Led by Helmholtz Munich, scientists have developed an accessible software solution specifically designed for the evaluation of complex medical health data. The open-source software called ‘ehrapy’ enables researchers to structure and systematically examine large, heterogeneous datasets. The software is offered to the worldwide scientific community to make use of and further develop.
Ehrapy is meant to fill a critical gap within the evaluation of health data, says Lukas Heumos, one in every of the major developers and a scientist on the Institute of Computational Biology at Helmholtz Munich and the Technical University of Munich (TUM): “Until now, there have been no standardized tools for systematically and efficiently analyzing diverse and complicated medical data. We have modified that with ehrapy.” The team behind ehrapy comes from biomedical research and has extensive experience in analyzing complex scientific datasets. “The healthcare sector faces similar challenges in data evaluation as those working in laboratories,” noted Heumos initially of the ehrapy project.
Exploratory Approach — Hypothesis-Free Evaluation
Along with many other contributors, Heumos has used his expertise in scientific software development to create an answer for analyzing patient data: “Ehrapy can uncover latest patterns and generate insights without having to investigate the information based on a selected assumption or hypothesis.” This exploratory approach, says Heumos, is a novel feature of ehrapy.
Ehrapy allows researchers to sort, group, and analyze large, heterogeneous, and complicated datasets with none pre-existing hypotheses. This opens up latest insights that may then be explored further. Heumos explains: “The exploratory approach brings fresh perspectives to health data evaluation. Attributable to their complexity and heterogeneity, these data are sometimes not analyzed as effectively as they could possibly be.” Ehrapy thus opens latest avenues for making health data more useful for medical research and practice.
The Long-Term Goal: Routine Use in Clinical Practice
Ehrapy was designed as open-source software from the start. “It was necessary to us to make the software available to the scientific community from day one,” emphasizes Heumos. The software is offered as a Python package on GitHub, a web-based platform for software development, and might be used and further developed by researchers worldwide.
Currently, ehrapy focuses on efficiently and quickly analyzing research datasets, similar to those stored in large health research centers. “Routine use in clinical practice is a long-term goal, but for now, we’re concentrating on providing the research community with a strong tool,” says Heumos.
In the long run, the team plans to offer standardized databases for electronic health records (EHRs). These databases will enable higher integration and evaluation of enormous volumes of medical data. Moreover, this can facilitate the event of EHR atlases that may function reference datasets for contextualizing and annotating latest datasets.
A Long Journey
“Ehrapy enables comprehensive data evaluation across systems, which is usually a key step for future AI systems in medicine. I subsequently hope for a comparatively quick adoption at various sites,” says Prof. Fabian Theis, Director of the Institute of Computational Biology at Helmholtz Munich and TUM Professor: “Establishing such technologies in medicine is a lengthy process that may take many years. Our goal is to bridge the gap between biomedical research and practical application in medicine.” Theis further explains that the event team is specializing in exploratory data evaluation methods in a holistic form to more easily reveal hidden connections. “We’re also attempting to support academic and industrial players within the healthcare sector.”
Ehrapy on GitHub: https://github.com/theislab/ehrapy
