Machine Learning For Health Sciences

by | Oct 12, 2018 | Case Study | 0 comments

Using Robotic Process Automation to improve Clinical Trials

Realization of value and implementation

Clinical research requires analyzing large amounts of data to validate results and ensure product safety. Using outdated methods results in increased cost due to extensive human interaction and long timelines.

Machine Learning (ML) can reduce cost by increasing the processing speed of the data required for the trial. By automating the data analysis process, a team can accomplish more in less time with the same number of people.


• Analysis of potential candidates for clinical trials

• Analysis of trial data to identify new research directions and areas to reduce clinical risk

• Collection of data from health records

• Optimization of trial sample sizes for greater accuracy

Advoqt Technology Group drives Digital Transformation for mid-sized businesses. Seasoned consultants, proprietary Machine Learning and Robotic Process Automation technology, and a vetted ecosystem of niche partners allow us to deliver integrated solutions that give you a measurable competitive advantage.

Healthsciences and pharmaceutical companies worldwide are adopting machine learning to reduce clinical risk and decrease time to market. ML and robotic process automation allow companies to analyze larger and more complex data sets, and deliver faster, more accurate results. From clustering to trial result segmentation to risk prediction — Robotic Process Automation can pave the way for more efficient and successful clinical trials. ML is frequently coupled with data science and big data analysis to increase the value of the technology in clinical trials.

ML models perform unstructured data mining to measure the impact of experimental treatments for patients and learn of any potential side effects quicker.

The results of the initial data analysis are then used to adjust the treatment plans and generate new data sets. The machine learning model continuously analyses data and can be programmed to trigger alerts based on patterns that would normally be harder to detect by a person.

The value of the initial investment in a ML solution can be realized in future trials. Artificial Intelligence engineers can create reusable ML processes that are applicable to multiple clinical trials which means that the additional cost per trial is kept to a minimum. For more complex or unique scenarios, engineers can build a custom ML solution that can be reused in future trials of the same type. Learning models can be programed to self-train using previous decisions and corrections made by human supervisors, which means the solution can be used multiple times with minimal risk of loss of accuracy.

Implementation of machine learning in clinical trials results in lower cost, decreased time to market, and increased in safety. To learn more about how Advoqt can help you implement ML in your organization please visit our website at www.Advoqt.com or call (617) 600-8161.

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