Natural language processing in pathology: A scoping review

Journal of Clinical Pathology

Volumn 69, Issue 11, 2016, Pages 949-955

  Burger, Gerard ^a,b   Abu Hanna, Ameen ^b   De Keizer, Nicolette ^b   Cornet, Ronald ^b,c  

^a Symbiant Pathology Expert Centre   (Netherlands)

^b UNIVERSITY OF AMSTERDAM   (Netherlands)

^c LINKÖPING UNIVERSITY   (Sweden)

Author keywords

COMPUTER SYSTEMS; REPORTS; SURGICAL PATHOLOGY

Indexed keywords

CLINICAL PRACTICE; COLONOSCOPY; HUMAN; LIBRARY; MACHINE LEARNING; MEDICAL RECORD; MEDLINE; NATURAL LANGUAGE PROCESSING; PATHOLOGY; PRIORITY JOURNAL; REVIEW;

EID: 84979084834     PISSN: 00219746     EISSN: 14724146     Source Type: Journal    
DOI: 10.1136/jclinpath-2016-203872     Document Type: Review

References (57)

1. Friedman C. A broad-coverage natural language processing system. Proc AMIA Symp 2000; 270-4
2. De Bruijn LM, Hasman A, Arends JW. Automatic SNOMED classification-A corpus-based method. Comput Methods Programs Biomed 1997; 54: 115-22
3. Biese KJ, Forbach CR, Medlin RP, et al. Computer-facilitated review of electronic medical records reliably identifies emergency department interventions in older adults. Acad Emerg Med 2013; 20: 621-8
4. Shah AD, Martinez C, Hemingway H. The freetext matching algorithm: a computer program to extract diagnoses and causes of death from unstructured text in electronic health records. BMC Med Inform Decis Mak 2012; 12: 1-13
5. Pratt A, Thomas L. An information processing system for pathology data. Pathology Annual 1966; 1: 1-21
6. Dunham GS, Pacak MG, Pratt AW. Automatic indexing of pathology data. J Am Soc Info Sci 1978; 29. p81-90
7. Jouhet V, Defossez G, Burgun A, et al. Automated classification of free-Text pathology reports for registration of incident cases of cancer. Methods Inf Med 2012; 51: 242-51
8. Hanauer DA, Miela G, Chinnaiyan AM, et al. The registry case finding engine: an automated tool to identify cancer cases from unstructured, free-Text pathology reports and clinical notes. J Am Coll Surg 2007; 205: 690-7
9. Lowe HJ, Huang Y, Regula DP. Using a statistical natural language Parser augmented with the UMLS specialist lexicon to assign SNOMED CT codes to anatomic sites and pathologic diagnoses in full text pathology reports. AMIA Annu Symp Proc 2009; 2009: 386-90
10. Nguyen A, Moore D, McCowan I, et al. Multi-class classification of cancer stages from free-Text histology reports using support vector machines. Conf Proc IEEE Eng Med Biol Soc 2007; 2007: 5140-3
11. Carter KJ, Rinehart S, Kessler E, et al. Quality assurance in anatomic pathology: automated SNOMED coding. J Am Med Inform Assoc 1996: 270-2
12. Schadow G, Mcdonald CJ. Extracting structured information from free text pathology reports. AMIA Annu Symp Proc 2003; 584-8
13. Schlangen D, Stede M, Bontas EP. Feeding OWL: extracting and representing the content of pathology reports. NLPXML '04: Proceeedings of the Workshop on NLP and XML. 2004
14. Happe A, Cuggia M, Turlin B, et al. Design of an automatic coding algorithm for a multi-Axial classification in pathology. Stud Health Technol Inform 2008; 136: 815-20
15. Liang YF, Chu PY, Chang CS, et al. Developing and evaluating a simple, spreadsheet-based pathology report extraction system for cancer registrars. AMIA Annu Symp Proc 2006; p1008
16. De Bruijn LM, Hasman A, Arends JW. Automatic coding of diagnostic reports. Methods Inf Med 1998; 37: 260-5
17. Stanfill MH, Williams M, Fenton SH, et al. A systematic literature review of automated clinical coding and classification systems. J Am Med Inform Assoc 2010; 17: 646-51
18. Buckley JM, Buckley JM, Coopey SB, et al. The feasibility of using natural language processing to extract clinical information from breast pathology reports. J Pathol Inform 2012; 3: 23
19. Casati B, Haugland HK, Barstad GMJ, et al. Implementation and use of electronic synoptic cancer reporting: an explorative case study of six Norwegian pathology laboratories. Implement Sci 2014; 9: 111
20. Lankshear S, Srigley J, Mcgowan T, et al. Standardized synoptic cancer pathology reports-so what and who cares?. A population-based satisfaction survey of 970 pathologists, surgeons, and oncologists. Arch Pathol Lab Med 2013; 137: 1599-602
21. Dalkey N, Helmer O. Crawford ES, et al. An experimental application of the Delphi method to the use of experts. Manag Sci 1963; 9: 458-67
22. Jurafsky D, Martin JH. Introduction, in Speech and Language Processing. 2014, Pearson. 5-6
23. Gaizauskas R, Cunningham H, Wilks Y, et al. GATE: An Environment to Support Research and Development in Natural Language Engineering. Proceedings of the 8th IEEE International Conference on Tools with Artificial Intelligence. 1996
24. Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010; 17: 507-13
25. Frank E, Hall M, Trigg L, et al. Data mining in bioinformatics using Weka. Bioinformatics 2004; 20: 2479-81
26. D'Avolio LW, Nguyen TM, Farwell WR, et al. Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC). J Am Med Inform Assoc 2010; 17: 375-82
27. Joachims T. Making large-scale SVM learning practical. Universität Dortmund, 1998
