The information technology played an important role in information
and knowledge dissemination in the last decade. The usage of IT to
transfer information and knowledge in the animal health care domain
using expert systems is one of the areas investigated by many
institutions. The current era is witnessing a vast development in all
fields of animal health care. Therefore there is a need for an
unconventional method to transfer the knowledge of experts in this
domain to the general public of livestock holders, especially that the
number of experts in new technologies is lesser than their demand in a
certain domain. The transfer of knowledge from veterinary consultants
& scientists to livestock holders represents a bottleneck for the
development of animal health care in any country. Expert systems are
simply computer software programs that mimic the behaviour of human
experts. They are one of the successful applications of the Artificial
Intelligence field, a branch in Computer Science that investigates how
to make the machine think like human or do tasks that humans do.
Expert Systems are very helpful to ensure an effective and nationally
coordinated approach in response to emergency incidents and in routine
bio-security activities. Such systems enable better management of the
information and resources used to manage animal’s diseases and
emergency responses to incursions.
Its my utmost belief that Kenya and other developing countries should be in the mainstream of adapting technology in excellent service delivery.
Veterinary Medicine applications of technology can improve education and service delivery.Here i highlight Informatics, Diagnostics,Biotechnology.Data analysis,Simualtion modelling and networks to outline policy changes for Kenya
This document provides an overview of various technologies impacting nursing and healthcare, including robotics in surgery, robots used for diagnostics and therapy, robots as direct care providers, biometrics, smart cards/objects, point of care testing, electronic health records, telehealth/telenursing, and nursing informatics. It discusses the benefits and limitations of these technologies, as well as their implications for nursing practice and patient outcomes.
Computer systems and the internet have greatly improved healthcare in several ways:
1. Electronic medical records allow doctors to access complete patient histories instantly and share information between hospitals. Computerized prescriptions reduce errors.
2. Diagnostic tools like CT scans, MRIs, and ultrasounds can identify medical issues much faster and more accurately than before. Monitoring equipment keeps close tabs on patients' vital signs.
3. Treatments are also enhanced through robotics in surgery, pacemakers, ventilators, and prosthetics that can mimic natural limb movement. Online support groups and research databases help patients.
4. However, self-diagnosis online risks missing issues, and purchasing medications without a prescription
The document discusses the concept of a "smart hospital" and how information and communication technologies (ICT) can help digitize healthcare and make it smarter by reducing errors, improving access to patient information, and helping address the fragmented nature of healthcare through standards-based health information exchange. The talk outlines how ICT can add value to healthcare through improved guideline adherence, safety, decision making, and patient education.
Advance IoT-based BSN Healthcare System for Emergency Response of Patient wit...IJMTST Journal
BSN is a prior and emerging technology in the medical field .BSN care system first deploy light weight ,tinypowered
sensors on patient body which communicate with each other and the coordinator node which is on
the body.This system mainly focuses on the measurement and estimation of important parameters like
ECG,temperature,level of blood. This real time system focuses on several parameters like patient location,
data storage, motion detection and transmission of data as well as alert messages to first responder and
server of hospital.In this system we are using three types of sensors ECG sensor,temperature sensor and
level sensor which gathers patients information and sends to micro-controller via which it goes to
BTC(Bluetooth Controller) through it is transferred to an Android smart phone of patient via Wi-Fi/Internet it
goes to server and from the server data is sends to doctors Android App. We are additionally using NFC(Near
Field Communication) technology,motion detection of patient by continuous grabbing of feed from
camera.Subsequently, by using this customized algorithm we proposed IOT based healthcare system using
body sensors which efficiently accomplish requirement.
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.
This document provides an overview of robots and artificial intelligence (AI), genomic medicine, and biometrics. It discusses how each technology works and has developed over time. The document also examines how each could transform how governments deliver services and enhance efficiency. For example, AI and robots may support automation, personalization, and prediction across various government functions. Genomic medicine could help diagnose and treat rare diseases. Biometrics could improve security and targeted welfare programs. However, each technology also raises ethical issues that governments will need to address through new policies and regulations to balance their benefits and risks. The report aims to help policymakers understand and respond to these advanced scientific developments.
Artificial intelligence in healthcare past,present and futureErrepe
This document discusses artificial intelligence (AI) applications in healthcare. It surveys the current status of AI in healthcare and major disease areas where AI is used, including cancer, neurology, and cardiology. The document also reviews AI applications in stroke, including early detection and diagnosis, treatment, and outcome prediction. Popular AI techniques discussed are machine learning methods for structured data like images and neural networks, and natural language processing for unstructured data like clinical notes. The document concludes that while AI cannot replace physicians, it can assist clinicians by analyzing large amounts of healthcare data to support clinical decision making.
Its my utmost belief that Kenya and other developing countries should be in the mainstream of adapting technology in excellent service delivery.
Veterinary Medicine applications of technology can improve education and service delivery.Here i highlight Informatics, Diagnostics,Biotechnology.Data analysis,Simualtion modelling and networks to outline policy changes for Kenya
This document provides an overview of various technologies impacting nursing and healthcare, including robotics in surgery, robots used for diagnostics and therapy, robots as direct care providers, biometrics, smart cards/objects, point of care testing, electronic health records, telehealth/telenursing, and nursing informatics. It discusses the benefits and limitations of these technologies, as well as their implications for nursing practice and patient outcomes.
Computer systems and the internet have greatly improved healthcare in several ways:
1. Electronic medical records allow doctors to access complete patient histories instantly and share information between hospitals. Computerized prescriptions reduce errors.
2. Diagnostic tools like CT scans, MRIs, and ultrasounds can identify medical issues much faster and more accurately than before. Monitoring equipment keeps close tabs on patients' vital signs.
3. Treatments are also enhanced through robotics in surgery, pacemakers, ventilators, and prosthetics that can mimic natural limb movement. Online support groups and research databases help patients.
4. However, self-diagnosis online risks missing issues, and purchasing medications without a prescription
The document discusses the concept of a "smart hospital" and how information and communication technologies (ICT) can help digitize healthcare and make it smarter by reducing errors, improving access to patient information, and helping address the fragmented nature of healthcare through standards-based health information exchange. The talk outlines how ICT can add value to healthcare through improved guideline adherence, safety, decision making, and patient education.
