IEEE Published Work: Medicine Expenditure Prediction

Machine learning (ML) offers a wide range of techniques to predict medicine expenditures using historical expenditures data as well as other healthcare variables. For example, researchers have developed multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN) models for predicting healthcare outcomes. However, recently proposed generative approaches (e.g., generative adversarial networks; GANs) are yet to be explored for time-series prediction of medicine-related expenditures. The primary objective of this research was to develop and test a generative adversarial network model (called “variance-based GAN or V-GAN”) that specifically minimizes the difference in variance between model and actual data during model training. For our model development, we used patient expenditure data of a popular pain medication in the US. In the V-GAN model, we used an LSTM model as a generator network and a CNN model or an MLP model as a discriminator network. The V-GAN model’s performance was compared with other GAN variants and ML models proposed in prior research such as linear regression (LR), gradient boosting regression (GBR), MLP, and LSTM. Results revealed that the V-GAN model using an LSTM generator and a CNN discriminator outperformed other GAN-based prediction models, as well as the LR, GBR, MLP, and LSTM models in correctly predicting medicine expenditures of patients. Through this research, we highlight the utility of developing GAN-based architectures involving variance minimization for predicting patient-related expenditures in the healthcare domain.

Human Activity Recognition Using Pharma ML 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:


Leveraging the Power of Real-time ETL for Better Pharmaceutical Insights

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.


Delivering Value from LAAD

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.”


Machine Learning for Pharma using Random Forest, Part II

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.


Machine Learning and AI in Healthcare: Practical Applications (Part 2)

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.


Machine Learning and AI in Healthcare Coming Up Short (Part 1)

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?”