Electronic case report forms generation from pathology reports by ARGO, automatic record generator for onco-hematology.

Autor: Zaccaria GM; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy. g.m.zaccaria@oncologico.bari.it., Colella V; Department of Electrical and Information Engineering, Politecnico of Bari, Bari, Italy., Colucci S; Department of Electrical and Information Engineering, Politecnico of Bari, Bari, Italy., Clemente F; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy., Pavone F; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy., Vegliante MC; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy., Esposito F; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy.; Department of Mathematics, University of Bari Aldo Moro, Bari, Italy., Opinto G; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy., Scattone A; Pathology Department, IRCCS Istituto Tumori 'Giovanni Paolo II', Bari, Italy., Loseto G; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy., Minoia C; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy., Rossini B; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy., Quinto AM; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy., Angiulli V; Clinical Engineering Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Bari, Italy., Grieco LA; Department of Electrical and Information Engineering, Politecnico of Bari, Bari, Italy., Fama A; Hematology, Azienda USL - IRCCS Di Reggio Emilia, Reggio Emilia, Italy., Ferrero S; Division of Hematology 1, AOU 'Città Della Salute e Della Scienza di Torino', Torino, Italy.; Department of Molecular Biotechnologies and Health Sciences, University of Torino, Torino, Italy., Moia R; Division of Hematology, Azienda Ospedaliero-Universitaria Maggiore Della Carità Di Novara, Novara, Italy., Di Rocco A; Unit of Hematology, Azienda Ospedaliero-Universitaria Policlinico Umberto I, Roma, Italy., Quaglia FM; Department of Medicine, Section of Hematology, University of Verona, Verona, Italy., Tabanelli V; Division of Diagnostic Haematopathology, European Institute of Oncology, IRCCS, Milano, Italy., Guarini A; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy., Ciavarella S; Hematology and Cell Therapy Unit, IRCCS Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco, 65, Bari, Italy.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2021 Dec 10; Vol. 11 (1), pp. 23823. Date of Electronic Publication: 2021 Dec 10.
DOI: 10.1038/s41598-021-03204-z
Abstrakt: The unstructured nature of Real-World (RW) data from onco-hematological patients and the scarce accessibility to integrated systems restrain the use of RW information for research purposes. Natural Language Processing (NLP) might help in transposing unstructured reports into standardized electronic health records. We exploited NLP to develop an automated tool, named ARGO (Automatic Record Generator for Onco-hematology) to recognize information from pathology reports and populate electronic case report forms (eCRFs) pre-implemented by REDCap. ARGO was applied to hemo-lymphopathology reports of diffuse large B-cell, follicular, and mantle cell lymphomas, and assessed for accuracy (A), precision (P), recall (R) and F1-score (F) on internal (n = 239) and external (n = 93) report series. 326 (98.2%) reports were converted into corresponding eCRFs. Overall, ARGO showed high performance in capturing (1) identification report number (all metrics > 90%), (2) biopsy date (all metrics > 90% in both series), (3) specimen type (86.6% and 91.4% of A, 98.5% and 100.0% of P, 92.5% and 95.5% of F, and 87.2% and 91.4% of R for internal and external series, respectively), (4) diagnosis (100% of P with A, R and F of 90% in both series). We developed and validated a generalizable tool that generates structured eCRFs from real-life pathology reports.
(© 2021. The Author(s).)
Databáze: MEDLINE
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