Advance IoT-based BSN Healthcare System for Emergency Response of Patient wit...IJMTST Journal
BSN is a prior and emerging technology in the medical field .BSN care system first deploy light weight ,tinypowered
sensors on patient body which communicate with each other and the coordinator node which is on
the body.This system mainly focuses on the measurement and estimation of important parameters like
ECG,temperature,level of blood. This real time system focuses on several parameters like patient location,
data storage, motion detection and transmission of data as well as alert messages to first responder and
server of hospital.In this system we are using three types of sensors ECG sensor,temperature sensor and
level sensor which gathers patients information and sends to micro-controller via which it goes to
BTC(Bluetooth Controller) through it is transferred to an Android smart phone of patient via Wi-Fi/Internet it
goes to server and from the server data is sends to doctors Android App. We are additionally using NFC(Near
Field Communication) technology,motion detection of patient by continuous grabbing of feed from
camera.Subsequently, by using this customized algorithm we proposed IOT based healthcare system using
body sensors which efficiently accomplish requirement.
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.
This document provides an overview of robots and artificial intelligence (AI), genomic medicine, and biometrics. It discusses how each technology works and has developed over time. The document also examines how each could transform how governments deliver services and enhance efficiency. For example, AI and robots may support automation, personalization, and prediction across various government functions. Genomic medicine could help diagnose and treat rare diseases. Biometrics could improve security and targeted welfare programs. However, each technology also raises ethical issues that governments will need to address through new policies and regulations to balance their benefits and risks. The report aims to help policymakers understand and respond to these advanced scientific developments.
Artificial intelligence in healthcare past,present and futureErrepe
This document discusses artificial intelligence (AI) applications in healthcare. It surveys the current status of AI in healthcare and major disease areas where AI is used, including cancer, neurology, and cardiology. The document also reviews AI applications in stroke, including early detection and diagnosis, treatment, and outcome prediction. Popular AI techniques discussed are machine learning methods for structured data like images and neural networks, and natural language processing for unstructured data like clinical notes. The document concludes that while AI cannot replace physicians, it can assist clinicians by analyzing large amounts of healthcare data to support clinical decision making.
The document discusses opportunities and challenges in e-governance and mHealth. It provides an overview of mHealth, including how mobile devices can be used for various aspects of healthcare. The document also discusses a vision for machine augmented mindfulness in healthcare, where machines could help monitor health, diagnose conditions, and potentially cure diseases at a molecular level through technologies like gene therapy and wearables. Realizing this vision faces challenges, but also opportunities to help the millions lacking access to proper healthcare through technical research and mobile health applications.
Optimising maternal & child healthcare in India through the integrated use of...Skannd Tyagi
This paper is a literature review on the present condition of pre-natal and post-natal Maternal and Child healthcare in Rural India. This is a first step on finding the several possibilities using AI, Big Data and Telemedicine in identifying patterns and provide more structured and streamlined support to rural and semi-urban communities. Our endeavour with this research paper is to identify the pain points and attempt to find solutions using current technologies.
This document summarizes an intelligent mobile health monitoring system (IMHMS) that collects biomedical and environmental data from sensors, analyzes the data using an intelligent medical server, and provides medical feedback to patients through their mobile devices. The system aims to improve healthcare access and provide personalized health monitoring anywhere through integration of biosensors, wireless networks, and mobile computing. It discusses related works in mobile health monitoring and care. Key aspects of IMHMS include its system architecture, characteristics like long-term ambulatory monitoring and real-time updates, impact on healthcare research through data mining, and future directions like developing the intelligent medical server.
Artificial intelligence in medicine (projeck)YasserAli152984
The document discusses various uses of artificial intelligence in medicine, including disease detection, diagnostics, scientific experiments, surgery robots, and cancer detection. It notes that AI has made progress in areas like analyzing large datasets, aiding physicians, and automating administrative tasks. However, the integration of human and AI is seen as key to revolutionizing healthcare.
Spatiotemporal Analysis of Infant and Maternal Morality of Mother and Child T...IJMTST Journal
Community healthcare is significant societal issue with profound suggestions for government organization
and with huge effect on individual standards of livelihood. The maintaining and monitoring of numerous
health associated government schemes are done through information technology where involvement of
Geographic Information System (GIS) is missing. This Project intends in integrating a GIS with Infant and
Maternal Mortality of Mother and Child Tracking System (MCTS). Thematic Mapping is a component of GIS
where it has constantly characterized to demonstrate the current status of health issues in an influential
way. The open source tool Quantum GIS (QGIS) is used to represent Infant and Maternal Mortality on
Thematic Maps which helps in identifying the areas with higher and weaker distributions of affected
population.
The document discusses challenges and opportunities for information and communication technology (ICT) in India's healthcare sector. It notes that while ICT could help address issues like the shortage of doctors and hospital beds in rural areas, the sector faces challenges like low government healthcare spending, lack of infrastructure, and lack of awareness and access in rural areas. The document advocates for government policies to better implement ICT and realize its potential to improve healthcare access, quality and lower costs.
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.
Transforming the Kenya Health Information System (KHIS) to an Early Warning a...Stephen Olubulyera
Transforming the Kenya Health Information System (KHIS) to an Early Warning and Real-Time Electronic Disease Notification System: Optimization for Epidemiology, Disease Surveillance and Response in Kenya.
To develop a concept on transforming the Kenya Health Management Information System (KHIS) to an electronic disease early warning and real-time notification system through optimization of disease surveillance indicators: automatic and real-time notifications integrated into an informative standardised tool. The concepts will enhance and develop part of the list of diseases mandatorily reported within the stipulated period in disease occurrence depending on the case definition of the diseases, virulence and the degree of spread of new emerging diseases that have not been defined e.g. a new infectious disease.
Mie2014 workshop: Gap Analysis of Personalized Health Services through Patien...Pei-Yun Sabrina Hsueh
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
Pei-Yun Sabrina HSUEH, , Michael MARSCHOLLEK, Yardena PERES, Stefan von CAVALLAR and Fernando J. MARTIN-SANCHEZ
IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
Hannover Medical School, Germany
IBM Research Lab in Haifa, Israel
IBM Research Lab in Melbourne, Australia
Melbourne Medical School, Australia
Mobile computing, wearable and embedded tech entail new and different styles of healthcare data processing, clinical and wellness decision support, and patient engagement schemes. This is especially important to the preventive and disease management scenarios that require better understanding of disease progression previously unable to achieve due to the lack of reliable means to capture granular patient-generated data in non-clinical settings. The new sources of data, when coupled with a framework to integrate analytical insights with feasible service models, enable reliable detection of inflection points, habit formation cycles and assessments of treatment efficacy. Research into data collection, recording, management and analysis of behavioral manisfestations and triggers will help address these challenges in areas spanning from simple fall detection to situations requiring complicated, multi-modal health monitoring such as Alzheimer’s progression and other adherence management cases. Leveraging recent advance in health devices and sensors as well as expertise in healthcare practice and informatics, the proposed workshop will help form a deeper understanding of requirements on patient-controlled devices to address unique healthcare challenges, identify care flow gaps and translate these findings to the design of platforms for patient-controlled devices and a portfolio of potential service models for preventive care and disease management.
