IEEE Published Work: Medicine Expenditure Prediction

Abstract:
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.
 
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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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Machine Learning with CMS Public Healthcare Dataset, Part I

Background

From the outset, the term “Machine Learning” can seem very daunting to those unfamiliar with the technicalities of what this actually means, or so it seemed to me when I was initially assigned to develop a use case for one of these algorithms during my time here at RxDataScience.  From a quick study into the topic, Machine Learning (ML), put simply, is a branch of Artificial Intelligence (AI) that allows a system to automatically learn and improve itself without being explicitly instructed to, by using past and present data to predict certain outcomes [1].  The following video provides a gentle introduction into what ML is all about:

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Mental Health and Big Data: A Step in the Right Direction


  • Kate Spade – depression and anxiety
  • Robin Williams – severe depression
  • Pete Davidson – Borderline Personality Disorder
  • Demi Lovato – Bipolar disorder

Two gone, two battling a lifelong battle, like many Americans. With the stigma and bias surrounding the topic, the mental health community struggles to move forward. The research and treatment is at least a decade behind common chronic conditions, mainly because no one wants to talk about mental health due to the complexity and lack of understanding. 

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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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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.

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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.”

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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.

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Deep Diving into the Genetics of Cancer – An MLPerspective, 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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Big Data Application in Healthcare For Effective Patient Treatment

Within the first decade of the 21st century, the use of big data became very popular in many big industries. The methods for capturing big data have since evolved from traditional data lake systems to more integrated technologies that combine big data with all other systems within a company.1

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