, but also our experiences, opinions,
expectations, and emotions as humans.
Mei Chen 2
4. When people search for information on the Internet,
it is always for a purpose
Mei Chen
5. Current Internet information search
Search
engine
User
Input
System
Outputs
Relevance:
• Symbolic data match
• Popularity measures
Related
search A
Related
search B
Related
search C
Related
search DRelated
search N
People also search for:
6. Our cognitive-based semantic approach
to search
Search
engine
Input on
a subject
matter
Output A for
Task 1
Output A for
Task 2
Output A for
Task 3
Output A for
Task 4
Output A for
Task 5
Output B for
Task 1
Output B for
Task 2
Output B for
Task 3
Output B for
Task 4
Output B for
Task 5
Output C for
Task 1
Output C for
Task 2
Output C for
Task 3
Output C for
Task 4
Output C for
Task 5
Output ...N
Agent in
Situation A
with Goal A
Agent in
Situation A
with Goal C
Redefine
the relevance:
• Subject-matter
• Agent
• Situation
• Goal
• Tasks
Agent in
Situation A
with Goal B
Agent in
Situation A
with Goal N
7. Semantic computing: A promising
perspective
• Addressing topics important for understanding digital
content such as “meaning”, “context”, “intention”
• Offering more comprehensive and cohesive views to
address the multi-faceted nature of automatic analysis of
text;
• Integrated and coherent approaches have not yet been
developed (Sheu, 2015; Wang, 2010).
8. Our cognitive-based semantic approach
to search: Seenso.com
A cognitive-based semantic approach to deep content
analysis:
• Using rule-based natural language processing technology in
conjunction with a world model and cognitive frameworks to
semantically analyze, rank, select, retrieve, and extract web
content.
• Analyzing meaning of the content of web content in relation to
user’s intention, tasks, context, and the functional use of
knowledge in a given subject-matter area.
9. Key components
(a) A world model:
The world model mimics the real world and things in it, and it serves as
the basis for understanding the topics to be analyzed.
(b) Cognitive frames (model of knowledge ):
Cognitive frames reflect the interaction with the world and things that
people should know or do in such interactions.
(c) Semantic rules:
Semantic rules are possible linguistic expressions that describe the
meaningful aspects of entities, attributes, relations, actions, and
interactions.
10. A world model
Macro World
(World Model
Of Everyday Things)
People
Objects
Food
Plants
Animals
Places
Organiza
-tions
Events
Academic
disciplines
Industries
The world model mimics
the real world and things in
it, and it serves as the
basis for understanding
the topics to be analyzed.
11. The micro world
The micro world represents
domain-specific entities or
object classes, particularly
entities important for
understanding the domain-
specific nature of users'
interactions.
• Disease entity > 25,000
• Symptom entity > 4,500
• Injury and accident entity > 1,500
• Medical procedure entity > 9,500
• Drug entity > 8,000
• Other health related object classes
Micro world
in medicine
Diseases
Symptoms
Injuries
DrugsProcedures
Specialists
Treatment
modalities
12. User interactions from a cognitive
perspective
HigherLevelsofInteraction
withtheWorld
Sense making
Performance
Planning
Decision making
Risk management
Diagnostic
problem solving
Experiment & test
Design & Creation
Communication &
socialization
Cognitive frames reflect
users’ interactions with
the world and things that
people should know or
do in such interactions.
13. Cognitive frame of diseases
• What it is
• Definition
• Clinical characteristics
• Symptoms
• Types & classification
• Development & stages
• Who are at risk
• Causes & risk factors
• Prevention & early detection
• Tests & examinations
• Making diagnosis
• Interpreting test results
• Making differential diagnosis
• Deciding the diagnosis
• Validating the diagnosis
• Treatments
• Drug therapy
• Alternative medicine
• Other medical intervention
• Potential complications & precautions
• Making informed treatment decisions
• Threshold for treatment
• Assessing treatment options
• Treatment effectiveness
• What is effective
• For what subtype of disease it is effective
• For whom it is effective
• When it is effective
• Treatment safety
• Adverse effects
• Treatment financial costs
• Opportunity cost
• How to choose a treatment?
