One of the major purposes manufacturers incorporate AI or ML in their applications is to ease software computations and to predict precise results. I think compared to any other application, a medical application requires a lot of precise computations and therefore, AI is a perfect solution to enhance performance and productivity. While reading the health-tech news, I came across recent research in this regard, the use of AI in predicting a potential stroke or cardiac arrest. ..
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field.
A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBaseijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Chaitanya | G. Sai Kiran"A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11402.pdf http://www.ijtsrd.com/computer-science/data-miining/11402/a-heart-disease-prediction-model-using-logistic-regression-by-cleveland-database/k-sandhya-rani
A Heart Disease Prediction Model using Logistic Regressionijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Manoj | G. Suguna Mani"A Heart Disease Prediction Model using Logistic Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11401.pdf http://www.ijtsrd.com/computer-science/data-miining/11401/a-heart-disease-prediction-model-using-logistic-regression/k-sandhya-rani
Heart Disease Prediction Using Data Mining TechniquesIJRES Journal
There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease to be established. Performance of these techniques is compared through sensitivity, specificity and accuracy. It has been observed that Artificial Neural Networks outperform K Means clustering in all the parameters i.e. Sensitivity, Specificity and Accuracy.
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field.
A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBaseijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Chaitanya | G. Sai Kiran"A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11402.pdf http://www.ijtsrd.com/computer-science/data-miining/11402/a-heart-disease-prediction-model-using-logistic-regression-by-cleveland-database/k-sandhya-rani
A Heart Disease Prediction Model using Logistic Regressionijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Manoj | G. Suguna Mani"A Heart Disease Prediction Model using Logistic Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11401.pdf http://www.ijtsrd.com/computer-science/data-miining/11401/a-heart-disease-prediction-model-using-logistic-regression/k-sandhya-rani
Heart Disease Prediction Using Data Mining TechniquesIJRES Journal
There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease to be established. Performance of these techniques is compared through sensitivity, specificity and accuracy. It has been observed that Artificial Neural Networks outperform K Means clustering in all the parameters i.e. Sensitivity, Specificity and Accuracy.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
Psdot 14 using data mining techniques in heartZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai
Human heart can be described as a compound body organ contains muscles together with
biological nerves. Human heart pumps nearly 5 litre of blood in the body providing the human body
with renewed material [6]. If operation of heart is not proper, it will affect the other body parts of
human such as brain, kidney etc. various study revealed that heart disease have emerged as the
number one killer in world. About 25 per cent of deaths in the age group of 25-69 years occur
because of heart disease. There are number of factors, which increase the risk of heart disease such
as smoking, cholesterol, high blood pressure, obesity and low physical exercise etc. The World
Health Organisation (WHO) has estimated that 12 million deaths occur worldwide, every year due to
heart diseases. WHO estimated by 2030, almost 23.6 million people will die due to Heart
disease.Cardiovascular disease includes coronary heart disease (CHD), cerebrovascular disease
(stroke), Hypertensive heart disease, congenital heart disease, peripheral artery disease, rheumatic
heart disease, inflammatory heart disease [5].
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
This describes the techniques that are used for prediction of heart diseases using the concept of data mining.It states about IHPDS(Intelligent Heart Disease Prediction System)
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
Classification of ECG signals into different types of arrhythmias using ML
-In this study, an intellectual based electrocardiogram (ECG) signal classification approach utilizing Deep Learning (DL) is being developed. ECG plays important role in diagnosing various Cardiac ailments. The ECG signal with irregular rhythm is known as Arrhythmia such as Atrial Fibrillation, Ventricular Tachycardia, Ventricular Fibrillation, and so on. The main aspire of this task is to screen and distinguish the patient with various cardio vascular arrhythmia
In my software engineering career, I was lucky enough to work on some amazing frameworks and technologies, but nothing compares to the features and computing power enabled by Artificial Intelligence (AI). It is often utilized in cases of predictions, reducing computations, recommendations, and now based on my research, it is heavily being used in the healthcare sector...
