Say you need to implement some machine learning system. Should you purchase a product, re-use open-source code, or develop your own algorithms? The decision does not need to be a binary one. I discuss the pluses and minuses of both options. Combining them offers the best of both worlds.

Say you need to implement some machine learning system. Should you purchase a product, re-use open-source code, or develop your own algorithms? The decision does not need to be a binary one. I discuss the pluses and minuses of both options. Combining them offers the best of both worlds.

That same month, researchers at Baylor College of Medicine and India's Amity University announced that they had developed an AI platform that can target not only Covid-19 but Chagas disease, an infectious ailment common in South America that results in damage to the heart and central nervous system. AI is capable of analyzing data from various sources--electronic health records, images, therapies, etc.--and developing models that will predict the best possible approach to any given patient's care journey, thereby streamlining operations and ensuring the most favorable outcomes. IBM researchers, for example, have partnered with scientists from two healthcare systems to use AI to examine EHRs for clues about the warning signs of heart failure, which has long been the leading cause of death in the U.S. As a result, the team was able to develop a model that predicted this malady as much as two years earlier than previous methods.

It is expected that, in 2022, artificial intelligence and machine learning will continue to impact healthcare in a multitude of ways.

What are the advantages of different classification algorithms? For instance, if we have large training data set with approx more than 10,000 instances and more than 100,000 features, then which classifier will be best to choose for classification? This list may seem a bit daunting because there are many issues that are not straightforward to answer. The good news though is, that as many problems in life, you can address this question by following the Occam's Razor principle: use the least complicated algorithm that can address your needs and only go for something more complicated if strictly necessary. To read the full article (posted as a Quora question, including 22 answers), click here.

What are the advantages of different classification algorithms? For instance, if we have large training data set with approx more than 10,000 instances and more than 100,000 features, then which classifier will be best to choose for classification? This list may seem a bit daunting because there are many issues that are not straightforward to answer. The good news though is, that as many problems in life, you can address this question by following the Occam's Razor principle: use the least complicated algorithm that can address your needs and only go for something more complicated if strictly necessary. To read the full article (posted as a Quora question, including 22 answers), click here.