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Feature engineering is an important step in analytics. Some may say this is THE most important step. I typically spend between 50-70% of the time of an analytic exercise on feature engineering. What is it: In the predictive modeling context, features are basically elements of the data. E.g. age of patients, diagnoses and procedures patients have had … Continue reading Feature engineering for health analytics
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
In this post, I describe risk adjustment, an important tool in health analytics. I’ll cover what it is, how it is used, how it is done and how well it performs. What it is In the sport of boxing, matches are held between two boxers of the same weight class and gender. A match would … Continue reading Risk Adjustment 101
In this post I describe how I would go about using health analytics to identify patients most at risk of an opioid overdose. The Facts In 2016, there were 42,249 deaths in the US that involved opioids. That equates to a 13.3/100,000 mortality rate, and approx. one opioid related death every 10 minutes! By the time … Continue reading On Opioids and Analytics
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