• Shared medical decision making
• Getting a second opinion
• Self-care tips
14. Cognitive frame of medical procedures
• Understanding the procedure
Function, Effectiveness, Safety
• Assessing whether the procedure works for your
Medical condition to be treated, Preexisting disease and other health conditions to avoid
• Preparing for the procedure
Potential complications, Precautions
• Undergoing the procedure
Things to expect and do during the procedure
• Patients’ surgical safety checklist
Common medical errors and prevention
• Post-operation self-care guides
• Advice for caregivers
• Clinical guidelines
15. Cognitive frame of drugs
• What it is
• Classification
• Composition
• Mechanism of action
• Used for
• Disease
• Symptom
• Injury & accident
• People with other conditions
• Off-label use
• Drug administration
• Dose
• Route
• Used in combination
• Storage
• What to expect?
• Side effects
• Drug interaction
• Warning & precautions
• Drug accidents & overdose
• Signs of overdosing
• What to do if overdosing
• Clinical evidence
• Effectiveness
• Safety
16. Semantic rules
Linguistic descriptions of entities, attributes, relations,
processes, actions, and interaction
Strict semantic rules:
• DRUG (S) treat (V) DISEASE (O).
• DRUG (S) is (Be) effective (Adj) for treating
(V) DISEASE (O).
• SPECIALISTS (S) use (V) DRUG (O) as
first-line treatment (O) for DISEASE patients
(O).
• SPECIALISTS (S) use (V) DRUG (O) to
treat (V) patients (O) who (S) suffer from (V)
DISEASE (O) (subordinate clause).
• Patients (S) need (V) to take (V) DRUG (O)
if they (S) suffer from (V) DISEASE (O)
(conditional clause).
Loose semantic rules:
• DRUG treat * DISEASE
• DRUG is effective for * DISEASE
• SPECIALISTS use DRUG + treatment +
DISEASE
18. Analyzing the deep content of texts
Themacroworld
Input
Text 1
Text 2
Text 3
Text 4
Text 6
…
Text n
Themicromedicalworld
Genericcognitiveframe
Domain-specificcognitive
frames
Output
(Semantic
representat
ions)
SR 1
SR 2
SR 3
…
SR n
Basicnaturallanguage
processing
Genericsemanticrules
Domain-specificsemantic
rules
20. The assumptions about users’ situations
• The insights from cognitive studies of learning, problem solving, and
performance
• Being at risk
• Prevention and early direction
• Experiencing certain symptoms
• Undergoing a medical test
• Getting an abnormal test result
• Being diagnosed with a chronic disease
• Understanding treatment options
• Making informed treatment decisions
• Undergoing a medical treatment/procedure
• Pain & symptom relief
• Living with the disease
23. Current focus:
Health information for self-care
Self-care education is urgently needed to overcome the global
health care challenges (U.S. data)
• A high prevalence of chronic diseases
75% of national health care spending, 7 out of 10 deaths
• High associated healthcare costs
$3.3 to 3.6 trillion annual health expenditure
• Aging populations
47.8 million seniors
• Low health literacy
Cost due to low health literacy: up to $238 billion (in 2003)
• High rates of preventable medical errors
Third leading cause of medical related deaths
24. Self-care education opportunities
offered by the Internet
• The Internet can deliver a variety of content (webpages, audio,
and videos) to users almost anytime and anywhere, it can
become a powerful tool for promoting public health education.
• A large amount of high-quality health information is available on
the Internet;
• Over 70% of people use the Internet to search for health-related
information;
• Searching for health information on the Internet provides a
window of opportunity to promote public health education and
public health.
25. Challenges in searching health
information on the Internet
• Difficulty to get the right information at the right time;
• The concerns of reliability of health information on the Internet;
• Acting on fragmented and inadequate information can lead to
counterproductive feelings, decisions, actions, and serious
consequences;
• Better search methods are needed to improve the quality and
usefulness of health information for supporting self-care.
