Name: Juho Lähteenmaa
Function: Data Analyst
Partner: HUS
Short biography
I am currently a PhD Statistic student in Aalto University. I have a master’s degree in economics from University of Helsinki. I am working currently in HUS as a data analyst in ONCOVALUE project. I have worked at HUS as a data analyst for almost four years: this work has included data mining from the Data Lake, data consulting in reporting projects, and offering data-based knowledge for clinicians, for example epidemic forecasting during COVID-19 pandemic. I have earlier experience in social science research and data analysis in ICT industry.
Contribution to ONCOVALUE Research
My primary focus involves working with HUS’s EMR data. This includes tasks such as investigating the data, conducting data wrangling, harmonizing it into a common data format, and performing data analysis.
I am developing the statistical methods that can be used in creating real-world evidence about the cancer treatments. All this work is done in close collaboration with other ONCOVALUE partners. An important part of ONCOVALUE and thereof my work is to document and share the cumulated knowledge, methods, and codes to benefit a broader audience.
Additionally, my contribution to the project includes writing of scientific articles that delve into interpreting observed treatment outcomes, comparing these results to previous trial evidence, and exploring the distribution of outcomes among different patient groups.
Challenges in ONCOVALUE
Creating comprehensive and comparable real-world evidence from clinical data, particularly for complex disease groups like cancers, is a non-trivial task. The biggest challenges to ONCOVALUE project are defining the common data concepts, collect the data robustly, and harmonize the data. It involves multiple crucial steps that require close collaboration among clinicians, technical experts, and consortium partners.
These steps include defining relevant data, developing processes and methods for collecting real-world data as part of clinical routines, and harmonizing data and concepts to ensure comparability across different clinics. Even with the utilization of machine learning methods in structuring data, collaboration remains essential. Both technical experts and clinicians play vital roles in the development of machine learning models, which is something that is also part of ONCOVALUE objects.
Expectations and Aspirations
I am thrilled to be part of this multidisciplinary team, providing an exciting opportunity to learn from experts in oncology, health technology assessment (HTA), data, and technology. The ONCOVALUE project encompasses a wide array of intriguing subjects, including health economics, natural language processing, image AI, federated learning, statistical methods, with a central focus on cancer treatments. I am eager to delve deeply into these diverse and captivating topics. I am very happy to do my PhD studies to this project.
