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predictivehealthcare.pennmedicine.org | ||
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douno.net
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nicolas.kruchten.com
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| | | | | The business world is full of streams of items that need to be filtered or evaluated: parts on an assembly line, resumés in an application pile, emails in a delivery queue, transactions awaiting processing. Machine learning techniques are increasingly being used to make such processes more efficient: image processing to flag bad parts, text analysis to surface good candidates, spam filtering to sort email, fraud detection to lower transaction costs etc.In this article, I show how you can take business fa... | |
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erik.wiffin.com
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| | | | | Executive Summary A major academic medical center faced challenges with high patient no-show rates for scheduled surgical procedures, leading to suboptimal utilization of limited operating room capacity and lost revenue opportunities. To address this problem, I was contracted to develop a machine learning model to predict the likelihood of patient no-shows. By integrating this predictive capability into their scheduling workflows, the hospital was able to proactively identify high-risk no-show patients and take steps to mitigate the issue. | |
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jaketae.github.io
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| | | In this short post, we will take a look at variational lower bound, also referred to as the evidence lower bound or ELBO for short. While I have referenced ELBO in a previous blog post on VAEs, the proofs and formulations presented in the post seems somewhat overly convoluted in retrospect. One might consider this a gentler, more refined recap on the topic. For the remainder of this post, I will use the terms "variational lower bound" and "ELBO" interchangeably to refer to the same concept. I was heavily inspired by Hugo Larochelle's excellent lecture on deep belief networks. | ||