This document provides a historical perspective on nursing and computers from the 1960s to present. It discusses how computer technology initially emerged in response to changes in healthcare and was used for basic office functions. Over time, computers became integrated into various areas of nursing including practice, administration, education, and research. The document outlines standards and initiatives that helped define the role of informatics in nursing and healthcare more broadly. Landmark events over the past 35 years helped establish nursing informatics as a specialty and integral part of modern nursing practice.
This document discusses a presentation about ICT applications for healthcare given by Dr. Nawanan Theera-Ampornpunt. It provides background on her education and experience in health informatics. The presentation covers why healthcare needs ICT due to issues like errors, fragmentation, and large amounts of information. It defines key terms like health IT, eHealth, and examples of ICT applications like EHRs, telemedicine, and clinical decision support systems. It discusses the need for standards, interoperability, and a vision for connected healthcare information exchange.
The document provides an overview of artificial intelligence in healthcare. It discusses the history of AI, the stages of AI from narrow to general to super intelligence. It then discusses the need for and applications of AI in healthcare, including predicting health trajectories, recommending treatments, guiding surgical care, monitoring patients, and automating tasks. The document also discusses challenges in the Indian healthcare system and how AI can help address issues like shortages and access to care.
This document provides an overview of information and communications technology (ICT) in healthcare. It discusses the concept of a "smart hospital" and how digitizing healthcare can help hospitals become smarter. A smart hospital is focused on using health IT and digital tools to improve quality of care, patient outcomes, and care delivery processes. The document outlines challenges to making healthcare smarter and provides examples of how technologies like electronic health records, clinical decision support, and health information exchange can help address issues like medical errors and support high quality care. The overall goal of health IT initiatives should be to link technology investments to meaningful improvements in healthcare quality, safety, efficiency and patient-centered care.
The document discusses the application of information and communications technology (ICT) for clinical care improvement. It outlines how healthcare is error-prone due to human fallibility, and how health information technology (IT) such as computerized provider order entry (CPOE) and clinical decision support systems can help reduce errors. The document also explains why access to complete and accurate patient information through electronic health records improves care delivery and coordination across different healthcare providers and settings.
Big Data, CEP and IoT : Redefining Holistic Healthcare Information Systems an...Tauseef Naquishbandi
Healthcare industry has been a significant area for innovative application of various technologies over decades. Being an area of social relevance governmental spending on healthcare have always been on the rise over the years. Event Processing (CEP) has been in use for many years for situational awareness and response generation. Computing technologies have played an important role in improvising several aspects of healthcare. Recently emergent technology paradigms of Big Data, Internet of Things (IoT) and Complex Event Processing (CEP) have the potential not only to deal with pain areas of healthcare domain but also to redefine healthcare offerings. This paper aims to lay the groundwork for a healthcare system which builds upon integration of Big Data, CEP and IoT.
This document discusses the various applications of information technology in veterinary science. It begins by introducing veterinary informatics and some key areas where IT is applied, including disease surveillance using geo-informatics, disease diagnosis using various imaging technologies, artificial intelligence in health management, and data analysis. It then discusses veterinary hospital management software and its features and advantages. Next, it covers dairy herd management software and its benefits. Finally, it briefly mentions telemedicine and its role in veterinary care.
This document provides an overview of digital health for nursing students. It defines digital health and discusses key areas like big data, genomics, and artificial intelligence. It outlines drivers of digital health in India like lifestyle diseases and an aging population. Key aspects of digital health are discussed, including telemedicine, electronic health records, robot-assisted surgery, self-monitoring devices, the internet of medical things, and mHealth. The future of digital health in India is seen to involve expanded telemedicine, electronic medical records, artificial intelligence, and more. Digital health tools were also discussed in the context of COVID-19, along with advantages and challenges of digital health.
The document discusses opportunities and challenges in e-governance and mHealth. It provides an overview of mHealth, including how mobile devices can be used for various aspects of healthcare. The document also discusses a vision for machine augmented mindfulness in healthcare, where machines could help monitor health, diagnose conditions, and potentially cure diseases at a molecular level through technologies like gene therapy and wearables. Realizing this vision faces challenges, but also opportunities to help the millions lacking access to proper healthcare through technical research and mobile health applications.
Optimising maternal & child healthcare in India through the integrated use of...Skannd Tyagi
This paper is a literature review on the present condition of pre-natal and post-natal Maternal and Child healthcare in Rural India. This is a first step on finding the several possibilities using AI, Big Data and Telemedicine in identifying patterns and provide more structured and streamlined support to rural and semi-urban communities. Our endeavour with this research paper is to identify the pain points and attempt to find solutions using current technologies.
This document summarizes an intelligent mobile health monitoring system (IMHMS) that collects biomedical and environmental data from sensors, analyzes the data using an intelligent medical server, and provides medical feedback to patients through their mobile devices. The system aims to improve healthcare access and provide personalized health monitoring anywhere through integration of biosensors, wireless networks, and mobile computing. It discusses related works in mobile health monitoring and care. Key aspects of IMHMS include its system architecture, characteristics like long-term ambulatory monitoring and real-time updates, impact on healthcare research through data mining, and future directions like developing the intelligent medical server.
Artificial intelligence in medicine (projeck)YasserAli152984
The document discusses various uses of artificial intelligence in medicine, including disease detection, diagnostics, scientific experiments, surgery robots, and cancer detection. It notes that AI has made progress in areas like analyzing large datasets, aiding physicians, and automating administrative tasks. However, the integration of human and AI is seen as key to revolutionizing healthcare.
Spatiotemporal Analysis of Infant and Maternal Morality of Mother and Child T...IJMTST Journal
Community healthcare is significant societal issue with profound suggestions for government organization
and with huge effect on individual standards of livelihood. The maintaining and monitoring of numerous
health associated government schemes are done through information technology where involvement of
Geographic Information System (GIS) is missing. This Project intends in integrating a GIS with Infant and
Maternal Mortality of Mother and Child Tracking System (MCTS). Thematic Mapping is a component of GIS
where it has constantly characterized to demonstrate the current status of health issues in an influential
way. The open source tool Quantum GIS (QGIS) is used to represent Infant and Maternal Mortality on
Thematic Maps which helps in identifying the areas with higher and weaker distributions of affected
population.
