MEDSYNDIKATE - a natural language system for the extraction of medical information from findings reports

被引:60
|
作者
Hahn, U
Romacker, M
Schulz, S
机构
[1] Univ Freiburg, Text Knowledge Engn Lab, D-79104 Freiburg, Germany
[2] Freiburg Univ Hosp, Dept Med Informat, D-79085 Freiburg, Germany
关键词
information extraction; text understanding; evaluation;
D O I
10.1016/S1386-5056(02)00053-9
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
MEDSYNDIKATE is a natural language processor, which automatically acquires medical information from findings reports. In the course of text analysis their contents is transferred to conceptual representation structures, which constitute a corresponding text knowledge base. MEDSYNDIKATE is particularly adapted to deal properly with text structures, such as various forms of anaphoric reference relations spanning several sentences. The strong demands MEDSYNDIKATE poses on the availability of expressive knowledge sources are accounted for by two alternative approaches to acquire medical domain knowledge (semi)automatically. We also present data for the information extraction performance Of MEDSYNDIKATE in terms of the semantic interpretation of three major syntactic patterns in medical documents. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.
引用
收藏
页码:63 / 74
页数:12
相关论文
共 50 条
  • [1] Information extraction from weakly structured radiological reports with natural language queries
    Dada, Amin
    Ufer, Tim Leon
    Kim, Moon
    Hasin, Max
    Spieker, Nicola
    Forsting, Michael
    Nensa, Felix
    Egger, Jan
    Kleesiek, Jens
    [J]. EUROPEAN RADIOLOGY, 2024, 34 (01) : 330 - 337
  • [2] Information extraction from weakly structured radiological reports with natural language queries
    Amin Dada
    Tim Leon Ufer
    Moon Kim
    Max Hasin
    Nicola Spieker
    Michael Forsting
    Felix Nensa
    Jan Egger
    Jens Kleesiek
    [J]. European Radiology, 2024, 34 : 330 - 337
  • [3] Image Text Extraction and Natural Language Processing of Unstructured Data from Medical Reports
    Malashin, Ivan
    Masich, Igor
    Tynchenko, Vadim
    Gantimurov, Andrei
    Nelyub, Vladimir
    Borodulin, Aleksei
    [J]. MACHINE LEARNING AND KNOWLEDGE EXTRACTION, 2024, 6 (02): : 1361 - 1377
  • [4] NATURAL LANGUAGE PROCESSING OF ESOPHAGOGASTRODUODENOSCOPY REPORTS FOR INFORMATION EXTRACTION OF GASTRIC DISEASES
    Bae, Jung Ho
    Han, Hyun Wook
    Song, Gyuseon
    [J]. GASTROINTESTINAL ENDOSCOPY, 2022, 95 (06) : AB247 - AB248
  • [5] Compositional Information Extraction Methodology from Medical Reports
    Rani, Pratibha
    Reddy, Raghunath
    Mathur, Devika
    Bandyopadhyay, Subhadip
    Laha, Arijit
    [J]. DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, PT II, 2011, 6588 : 400 - +
  • [6] Automated StrokeRelated Information Extraction From Diagnostic Imaging Reports Using Natural Language Processing
    Liu, Zhongyu Anna
    Mamdani, Muhammad
    Aviv, Richard
    Pou-Prom, Chloe
    Yu, Amy
    [J]. STROKE, 2020, 51
  • [7] Fiscal data in text: Information extraction from audit reports using Natural Language Processing
    Beltran, Alejandro
    [J]. DATA & POLICY, 2023, 5
  • [8] Information Extraction from Cancer Pathology Reports with Graph Convolution Networks for Natural Language Texts
    Yoon, Hong-Jun
    Gounley, John
    Young, M. Todd
    Tourassi, Georgia
    [J]. 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2019, : 4561 - 4564
  • [9] Anatomic stage extraction from medical reports of breast Cancer patients using natural language processing
    Deshmukh, Pratiksha R.
    Phalnikar, Rashmi
    [J]. HEALTH AND TECHNOLOGY, 2020, 10 (06) : 1555 - 1570
  • [10] Anatomic stage extraction from medical reports of breast Cancer patients using natural language processing
    Pratiksha R. Deshmukh
    Rashmi Phalnikar
    [J]. Health and Technology, 2020, 10 : 1555 - 1570