Scientists create a labor-saving automated method for studying
electronic health records
Study suggests new method is as effective as manually-based 'gold-
standard' at classifying a diagnosis

Date:
September 2, 2021
Source:
The Mount Sinai Hospital / Mount Sinai School of Medicine
Summary:
A new, automated, artificial intelligence-based algorithm can
learn to read patient data from electronic health records. In
a side-by-side comparison, scientists showed that their method
accurately identified patients with certain diseases as well as
the traditional, 'gold- standard' method, which requires much more
manual labor to develop and perform.



FULL STORY ==========================================================================
In an article published in the journal Patterns, scientists at the
Icahn School of Medicine at Mount Sinai described the creation of a new, automated, artificial intelligence-based algorithm that can learn to read patient data from electronic health records. In a side-by-side comparison,
they showed that their method, called Phe2vec (FEE-to-vek), accurately identified patients with certain diseases as well as the traditional, "gold-standard" method, which requires much more manual labor to develop
and perform.


========================================================================== "There continues to be an explosion in the amount and types of data electronically stored in a patient's medical record. Disentangling this
complex web of data can be highly burdensome, thus slowing advancements in clinical research," said Benjamin S. Glicksberg, PhD, Assistant Professor
of Genetics and Genomic Sciences, a member of the Hasso Plattner Institute
for Digital Health at Mount Sinai (HPIMS), and a senior author of the
study. "In this study, we created a new method for mining data from
electronic health records with machine learning that is faster and less
labor intensive than the industry standard. We hope that this will be
a valuable tool that will facilitate further, and less biased, research
in clinical informatics." The study was led by Jessica K. De Freitas,
a graduate student in Dr.

Glicksberg lab.

Currently, scientists rely on a set of established computer programs, or algorithms, to mine medical records for new information. The development
and storage of these algorithms is managed by a system called the
Phenotype Knowledgebase (PheKB). Although the system is highly effective
at correctly identifying a patient diagnosis, the process of developing
an algorithm can be very time-consuming and inflexible. To study a
disease, researchers first have to comb through reams of medical records looking for pieces of data, such as certain lab tests or prescriptions,
which are uniquely associated with the disease. They then program the
algorithm that guides the computer to search for patients who have those disease-specific pieces of data, which constitute a "phenotype." In turn,
the list of patients identified by the computer needs to be manually double-checked by researchers. Each time researchers want to study a
new disease, they have to restart the process from scratch.

In this study, the researchers tried a different approach -- one in
which the computer learns, on its own, how to spot disease phenotypes
and thus save researchers time and effort. This new, Phe2vec method was
based on studies the team had already conducted.

"Previously, we showed that unsupervised machine learning could be a
highly efficient and effective strategy for mining electronic health
records," said Riccardo Miotto, PhD, a former Assistant Professor at
the HPIMS and a senior author of the study. "The potential advantage
of our approach is that it learns representations of diseases from the
data itself. Therefore, the machine does much of the work experts would normally do to define the combination of data elements from health records
that best describes a particular disease." Essentially, a computer was programmed to scour through millions of electronic health records and
learn how to find connections between data and diseases.

This programming relied on "embedding" algorithms that had been previously developed by other researchers, such as linguists, to study word
networks in various languages. One of the algorithms, called word2vec,
was particularly effective. Then, the computer was programmed to use
what it learned to identify the diagnoses of nearly 2 million patients
whose data was stored in the Mount Sinai Health System.

Finally, the researchers compared the effectiveness between the new and
the old systems. For nine out of ten diseases tested, they found that
the new Phe2vec system was as effective as, or performed slightly better
than, the gold standard phenotyping process at correctly identifying a diagnoses from electronic health records. A few examples of the diseases included dementia, multiple sclerosis, and sickle cell anemia.

"Overall our results are encouraging and suggest that Phe2vec is
a promising technique for large-scale phenotyping of diseases in
electronic health record data," Dr. Glicksberg said. "With further
testing and refinement, we hope that it could be used to automate many
of the initial steps of clinical informatics research, thus allowing
scientists to focus their efforts on downstream analyses like predictive modeling." This study was supported by the Hasso Plattner Foundation,
the Alzheimer's Drug Discovery Foundation, and a courtesy graphics
processing unit donation from the NVIDIA Corporation.

========================================================================== Story Source: Materials provided by The_Mount_Sinai_Hospital_/_Mount_Sinai_School_of Medicine. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Jessica K. De Freitas, Kipp W. Johnson, Eddye Golden, Girish
N. Nadkarni,
Joel T. Dudley, Erwin P. Bottinger, Benjamin S. Glicksberg,
Riccardo Miotto. Phe2vec: Automated disease phenotyping based on
unsupervised embeddings from electronic health records. Patterns,
2021; 100337 DOI: 10.1016/j.patter.2021.100337 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210902125116.htm

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