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
Psdot 14 using data mining techniques in heartZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai
Human heart can be described as a compound body organ contains muscles together with
biological nerves. Human heart pumps nearly 5 litre of blood in the body providing the human body
with renewed material [6]. If operation of heart is not proper, it will affect the other body parts of
human such as brain, kidney etc. various study revealed that heart disease have emerged as the
number one killer in world. About 25 per cent of deaths in the age group of 25-69 years occur
because of heart disease. There are number of factors, which increase the risk of heart disease such
as smoking, cholesterol, high blood pressure, obesity and low physical exercise etc. The World
Health Organisation (WHO) has estimated that 12 million deaths occur worldwide, every year due to
heart diseases. WHO estimated by 2030, almost 23.6 million people will die due to Heart
disease.Cardiovascular disease includes coronary heart disease (CHD), cerebrovascular disease
(stroke), Hypertensive heart disease, congenital heart disease, peripheral artery disease, rheumatic
heart disease, inflammatory heart disease [5].
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
This describes the techniques that are used for prediction of heart diseases using the concept of data mining.It states about IHPDS(Intelligent Heart Disease Prediction System)
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
Classification of ECG signals into different types of arrhythmias using ML
-In this study, an intellectual based electrocardiogram (ECG) signal classification approach utilizing Deep Learning (DL) is being developed. ECG plays important role in diagnosing various Cardiac ailments. The ECG signal with irregular rhythm is known as Arrhythmia such as Atrial Fibrillation, Ventricular Tachycardia, Ventricular Fibrillation, and so on. The main aspire of this task is to screen and distinguish the patient with various cardio vascular arrhythmia
In my software engineering career, I was lucky enough to work on some amazing frameworks and technologies, but nothing compares to the features and computing power enabled by Artificial Intelligence (AI). It is often utilized in cases of predictions, reducing computations, recommendations, and now based on my research, it is heavily being used in the healthcare sector...
Precision Algorithms in Healthcare: Improving treatments with AIDay1 Technologies
It’s 2020 and we can safely say that the year hasn’t been our best or what we wanted it to be like. The alarming spread of COVID-19, and its aftermath has people unrooted and shaken to their toes, and literally everyone is looking at technology and healthcare innovations to find an answer to the pandemic. And fast.
Artificial Intelligence in Healthcare.pdfayushiqss
Imagine a parallel world, where everyone could know about their future health and any diseases they might have in later years. Now, come back to the real world where you no longer need to imagine anything. Everything is possible now with the integration of Artificial Intelligence in healthcare. Humans are developing the best AI and ML-powered devices that can predict your future health.
Caption Guidance – FDA’s First Authorized AI-Based Cardiac Ultrasound SoftwareEMMAIntl
Happy American Heart Association month, everyone! While celebrating this month at EMMA International, we bring you this blog on a medical software tool that utilizes Artificial Intelligence (AI) for providing echocardiography. This is the story of Caption Guidance, the world’s first and only FDA-granted AI-guided cardiac ultrasound software...
SaMD or Software as a Medical Device can be described as a software constructed to be used in medical devices. These softwares can be run on different operating systems and virtual platforms.
1. The basic programming model of a SaMD is given below.
2. Different softwares are used for medical purposes, and they include the following:
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Producing Better and Affordable Healthcare Services Using Computational Intel...EMMAIntl
Computational Intelligence (CI) is one of the major pillars of Artificial Intelligence. It is the study, design, and development of intelligent software based on the theory of evolution. Within the past decade, healthcare has become expensive. Also, with the declining doctor-patient ratio, there are constant needs for computing systems for everything from executing simple tasks, such as booking appointments, to major services such as consulting and diagnosis...
