Exciting research at the frontier of artificial intelligence and data science in laboratory medicine was presented at ADLM 2024 (formerly the AACC Annual Scientific Meeting & Clinical Lab Expo).
One study leveraged a National Institutes of Health (NIH) research cohort and a machine learning model to predict outcomes for patients with multiple myeloma, and another introduced a model that could help to lower worldwide mortality rates from sepsis.
Advancing precision medicine for multiple myeloma
Diagnosing and monitoring the progression of the blood cancer multiple myeloma involves many factors and is complicated by disparities across demographic groups, as well as imbalanced datasets. To better predict outcomes for patients with multiple myeloma, a team of researchers from the NIH’s All of Us Research Program, led by Dr. Thomas Houze, developed machine learning models tailored for different demographic groups diagnosed with multiple myeloma.
The All of Us Research Program is an NIH initiative that seeks to collect and study the health data of 1 million or more people living in the U.S. Because the All of Us database contains a wide range of participants from diverse backgrounds, it is an extremely valuable resource for training a machine learning model to make precise, individualized predictions for patients with a complex disease like multiple myeloma.
When developing their model, the NIH researchers employed the Synthetic Minority Over-Sampling Technique (SMOTE), a machine learning technique used to resolve imbalanced datasets. This ensures that the model makes useful and accurate predictions for smaller groups in the database. Without SMOTE, “the larger datasets would dominate the signal, so you get very good predictions for people of European genetic ancestry, but very poor predictions for people of Asian or African genetic ancestry,” Dr. Houze said. “This was something that I thought needed to be done and it’s a very recent capability in this field.”
Applying SMOTE to the data resulted in significant improvements in prediction accuracy for minority groups within the multiple myeloma patient population, the researchers found, which in turn could improve care for these groups. The technique may also enable precision medicine in areas beyond oncology, according to Dr. Houze.
“Once you get this methodology working with our data, you can apply it to Alzheimer’s, cardiovascular disease, mental health, and other areas,” Dr. Houze said.
Predicting sepsis risk with machine learning
Sepsis is a major global health concern. It’s responsible for approximately 11 million deaths annually, representing the leading cause of hospital readmissions and mortality worldwide. Early diagnosis and appropriate treatment could prevent 80% of sepsis-related deaths, but the majority of sepsis cases occur outside the hospital, making timely detection challenging.
Using data from more than 25,000 sepsis and non-sepsis cases, Dr. Raj Gopalan of BSRM Consulting created a machine learning model to identify a patient’s risk of developing sepsis up to 1 week before hospital admission. The model’s input parameters include age, gender, and data from routine blood tests such as complete blood counts, differential counts, comprehensive metabolic panels, and lipid panels that were recorded up to 1 week before sepsis diagnosis. The model demonstrated 99% accuracy in predicting sepsis risk, and identified calcium, protein, liver enzymes, hematocrit, white blood cells, and cholesterol as key contributors to sepsis risk prediction.
Going forward, this model might be used alongside other similar models to make accurate predictions across various health conditions, according to Dr. Gopalan.
“As soon as you receive blood test results, they can be processed through various cancer and chronic disease models — not just one, but 40 or 50 — providing insights into a patient’s risk levels. This allows for additional, specific testing to confirm or rule out any risks associated with these conditions,” he said.
