Codiant - A YASH Technologies Company’s Post

Causal machine learning is changing how we make clinical decisions. Unlike traditional machine learning that predicts outcomes, causal machine learning explains the "why" and "what if" behind those predictions. This shift from correlation to causation allows for personalized medicine at its best. Imagine knowing not just which treatment could work, but why it’s the best option for a specific patient. 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗟 𝗮𝗻𝗱 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗟: 𝟭. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗖𝗮𝘂𝘀𝗮𝘁𝗶𝗼𝗻 𝘃𝘀. 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻: - 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Identifies correlations and makes predictions based on patterns observed in data. It's primarily concerned with "what" happens. - 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Looks to establish cause-and-effect relationships, answering "why" something happens and "what if" different interventions are applied. 𝟮. 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: - 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Often uses algorithms that can be black boxes, focusing on accuracy and pattern detection over interpretability. - 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Employs models that incorporate causal assumptions, making them more transparent and interpretable, as they mimic human reasoning more closely. 𝟯. 𝗗𝗮𝘁𝗮 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀: - 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: : Can work effectively with large volumes of observational data. - 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Often requires structured experimental data or sophisticated designs to infer causality, such as randomized control trials or longitudinal studies. Recent studies highlight its power: Causal machine learning can potentially reduce trial and error in treatment plans, increasing efficiency and patient outcomes. This method is not just about data, but about making the data work for us in the most impactful ways. 🔗 Link to study in the comments below. For those in evidence generation, this means richer, more actionable insights that can accelerate innovation and patient care. The potential for this technology to improve lives is immense. 👉 Follow xCures Read our LinkedIn Newsletter: https://lnkd.in/dnNJV2ti https://xcures.com/ 👀 #HealthTech #AIinHealthcare

Life Science Analytics Soars The life science analytics software market, valued at $27 billion, is projected to surge to $77.4 billion by 2032. This growth is fueled by the adoption of AI, machine learning, and big data analytics in healthcare, revolutionizing patient care and drug development. #LifeScience #HealthcareAnalytics #AIinHealthcare #BigData #FutureOfMedicine #AMR Read more : https://lnkd.in/d4cS4MKC.

🚀 Excited to share my latest Medium blog post! 🚀 🔍 Dive into the transformative world of healthcare with my newest article: "The Impact of Machine Learning on Healthcare." Discover how cutting-edge technology is revolutionizing patient care and medical practices. From early disease detection to personalized treatment plans, machine learning is reshaping the landscape of healthcare like never before! 🔗 Read the full article here: https://lnkd.in/d2XYnUk2 💡 Are you curious about how AI is enhancing healthcare outcomes? Join the conversation and explore the potential of machine learning in revolutionizing the way we approach healthcare. Don't miss out on this insightful read! #HealthTech #MachineLearning #HealthcareInnovation #AIinMedicine #FutureofHealthcare #DataDrivenHealthcare #DigitalHealth #MedicalAI #InnovationsInHealthcare #TechForGood #HealthcareTransformation #MediumArticle #HealthcareTechnology

🚀 Excited to share that I recently completed a two-day National Workshop on Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications! ⭐️ I gained valuable insights into Predictive modeling, Data preprocessing, and real-world applications of AI in improving patient outcomes. Looking forward to applying this knowledge to drive innovation in healthcare!

🚀 Unleashing My Latest ML Breakthrough! 🚀 🔮 Can Data Predict Medical Costs? Absolutely! 🔮 I've just wrapped up a thrilling machine learning project where I tackled the challenge of predicting healthcare expenses using the "Medical Cost Personal Datasets". This dataset includes fascinating details like age, BMI, smoking status, and more - everything that can influence medical costs. 🔧 Tech Arsenal: - Linear Regression: The classic workhorse. - Elastic Net Regression: The powerhouse that blends Lasso and Ridge for superior results! 🌟 Project Highlights: - Data Wizardry: Transformed raw data with cleaning, encoding, and scaling. - Model Battle: Elastic Net emerged victorious, delivering sharper predictions! - Insightful Discoveries: Unearthed patterns that could revolutionize healthcare pricing. 🌐 Why This Rocks: Imagine a future where your healthcare costs are accurately predicted and personalized, making insurance fairer and more efficient! 🔗 Dive into the model: https://lnkd.in/eSPfpAnq I’m super excited about the endless possibilities this project reveals. Let’s connect, brainstorm, and push the boundaries of machine learning and data science together! #MachineLearning #DataScience #AI #HealthcareRevolution #MedicalCostPrediction #Innovation