Heart Disease Prediction using Machine Learning Algorithmijtsrd
Nowadays, Heart disease has become dangerous to a human being, it effects very badly to human body. If anyone is suffering from heart disease, then it leads to blood clotting. Heart disease prediction is very difficult task to predict in the field of medical science. Affiliation has predicted that 12 million people fail horrendously every year as a result of heart disease. In this paper, we propose a k Nearest Neighbors Algorithm KNN way to deal with improve the exactness of heart determination. We show that k Nearest Neighbors Algorithm KNN have better accuracy than random forest algorithm for viewing heart disease. The k Nearest Neighbors Algorithm give more precise and exact outcome . We have taken 13 attributes in the dataset and a target attribute, by applying machine learning we achieved 84 accuracy in the heart disease detection. Ravi Kumar Singh | Dr. A Rengarajan "Heart Disease Prediction using Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38358.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38358/heart-disease-prediction-using-machine-learning-algorithm/ravi-kumar-singh
For the full video of this presentation, please visit:
https://www.embedded-vision.com/industry-analysis/video-interviews-demos/vision-opportunities-healthcare-presentation-woodside-capit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Rudy Burger, Managing Partner, and Vini Jolly, Executive Director, both of Woodside Capital Partners, deliver the presentation "Vision Opportunities in Healthcare" at the Embedded Vision Alliance's December 2019 Vision Industry and Technology Forum. Burger and Jolly outline trends and opportunities in computer vision for healthcare applications.
The Power of Sensors in health & healthcareD3 Consutling
In a series of reports we explore key digital health trends and related opportunities for technology companies, healthcare providers and patients-consumers. We take both an international and Flemish perspective, the latter based on interviews with local stakeholders. In this report we focus on sensor-based applications.
Many attempts have been made to produce a long-term, cost-effective, and biocompatible scaffold; however, most attempts fail to achieve this. An example would be collagen-based scaffolds. Collagen is the body’s most abundant protein and is thus highly biocompatible. Unfortunately, collagen-based scaffolds have disappointing long-term properties including poor shape retention and mechanical strength. Many types of these bio-scaffolds exist including protein-based, carbohydrate-based, polymer-based, or a combination of these...
Stability Testing of Pharmaceuticals and SupplementsEMMAIntl
Whether you are working on a prescription drug, over-the-counter (OTC) drug, or even a dietary supplement, stability testing is required depending on the location of registration and agencies involved in its approval. Stability testing is the method of testing a product's safety, efficacy, and chemical composition after a set period...
Millions in the United States alone have an allergic condition, with many of these allergies being related to food. According to the Food Allergy Research & Education organization (FARE) 32 million Americans have food allergies. Of those 32 million, 200,000 require emergency medical care for allergic reactions from those foods. A common misconception is that food intolerance is a food allergy when in actuality that is its own unique category...
The field of biomedical engineering is a new, widely researched, and well-funded industry that aims to tackle problems in medicine and health by providing engineered solutions. These solutions might be delivered in the form of electrical hardware, chemicals, or even software. Given the extensive range of applications that exist in the medical device industry, the field is continuously accelerating its innovations in technology via an abundance of research and innovation outlets in countless interrelated fields. One of the many fields that are fundamentally fueling the growth of the biomedical industry is material science...
Investigating Ketamine for Parkinson’s DiseaseEMMAIntl
In May 2021, the FDA approved an Investigational New Drug (IND) application from PharmaTher Holdings Ltd., to proceed with a Phase Two clinical trial. PharmaTher Holdings Ltd. is a psychedelics biotech organization that focuses on research and development, and commercialization of psychedelics to treat pain and neurological disorders, and mental illnesses. This company is headquartered in Vancouver, Canada...
