Whether you're exploring data analytics for the first time or refining advanced machine learning models, ADLM's data science challenges offer hands-on opportunities to build practical skills using authentic laboratory datasets.
Note: In July 2023, AACC (American Association for Clinical Chemistry) became the Association for Diagnostics & Laboratory Medicine (ADLM). Some historical references may use our former name.
The ADLM Data Analytics Steering Committee has developed a progressive series of challenges on the Artery platform where you can develop foundational to intermediate data skills at your own pace. These challenges encourage peer learning and knowledge sharing within the laboratory medicine community.
Submit solutions, ask questions, and learn from colleagues tackling the same problems. ADLM membership required to access Artery content.
The Challenge: Build an AI tool that rapidly extracts and presents information from complex laboratory document repositories.
Why It Matters: Laboratory professionals spend countless hours searching through policies, procedures, and technical documentation. An intelligent extraction tool would free up time for high-impact work while ensuring quick access to critical information.
How to Participate: View the complete challenge description and submit your solution on GitHub. Deadline: November 15, 2025
In early 2026, there will be a finalists webinar. Please come back to this page to learn more.
The Challenge: Create an accessible, shareable tool that visualizes fairness metrics and provides actionable insights to advance health equity in laboratory medicine.
What Made It Unique: This challenge encouraged participants to partner with local stakeholders and apply their tools to real institutional datasets, driving tangible equity improvements.
The Winners: Nathan Breit, Jing Zhang, Joyce Liao, and Kate Crawford from the University of Washington presented their solution at the ADLM 2024 Health Equity & Access pision breakfast.
Explore the Solutions: Review all competition entries and access the dataset for your own dashboard development on GitHub.
Developed in collaboration with the Informatics Section at Washington University School of Medicine and ADLM's Health Equity & Access and Informatics pisions.
The Challenge: Develop an algorithm to identify which phlebotomists would benefit most from hemolysis prevention training by predicting potential cost savings over the following year.
The Impact: Sample hemolysis leads to specimen rejection, patient redraw, delayed results, and increased costs. This challenge addressed a universal laboratory pain point through predictive analytics.
The Winners: Eric Olson (Babson Diagnostics), Dave DeCaprio (ClosedLoop), and Ethan Olson shared their winning approach at the 2023 ADLM Annual Scientific Meeting.
Test Your Skills: Access the competition description and dataset on Kaggle.
Developed in collaboration with the Informatics Section at Washington University School of Medicine.
The Challenge: Build a predictive model for parathyroid hormone-related protein (PTHrP) results using laboratory data available at the time of order.
Why It Mattered: Accurate prediction of PTHrP results could optimize test utilization and reduce unnecessary testing while maintaining diagnostic quality.
The Winners: Yingheng Wang, Weishen Pan, He Sarina Yang, and Fei Wang from Cornell University presented their machine learning approach at the 2022 AACC Annual Scientific Meeting.
Learn More:
Developed in collaboration with the Informatics Section at Washington University School of Medicine.
Skill Development: Progress from basic data manipulation to advanced machine learning using real laboratory datasets
Networking: Connect with data-savvy colleagues across clinical chemistry, informatics, and laboratory medicine
Recognition: Winning teams present their solutions at ADLM's Annual Scientific Meeting and related events
Real-World Impact: Address genuine challenges facing laboratory professionals today
Ready to enhance your data science skills? Explore the Artery challenges or data science competitions. All experience levels are welcome.