26. Future directions
• Automatically curate high-quality health information from the best
medical websites and create a practical self-care knowledge base
for supporting
• Patient education and self-care;
• The development of smart consumer digital applications.
27. Future directions
• Create intelligent interfaces to communicate such knowledge to
users in all forms of consumer digital health products and
services
Strategies to incorporate self-care knowledge base in EHR systems, mobile
self-monitoring devices & mobile apps:
• Smart AI conversation agents:
• Understanding health-related instruction and actions,
• Carrying meaningful conversations with users via speech and text.
• Contextualized health information support;
• On-demand mini courses on major diseases and common surgical procedures.
- As humans, we learn about the world through our interaction with the world.
- Our interactions with people, with objects, and with all the things we encounter.
-The rich information that we recorded on the Internet reflects the knowledge that we gained through such interactions.
- The reason we want to retrieve information is to support further new interactions with the world through our meaningful activities.
- We do not retrieve information out of a vacuum just for the sake of it.
- Retrieval is always done within the context of a purposeful human activity. It’s to help with something, learn about a person, carry out a task, make a decision, solve a problem, find a job or even have fun.
- So when people search for information on the Internet, it’s always for a purpose.
- However, the automated text analysis and search engine technology so far has paid little attention to the human activities, or the use of information for interacting with the world.
- Ontologies and key words normally do not reflect the interaction.
- If we want to improve the relevance and usefulness of search engine technology, we need to find a way to account for the interactions.
- Textual content exists for a purpose and it should also be retrieved with the knowledge that it serves a purpose.
- However, most indexing and retrieval methods view text essentially as "symbolic data" and the search and retrieval process as "symbol manipulation".
- For this reason, most of those methods will work well across languages without even having to know anything about the specifics of each language because it only cares about matching queries with content at a symbolic level without the need to understand why someone would make such a query.
- But if you start to care about why a certain query was made and why a certain page was created then you need to go deeper.
For instance, If someone searches for ”back pain", it is most probably because he or she is suffering from it, or wants to know the cause of it, or the remedy or the prognosis.
Then it becomes important to understand which of those aspects are represented in the content that is retrieved even though they are not explicitly mentioned in the query.
Understanding the functional use of the information gives us an opportunity to retrieve more useful content
- The emerging field of semantic computing seems to offer more comprehensive and cohesive views to address the multi-faceted nature of automatic analysis of text.
- Interesting frameworks and models are being defined that try to place the focus beyond symbolic data and more towards the cognitive and knowledge level
- But integrated and coherent approaches have not yet been fully developed.
At Cogilex, we have been working on a cognitive-based semantic approach to search for which we have done a first large-scale implementation in a publicly available medical search engine called Seenso
The core of this approach is to match every sentence of every page to a functional model of knowledge in a specific subject matter area.
In Seenso, all content is indexed in terms of its functional use by users
The ranking of content is done solely and entirely based on the semantic content of the page in terms of cognitive tasks and functions.
There are three main components in our approach:
- A model of the world in terms of classes and hierarchy of concepts
- A model of the knowledge and its functional use
- And semantic rules that matches text with those two models
The world model is composed of detailed ontologies that defines the classes and hierarchy of objects to be found in any text.
It serves as the basis for understanding the topics to be analyzed.
Detailed micro-worlds are then defined for each subject matter.
In our implementation we have created a rich medical micro world that includes for instance 25,000 medical conditions and their relationships as well as symptoms, medical procedures, drugs, etc.
Next we define a model of knowledge in terms of cognitive frames that reflect the interaction and things that people should know or do in their relations with the world.
Those frames represent the cognitive, social and emotional function of information.
When we see text, sentences, paragraphs, we need to know if it describes how to understand something, or to plan something, or to evaluate a risk or to diagnose something.
Text needs to be linked to a purposeful function
For instance, this is a cognitive frame we have created for diseases in our medical world.
When we analyse a text about diseases, we try to fit any utterance into one or more of those elements.