Aduhelm, an Accelerated Approval for Alzheimer’sEMMAIntl
Alzheimer’s disease is the most common cause of dementia, especially in patients aged 65 and older1. Alzheimer’s disease is a neurodegenerative disease that has a direct correlation to age: as age increases, the likelihood of developing Alzheimer’s increases as well. Alzheimer’s has long been a subject of discussion in the pharmaceutical industries and, until the FDA’s recent accelerated approval of Aduhelm earlier this month, the most recent treatment approved for Alzheimer’s was in 2003, almost two decades ago. The FDA’s approval of Aduhelm represents the first-of-its-kind treatment and is the first therapy that aims to interrupt the underlying physiological pathway of Alzheimer’s, rather than simply attempt to treat its symptoms...
Every June 14th, the World Health Organization (WHO) hosts World Blood Donor Day to raise awareness all over the globe for how crucial the need for safe blood is in the healthcare industry. In the US and Canada alone, 43,000 pints of blood are used each day for life-saving procedures and treatments...
Starting in Summer 2021, a new type of COVID vaccine could be available. Known as a protein subunit vaccine, this vaccine contains a spike protein that the other three vaccines are missing. The other vaccines, Pfizer, Moderna, and Johnson & Johnson, contain instructions for the spike protein but do not actually include the spike protein in the vaccination. The three vaccines allow our cell bodies to make the protein up for itself...
June 14th through the 20th is Men’s Health Week, which is a great opportunity to heighten awareness for men’s depression. There is a theme in society applicable to most men as they tend to internalize depressive thoughts, not allowing for a proper diagnosis. There are four major reasons men do not reach out for help with their depression: failure to recognize the depression consuming them, downplaying signs and symptoms, reluctance to converse about their feelings with others, and resisting mental health treatment...
Celebrating Pride Month at EMMA InternationalEMMAIntl
June 1 started the celebration of Pride Month, which commemorates Lesbian, Gay, Bisexual, and Transgender members. Celebrating Pride Month is also more than celebrating members of the LGBTQIA+, this month is also about recognizing that diversity fuels innovation and collaboration among a variety of industries, including the life sciences...
Growth and Integration of ML/AI in BiotechEMMAIntl
The biotechnology and pharmaceutical industries are heavily reliant on collecting, storing, and analyzing data for both R&D as well as production purposes. The large, countless, and rapidly growing sets of data are critical for researchers and scientists to accelerate progress in the medical industry. As our technologies advance and our capacity to store data continue to increase, we must continue to find new ways to efficiently analyze data. Researchers at the European Bioinformatics Institute (EMBL-EBI) have determined that nucleotide and proteomics data is growing at an exponential rate, with the amount of data stored on their servers doubling each year...
Quality Function Deployment, or QFD, is a decades-old methodology focused on the voice of the customer. It was initially developed in Japan in the 1960s but was popularized in the US by the automotive industry in the 1980s . QFD is a tool often leveraged by Total Quality Management (TQM), which is a quality principle that customers define quality and subsequently should be prioritized at all stages of the product, both pre-and post-production...
New digital health technology is coming out every day and is changing the course of the MedTech industry as we know it. Many physicians are making the transition to using these digital health devices and technologies to improve patient care and outcomes. Some of this increase can be attributed to COVID-19 of course as it enabled them to provide care for patients remotely. However, many of these digital health devices and technologies have been around for a bit, so what caused the hesitation in adapting them sooner and what are some of the great perks of this new wave of medical care?
Immune Systems After the COVID-19 PandemicEMMAIntl
Everyone has heard that immune systems weaken when they are sheltered, but is that really the case? As we are now over one year into lockdowns and social distancing, many are becoming concerned that after the pandemic immune systems are going to falter after being isolated for such a long period, and many adults are concerned to resume a “normal” life due to this...
Stability Testing Requirements for PharmaceuticalsEMMAIntl
Deciding how and when to conduct stability tests on your new drug can be challenging. Stability tests provide evidence data on how the quality of a drug substance or drug product varies with time under the influence of a variety of environmental factors. It also establishes a retest period for the drug substance or a shelf life for the drug product and recommended storage conditions...
