Geisinger and Eisai to test real-world validity of AI-powered Passive Digital Marker (PDM) in detecting early cognitive impairment and dementia

Geisinger and Eisai to test real-world validity of AI-powered Passive Digital Marker (PDM) in detecting early cognitive impairment and dementia
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Research collaboration will test novel algorithm’s effectiveness on Geisinger data (press release):

Geisinger and Eisai Inc. Today announced a collaborative effort to study the potential effectiveness of an artificial intelligence (AI) tool in the detection of cognitive impairment that could identify dementias, including Alzheimer’s disease (AD). If effective, the AI ​​tool could potentially be developed to support the early detection and staging of cognitive impairment and dementia, leading to appropriate additional testing for the clinical, biological diagnosis and treatment of dementias such as AD.

The research collaboration will study the use of an algorithm trained on a set of de-identified patient data to identify individuals likely to have cognitive impairment. The algorithm, known as a Passive Digital Marker (PDM), was developed and tested by researchers at Purdue University and Indiana University … “AI technology has the potential to transform medicine,” said Yasser El-Manzalawy, Ph.D., principal investigator and assistant professor of Translational Data Science and Informatics at Geisinger. “AI-based tools can efficiencies scan massive amounts of healthcare data and identify hidden patterns. These patterns can be used to detect diseases, like cancer and dementia, at an early stage…”

As an implementation scientist, it is always exciting to have other scientists evaluate the reproducibility of the performance of our passive digital marker in very different populations,” said Malaz Boustani, MD, Richard M. Fairbanks Professor of Aging Research at Indiana University. “Reproducibility is the cornerstone of scientific progress.”

About the Passive Digital Marker (PDM):

Predicting dementia with routine care EMR data (Artificial Intelligence in Medicine). From the Abstract:

Our aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia.

Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source. These data embody a rich knowledge and make related medical applications easy to deploy at scale in a cost-effective manner. Specifically, the model is trained by using structured and unstructured data from three EMR data sets: diagnosis, prescriptions, and medical notes…

The results show that the combined model is generalizable across multiple institutions and is able to predict dementia within one year of its onset with an accuracy of nearly 80% despite the fact that it was trained using routine care data. Moreover, the analysis of the models identified important predictors for dementia. Some of these predictors (eg, age and hypertensive disorders) are already confirmed by the literature while others, especially the ones derived from the unstructured medical notes, require further clinical analysis.

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