November 14, 2021 - Parul Saini, Webmedy Team
Clinical Trials are expensive, but these are essential processes as modern medicine sees new drug advancements. Clinical trials are the process of disease research that helps pharmaceutical companies learn more about how drugs function and which side effects they might cause before beginning human testing. A clinical trial is conducted as a group of clinical studies that are designed to answer specific questions about the safety or effectiveness of an investigational drug.
Analytics plays a big role in modeling clinical trials and predictive analytics is one such technique that has been embraced by clinical researchers. Predictive models are a tool of increasing value for practicing personalized, patient-centered medicine, providing both patients and their clinicians with individualized information on prognosis or response to therapy.
Now more and more companies are using Big Data, Predictive Analytics, and predictive modeling technology to enhance their decision-making. These models find patterns in historical clinical trials data and the latest advancements in drug design to find an eligible patient for a trial. Predictive models primarily capture relationships among many factors to assess the risks.
Predictive modeling is a widely used clinical trials application of predictive analytics that can be applied to extract useful information from clinical trial datasets, trends, and associations in large clinical trial datasets with many variables for better decision making – ultimately leading to more accurate clinical research results. Predictive analytics is a clinical research tool that can be used to improve the success rate of clinical trials. Predictive analytics are being applied in clinical research to improve the success rate of clinical studies.
Predictive models use many techniques ranging from Data Mining to Machine Learning (ML) and Artificial Intelligence (AI). These models find patterns in historical clinical trials data and the latest advancements in drug design to find an eligible patient for a trial. Predictive models primarily capture relationships among many factors to assess the risks. It makes this branch of data analytics well-suited to address the most profound challenges that researchers face in clinical trials.
Predictive models extract useful information from a patients' medical record and compare it with the ongoing trials to suggest matching studies. By extracting information from EHRs and medical image databases, independent software vendors (ISVs) can help researchers make better predictions about patient eligibility, giving them a robust solution for their patient enrollment problems.
Predictive analytics are being used to predict which patients will respond favorably or poorly to a treatment based on their genetic make-up, age, medical history, and other information. Clinical research analysts may also use predictive analytics to detect adverse events during clinical trials by analyzing real-world evidence sources such as EHRs and claims data, in addition to clinical studies. This can be done through predictive analytics models that examine potential clinical events that could affect or influence clinical trials, such as hospitalization or death.
Assessing interactions between drugs used to treat different diseases or disorders. Predictive/machine learning modeling can be used to extract insights into the adverse events that could occur when two or more drugs are given together. It can also help to identify lower-risk interactions through analysis of available clinical data and in silicon clinical studies.
Clinical trial researchers can use clinical research data and predictive analytics to predict which patients will be most likely to experience certain side effects.
Clinical research analysts may also use predictive analytics to predict which patients will respond favorably or poorly to a treatment based on their genetic make-up age medical history and other information. Big Data is also allowing for more complex trials by permitting iterative changes in parameters based on real-time outcomes. This is only the tip of the iceberg, with these technologies improving virtually every aspect of the clinical trials process, from enrollment and planning to data collection, data management, and comprehensive data analysis.
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