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. 2021 Dec 10;11:23823. doi: 10.1038/s41598-021-03204-z

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Graphical description of the framework. (A) Each paper-based report is manually transformed into an image file by a common digital scanner (right upside, an example of paper-based report from the Pathology Unit of the IRCCS Istituto Tumori “Giovanni Paolo II” of Bari, Italy). Then, the image is uploaded into ARGO through a web interface (black block), transformed in structured text through OCR and saved (by an NLP approach) as structured data in a database via webserver. “Diagnosis” attribution is carried out via API connecting ARGO with SEER servers (blue block). Finally, ARGO automatically populates eCRFs via API (red block). (B) Representative picture of REDCap dashboard for a single case report including “Demography” and “Disease parameters” forms (red bullets). Abbreviations. ARGO: Automatic Record Generator for Onco-hematology, OCR: Optical Character Recognition, NLP: Natural Language Processing, SEER: Surveillance, Epidemiology, and End Results, eCRFs: electronic Case Report Forms, API: Application Programming Interface, REDCap: Research Electronic Data-Capture, ID: Identification.