The document discusses challenges and opportunities for information and communication technology (ICT) in India's healthcare sector. It notes that while ICT could help address issues like the shortage of doctors and hospital beds in rural areas, the sector faces challenges like low government healthcare spending, lack of infrastructure, and lack of awareness and access in rural areas. The document advocates for government policies to better implement ICT and realize its potential to improve healthcare access, quality and lower costs.
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.
Transforming the Kenya Health Information System (KHIS) to an Early Warning a...Stephen Olubulyera
Transforming the Kenya Health Information System (KHIS) to an Early Warning and Real-Time Electronic Disease Notification System: Optimization for Epidemiology, Disease Surveillance and Response in Kenya.
To develop a concept on transforming the Kenya Health Management Information System (KHIS) to an electronic disease early warning and real-time notification system through optimization of disease surveillance indicators: automatic and real-time notifications integrated into an informative standardised tool. The concepts will enhance and develop part of the list of diseases mandatorily reported within the stipulated period in disease occurrence depending on the case definition of the diseases, virulence and the degree of spread of new emerging diseases that have not been defined e.g. a new infectious disease.
Mie2014 workshop: Gap Analysis of Personalized Health Services through Patien...Pei-Yun Sabrina Hsueh
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
Pei-Yun Sabrina HSUEH, , Michael MARSCHOLLEK, Yardena PERES, Stefan von CAVALLAR and Fernando J. MARTIN-SANCHEZ
IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
Hannover Medical School, Germany
IBM Research Lab in Haifa, Israel
IBM Research Lab in Melbourne, Australia
Melbourne Medical School, Australia
Mobile computing, wearable and embedded tech entail new and different styles of healthcare data processing, clinical and wellness decision support, and patient engagement schemes. This is especially important to the preventive and disease management scenarios that require better understanding of disease progression previously unable to achieve due to the lack of reliable means to capture granular patient-generated data in non-clinical settings. The new sources of data, when coupled with a framework to integrate analytical insights with feasible service models, enable reliable detection of inflection points, habit formation cycles and assessments of treatment efficacy. Research into data collection, recording, management and analysis of behavioral manisfestations and triggers will help address these challenges in areas spanning from simple fall detection to situations requiring complicated, multi-modal health monitoring such as Alzheimer’s progression and other adherence management cases. Leveraging recent advance in health devices and sensors as well as expertise in healthcare practice and informatics, the proposed workshop will help form a deeper understanding of requirements on patient-controlled devices to address unique healthcare challenges, identify care flow gaps and translate these findings to the design of platforms for patient-controlled devices and a portfolio of potential service models for preventive care and disease management.
This document provides a historical perspective on nursing and computers from the 1960s to present. It discusses how computer technology initially emerged in response to changes in healthcare and was used for basic office functions. Over time, computers became integrated into various areas of nursing including practice, administration, education, and research. The document outlines standards and initiatives that helped define the role of informatics in nursing and healthcare more broadly. Landmark events over the past 35 years helped establish nursing informatics as a specialty and integral part of modern nursing practice.
This document discusses a presentation about ICT applications for healthcare given by Dr. Nawanan Theera-Ampornpunt. It provides background on her education and experience in health informatics. The presentation covers why healthcare needs ICT due to issues like errors, fragmentation, and large amounts of information. It defines key terms like health IT, eHealth, and examples of ICT applications like EHRs, telemedicine, and clinical decision support systems. It discusses the need for standards, interoperability, and a vision for connected healthcare information exchange.
The document provides an overview of artificial intelligence in healthcare. It discusses the history of AI, the stages of AI from narrow to general to super intelligence. It then discusses the need for and applications of AI in healthcare, including predicting health trajectories, recommending treatments, guiding surgical care, monitoring patients, and automating tasks. The document also discusses challenges in the Indian healthcare system and how AI can help address issues like shortages and access to care.
This document provides an overview of information and communications technology (ICT) in healthcare. It discusses the concept of a "smart hospital" and how digitizing healthcare can help hospitals become smarter. A smart hospital is focused on using health IT and digital tools to improve quality of care, patient outcomes, and care delivery processes. The document outlines challenges to making healthcare smarter and provides examples of how technologies like electronic health records, clinical decision support, and health information exchange can help address issues like medical errors and support high quality care. The overall goal of health IT initiatives should be to link technology investments to meaningful improvements in healthcare quality, safety, efficiency and patient-centered care.
The document discusses the application of information and communications technology (ICT) for clinical care improvement. It outlines how healthcare is error-prone due to human fallibility, and how health information technology (IT) such as computerized provider order entry (CPOE) and clinical decision support systems can help reduce errors. The document also explains why access to complete and accurate patient information through electronic health records improves care delivery and coordination across different healthcare providers and settings.
Big Data, CEP and IoT : Redefining Holistic Healthcare Information Systems an...Tauseef Naquishbandi
Healthcare industry has been a significant area for innovative application of various technologies over decades. Being an area of social relevance governmental spending on healthcare have always been on the rise over the years. Event Processing (CEP) has been in use for many years for situational awareness and response generation. Computing technologies have played an important role in improvising several aspects of healthcare. Recently emergent technology paradigms of Big Data, Internet of Things (IoT) and Complex Event Processing (CEP) have the potential not only to deal with pain areas of healthcare domain but also to redefine healthcare offerings. This paper aims to lay the groundwork for a healthcare system which builds upon integration of Big Data, CEP and IoT.
This document discusses the various applications of information technology in veterinary science. It begins by introducing veterinary informatics and some key areas where IT is applied, including disease surveillance using geo-informatics, disease diagnosis using various imaging technologies, artificial intelligence in health management, and data analysis. It then discusses veterinary hospital management software and its features and advantages. Next, it covers dairy herd management software and its benefits. Finally, it briefly mentions telemedicine and its role in veterinary care.
This document provides an overview of digital health for nursing students. It defines digital health and discusses key areas like big data, genomics, and artificial intelligence. It outlines drivers of digital health in India like lifestyle diseases and an aging population. Key aspects of digital health are discussed, including telemedicine, electronic health records, robot-assisted surgery, self-monitoring devices, the internet of medical things, and mHealth. The future of digital health in India is seen to involve expanded telemedicine, electronic medical records, artificial intelligence, and more. Digital health tools were also discussed in the context of COVID-19, along with advantages and challenges of digital health.