For instance, is a sentence talking about
- Who is at risk
- What are the causes of this disease
- How to prevent it
- How to treat it
Different topics will have different cognitive frames
Here is a subset of our frame for medical procedures
So, is our sentence talking about
Preparing for the procedure
Undergoing the procedure
Possible complications of the procedures
This is yet a different cognitive frame for information on drugs
What is it used for
How is it administered
What are the side effects
What to do in case of overdose
Next, we define semantic rules that match any text, for instance web content and users queries, to the cognitive frames and micro-worlds
Those rules represent possible linguistic expressions that describe the meaningful aspects of entities, attributes, relations, actions, and interactions.
The rules shown here are pattern matching rules but we also have used strict grammatical parsing with a dependency syntax approach.
We have found that it is important to use different levels of precision and coverage in order to more accurately capture and describe the nature of a paragraph or of a whole page.
Doing a shallower parsing with a certain strictness control like the one described here allows for a higher level of coverage that better helps to determine the cognitive frame coverage of a whole page.
On the other hand, we also need to do more precise fine grain extraction on key content in order to create databases of medical knowledge from individual sentences
So the best parsing mechanism for indexing and retrieving will not be the same as the best mechanism for extracting and inferencing.
Using a combination of methods gives us much flexibility
So in summary, this approach of identifying the meaningful content of webpages in terms of micro-world objects linked to cognitive frames representing users goals and tasks enables the search engine to identify and provide information that is central to users’ functional needs.
So, the process that our system follows when analyzing a web page goes a bit like this
- First, we apply a generic Natural Language Processing component to discover sentence and paragraph boundaries, assign parts of speech, normalize spelling, identify noun phrases boundaries.
Then, using our micro-worlds, we discover, tag and store all entities on the page. We also determine the topic and subtopics of the page
Next, we apply all semantic rules at every position of every sentence and generate semantic representations associated with every sentence
- Those representations are basically predicate structures where the predicate is a frame element like Disease:Cause and the arguments are objects in the micro-world, like “smoking” and “cancer”.
Finally, we assign a ranking or score for the whole page for every node of every cognitive frames present on that page. For instance, a page about diabetes, will receive a score for prevention, treatment, causes and, of course, an overall score for the disease frame. Any drug or procedure mentioned on that page will also be ranked in their respective frames. I will not go into the details of this evaluation but it is mostly based on the relative weight of of each branch in the cognitive frame. Those weights are determined by the knowledge engineer according to our knowledge model.
After all this is done, we now have millions of pages that are classified and indexed by object classes and cognitive frame elements
We are now ready to answer users by applying a similar process on their queries
A fundamental aspect of our work is that we put the user at the very center of the search process.
Before any querying is done or any indexing is done, there is a user.
A user who may not possess the right terminology; who may lack effective search strategies
But a user who might experience some symptoms or have some concerns;
Then the search query given by this user might be just a symptom name or a disease name
But what does our user really need?
It could be to make sense of a situation, to know what caused it, assess whether it is serious or not, decide to consult a doctor or not.
Most search engines assume that if we just return the most popular URLs for the keywords entered, this will be good enough.
It is certainly good enough if we just need to satisfy a query at its face value as if it was a formal object but what about the intention and the person behind the query?
If we really want to support a human being who is asking for information for a real reason, we believe that a ranking mechanism like the one we propose will allow the search engine to retrieve content organized in terms of useful functional information that we can use to guide our user more purposefully and more adequately.
To build our situational model of users, we rely on the insights from cognitive studies of learning and problem solving. For our model of diseases, for instance, we first specified all possible situations that users may encounter, then we identified the goals and tasks of users in these situations, finally we decided what kinds of information would help users perform these tasks successfully based on instructional principles of learning.
For example, if somebody searches for a particular risk factor known for a disease, this model helps us determine what kind of information would be most useful. For instance, statistics about this risk factor, what may influence the odds, what kind of self-monitoring needs to be done, what are recommended screening etc.
We do not have time to do a live demo of the system but you can try it online at your heart’s content at Seenso.com
Here is a screenshot of a Seenso search for Alzheimer’s disease
First, as I have explained, the system ranks the research results based on the meaning of the text on the page and its relevance to user’s goal and task.
