#Machine Learning Healthcare

[lets-blogoff]

The Impact Of Artificial Intelligence On The Medical Industry

The Impact Of Artificial Intelligence On The Medical Industry - #letsblogoff #AI, #PopularPosts, #Technology, #Tips - https://www.letsblogoff.com/the-impact-of-artificial-intelligence-on-the-medical-industry/

[ai-innova7ions]

Neturbiz Enterprises - AI Innov7ions

Our mission is to provide details about AI-powered platforms across different technologies, each of which offer unique set of features. The AI industry encompasses a broad range of technologies designed to simulate human intelligence. These include machine learning, natural language processing, robotics, computer vision, and more. Companies and research institutions are continuously advancing AI capabilities, from creating sophisticated algorithms to developing powerful hardware. The AI industry, characterized by the development and deployment of artificial intelligence technologies, has a profound impact on our daily lives, reshaping various aspects of how we live, work, and interact.

[knifekris]

every day i struggle to make choices

[cbirt]

As computing power has exponentially grown, machine learning (ML) techniques have gained immense popularity in healthcare – including predicting outcomes for patients with devastating illnesses like cancer. By estimating survival rates and life expectancy, statistical models help patients and doctors make difficult treatment decisions, weighing benefits against risks. Now, researchers at The University of Texas at Arlington have developed a pioneering new ML approach that significantly improves prognostic accuracy for cancer patients. Published recently, their work demonstrates how artificial intelligence (AI) continues advancing precision oncology to avoid under or over-treatment.

The Drive to Perfect Patient Survival Predictions

For patients battling cancer, determining their odds of surviving with or without intense interventions can dictate the quality of life versus longevity tradeoffs. However, traditional statistical models have faced limitations.

As lead author Dr. Suvra Pal explains, “Previous techniques don’t fully capture complex relationships between survival and factors like patient age or treatment details. They also offer limited interpretability into why certain predictions are made.”

By amalgamating sophisticated machine learning with traditional parametric cure models, his team aimed to enhance predictions that guide patients and doctors during difficult treatment decisions.

Continue Reading

[red1culous]

Take Care of Maya

I watched this last night and cried my eyes out. It's a documentary about the Kowalski's and their struggles not so much with Maya's CRPS affliction but more so with Johns Hopkins All Children’s Hospital for allegedly abusing Maya Kowalski, then 10, and accusing her mother of Munchausen by proxy.

[danieldavidreitberg]

Sun-Kissed Style: Solar Clothing Charges Up the Future of Fashion - Daniel Reitberg

There will soon be a clearer line between fashion and usefulness. Solar-powered clothes is a big step forward in terms of both style and environmental responsibility. Now, solar cells are being woven into fabrics, which turns regular clothes into tiny power plants. Imagine that your backpack could charge your phone while you're on the go or that your hiking jacket could charge your fitness tracker while you're out on a journey.

Embedded solar cells in this new technology make energy, which could power a variety of wearable tech. Even though the current level of efficiency needs to be improved before smartphones can be charged fully, the promise is clear. Solar clothing shows us a possible future where our clothes not only look good on us but also provide us with clean energy when we're on the go.

This exciting new development has a lot of potential for people who care about the environment and people who love the outdoors. As study goes on and clothes get more efficient, solar clothing could change the way we power our wearable tech, making the future of fashion and tech more eco-friendly and convenient.

[aarvyedutech]

TOP 10 courses that have generally been in high demand in 2024-

Data Science and Machine Learning: Skills in data analysis, machine learning, and artificial intelligence are highly sought after in various industries.

Cybersecurity: With the increasing frequency of cyber threats, cybersecurity skills are crucial to protect sensitive information.

Cloud Computing: As businesses transition to cloud-based solutions, professionals with expertise in cloud computing, like AWS or Azure, are in high demand.

Digital Marketing: In the age of online businesses, digital marketing skills, including SEO, social media marketing, and content marketing, are highly valued.

Programming and Software Development: Proficiency in programming languages and software development skills continue to be in high demand across industries.

Healthcare and Nursing: Courses related to healthcare and nursing, especially those addressing specific needs like telemedicine, have seen increased demand.

Project Management: Project management skills are crucial in various sectors, and certifications like PMP (Project Management Professional) are highly valued.

Artificial Intelligence (AI) and Robotics: AI and robotics courses are sought after as businesses explore automation and intelligent technologies.

Blockchain Technology: With applications beyond cryptocurrencies, blockchain technology courses are gaining popularity in various sectors, including finance and supply chain.

Environmental Science and Sustainability: Courses focusing on environmental sustainability and green technologies are increasingly relevant in addressing global challenges.

Join Now

learn more -

[marutitech]

AI-Powered Medical Records Summarization: A Game-Changer

In the world of healthcare, medical records are the lifeblood of patient care. They contain crucial information about a patient's medical history, diagnosis, treatment, doctor's notes, prescriptions, and progress. These records are paramount to healthcare providers, legal firms, and insurance companies.

Doctors and caregivers need timely access to patients' medical histories and health reports to make precise diagnoses and develop effective treatment plans. Similarly, legal firms rely on these records to establish relevant facts and prepare a solid case. 

However, managing extensive and complex medical records with specialized terminology takes time and effort. Professionals spend hours navigating through stacks of documents, and missing or misplacing crucial information can have serious consequences. This is where medical records summarization comes in.

Medical records summarization concisely summarizes a patient’s entire medical history. It highlights all the essential information in a structured manner that helps track medical records quickly and accurately.

Text summarization is an essential Natural Language Processing (NLP) task that involves constructing a brief and well-structured summary of a lengthy text document. This process entails identifying and emphasizing the text's key information and essential points within the text. The process is referred to as document summarization when applied to a specific document.

