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
EHRs… ahhh, love them, hate them. If you do any work in the medical space, you will need to interact with them. In this post, I talk about what they are, what they do, their strengths and weaknesses. What are they? EHRs (electronic health records) are digital records of patient’s clinical records. EHR platforms are software platforms … Continue reading EHRs, what’s all the fuss about?
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
This post describes some of the commonly seen types of errors in healthcare data. Occasionally, memories flash across my mind, of the days I spent in maths lectures, of being amazed at the clean, efficient and elegant proofs that demonstrate the logical integrity of abstract maths theorems. Fast forward to today, I more often than … Continue reading Ugly side of healthcare data