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:(more…)
The ETL (extract, transform and load) process by which organizations prepare data for storage is an essential part of modern database systems, particularly used for business intelligence applications. The problem is that it can be inefficient and slow — too slow for companies to do real-time and streaming analytics.(more…)
IMS Health’s Longitudinal Access and Adjudication Data (LAAD) has revolutionized how companies view data, and structures the information in an easier way for all parties involved. In short, “LAAD is an anonymized patient sample dataset that captures longitudinal pharmacy (both retail and specialty) and medical claims. It was used to quantify payer channel distribution, co-pay card penetration, and low-income subsidy prevalence for these products.”(more…)
Introduction to Machine Learning with Random Forest (Pharma/Genetics)
To pick up where we left off last blog post, we discussed the potential of predictive analytics in the genetics of cancer. I aim to achieve this by using the aforementioned Random Forest classification algorithm.(more…)
How can AI technology improve the healthcare industry?
Our last blog post explored the implications of a recent study on artificial intelligence (AI) and machine learning business application. While the healthcare industry can expect incredible benefits from the adoption of AI technology, it remains under-adopted and receives below-average attention when compared to other industries.
This post aims to highlight the areas within healthcare where AI has potential. This technology can improve operations, patient experiences, medical procedures, and solve regulatory challenges.(more…)
An independent study on machine learning and artificial intelligence (AI) was released by the McKinsey Global Institute (MGI) in June 2017, focusing on the following central question: “Is artificial intelligence the next digital frontier, and if so, are businesses ready for it?”(more…)