EMMA International is continuing to celebrate Women’s Health Week! While there are so many reasons Women’s Health Week is important, one of the best things to come out of this week is the attention it brings and the reminders that we should all consider our health and take steps to ensure a healthy future...
Happy National Women’s Health Week! In honor of this week bringing light to important women’s health issues, I wanted to walk through a brief history of innovations that shaped one of the largest facets of women’s health – reproductive health...
In the work from home era, we all realized how important it is to digitize our important documents and what a lifesaver digital signatures are. With everything now getting electronically stored, electronic signatures and documentation are slowly replacing the paper-based system. That means we must now get ready to expand our digital storage plans rather than buying new filing cabinets...
Considerations for Biocompatibility EvaluationEMMAIntl
Biocompatibility is one of the most critical performance studies that manufacturers need to perform as part of their product development process. ISO 10993-5 and ISO 10993-10 are FDA-recognized standards for biocompatibility. Whether you perform these studies in-house or send out samples to a third-party lab the protocol for biocompatibility assessment must be conducted in accordance with ISO 10993...
Restoring the Earth for a Healthier FutureEMMAIntl
Today marks the 52nd anniversary of the birth of the true modern environmental movement, however, you probably know it as Earth Day. Early in the pandemic, many hoped that the lockdowns would help the Earth heal as people began to stay home, stopped commuting, and some factories even had paused production. Unfortunately, as things are beginning to open back up emissions are on the rise again and we need to continue to think about the future consequences...
We understand the unique challenges pickleball players face and are committed to helping you stay healthy and active. In this presentation, we’ll explore the three most common pickleball injuries and provide strategies for prevention and treatment.
Leading the Way in Nephrology: Dr. David Greene's Work with Stem Cells for Ki...Dr. David Greene Arizona
As we watch Dr. Greene's continued efforts and research in Arizona, it's clear that stem cell therapy holds a promising key to unlocking new doors in the treatment of kidney disease. With each study and trial, we step closer to a world where kidney disease is no longer a life sentence but a treatable condition, thanks to pioneers like Dr. David Greene.
CHAPTER 1 SEMESTER V - ROLE OF PEADIATRIC NURSE.pdfSachin Sharma
Pediatric nurses play a vital role in the health and well-being of children. Their responsibilities are wide-ranging, and their objectives can be categorized into several key areas:
1. Direct Patient Care:
Objective: Provide comprehensive and compassionate care to infants, children, and adolescents in various healthcare settings (hospitals, clinics, etc.).
This includes tasks like:
Monitoring vital signs and physical condition.
Administering medications and treatments.
Performing procedures as directed by doctors.
Assisting with daily living activities (bathing, feeding).
Providing emotional support and pain management.
2. Health Promotion and Education:
Objective: Promote healthy behaviors and educate children, families, and communities about preventive healthcare.
This includes tasks like:
Administering vaccinations.
Providing education on nutrition, hygiene, and development.
Offering breastfeeding and childbirth support.
Counseling families on safety and injury prevention.
3. Collaboration and Advocacy:
Objective: Collaborate effectively with doctors, social workers, therapists, and other healthcare professionals to ensure coordinated care for children.
Objective: Advocate for the rights and best interests of their patients, especially when children cannot speak for themselves.
This includes tasks like:
Communicating effectively with healthcare teams.
Identifying and addressing potential risks to child welfare.
Educating families about their child's condition and treatment options.
4. Professional Development and Research:
Objective: Stay up-to-date on the latest advancements in pediatric healthcare through continuing education and research.
Objective: Contribute to improving the quality of care for children by participating in research initiatives.
This includes tasks like:
Attending workshops and conferences on pediatric nursing.
Participating in clinical trials related to child health.
Implementing evidence-based practices into their daily routines.
By fulfilling these objectives, pediatric nurses play a crucial role in ensuring the optimal health and well-being of children throughout all stages of their development.
