Learner, Practitioner & Teacher
SHAP (SHapley Additive explanations) is another popular method for explaining the predictions of machine learning models. In this blog post, we will explore the SHAP technique, how it works, and its application in healthcare using a use case. What
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LIME (Local Interpretable Model-agnostic Explanations) is a popular method for explaining the predictions of machine learning models, especially black-box models. In this blog post, we will explore the LIME technique, how it works, and its application in healthcare using a
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As healthcare providers strive to provide the best possible care to their patients, understanding the effects of different treatments becomes increasingly important. This is especially true for data in longitudinal nature, where patients are followed over time and treatment effects
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As technology continues to advance, there has been much speculation about whether AI will replace doctors in the field of medicine. However, it is important to note that AI is not meant to replace doctors, but rather to assist them
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NEED In recent years, the healthcare industry has witnessed the emergence of Artificial Intelligence (AI) and Machine Learning (ML) technologies, which are revolutionizing the way healthcare providers operate. These technologies enable healthcare providers to collect and analyze large amounts of
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The healthcare industry has always recognized the importance of prevention. After all, as the famous adage goes, “Prevention is better than cure.” However, in the past, healthcare has largely been a reactive field, with physicians and clinicians treating patients after
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Objective: In today’s world, customers expect personalized and intelligent products that meet their unique needs. This expectation is not limited to consumer products but extends to the healthcare sector as well. Medeva, a leading provider of healthcare technology solutions, has
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Need: As healthcare providers, we are always striving to provide the best possible care to our patients. One critical aspect of this is efficient practice management, which involves streamlining data entry and analysis, monitoring patient progress, and making data-driven decisions.
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There are various methods to compute the global importance of features in machine learning models. Here are some of the most common methods: Permutation Importance: This method measures the impact of shuffling a feature on the model’s performance. It involves
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Explainable AI (XAI) is becoming increasingly important in today’s world of machine learning and artificial intelligence. As these technologies are increasingly used to make critical decisions, it is essential that they are transparent and understandable to ensure their decisions are
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