For instance, the page from the National Institute of Health, is first because it is the one whose content covers best the cognitive tree for disease. Popularity and linking played no role here. However, we do take into account the quality of the sources evaluated both by manual and automatic means.
We also index thousands of medical news sources daily using the same algorithms.
We also index a large number of videos from very good sources based on the text descriptions of those videos. This is very good content that is largely ignored by traditional search engine as hospitals and doctor’s videos for instance are not heavily linked by other pages but as we do our analysis based on content, this does not matter any more so some jewels come up to the surface.
The tree on the left is a subset of the cognitive frame for disease that serves as a knowledge maps of key information to guide users’ search. This knowledge map also represents a model of expertise, reflecting important things we want people to know or do in their interactions with the world, in this case, dealing with a disease. This tree will change depending on the search query.
So with this guided exploration, users can choose different issues to explore, based on their goals, tasks, and preferences.
Using machine learning on big data, we also discover some hidden relations among different medical entities, which allows us to extract and expand self-care information for specific diseases. For example, it allows users to discover what are the most common symptoms, tests, treatment modalities, drugs, and even dietary plans related to a specific disease.
For instance in this example, the system discovered that Resection, Colectomy and Laparoscopy are common related procedures for Colorectal Cancer. The system knows this because it actually read it many times it in multiple quality sources and within multiple sentence structures.
This is really powerful because users now can explore a wide of range of important relations that are not mentioned in any single document, but that are important for them to understand their health conditions, make informed decision-making concerning tests they need, treatment suitable to them, or even learn how the change of dietary plan can affect their condition.
As you can witness, we focused the application of our work on self-care related content. Why is that?
Countries all over the world are facing great challenges in their healthcare systems.
But as most chronic disease are preventable, treatment outcomes can be significantly improved through better patient education and more effective self-care
So, as a society, we must provide better health information for supporting self-care both for our personal well-being and for our countries to be able to afford health care for all.
The reality is that now, the Internet, rather than physicians, has become the primary source for people to obtain health-related information;
Also searching for health information on the Internet provides a great opportunity to promote public health education and we believe this is the most cost-effective way to educate the public about self-care.
So we need to embrace the use of the Internet as a source of medical knowledge for all.
But it needs to be good and there are challenges to overcome.
The biggest challenge of doing this is to provide the right information at the right time.
Information needs to be reliable, complete and trustworthy.
We think that the kind of semantic technology that we propose is suitable for analyzing procedure-oriented tasks involved in self-care and that it can help provide the right types of information at the time when users are seeking it.
We are pretty happy with our first implementation but many things need to be improved, besides of course lots of fine tuning to make the search results better.
We want to to go much further in generating inferences from our data in order to build a useful database of curated medical information. This data is very rich and we are just touching the surface of what can be done with it.
The ultimate goal is to use this technology to create a database of high-quality, up-to-date, and practical self-care knowledge base from the best medical websites on the Internet
Then, this knowledge base can be used not only for supporting self-care, but also for the development of smart consumer digital health applications.
We also need to go much further in communicating knowledge to users.
Providing exploratory tools is good but is not enough and we need to directly engage users.
At the moment, we are working on a prototype of an AI conversation agent based on our data and our cognitive models.
We believe that this kind of work can make it easier for users to obtain, understand, and use health information in a variety of contexts such as in electronic health record systems, in smart mobile apps and mobile self-monitoring devices.
We have done this work in the field of medicine but the same exact semantic approach can also be used to develop other vertical search engines to support learning about sports, arts, troubleshooting, and virtually, anything we do. This can be done by simply defining the cognitive frames and the micro-world for each of those subject areas. All the rest stays the same.
We do believe that ultimately an approach like the one we propose can be used to transform massive, unstructured text into organized and functional knowledge for supporting human learning and performance in a variety of contexts including intelligent systems and services.
Most importantly, we believe that the successful integration of semantic search technology with cognitive models of user’s interactions will result in better search engines that will serve not only as information retrieval systems, but also as learning platforms, decision-making aids, and self-care supporting systems.