Livestock are farm animals who are raised to generate profit. They are used for the commodities such as meat, eggs, milk, fur, leather and wool. Livestock animals usually distribute in remote areas, with relatively poor condition of disease diagnosis. Generally, it is difficult to carry out disease diagnosis rapidly and accurately.
Livestock diseases often pose a risk to public health and even affects the economy at large extent as we are quite dependent on the essential commodities we procure from the livestock. It is necessary to detect the disease outcome in the livestock to take the precautionary measures in order to avoid spread amongst them. So, there is a need for a system which can help in predicting the diseases among livestock on the basis of symptoms and suggest the precautionary measures to be taken with respect to the disease predicted. Our proposed system will predict the livestock (Cow, Sheep and Goat) disease using SVC (Support Vector Classifier) multi-class classification algorithm based on the symptoms and also provide the precautionary measures on the basis of disease predicted. There are some diseases which can prove to be fatal. So, our system will also alert the livestock owner if the predicted disease may cause a sudden death.
Dr Gupta spoke at the Indo-French dialogue on Telemedicine in Healthcare — with Christophe Saint Martin, Dr K Ganapathy, Vijay Agarwal and Shobha Mishra Ghosh.
http://www.ambafrance-in.org/Indo-French-dialogue-on
Artificial intelligence is being used increasingly in health care to improve outcomes. It can help detect diseases like cancer more accurately, review medical images much faster than humans, and provide personalized treatment recommendations. AI systems analyze large amounts of medical data to support clinical decision making. Chatbots and digital consultations using AI can provide medical advice by comparing symptoms to illnesses. Machine learning algorithms also help with tasks like medication management and molecular epidemiology research. AI shows promise in improving health globally by making better use of data and resources.
This document discusses telemedicine, including its history, types, technologies used, and applications in clinical research. It began with definitions of telemedicine and telehealth. Historically, NASA and the National Library of Medicine played important roles in early telemedicine development to provide health care to remote areas. Common telemedicine types are store-and-forward, remote monitoring, and interactive videoconferencing. Emerging technologies discussed that are useful for clinical research include robotic process automation, artificial intelligence, augmented/virtual reality, the internet of things, nanotechnology, and 3D printing. The document also reviews current telemedicine efforts and utilities in India as well as ongoing challenges.
The Future of mHealth - Jay Srini - March 2011LifeWIRE Corp
Jay Srini's presentation of her take on the Future of mHealth, presented at the 3rd mHealth Networking Conference, March 30, 2011. Aside from being one of the preeminent thought leader in the area of innovation and mhealth, she holds a number of positions including Assistant Professor at the University of Pittsburgh and CIO for LifeWIRE Corp.
Health Care IT Legal Issues:
1. Enabling IT from Mobile Devices: mHealth, mDevices and Telemedicine.
2. Current Hot Topics in Health Care IT Contracting.
3. Medical management System Architecture.
Proposed remote healthcare system for rural developmentIAEME Publication
This document summarizes a proposed remote healthcare system for rural development. The system would use wireless biomedical sensors and a wireless network to allow remote patient monitoring. This would provide enhanced mobility and comfort to patients during hospitalization. The system is proposed to have three tiers - a call center level with a medical expert, a weekly referral clinic, and a hospital level. The goal is to improve access to healthcare in rural areas using communication technologies like wireless networks and sensors.
AI has made significant advances in medicine and healthcare by transforming how care is delivered, diagnosed, and managed. Some key areas AI is being applied include medical imaging analysis to help detect diseases, using genomic data to identify disease predispositions, and predictive analytics to anticipate patient needs. While AI shows great potential, it is not intended to replace doctors but rather enhance their capabilities and lead to better patient outcomes.
PLANT LEAFLET MALADY REVELATION UTILIZING CNN ALONGSIDE FOG SYSTEMIRJET Journal
This document describes a study that used a convolutional neural network (CNN) and fog system to identify plant leaf diseases. The study collected a dataset of 9,600 images of healthy and diseased leaves from various plant species. It then used data processing techniques and CNN to build a model for classifying leaf images as healthy or diseased. If the model detected a disease, the fog system would activate to apply the appropriate treatment. The results showed that the CNN model achieved accurate detection and classification of plant diseases from leaf images. The fog system then helped cure detected diseases, reducing agricultural losses.
E-Health is alluded to as utilizing of information and communication technologies (ICT) in restorative field to control treatment of patients, research, and wellbeing training and checking of general wellbeing. The reason for this paper is thusly to investigate an institutionalized system for E-Health challenges confronted
by e-wellbeing A rundown of both e-wellbeing difficulties are given and a proposed structure is likewise accommodated E-Health and could give direction in the execution of e-wellbeing To understand the motivation behind the paper, an inductive substance examination procedure was taken after. The
fundamental outcomes were that in spite of the fact that the difficulties exceeds the advantages in the gave records, there is still trust that through appropriate ICT arrangements the advantages of e-wellbeing can develop all the more quickly. This can prompt to enhanced e-wellbeing administration conveyance and nationals in nations can all profit by this.
Modern Era of Medical Field : E-HealthFull Text ijbbjournal
E-Health is alluded to as utilizing of information and communication technologies (ICT) in restorative field
to control treatment of patients, research, and wellbeing training and checking of general wellbeing. The
reason for this paper is thusly to investigate an institutionalized system for E-Health challenges confronted
by e-wellbeing A rundown of both e-wellbeing difficulties are given and a proposed structure is likewise
accommodated E-Health and could give direction in the execution of e-wellbeing To understand the
motivation behind the paper, an inductive substance examination procedure was taken after. The
fundamental outcomes were that in spite of the fact that the difficulties exceeds the advantages in the gave
records, there is still trust that through appropriate ICT arrangements the advantages of e-wellbeing can
develop all the more quickly. This can prompt to enhanced e-wellbeing administration conveyance and
nationals in nations can all profit by this
Using Social Media and Health IT to Promote Health and Wellness and Provide Healthcare Education to Health Workers Manish Nachnani
Telemedicine and Use of Emerging Technologies - Kinect(microsoft) and Augmented Reality Manish Nachnani,
Social Media- Health IT - Behavioural Finance Improving Healthcare Behaviour by Using Social Media and Health 2.0 Manish Nachnani,
Social Media for Health and Wellness Promotion Manish Nachnani,
Survey of IOT based Patient Health Monitoring Systemdbpublications
The Internet of things has provided a promising opportunity and applications for medical services is one of the most important way or solution for taking care of population which is in rapid growth. Internet of things consists of communication and sensors; wireless body area network is highly suitable tool for the medical IOT device. In this survey we discuss mainly on practical issues for implementation of WBAN to health care service tool for the medical devices. The IoT applications are key enabling technologies in industries. A main aim of this survey paper is that it summarizes the present state-of-the-art IOT in industries and also in workflow hospitals systematically. In recent years wide range of opportunity and powerful of IOT applications are developed in industry. The health monitoring system is a big challenge for several researchers. In this paper introduced on the survey of different IOT applications are used for the health monitoring system. The IoT applications are used to decrease the problems which are related to health care system.