CHAPTER 1 SEMESTER V PREVENTIVE-PEDIATRICS.pdfSachin Sharma
This content provides an overview of preventive pediatrics. It defines preventive pediatrics as preventing disease and promoting children's physical, mental, and social well-being to achieve positive health. It discusses antenatal, postnatal, and social preventive pediatrics. It also covers various child health programs like immunization, breastfeeding, ICDS, and the roles of organizations like WHO, UNICEF, and nurses in preventive pediatrics.
Defecation
Normal defecation begins with movement in the left colon, moving stool toward the anus. When stool reaches the rectum, the distention causes relaxation of the internal sphincter and an awareness of the need to defecate. At the time of defecation, the external sphincter relaxes, and abdominal muscles contract, increasing intrarectal pressure and forcing the stool out
The Valsalva maneuver exerts pressure to expel faeces through a voluntary contraction of the abdominal muscles while maintaining forced expiration against a closed airway. Patients with cardiovascular disease, glaucoma, increased intracranial pressure, or a new surgical wound are at greater risk for cardiac dysrhythmias and elevated blood pressure with the Valsalva maneuver and need to avoid straining to pass the stool.
Normal defecation is painless, resulting in passage of soft, formed stool
CONSTIPATION
Constipation is a symptom, not a disease. Improper diet, reduced fluid intake, lack of exercise, and certain medications can cause constipation. For example, patients receiving opiates for pain after surgery often require a stool softener or laxative to prevent constipation. The signs of constipation include infrequent bowel movements (less than every 3 days), difficulty passing stools, excessive straining, inability to defecate at will, and hard feaces
IMPACTION
Fecal impaction results from unrelieved constipation. It is a collection of hardened feces wedged in the rectum that a person cannot expel. In cases of severe impaction the mass extends up into the sigmoid colon.
DIARRHEA
Diarrhea is an increase in the number of stools and the passage of liquid, unformed feces. It is associated with disorders affecting digestion, absorption, and secretion in the GI tract. Intestinal contents pass through the small and large intestine too quickly to allow for the usual absorption of fluid and nutrients. Irritation within the colon results in increased mucus secretion. As a result, feces become watery, and the patient is unable to control the urge to defecate. Normally an anal bag is safe and effective in long-term treatment of patients with fecal incontinence at home, in hospice, or in the hospital. Fecal incontinence is expensive and a potentially dangerous condition in terms of contamination and risk of skin ulceration
HEMORRHOIDS
Hemorrhoids are dilated, engorged veins in the lining of the rectum. They are either external or internal.
FLATULENCE
As gas accumulates in the lumen of the intestines, the bowel wall stretches and distends (flatulence). It is a common cause of abdominal fullness, pain, and cramping. Normally intestinal gas escapes through the mouth (belching) or the anus (passing of flatus)
FECAL INCONTINENCE
Fecal incontinence is the inability to control passage of feces and gas from the anus. Incontinence harms a patient’s body image
PREPARATION AND GIVING OF LAXATIVESACCORDING TO POTTER AND PERRY,
An enema is the instillation of a solution into the rectum and sig
Navigating the Health Insurance Market_ Understanding Trends and Options.pdfEnterprise Wired
From navigating policy options to staying informed about industry trends, this comprehensive guide explores everything you need to know about the health insurance market.
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R3 Stem Cells and Kidney Repair: A New Horizon in Nephrology" explores groundbreaking advancements in the use of R3 stem cells for kidney disease treatment. This insightful piece delves into the potential of these cells to regenerate damaged kidney tissue, offering new hope for patients and reshaping the future of nephrology.