Document summarizations are of three major types:

Extractive: In an extractive summary, the output comprises the most relevant and important information from the source document. 

Abstractive: In an abstractive summary, the output is more creative and insightful. The content is not copied from the original document.

Mixed: In a mixed approach, the summary is newly generated but may have some details intact from the original document.

The comprehensive and concise nature of medical record summaries greatly contributes to the effectiveness and efficiency of both the healthcare and legal sectors.

Issues With Summarizing Medical Records

Though summarizing medical records has several benefits, they have their challenges. Even automated summary generation for medical records is not 100% accurate.

Some of the most common issues with summarizing medical records include:

Dealing With Biomedical Text

Summarizing biomedical texts can be challenging, as clinical documents often contain specific values of high significance. Here, lexical choices, numbers, and units matter a lot. Hence, creating an abstract summary of such texts becomes a significant challenge.

Identifying Key Information

Medical records contain a large amount of information. But the summary must only include relevant information that aligns with the intended purpose. Identifying and extracting relevant information from medical records can be challenging.

Maintaining Accuracy and Completeness

The medical records summarization process must include all the key components of a case. The key features include:

Consent for treatment

Legal documents like referral letter

Discharge summary

Admission notes, clinical progress notes, and nurse progress notes

Operation notes

Investigation reports like X-ray and histopathology reports

Orders for treatment and modification forms listing daily medications ordered

Signatures of doctors and nurse administrations

Maintaining accuracy and completeness, in summary, could be a challenge considering the complexity of medical documents.

What Are The Different Types Of Text Summarization?

There are two main approaches to getting an accurate summary and analysis of medical records: extractive summarization and abstractive summarization.

Extractive Summarization

Extractive summarization involves selecting essential phrases and lines from the original document to compose the summary. However, managing extensive and complex medical records with specialized terminology takes time and effort. LexRank, Luhn, and TextRank algorithms are among the top-rated tools for extractive summarization.

Abstractive Summarization

In abstractive summarization, the summarizer paraphrases sections of the source document. In abstractive summarization, the summarizer creates an entirely new set of text that did not exist in the original text. The new text represents the most critical insights from the original document. BARD and GPT-3 are some of the top tools for abstractive summarization.

Comparison Between Extractive and Abstractive Summarization

When comparing abstractive and extractive approaches in text summarization, abstractive summaries tend to be more coherent but less informative than extractive summaries. 

Abstractive summarization models often employ attention mechanisms, which can pose challenges when applied to lengthy texts.

On the other hand, extractive summary algorithms are relatively easier to develop and may not require specific datasets. In contrast, abstractive approaches typically require many specially marked-up texts. 

[iskierka]

i don’t know how to tell people on here that “just giving vacant houses” isn’t going to solve homelessness. like yes making housing available to currently unhoused ppl is essential but there is SO much more

[mtariqniaz]

The Transformative Benefits of Artificial Intelligence

Title: The Transformative Benefits of Artificial Intelligence Artificial Intelligence (AI) has emerged as one of the most revolutionary technologies of the 21st century. It involves creating intelligent machines that can mimic human cognitive functions such as learning, reasoning, problem-solving, and decision-making. As AI continues to advance, its impact is felt across various industries and…

[modiguru]

Mastering Export Marketing: A Comprehensive Guide to Finding Buyers for Your Export Business In This Training, You'll Discover.

[techiexpertnews]

5 Health Tech Trends Shaping The Future Of Healthcare

Use of AI and machine learning : The development of AI and machine learning to make healthcare genuinely smart.

Use of 3D printing : Using digital replicas and simulation technologies - such as virtual reality, augmented reality, digital twins and 3D printing to hack the real world.

Remote patient care : Using digital means to provide care that meets the patient where they need it.

Healthcare detect technologies : Health system adaptability and dynamism : technologies that foster system resilience and adaptability in times of rapid change.

Use of biotechnology : Building the future of healthcare through truly exciting science and technology development.

[cbirt]

A new ray of hope for non-invasive cancer detection has emerged. A machine learning algorithm called GEMINI, created by John Hopkins University and Boston University researchers, can effectively diagnose cancer patients using CT scans, even in the early stages (stages I and II), without intrusive treatments like biopsies. This model focuses on the DNA changes in malignant cells that are present not just in tumors but also in the blood’s freely moving cells-free DNA (cfDNA). GEMINI successfully demonstrated accuracy in detecting lung cancer in more than 90% of the patients in the early stages itself.

Many lives are lost to cancer primarily because of late detection, which also renders cancer treatment methods less effective. Early cancer detection certainly improves the chances of saving lives, but implementing effective screening methods is a huge hassle. A screening test called low-dose computed tomography (LDCT) is recommended for some high-risk individuals, but it comes with potential harm and low adherence rates.

Liquid biopsies offer a non-invasive approach to cancer detection by analyzing small fragments of DNA released into the bloodstream by cancer cells, called cell-free DNA (cfDNA). But, a hindrance to the efficacy of this method is the presence of minutely low proportions of cancer-specific mutations in cfDNA, which makes it difficult to distinguish those mutations from other background changes. The existing methods are confined to specific genes only, limiting successful early cancer detection.

Continue Reading

[ds4u]

Machine learning is a powerful technology, but it does have some limits. It works well if it has a lot of data, but if the data it uses needs to be completed or biased, the result will be wrong. Further, machine learning systems are complex and difficult to explain and understand. Despite these headaches, machine learning development company makes healthcare less costly and more accurate for doctors while using it to give people better care.