In July 2023, we changed our name from AACC (short for the American Association for Clinical Chemistry) to the Association for Diagnostics & Laboratory Medicine (ADLM). The following page was written prior to this rebranding and contains mentions of the association’s old name.
Artery challenges
The Association for Diagnostics & Laboratory Medicine’s (formerly AACC) Data Analytics Steering Committee created a series of data challenges on the Artery. The challenges are a chance to learn and share data analytics skills with like-minded colleagues. You can submit an answer to these challenges or ask a question about them at any time. The Association for Diagnostics & Laboratory Medicine (ADLM) membership is required to access Artery posts.
- Basic Data Management
- Histograms
- Evaluating Correlations
- Calculation of the Anion Gap
- Subgroup Analyses
- Dirty Data
- Data Joining
- Data Joining 2
Predicting PTHrP results
In collaboration with the Informatics Section of the Department of Pathology and Immunology at Washington University School of Medicine, the ADLM Data Analytics Steering Committee invited competitors to develop an algorithm that predicts a patient's PTHrP result using laboratory data available at the time of order. The winning team—Yingheng Wang, Weishen Pan, He Sarina Yang, and Fei Wang, all of Cornell University—was invited to share their approach during the ADLM Informatics Division luncheon at the 2022 AACC Annual Scientific Meeting.
See the competition description and download the dataset to test your own skills at Kaggle.
Read more about the competition and meet the winners in CLN.
In this video, Yingheng Wang describes the winning team's approach to predicting PTHrP results.
Help with hemolysis
In collaboration with the Informatics Section of the Department of Pathology and Immunology at Washington University School of Medicine, the ADLM Data Analytics Steering Committee invited competitors to develop an algorithm to rank blood specimen collectors based on how much money could be saved over the following year if they are trained on how to prevent sample hemolysis. The winning team—Eric Olson of Babson Diagnostics, Dave DeCaprio of ClosedLoop, and Ethan Olson—was invited to share their approach at a scientific session during the 2023 ADLM Annual Scientific Meeting.
See the competition description and download the dataset to test your own skills at Kaggle.
FairLabs
The ADLM Data Analytics Steering Committee again collaborated with the Informatics Section of the Department of Pathology and Immunology at Washington University School of Medicine, as well as with ADLM's Health Equity & Access Division and Informatics Division, in challenging participants to create a sharable tool that effectively presents fairness metrics and actionable information to drive meaningful improvement in health equity. Competitors were also incentivized to engage with local experts and datasets to use their tools to further health equity at their own institutions.
The winning team—Nathan Breit, Jing Zhang, Joyce Liao, and Kate Crawford, all of the University of Washington—was invited to share their approach during the ADLM Health Equity & Access Division breakfast at ADLM 2024.
See the competition description and all of the entries at GitHub, and download the dataset to practice your own dashboard-building skills.
LabDocs unlocked
The 2025 ADLM Data Science Challenge seeks to leverage the power of AI to create a tool capable of quickly and accurately extracting and presenting user-requested information from complex document stores. The proposed solution would ensure that laboratory professionals can focus on impactful work rather than time-intensive document searches.
See the full competition description and enter your solution at GitHub. The deadline for entries is November 15, 2025.