Clinical Research

Machine Learning and Clinical Research

Machine Learning and Clinical Research

The healthcare and pharmaceutical drug industry is a profitable market in spite of experiencingsome fluctuationsin recent times. Machine learning-based clinical trial management tools are still at early stages of creating a revolution in this industry.

Challenges in Clinical Research

The clinical trial is a costly foundational pillar of thepharmaceutical drugor healthcare discovery process. Clinical trials are research studies that determine safety and efficacy of medical devices or treatments or therapeutic methods in humans. Major challenges faced by researchers in structuring clinical trials are low rates of success and high costs. Clinical trial data is collected from a variety of formats and sources which is increasing in complexity and volume on daily basis. In search of the next great medical breakthrough, Contract research organization and Trial sponsors deal with large digitized data sets and more complex treatment protocols. Studies are spreading across multiple sites to become more geographically expansive to countries for the right target patient population.

Machine learning in Clinical trials

Machine learning is a type of artificial intelligence (AI) that infers knowledge from raw data, concerned with development of algorithms and provides actionable recommendations to help researchers flag, predict and prevent risks associated with clinical research. Machine learning can improve the reliability, quality and safety of clinical trials by generating the knowledge required for better monitoring and decision making process during clinical trial. This includes patient adherence, site management and impact of diseases or treatments on patients participating in clinical trials.

Highly customizable platform can analyze and consume data from electronic trial master files, electronic health records, electronic patient outcomes or reports, electronic data capture systems, central lab data or safety data, quality management or issue management systems, e-Consent and e-Source technologies, wearable data and other formats. This platform uses data from any source or format to detect, predict, analyze, and manage risk in clinical trials without the cost and other challenges of upgrading or maintaining a data warehouse. Such platforms for better clinical trial management work on several machine learning algorithms that help users understand the data and offer timely recommendations on identifying common and specific risks. The customizable user interface algorithms can fit individual study requirements to run on demand or schedule and follow strict or timely recommendation criteria. Predictive machine learning closes issues and risks not visible to humans using statistical computation. A machine program provides almost real time analysis and accordingly alerts clinical trialprofessionals.

The majority of machine learning cases and emerging technologies for clinical trials have three primary applications of Patient Recruitment, Clinical Trial Design and Clinical Trial Optimization. Applications provided by majority of companies in their first few years of operation target patient recruitment with the strongest traction. Patient optimization and engagement are some of the newest applications of machine learning. International studies with multiple language options for thousands of patients will meet the needs of participants around the globe. Machine learning applications offer potential value to the clinical trial process and the pharmaceutical drug industry for complex and voluminous clinical data.

Online Course in Clinical Research and Data Analytics

James Lind Institute (JLI) provides online programs in Clinical Research &Pharmacovigilance, Medical Writing, Clinical Data Management, Regulatory Affairs & Quality Assurance, Pharmaceutical Medicine and Translational Medicine.

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