28. Moore GW, Berman JJ. Automatic SNOMED coding. Proc Annu Symp Comput Appl Med Care 1994; 225-9
29. Sada Y, Hou J, Richardson P, et al. Validation of case finding algorithms for hepatocellular cancer from administrative data and electronic health records using natural language processing. Med Care 2016; 54: e9-14
30. Eide MJ, Mark Tuthill J, Krajenta RJ, et al. Validation of claims data algorithms to identify nonmelanoma skin cancer. J Invest Dermatol 2012l; 132: 2005-9
31. Owens SR, Balis UGJ, Lucas DR, et al. Application of a rules-based natural language parser to critical value reporting in anatomic pathology. Am J Surg Pathol 2012; 36: 376-80
32. Thomas SM, Mamlin B, Schadow G, et al. A successful technique for removing names in pathology reports using an augmented search and replace method. Proc AMIA Symp 2002; 777-81
33. Beckwith BA, Mahaadevan R, Balis UJ, et al. Development and evaluation of an open source software tool for deidentification of pathology reports. BMC Med Inform Decis Mak 2006; 6: 12
34. Xu H, Anderson K, Grann VR, et al. Facilitating cancer research using natural language processing of pathology reports. Stud Health Technol Inform 2004; 107(Pt 1): 565-72
35. Kim BJ, Merchant M, Zheng C, et al. A Natural language processing program effectively extracts key pathologic findings from radical prostatectomy reports. J Endourol 2014; 28: 1474-8
36. Thomas AA, Zheng C, Jung H, et al. Extracting data from electronic medical records: validation of a natural language processing program to assess prostate biopsy results. World J Urol 2014; 32: 99-103
37. Coden A, Savova G, Sominsky I, et al. Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model. J Biomed Inform 2009; 42: 937-49
38. Wagholikar KB, Maclaughlin KL, Henry MR, et al. Clinical decision support with automated text processing for cervical cancer screening. J Am Med Inform Assoc 2012; 19: 833-9
39. Hou JK, Chang M, Nguyen T, et al. Automated identification of surveillance colonoscopy in inflammatory bowel disease using natural language processing. Dig Dis Sci 2013; 58: 936-41
40. Imler TD, Morea J, Kahi C, et al. Natural language processing accurately categorizes findings from colonoscopy and pathology reports. Clin Gastroenterol Hepatol 2013; 11: 689-94
41. Li Y, Martinez D. Information extraction of multiple categories from pathology reports. Proceedings of Australasian Language Technology Association Workshop. 2010: 41-8
42. Martinez D, Li Y. Information extraction from pathology reports in a hospital setting. Conference on Information and Knowledge Management. 2011: 1877-82
43. McCowan I, Moore D, Fry MJ. Classification of cancer stage from free-Text histology reports. Proceedings of the 28th IEEE EMBS Annual International Conference New York. 2006: 5153-6
44. McCowan IA, Moore DC, Nguyen AN, et al. Collection of cancer stage data by classifying free-Text medical reports. J Am Med Inform Assoc 2007; 14: 736-45
45. Ou Y, Patrick J. Automatic population of structured reports from narrative pathology reports. Australasian Workshop on Health Informatics and Knowledge Management. 2014: 41-50
46. Ashish N, Dahm L, Boicey C. University of California, Irvine-Pathology Extraction Pipeline: the pathology extraction pipeline for information extraction from pathology reports. Health Informatics J 2014; 288-305
47. Crowley RS, Castine M, Mitchell K, et al. caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research. J Am Med Inform Assoc 2010; 17: 253-64
48. Harkema H, Chapman WW, Saul M, et al. Developing a natural language processing application for measuring the quality of colonoscopy procedures. J Am Med Inform Assoc 2011; 18(Suppl 1): i150-6
49. Liu K, Mitchell KJ, Chapman WW, et al. Automating tissue bank annotation from pathology reports-comparison to a gold standard expert annotation set. AMIA Annu Symp Proc 2005; 460-4
50. Luo Y, Sohani AR, Hochberg EP, et al. Automatic lymphoma classification with sentence subgraph mining from pathology reports. J Am Med Inform Assoc 2014; 21: 824-32
51. Nguyen A, Moore J, Zuccon G, et al. Classification of pathology reports for cancer registry notifications. Stud Health Technol Inform 2012; 178: 150-6
52. Nguyen AN, Lawley MJ, Hansen DP, et al. Symbolic rule-based classification of lung cancer stages from free-Text pathology reports. J Am Med Inform Assoc 2010; 17: 440-5
53. Strauss JA, Chao CR, Kwan ML, et al. Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm. J Am Med Inform Assoc 2013; 20: 349-55
54. Mitchell KJ, Becich MJ, Berman JJ, et al. Implementation and evaluation of a negation tagger in a pipeline-based system for information extract from pathology reports. Stud Health Technol Inform 2004; 107(Pt 1): 663-7
55. Demner-Fushman D, Chapman WW, Mcdonald CJ. What can natural language processing do for clinical decision support?. J Biomed Inform 2009; 42: 760-72
56. Turchin A, Kolatkar NS, Grant RW, et al. Using regular expressions to abstract blood pressure and treatment intensification information from the text of physician notes. J Am Med Inform Assoc 2006; 13: 691-5
57. Zeng QT, Goryachev S, Weiss S, et al. Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Decis Mak 2006; 6: 30