Data scientists and their role in clinical research
Published: 08.22.2023
Data Scientists become increasingly important in the digital transformation of clinical research. Find out what Data Science is, what role Data Scientists play in clinical trials, and why their impact on the industry is more relevant today than ever before here.
What is Data Science?
Data Science is a multidisciplinary field concerned with extracting knowledge from large amounts of data. Data Scientists use advanced analytical techniques to identify patterns, trends, and relationships in data. The goal is to use these insights for practical applications and in decision making. They usually develop AI or machine learning models for this purpose, which can take various forms and perform analysis or predictions autonomously.
The importance of Data Science has grown significantly in recent years as more and more companies and organisations recognise the value of using data in their decision making. For example, data science helps
- identifying hidden patterns and relationships. These insights can drive breakthrough innovations.
- tailoring products, services and medical treatments to individual needs. Personalised approaches lead to better outcomes.
- identifying and optimising inefficient processes. Efficient workflows save resources.
- predicting trends and developments. These forecasts are vital for making informed decisions in many areas, such as finance, economics and utilities.
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What role does Data Science play in clinical research?
With their expertise and analytical skills, Data Scientists are indispensable players in modern clinical research. Artificial Intelligence and Machine Learning have become essential in clinical research.
Developing a drug - from the initial idea to preclinical development of the drug to market approval - is a long and costly process. Yet only about 10% of drugs that start in the first phase actually receive approval. Analysis and predictions by Data Scientists and AI models reduce both time and cost, providing patients with faster access to life-saving medicines.
One example of the use of Data Science in clinical research is in the area of imaging techniques such as X-rays or MRI in the diagnosis and monitoring of disease. In the Hanse study, for example, AI assisted investigators in identifying lung cancer at an early stage, enabling early treatment and significantly increasing patients' chances of survival.
What are typical tasks of Data Scientists in clinical research?
No matter what industry they work in, Data Scientists work with data. In the case of clinical research, this is usually patient data. To draw valuable insights from the volumes of data that come together in clinical trials, Data Scientists use their broad expertise in disciplines such as statistics, computer science, mathematics, and machine learning. Here are some of the tasks Data Scientists face on a daily basis.
Data cleansing and preparation: Data is often incomplete, erroneous, or unstructured. Data Scientists clean and transform data to create a solid foundation for analysis. They remove duplicates, correct errors, and structure the data to make it usable for further steps.
Data Analysis: Before Data Scientists develop models, it is important to thoroughly examine the data. By using statistical methods, visualisation techniques and data mining algorithms, they identify patterns, correlations and outliers in the data. These findings serve as the basis for further analysis.
Model Development and Machine Learning: Data Scientists develop models and algorithms to enable prediction and pattern recognition. They use machine learning to train and apply models to data. Using techniques such as supervised or unsupervised learning, they identify patterns in data and are able to predict future events.
Data Visualisation and Communication: Data Scientists must transform complex technical concepts and results into understandable information for non-technical stakeholders. They create meaningful visualisations and reports that enable others to understand the results of data analysis and make informed decisions from them.
Continuous Improvement and Monitoring: Data is dynamic and constantly changing. Data Scientists monitor models and algorithms to ensure they are producing relevant and accurate results. They continuously optimise and improve their models to keep pace with changing requirements and data streams.
Want to learn more about Data Science at Alcedis? Take a deep dive into Alcedis Labs, where breakthrough innovations happen.