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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 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
As mentioned before, I'm not a fan of using advanced analytic techniques for the sake of intellectual pursuits. I'm a HUGE fan of asking good questions, framing analysis well, knowing data well, quickly arriving at actionable insights. A trained data scientist's toolbox has many statistical techniques, each of which has strengths and weaknesses. A health … Continue reading Decision Trees – intro
In this post, I describe the statistical concepts that I have found most relevant in health data analytics. First and foremost, I’m not a fan of using advanced statistical techniques for the sake of using them. In healthcare, the audience of your analysis is often non-statisticians (bio statistics research arena aside), so advanced statistical concepts … Continue reading Just the Statistics you need
The Analytics Pro 