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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.
Application of computers in Epidemiology.pptxiqbal606601
Computers play an indispensable role in epidemiology by providing high precision, speed, and accuracy in data collection, analysis, and disease modeling and prediction. They allow epidemiologists to perform complex statistical analyses and simulations to measure disease transmission and identify risk factors. Computer technologies like disease surveillance systems, telemedicine, health management information systems, and geographic information systems have helped improve public health monitoring, access to care, and evidence-based decision making.
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Information Technology in Animal Health Care System
1. Information Technology in Animal Health Care System
Omankwu, Obinnaya Chinecherem, Nwagu, Chikezie Kenneth, and Inyiama, Hycient
1
Computer Science Department, Michael Okpara University of Agriculture, Umudike
Umuahia, Abia State, Nigeria
saintbeloved@yahoo.com
2
Computer Science Department, Nnamdi Azikiwe University, Awka Anambra State, Nigeria,
Nwaguchikeziekenneth@hotmail.com
3
Electronics & Computer Engineering Department, Nnamdi Azikiwe University, Awka
Anambra State, Nigeria.
ABSTRACT
The information technology played an important role in information
and knowledge dissemination in the last decade. The usage of IT to
transfer information and knowledge in the animal health care domain
using expert systems is one of the areas investigated by many
institutions. The current era is witnessing a vast development in all
fields of animal health care. Therefore there is a need for an
unconventional method to transfer the knowledge of experts in this
domain to the general public of livestock holders, especially that the
number of experts in new technologies is lesser than their demand in a
certain domain. The transfer of knowledge from veterinary consultants
& scientists to livestock holders represents a bottleneck for the
development of animal health care in any country. Expert systems are
simply computer software programs that mimic the behaviour of human
experts. They are one of the successful applications of the Artificial
Intelligence field, a branch in Computer Science that investigates how
to make the machine think like human or do tasks that humans do.
Expert Systems are very helpful to ensure an effective and nationally
coordinated approach in response to emergency incidents and in routine
bio-security activities. Such systems enable better management of the
information and resources used to manage animal’s diseases and
emergency responses to incursions.
Keywords— Artificial Intelligence, Expert System, IITV,
Ultrasound, Tomography, MRI, DSA , Endoscopy
INTRODUCTION
Livestock wealth is very precious for a developing country
like India. In India, animal husbandry is no longer a subsidiary
to agriculture or a backyard vocation. Animal husbandry has
metamorphosed into an industry and the latest reports suggest
that the contribution of animal husbandry sector to the GDP of
the nation is substantially higher despite the meager input.
Animal husbandry offers a better scope for marginal farmers
whose income from agriculture is dwindling fast due to vagaries
of monsoon, fragmentation of landholdings, pest problems, poor
pricing etc. Though the growth of livestock industry is very
promising, in order to make India a global leader in animal
husbandry, it is imperative to integrate it with developments in
other fields. The developments in Information Technology over
the past few decades are tremendous and offer great potential in
improving animal health through various measures like effective
disease forecasting, rapid and accurate disease diagnosis,
modern therapeutic measures etc.
INFORMATION TECHNOLOGY IN ANIMAL HEALTH
CARE
Medical diagnostic technology has made rapid strides
with the advent of the computer. Many of the advances in
human diagnostic technologies are translated into veterinary
medicine in developed countries. Newer branches like
Imaging, Radio diagnosis; Telemedicine, Telesonography
and Teleradiology have emerged. Broadly, the
instrumentation/devices which have been created with
modern technology in the present digital age are listed
below.
1) Image Intensifier TV system (IITV): Generally
used in orthopedic surgery. IITV helps in X-ray imaging of
the intra-operative site for orthopedic manipulations, and
the same can be stored for future reference.
2) Ultrasound: In small animal ultrasound is routinely
used as a diagnostic aid. Ultrasonography seems to have a
promising future in veterinary medicine, particularly for the
assessment of intra/peri-abdominal disease.
Ultrasonography is non-invasive and non-surgical
armamentarium of the veterinary clinician since the advent
of the fiber optic endoscope.
3) Computerized Tomography (CT): CT has been an
extremely significant development which has a unique
cross-sectional imaging ability useful for the diagnosis of
tumors, malformations, inflammation, degenerative and
vascular diseases and trauma.
4) Magnetic Resonance Imaging (MRI): MRI is a
highly sensitive and non-invasive technique providing
accurate and detailed anatomic images with good contrast
and spatial resolution. MRI is still in its infancy and its use
is infrequent. To date, MRI has been used in developed
countries in clinical cases as well as a research tool
especially for diseases in small animals.
5) Digital Subtraction Angiography (DSA): DSA is a
radiographic modality which allows dynamic imaging of
the vascular system following intravascular injection of
iodinated X-ray contrast media, through the use of image
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ISSN 1947-5500 International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 3, March 2018
216
2. intensification, enhancement of the iodine signal and digital
processing of the image data.
6) Laparoscopy: Only in the last 15 years, its use has been
extensively in various animal species for research and clinical
diagnostic and therapeutic purposes. The most advantageous
characteristic of laparoscopy is that it allows direct examination
of abdominal cavity with only minimal and superficial surgical
intervention.
7) Endoscopy: It is a minimal invasive diagnostic
modality which aids in documenting mucosal inflammation,
hyperemia, active bleeding, irregular mucosal surface etc. and
facilitates biopsy in tubular organs like GI tract, respiratory and
the urogenital systems.
As all the above techniques require human experts to analyse
the results of IT application, there is a need for an
unconventional method to transfer the knowledge of experts in
this domain to the general public of livestock holders through
the expert system. Expert system is one of the successful
applications of the Artificial Intelligence field. Artificial
intelligence may be defined by comparing computer and human
functions. If the computer performs a task that seems intelligent
when it is done by humans it can be said to be exhibiting
artificial intelligence. In medicine, most artificial intelligence
research has been devoted to creating computer systems that
contain detailed information about a specific medical subject.
