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## EHRoes Abstract
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**Background**
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With a growing amount of patient data such as sequencing, imaging, and medical records, electronic health records (EHR) became central to drive research and improve diagnosis of patients by using multimodal data. But EHR usage is challenging as they require tools to format, digitize and interpret data. Today’s EHR exploitation tools ecosystem is heavily fragmented among tools to create EHR (format and digitize) and tools to interpret the data (exploration and diagnosis). Also most of the tools are specialized for one specific type of data, multiplying the number of different software needed for multimodal approaches. There is a strong need for a simple, all-rounder and flexible platform.
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With a growing amount of patient data such as sequencing, imaging, and medical records, electronic health records (EHR) are now central to drive research and improve diagnosis of patients by using multimodal data. Exploiting patient data is a challenge as patient dataexploitation tools ecosystem is often fragmented into tools to digitize and format the data and those interpreting the data (exploration, diagnosis). Furthermore, as most of the tools are specialized in one type of data, a multitude of software is needed for multimodal approaches. There is a strong need for a simple, all-rounder and flexible platform.
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**Results**
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In this paper we present EHRoes, an all-in-one web application for patients’ data digitization and exploration. With its module-based architecture, we developed four modules to: (i) create a set of standard vocabulary for a domain, (ii) automatically digitize free-text data to a set of standard terms, (iii) annotate images with standard vocabulary using automatic segmentation and (iv) generate an automatic visualization dashboard to provide insight on the data and perform automatic diagnosis suggestions. Using this platform, we successfully digitized 89 muscle histology reports from patients with congenital myopathies and we obtained an accuracy of 0.75 for congenital myopathy subtypes classification.
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In this paper we present EHRoes, an all-in-one web application to digitize and explore patient data. EHRoes has a module-based architecture, composed of four modules to: (i) create a standard vocabulary for a domain (ii) automatically digitize free-text data to a set of standard terms (iii) annotate images with standard vocabulary using automatic segmentation and (iv) generate an automatic visualization dashboard to provide insight on the data and perform automatic diagnosis suggestions. We demonstrated the utility of EHRoes by digitizing 40 artificial muscle histology reports of patients with congenital myopathies.
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**Conclusions**
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With EHRoes we created a platform for both patients data digitization and exploration that can handle image data and free-text data. As it uses user designed standard vocabulary, it is highly flexible to fit any domain of research. It can be used both as a patient registry with automatic diagnosis or as a research tool to explore a cohort of patients.
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A demo instance of the application is available at https://ehroes.lbgi.fr.
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With EHRoes we created a platform for both digitization and exploration of patient data that can handle image data and free-text data. As it uses user-designed standard vocabulary, it is highly flexible to fit any domain of research. It can be used both as a patient registry with automatic diagnosis or as a research tool to explore a cohort of patients. A demo instance of the application is available at https://ehroes.lbgi.fr.
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