Microsoft's Medprompt's impact on patient outcomes 🔮 As a Public Health undergrad, I'm excited to share my insights on the groundbreaking impact of Generative AI (GenAI) in the field of public health. Microsoft's recent study on Medprompt, a prompting strategy that enables GPT-4 to outperform even specialized models like Med-PaLM-2 in medical question answering, is a testament to the transformative potential of GenAI. Medprompt's ability to combine dynamic few-shot selection, self-generated chain-of-thought, and choice shuffle ensembling has not only achieved state-of-the-art results on medical benchmarks but also shows promise in other domains such as law, philosophy, and psychology. This highlights the versatility of GenAI in tackling complex problems across various fields, including public health. The implications for public health are profound. GenAI-powered tools like Medprompt can assist healthcare professionals in making more accurate diagnoses, providing personalized treatment plans, and enhancing patient-provider communication. By reducing the reliance on expensive fine-tuning, these tools can be more widely accessible, benefiting underserved communities and reducing healthcare disparities. However, as the University of Wisconsin-Madison Global Health Institute study suggests, the successful integration of GenAI in real-world healthcare settings requires a delicate balance between prompt engineering and human factors engineering. Providers' adoption of AI-generated content remains a challenge, emphasizing the need for collaborative efforts between AI researchers and healthcare professionals. As we navigate this exciting frontier, it's crucial to prioritize the responsible development and deployment of GenAI in public health. By harnessing its potential while addressing ethical concerns, we can revolutionize the way we approach healthcare, ultimately improving patient outcomes and advancing health equity on a global scale. #MicrosoftResearch #Medprompt #GPT4 #MedPALM2 #UniversityOfWisconsinHealth #PublicHealth #GenAI #HealthcareAI

Unlock the future of medicine: Learn how to increment Deep learning in your healthcare app for better diagnoses and care. Know about healthcare software development, visit https://bit.ly/4cjXehK #healthcare #softwaredevelopment #mobileappdevelopment #ailoitte #healhtech #deeplearning #AI

Day 46: 8/11/2024 What is Causal Inference in Machine Learning? Causal inference in machine learning goes beyond finding patterns or correlations to determine actual cause-and-effect relationships from data. While traditional machine learning models are excellent at spotting patterns, they don't tell us why a change in one factor might impact another. Causal inference, on the other hand, is about understanding these underlying causal relationships, making predictions more accurate and decisions more actionable. Let’s take an example from healthcare to illustrate this. Suppose a machine learning model observes that patients who take a new medication seem to recover faster from an illness. This correlation suggests a relationship between the medication and recovery, but it doesn’t confirm causation—meaning, it doesn't prove the medication itself caused the improvement. Other factors, like the patient's overall health, age, or access to quality healthcare, might also contribute to the faster recovery. Causal inference steps helps us answer, is it really the medication that causes the improvement, or could other factors be at play? More on this in future posts. 😊 #DataScience #DeepLearning #KnowledgeDistillation #EdgeAI #MachineLearning

#deeplearning, #healthcare Deep Learning in Healthcare: Challenges, Applications, and Future Directions https://lnkd.in/dX7jzH4u

"Some of Med-Gemini’s key features that make the model better than existing AI models for healthcare include its ability to complete difficult queries from data in electronic health records through long-context processing and integration with search." Interesting how grounding GenAI with web search is becoming increasingly common as a way to minimize hallucinations. I wonder though, once the web is saturated with GenAI-generated junk content, how long this will be an effective solution (unless it is able to distinguish genuine information from the GenAI dross).