By focusing relevant knowledge on the problems facing the
physician, these programs are designed to act like consultants
and thereby have the potential of expanding the practitioner’s
expertise.
Expert systems are computer programs that typically contain
large amounts of knowledge for making decisions about specific
problem domains such as an area of medicine. In medicine,
several important experimental expert systems have been
developed. For example: INTERNIST - Diagnosis in internal
medicine, PIP - Renal disease, VM - Ventilator Management,
PUFF - Pulmonary function and ATTENDING - Anesthetic
Management. Similarly researchers have gone through
following different researches based upon the various expert
systems in animal health care domain.
REVIEW OF LITERATURE
Jeffrey C. Mariner1, Dirk U. Pfeiffer2(2011)
Through their research they suggested the Participatory
Epidemiology Network for Animal and Public Health
(PENAPH) seeks to facilitate research and information-sharing
among professionals interested in participatory approaches to
epidemiology and risk-based surveillance. As part of this
process, the network supports innovation in institutional
capacity by promoting minimum training guidelines, good
practice and continued advancement of methods through action
research.
Graeme Garner(2011)
This research indicates that trade and market access is a major
focus of surveillance in Australia. The animal health
surveillance system in Australia has evolved to meet a range of
regional, state/territory, national and industry needs including
Notifiable disease reporting, Trade and market access, Regional
and national animal disease management, Monitoring endemic
diseases and Early detection of exotic and emerging diseases
Jampour,M (2011)
In his research, he concludes that animal health and
domestic products health undoubtedly are the most basic
health factors, although, there are complete and correct
information in the disease of animal with neurological
involvement, however, generally defined neurological
diseases only on the basis of clinical symptoms is not so
simple as so proximity neurological signs and in most
instance veterinarians will doubt in diagnose. In this
research researcher use the fuzzy logic model approach to
determine and calculate lack or involvement of each the
possible disease with neurological signs and sufficiently
reduced natural Uncertainty regarding the diagnosis of
disease.
Gustavo Sotomayor(2011)
He commented that the Animal Protection Division of the
Agriculture and Livestock Service of Chile (SAG) has
moved from using file-based information and local
databases – in other words a nonstandard, non-
interconnected system – to a centralized database with
which users connect via a WAN (Wide Area Network).
Until 2004 the recording, storage and analysis of data
(information management) was mainly carried out using
local, spreadsheet-type files compiled by those responsible
for the different programmes. These were sent to the SAG
operational offices and then bound as management reports
or epidemiological analysis.
Hosein Alizadeh, Alireza Hasani-Bafarani, Hamid
Parvin, Behrouz Minaei, Mohammad R.
Kangavari(2008)
Through their research, researchers highlighted the
possibility of developing of an expert system for replacing
human expert investigated. Also, the knowledge extraction
methods are scribed. Fuzzy logic is used for dealing with
uncertainty. Finally, the Knowledge representation methods
are discussed and fuzzy rule base is proposed for
representing this knowledge.
Soegiarto(2011)
His research is based on Indonesian animal health service
using computerized information systems to assist in
managing animal and zoonotic disease for almost 20 years.
Initially these were adaptations of programs developed
internationally, but in the past ten years these have been
replaced by three nationally developed systems: SIKHNAS
for managing surveillance data, InfoLab used by regional
veterinary laboratories, and the HPAI Information System
for monitoring HPAI surveillance and control. These
applications are all standalone, which can lead to data
integration problems at a national level.
P.L. Nuthall, G.J. Bishop-Hurley(1999)
Their research is a section of a wider study involving expert
systems for feed management which covers the
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ISSN 1947-5500 International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 3, March 2018
217
3. development of a successful interface for expert systems and the
farmers attitudes to the expert systems themselves. Alternative
forms of the interface were created and presented to both
professionals and farmers for evaluation and use. Their
responses were used to conclude on a number of interface
design questions. A clear preference for data input through as
few screens as possible using pick lists and a mouse is evident,
as is the benefit from providing on-call pictures to visually
depict alternatives where the user has a choice.
Van Dang Ky(2011)
Through his research, he indicated that Viet Nam’s disease
information and surveillance system has been in place since the
1960s. However, before the year 2000 the system showed
limitations, such as slow outbreak detection and delayed
information transmission. Many outbreaks, therefore, could not
be detected early on and the implementation of control measures
was delayed, causing diseases to spread. At pesent, many
diseases are under intensive surveillance and monitoring. Rapid
response to outbreaks is performed well at different levels of the
veterinary system.
Dickens M Chibeu(2011)
The researcher has explained the role of the Animal Resources
Information System (ARIS) in decision-making, planning and
monitoring cannot be overstated. Specifically, ARIS is useful in
early warning and rapid response, allocating resources,
assessing the level of livestock contribution to livelihoods and
GDP, and formulating policy. About a decade ago, there was no
comprehensive information system at IBAR or in most Member
States (MS) capable of contributing efficiently to these
surveillance and decision-making activities. The focus then was
on disease reports for international organizations, with no
systematic data collection, analysis and information
dissemination. Data from different sections of Animal
Resources was fragmented, with a majority of MS using paper-
based data management rather than databases.
Kellaway, RC(1988)
His research is a design of CAMDAIRY, a computer model
containing a package of programs designed to help advisers,
farmers, students and research workers who are involved in the
feeding of dairy cows. Details of the model are given by Hulme
.The core program incorporates functions to predict nutrient
requirements, feed intake, substitution effects when feeding
concentrates, tissue mobilisation and partition of nutrient
utilisation between milk production and growth. Nutrient
partitioning is described by a series of asymptotic curves
relating energy intake to milk production, such that energy
requirements per litre increase progressively with level of milk
production.
Mokganedi Mokopasetso(2011)
He concludes that within the Southern African Development
Community (SADC) member states, livestock farming is
considered one of the main pillars for developing rural
livelihoods. In particular, there is a critical need to strengthen
national epidemic surveillance systems to enable timely
collection, reporting and analysis of animal disease data. The
overall project objective was to strengthen regional
preparedness against the spread of trans-boundary animal
diseases, and its main undertaking was to strengthen animal
disease surveillance through improving disease data
collection and processing for decision-making. This is the
context in which Digital Pen Technology (DPT) was
introduced to the region as an innovative way to collect and
send animal disease surveillance data from remote areas in
the field to Central Epidemiology Units for analysis and
decision-making.
