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.

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.


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?”