Learn how to analyze health data / Reinvent yourself in the workplace
My previous post discussed Challenges to doing ML in healthcare. This one suggests ways to apply AI in that overcome these challenges to realize the potential of AI in the real “healthcare” world. Strategically Choose use cases well, and design to get quick wins to prove ROI early. You will likely encounter a lot of resistance to … Continue reading Applying AI in real “healthcare” world
A number of trends have paved the way for increasing adoption of machine learning (ML) in healthcare. We’re capturing more volume and types of health data than ever. Software for ML are evolving fast. Hardware advances have made the computing power cheaper, more agile, modular and scaleable than ever. However, despite these significant advances, adoption … Continue reading Challenges to applying ML in healthcare
Some of you may remember the 1998 movie The Horse Whisperer. In it, Robert Redford’s character, who had a remarkable talent to understand horses, was able to help a horse and its owner overcome fears and recover from a tragic accident. It’s a lovely slow paced movie, well worth enjoying a popcorn over 🙂 In … Continue reading A health data whisperer’s tricks
Advances in computing power and in machine learning techniques are rapidly changing how humans utilize data. Aside from the core statistical issues, what are the questions that an analyst needs to consider when doing predictive modeling in practice? This post describes a few of these practical considerations. I like to use the 5 Ws to … Continue reading Practical considerations for Predictive Modeling
In this (technical) post, I illustrate how to calculate commonly used goodness of fit statistics. Goodness of fit statistics are essential components to doing statistical model building. They are how you tell whether your model is performing well ito explaining the patterns in your data. In practice, most of these are done automatically from the … Continue reading On being Sensitive and Specific – analytically speaking
The Health Analytics Guru 