Lawrence R. Jones(1990)
Technologies outlined in this research represent the
foundation of the next generation of computer applications
for dairy herd management. If adopted, these technologies
will allow the development of systems that are more
intuitive to use, are easier to learn to use, and provide more
complete access to management information. Integrated
decision support systems have the potential to supply dairy
herd managers and their consultants with a complete
computerized system to address many farm problems. As
these systems are augmented with more intelligent user
interfaces, they should eliminate many of the problems
facing dairy herd managers in selecting and using software.
The result of adopting such technologies will be better
informed management.
Mat Yamage and Mahabub Ahmed(2011)
Developed for Avian Influenza Technical Unit, Food and
Agriculture Organization of the United Nation Department
of Livestock Services, Dhaka, Bangladesh, his research
described the SMS gateway system which is a tool for
transmitting a large amount of information from the
grassroots level via a mobile phone to a central Internet
server and consolidating this information automatically for
handling by a single database manager. The flow of
information is bi-directional and timely instructions can be
given in response to a particular situation. The system is
suitable for the surveillance of HPAI H5N1 in Bangladesh
where the majority of poultry farms are in rural areas and
not readily accessible to the national veterinary services
owing to a shortage of human and material resources.
C. H. Burton, H. Menzi, P.J. Thorne, and P. Gerber,
Cemagref(2008)
Their research commented that dissemination and
knowledge transfer remain a challenge in many fields of
research. This is especially the case for the application of
livestock waste management in developing countries where
there an overwhelming volume of material is already
available to the farm advisor and the real need is often the
transfer of such knowledge to the local level. The object of
this project is to package up suitable techniques as an expert
and design system that can be applied directly to farm
situations across South East Asia. The software will
comprise both calculation models (e.g. nutrient excretion of
animals, nutrient balance, design and costs of manure
treatment facilities), and decision tree elements (e.g.
structured analysis of the present situation at a given farm).
Outputs will include summary reports providing specific
recommendations, specifications, case study examples and
supporting multimedia background information.
A.J. Mendes da Silva1, E. Brasil(2011)
Their research indicates that the Second Inter-American
Meeting on foot-and-mouth disease and Zoonoses Control
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218
4. (RICAZ), under Resolution I, took the first steps towards
establishing a Continental Epidemiological Information and
Surveillance System (SCIV). The proposal was put forward by
the Pan-American foot- and- mouth disease Centre
(PANAFTOSA), which had at that time already established
procedures by which member countries were urged to submit
periodically, epidemiologic information on the occurrence of
foot-and-mouth disease (FMD) and vesicular stomatitis, as well
as other diagnosed types and subtypes of virus.
Sanjay S. Chellapilla(2003)
This research describes the design and implementation of
DairyMAP, a Web-based benchmarking analysis and Expert
System for Dairy Herd Producers, as part of the Dairy
Management Analysis Program undertaken by the Edgar L.
Rhodes Center for Animal and Dairy Science. The system
consists of two major components – a preliminary statistical
benchmarking analysis (based on Dairy Herd Information
reports provided by the Dairy Records Management Systems,
Inc., in Raleigh, NC) and, a detailed expert evaluation of the
four major areas of dairy herd management, viz., Somatic Cell
Count and Mastitis, Reproduction, Genetics, and Milk
Production. The preliminary analysis provides information to
the producer about the areas of concern within each component
of dairy management, and suggests further evaluation and
diagnosis by the Expert System, concluding with comments and
recommendations for improving the producer’s herd.
T. Rousing, M. Bonde & J. T. Sorensen(2001)
Their research suggests a welfare assessment protocol for loose
housing systems for dairy caws based on four sources of
information being the system, management, animal behavior
and animal health. The animal behavior indicators refer to social
behavior, man-animal relationship and resting/rising behavior.
Health indicators focus on causes of pain and discomfort to the
animal:
Extreme body condition, skin injuries and disorders, udder and
teat lesions, lameness, hoof disorders and systemic diseases with
general affection of the animal. The listed indicators were
included in a protocol, which will be tested in ten commercial
dairy herds. The herds will be visited regularly during a one-
year period. System and management will be described and the
behavioral and health indicators will be measured on a sample
of the animals. The evaluation of the indicators will include
statistical analyses, expert opinion and interviews with the
articipating farmers.
A. Dagnino, J. I. Allen, M. N. Moore, K. Broeg, L. Canesi
and A. Viarengo(2007)
Through their research they developed an expert system which
is based on a set of rules derived from available data on
responses to natural and contaminant-induced stress of marine
mussels. Integration of parameters includes: level of biological
organization; biological significance; mutual inter-relationship;
and qualitative trends in a stress gradient. The system was tested
on a set of biomarker data obtained from the field and
subsequently validated with data from previous studies. The
results demonstrate that the expert system can effectively
quantify the biological effects of different levels of pollution.
The system represents a simple tool for risk assessment of the
harmful impact of contaminants by providing a clear indication
of the degree of stress syndrome induced by pollutants in
mussels.
J. Enting, R.B.M. Huirne, A.A. Dijkhuizen, M.J.M.
Tielen(1999)
For constructing a knowledge-based system in the field of
animal health management a documentation methodology
has been developed and is reported in their research. The
methodology was based on, among other things, the
CommonKADS technique.
It includes three subsequent phases: documenting concepts
and facts in hierarchies, documenting separate inferences
which integrate knowledge documented in hierarchies, and
documenting the strategy or sequence of the inferences to
be made. The method supports the full pathway of the
documentation process and addresses both declarative and
procedural knowledge. Also, the method provides a quick
insight into knowledge of a knowledge source (e.g. experts)
and comprehensible transcripts for the expert. The latter
facilitates the process of knowledge verification.
Michele Ruta, Floriano Scioscia, Eugenio Di
Sciascio(2009)
Their research is based on an innovative Decision Support
System for healthcare applications which is based on a
semantic enhancement of RFID standard protocols.
Semantically annotated descriptions of both medications
and animals, or person case history are stored in RFID tags
and used to help doctors in providing the correct therapy.
The proposed system allows discovering possible
incompatibilities in a therapy suggesting alternative
treatments.
From above reviews, it clear that most of them are based on
Animal Disease Surveillance to improve disease analysis,
early warning and predicting disease emergence and spread.
As a preventive measure, disease surveillance is aimed at
reducing animal health-related risks and major
consequences of disease outbreaks on food production and
livelihoods. Early warning systems are dependent on the
quality of animal disease information collected at all levels
via effective surveillance; therefore, data gathering and
sharing is essential to understand the dynamics of animal
diseases. Through the proposed expert system researchers
will utilize the experts knowledge for the best management
practices for developing rules in variety of animal health
care issues with special reference to lactating animals.
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