Machine Learning with CMS Public Healthcare Dataset, Part II

Introduction to CMS OpenPayments Data Analysis using Machine Learning

In the previous blog we discussed the fundamental concept of what Machine Learning is and how it can be applied in the modern world of Pharmaceuticals and Healthcare, further to this we explored CMS Open Payments, the federal program that collects information about the payments drug and device companies make to their potential clients.

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Human Activity Recognition Using Pharma Machine Learning and IoT Devices, Part II

Decision Trees

As mentioned in my previous exert, I will be delving further into the complexity of the algorithm I used in my study. Following some research into decision trees and the impact they have had on healthcare and pharma I found that their presence has been assisting across the field since the early 90’s in the form of Evidence Based Medicine (EBM). The stages detailed in this process where summarised to:

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Human Activity Recognition Using Machine Learning & iOT Devices, Part I

Introduction

Coming from a background in computer science, I was familiar with Machine Learning and its capabilities although admittedly I had never considered the impact it could have on the world of healthcare.  Subsequent to joining the team here at RxDataScience, I was tasked with implementing a decision tree, in order to detect what activity a person, in a room was undertaking using data collected via a radio frequency identification tag and sensors mounted in corners of the room.

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Deep Diving into the Genetics of Cancer – A Machine Learning Perspective, Part I

Machine Learning in Genomics & Cancer Treatment

When I first arrived in the RxDS (RxDataScience) Headquarters here in the Research Triangle Park as a Machine Learning researcher, I was informed that my first task would be to ‘Redefine Cancer Treatment’. As I’m sure you can understand, coming from a scientific background my curiosity levels were sky high. I was told that the key to bringing personalized medicine to cancer could be found within the genomes of cancer patients. Therein lies a multitude of mutations and variants, with some being benign passengers in this journey and others the core malignant drivers of the cancer itself (McFarland, Mirny and Korolev, 2014). Successfully interpreting these mutations and variants, using either traditional methods or contemporary data science solutions could lead to new treatments with each giving patients with the same underlying causes a fighting chance to overcome their individual cancers.

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