Two breaking studies presented at ADLM 2024
CHICAGO, July 31,
2024 /PRNewswire/ -- Exciting research at the
frontier of artificial intelligence and data science in laboratory
medicine was presented today 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.
"Sepsis is difficult to diagnose because the symptoms develop
rapidly and it often mimics other infectious conditions," Dr.
Gopalan said. "The model can synthesize a vast amount of patient
lab test results to make predictions."
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.
Session information
ADLM 2024 registration is free for members of the media.
Reporters can register online here:
https://xpressreg.net/register/adlm0824/media/landing.asp
Abstract B-111: Advancing precision medicine in multiple
myeloma: Addressing demographic variabilities and imbalanced data
in the NIH All of Us Research Program cohort
Abstract B-137: Sepsis risk prediction using machine learning
(ML) and routine blood markers up to 1 week before emergency
admission
Both abstracts will be presented during:
Scientific poster session
Wednesday, July 31
9:30 a.m. – 5
p.m. (presenting authors in attendance from 1:30 –
2:30 p.m.)
The session will take place in the Poster Hall on the Expo
show floor of McCormick Place, Chicago.
About ADLM 2024
ADLM 2024 (formerly the AACC Annual Scientific Meeting &
Clinical Lab Expo) offers 5 days packed with opportunities to learn
about exciting science from July 28-August
1 in Chicago. Plenary
sessions will explore the projected consequences of ending abortion
protection, new HIV prevention options, lymphoma biomarkers and
therapeutic targets, pharmacogenetic testing in precision health,
and the need for clinical trials of laboratory tests.
At the ADLM 2024 Clinical Lab Expo, more than 900 exhibitors
will fill the show floor of the McCormick Place Convention Center
in Chicago, with displays of the
latest diagnostic technology, including but not limited to
artificial intelligence, point-of-care, and automation.
About the Association for Diagnostics & Laboratory
Medicine (ADLM)
Dedicated to achieving better health through laboratory
medicine, ADLM (formerly AACC) brings together more than 70,000
clinical laboratory professionals, physicians, research scientists,
and business leaders from around the world focused on clinical
chemistry, molecular diagnostics, mass spectrometry, translational
medicine, lab management, and other areas of progressing laboratory
science. Since 1948, ADLM has worked to advance the common
interests of the field, providing programs that advance scientific
collaboration, knowledge, expertise, and innovation. For more
information, visit www.myadlm.org.
Christine DeLong
ADLM
Associate Director, Communications & PR
(p) 202.835.8722
cdelong@myadlm.org
Molly Polen
ADLM
Senior Director, Communications & PR
(p) 202.420.7612
(c) 703.598.0472
mpolen@myadlm.org
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