RxDataScience Blog


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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Machine Learning and Healthcare: Breast Cancer Diagnosis, Part I

Machine Learning and Healthcare: Breast Cancer Diagnosis

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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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Top Use Cases for Machine Learning in Pharma


Real-World Use Cases for AI & ML in Pharma

For decades, Pharmaceutical data analytics has been a largely manual and tedious task conducted by the commercial research, health outcomes, R&D and Clinical Study groups at Pharma companies both small and large. With the emergence of machine learning, artificial intelligence and other disruptive innovations, Pharma, like other industries has also started its slow but sure transition to a more agile, data-driven model – one where in-house research is supplemented by intelligence gathered by applying algorithms across terabytes of Physician Rx, Patient Claims and other related datasets.

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How Healthcare Can Prep for Artificial Intelligence, Machine Learning

Recently, Health IT Analytics published a blog on how the healthcare industry needs to prepare for advances in artificial intelligence and machine learning.

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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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Why Your Pharma CMO and Pharma CIO Need To Collaborate

In 2015, a survey by Accenture found that two-thirds of chief marketing officers (CMOs) in the pharmaceutical industry did not consider collaboration with their chief information officers (CIOs) as an important strategy. In the past, CMO/CIO collaboration was not a central focus as these two roles hardly ever coincided – CMOs generally focused on marketing technologies while CIOs focused on IT.

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Projected Pharmaceutical Data Analytics Trends 2018

Advanced analytics necessitate the use of complex statistics and mathematical systems to improve the business operations of a company. Evidence shows that companies that use advanced analytics have repeatedly reduced variability in product quality and simultaneously lowered costs and increased sales.

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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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How the 21st Century Cures Act and Real World Evidence Impacts the Drug Approval Process

The 21st Century Cures Act

In late December 2016 and with only three days remaining in session for the year, the U.S. Congress passed the 21st Century Cures Act, now commonly referred to as the Cures Act. This act laid out goals to “accelerate the discovery, development, and delivery of 21st century cures.” The law authorized $6.3 billion in funding, mostly for the National Institutes of Health. The Cures Act was first introduced nearly two years prior by seven bipartisan cosponsors, many of whom resided on the Science, Space, and Technology Committee. The legislation was supported by pharmaceutical manufacturers, as it had several important measures that would allow the healthcare and pharma industry to make more informed decisions to ultimately improve the patient journey. President Obama said the Cures Act will “modernize research and accelerate discovery” using data so that “treatment and healthcare can be tailored to individual patients.” It is a highly significant piece of legislation that will impact every pharmaceutical company and the way in which they manage, analyze, and leverage their real world data.

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