India Clinical Trials Market: Industry Size and Growth Trends [2030] Analyzed...Kumar Satyam
According to TechSci Research report, "India Clinical Trials Market- By Region, Competition, Forecast & Opportunities, 2030F," the India Clinical Trials Market was valued at USD 2.05 billion in 2024 and is projected to grow at a compound annual growth rate (CAGR) of 8.64% through 2030. The market is driven by a variety of factors, making India an attractive destination for pharmaceutical companies and researchers. India's vast and diverse patient population, cost-effective operational environment, and a large pool of skilled medical professionals contribute significantly to the market's growth. Additionally, increasing government support in streamlining regulations and the growing prevalence of lifestyle diseases further propel the clinical trials market.
Growing Prevalence of Lifestyle Diseases
The rising incidence of lifestyle diseases such as diabetes, cardiovascular diseases, and cancer is a major trend driving the clinical trials market in India. These conditions necessitate the development and testing of new treatment methods, creating a robust demand for clinical trials. The increasing burden of these diseases highlights the need for innovative therapies and underscores the importance of India as a key player in global clinical research.
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The dimensions of healthcare quality refer to various attributes or aspects that define the standard of healthcare services. These dimensions are used to evaluate, measure, and improve the quality of care provided to patients. A comprehensive understanding of these dimensions ensures that healthcare systems can address various aspects of patient care effectively and holistically. Dimensions of Healthcare Quality and Performance of care include the following; Appropriateness, Availability, Competence, Continuity, Effectiveness, Efficiency, Efficacy, Prevention, Respect and Care, Safety as well as Timeliness.
1. Using AI to Predict Strokes
By: Govind Yatnalkar
One of the major purposes manufacturers incorporate AI or ML in their applications is to
ease software computations and to predict precise results. I think compared to any other
application, a medical application requires a lot of precise computations and therefore, AI is a
perfect solution to enhance performance and productivity. While reading the health-tech news, I
came across recent research in this regard, the use of AI in predicting a potential stroke or
cardiac arrest.
This study was published in February 2020 in Circulation, The American Heart
Association Journal. The major component used in this research work is cardiovascular
magnetic resonance (CMR) imaging along with a contrast agent. Scanned CMRs provide the
quantitative measure of the blood flow to the heart and a contrast agent is a substance that
distinctively identifies certain elements during the standard imaging process. The lower the
quantitative value of the CMR (pure blood flow), the higher the risk of having a stroke.
Presumed to be one of the largest studies of its kind, the AI model was ‘learned’ or trained using
CMRs of more than 1000 patients. Following my research, I found that the AI model utilized
was Deep Convolutional Neural Network (DCNN).1 DCNNs are indeed the preferred AI model
when it comes to image training, testing, and prediction. Once the model was trained and ready,
new patient input data was supplied for checking the live predictive output. This input data was
constituted by new CMRs along with other health parameters such as age, gender, weight,
lifestyle habits, and other existing chronic diseases.2
Using the learned training data, the DCNN model predicted if there are signs of a
potential stroke or cardiac arrest for patients of whom new CMRs were supplied for testing.
With such predictions, patients can start early treatment and change their lifestyle habits to
reduce or even eliminate the chance of a stroke. This is a monumental achievement for
healthcare providers and patients alike.
To summarize, researchers in the healthcare sector are employing AI for predicting the
probability of a stroke. But before the software is deployed and installed, manufacturers should
ensure that their product is thoroughly validated. EMMA International has experience
validating complex software applications that include AI, Cloud, IoT, and Cybersecurity. Also,
our experienced quality and software experts can guide you through the FDA regulatory process
to ensure your AI/ML-based medical device is FDA compliant. Contact us at 248-987-4497 or
info@emmainternational.com for more information.
1
Knott, K. D., Seraphim,A., Augusto, J. B., Xue, H., Chacko, L., Aung, N., ... & Moon, J. C. (2020). The prognostic significance of
qu antitative myocardial perfusion:anartificial intelligence–based approachusing perfusion mapping.Circulation, 141(16), 1282-
1 291.
2
Alice Park (February 2020). How AI CanPredict HeartAttacksand Strokes. Retrieved on February 12th, 2021from
https://time.com/5784090/ai-heart-attack-stroke/.