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As a result, there is a significant demand for the digitization of information from these paper-based reports.\u00a0However,\u00a0the digitization of\u00a0paper-based\u00a0laboratory reports into a\u00a0structured data format can be challenging due to their\u00a0non-standard layouts, which includes various data types such as text,\u00a0numeric values,\u00a0reference\u00a0ranges,\u00a0and units. Therefore, it is crucial to develop a highly scalable and lightweight technique that can effectively identify and extract information from laboratory test reports and convert them\u00a0into a structured data format for downstream tasks.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We developed an end-to-end Natural Language Processing (NLP)-based pipeline for extracting information from paper-based laboratory test reports. Our pipeline consists of two main modules: an optical character recognition (OCR) module and an information extraction (IE) module. The OCR module is applied to locate and identify text from scanned laboratory test reports using state-of-the-art OCR algorithms. The IE module is then used to extract meaningful information from the OCR results to form digitalized tables of the test reports. The IE module consists of five sub-modules, which are time detection, headline position, line normalization, Named Entity Recognition (NER) with a Conditional Random Fields (CRF)-based method, and step detection for multi-column. Finally, we evaluated the performance of the proposed pipeline on 153 laboratory test reports collected from Peking University First Hospital (PKU1).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In the OCR module, we evaluate the accuracy of text detection and recognition results at three different levels and achieved an averaged accuracy of 0.93. In the IE module, we extracted four laboratory test entities, including test item name, test result, test unit, and reference value range. The overall F1 score is 0.86 on the 153 laboratory test reports collected from PKU1. With a single CPU, the average inference time of each report is only 0.78\u00a0s.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>In this study, we developed a practical lightweight pipeline to digitalize and extract information from paper-based laboratory test reports in diverse types and with different layouts that can be adopted in real clinical environments with the lowest possible computing resources requirements. The high evaluation performance on the real-world hospital dataset validated the feasibility of the proposed pipeline.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02346-6","type":"journal-article","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T14:02:09Z","timestamp":1699279329000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Extracting laboratory test information from paper-based reports"],"prefix":"10.1186","volume":"23","author":[{"given":"Ming-Wei","family":"Ma","sequence":"first","affiliation":[]},{"given":"Xian-Shu","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Ze-Yu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Shi-Yu","family":"Shang","sequence":"additional","affiliation":[]},{"given":"Ling","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Pei-Lin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Yu-Chen","family":"Han","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Zong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"key":"2346_CR1","doi-asserted-by":"publisher","first-page":"2174","DOI":"10.1377\/hlthaff.2015.0992","volume":"34","author":"J Adler-Milstein","year":"2015","unstructured":"Adler-Milstein J, DesRoches CM, Kralovec P, Foster G, Worzala C, Charles D, et al. 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Ethics approval was granted and the informed consent was waived by the Ethics Committee of Peking University First Hospital (18th May 2021\/No. 2021(190)). All methods were performed in accordance with the relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no conflicts of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"251"}}