Automated Generation of Synoptic Reports from Narrative Pathology Reports in University Malaya Medical Centre Using Natural Language Processing
Pathology reports represent a primary source of information for cancer registries. University Malaya Medical Centre (UMMC) is a tertiary hospital responsible for training pathologists; thus narrative reporting becomes important. However, the unstructured free-text reports made the information extrac...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/12/4/879 |
| _version_ | 1797436064490586112 |
|---|---|
| author | Wee-Ming Tan Kean-Hooi Teoh Mogana Darshini Ganggayah Nur Aishah Taib Hana Salwani Zaini Sarinder Kaur Dhillon |
| author_facet | Wee-Ming Tan Kean-Hooi Teoh Mogana Darshini Ganggayah Nur Aishah Taib Hana Salwani Zaini Sarinder Kaur Dhillon |
| author_sort | Wee-Ming Tan |
| collection | DOAJ |
| description | Pathology reports represent a primary source of information for cancer registries. University Malaya Medical Centre (UMMC) is a tertiary hospital responsible for training pathologists; thus narrative reporting becomes important. However, the unstructured free-text reports made the information extraction process tedious for clinical audits and data analysis-related research. This study aims to develop an automated natural language processing (NLP) algorithm to summarize the existing narrative breast pathology report from UMMC to a narrower structured synoptic pathology report with a checklist-style report template to ease the creation of pathology reports. The development of the rule-based NLP algorithm was based on the R programming language by using 593 pathology specimens from 174 patients provided by the Department of Pathology, UMMC. The pathologist provides specific keywords for data elements to define the semantic rules of the NLP. The system was evaluated by calculating the precision, recall, and F1-score. The proposed NLP algorithm achieved a micro-F1 score of 99.50% and a macro-F1 score of 98.97% on 178 specimens with 25 data elements. This achievement correlated to clinicians’ needs, which could improve communication between pathologists and clinicians. The study presented here is significant, as structured data is easily minable and could generate important insights. |
| first_indexed | 2024-03-09T10:57:17Z |
| format | Article |
| id | doaj.art-6cda3e9684284de49c9004c1ff6d2d91 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T10:57:17Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-6cda3e9684284de49c9004c1ff6d2d912023-12-01T01:32:20ZengMDPI AGDiagnostics2075-44182022-04-0112487910.3390/diagnostics12040879Automated Generation of Synoptic Reports from Narrative Pathology Reports in University Malaya Medical Centre Using Natural Language ProcessingWee-Ming Tan0Kean-Hooi Teoh1Mogana Darshini Ganggayah2Nur Aishah Taib3Hana Salwani Zaini4Sarinder Kaur Dhillon5Data Science & Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, MalaysiaLaboratory Department, Sunway Medical Centre, Bandar Sunway 47500, MalaysiaData Science & Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Information Technology, University Malaya Medical Centre, Kuala Lumpur 50603, MalaysiaData Science & Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, MalaysiaPathology reports represent a primary source of information for cancer registries. University Malaya Medical Centre (UMMC) is a tertiary hospital responsible for training pathologists; thus narrative reporting becomes important. However, the unstructured free-text reports made the information extraction process tedious for clinical audits and data analysis-related research. This study aims to develop an automated natural language processing (NLP) algorithm to summarize the existing narrative breast pathology report from UMMC to a narrower structured synoptic pathology report with a checklist-style report template to ease the creation of pathology reports. The development of the rule-based NLP algorithm was based on the R programming language by using 593 pathology specimens from 174 patients provided by the Department of Pathology, UMMC. The pathologist provides specific keywords for data elements to define the semantic rules of the NLP. The system was evaluated by calculating the precision, recall, and F1-score. The proposed NLP algorithm achieved a micro-F1 score of 99.50% and a macro-F1 score of 98.97% on 178 specimens with 25 data elements. This achievement correlated to clinicians’ needs, which could improve communication between pathologists and clinicians. The study presented here is significant, as structured data is easily minable and could generate important insights.https://www.mdpi.com/2075-4418/12/4/879pathology reportingsynoptic reportinginformation extractiontext miningnatural language processingrule based |
| spellingShingle | Wee-Ming Tan Kean-Hooi Teoh Mogana Darshini Ganggayah Nur Aishah Taib Hana Salwani Zaini Sarinder Kaur Dhillon Automated Generation of Synoptic Reports from Narrative Pathology Reports in University Malaya Medical Centre Using Natural Language Processing Diagnostics pathology reporting synoptic reporting information extraction text mining natural language processing rule based |
| title | Automated Generation of Synoptic Reports from Narrative Pathology Reports in University Malaya Medical Centre Using Natural Language Processing |
| title_full | Automated Generation of Synoptic Reports from Narrative Pathology Reports in University Malaya Medical Centre Using Natural Language Processing |
| title_fullStr | Automated Generation of Synoptic Reports from Narrative Pathology Reports in University Malaya Medical Centre Using Natural Language Processing |
| title_full_unstemmed | Automated Generation of Synoptic Reports from Narrative Pathology Reports in University Malaya Medical Centre Using Natural Language Processing |
| title_short | Automated Generation of Synoptic Reports from Narrative Pathology Reports in University Malaya Medical Centre Using Natural Language Processing |
| title_sort | automated generation of synoptic reports from narrative pathology reports in university malaya medical centre using natural language processing |
| topic | pathology reporting synoptic reporting information extraction text mining natural language processing rule based |
| url | https://www.mdpi.com/2075-4418/12/4/879 |
| work_keys_str_mv | AT weemingtan automatedgenerationofsynopticreportsfromnarrativepathologyreportsinuniversitymalayamedicalcentreusingnaturallanguageprocessing AT keanhooiteoh automatedgenerationofsynopticreportsfromnarrativepathologyreportsinuniversitymalayamedicalcentreusingnaturallanguageprocessing AT moganadarshiniganggayah automatedgenerationofsynopticreportsfromnarrativepathologyreportsinuniversitymalayamedicalcentreusingnaturallanguageprocessing AT nuraishahtaib automatedgenerationofsynopticreportsfromnarrativepathologyreportsinuniversitymalayamedicalcentreusingnaturallanguageprocessing AT hanasalwanizaini automatedgenerationofsynopticreportsfromnarrativepathologyreportsinuniversitymalayamedicalcentreusingnaturallanguageprocessing AT sarinderkaurdhillon automatedgenerationofsynopticreportsfromnarrativepathologyreportsinuniversitymalayamedicalcentreusingnaturallanguageprocessing |