medSynDiKATe—a natural language system for the extraction of medical information from findings reports

2002 ◽  
Vol 67 (1-3) ◽  
pp. 63-74 ◽  
Author(s):  
Udo Hahn ◽  
Martin Romacker ◽  
Stefan Schulz
Keyword(s):  
Natural Language ◽  
Medical Information ◽  
Language System

scholarly journals Comparison of disclosure/concealment of medical information given to conversational agents or to physicians

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822199486
Author(s):  
Nicholas RJ Frick ◽  
Felix Brünker ◽  
Björn Ross ◽  
Stefan Stieglitz
Keyword(s):  
Natural Language ◽  
Medical Information ◽  
Frame Of Reference ◽  
Future Research ◽  
Negative Consequences ◽  
Conversational Agents ◽  
Online Questionnaire ◽  
Computer Based

Within the anamnesis, medical information is frequently withheld, incomplete, or incorrect, potentially causing negative consequences for the patient. The use of conversational agents (CAs), computer-based systems using natural language to interact with humans, may mitigate this problem. The present research examines whether CAs differ from physicians in their ability to elicit truthful disclosure and discourage concealment of medical information. We conducted an online questionnaire with German participants ( N = 148) to assess their willingness to reveal medical information. The results indicate that patients would rather disclose medical information to a physician than to a CA; there was no difference in the tendency to conceal information. This research offers a frame of reference for future research on applying CAs during the anamnesis to support physicians. From a practical view, physicians might gain better understanding of how the use of CAs can facilitate the anamnesis.

Blog Snippets Based Drug Effects Extraction System Using Lexical and Grammatical Restrictions

2014 ◽  
Vol 5 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Shiho Kitajima ◽  
Rafal Rzepka ◽  
Kenji Araki
Keyword(s):  
Quality Of Life ◽  
Natural Language ◽  
Large Scale ◽  
Medical Information ◽  
Drug Effects ◽  
Large Scale Data ◽  
Medical Documents ◽  
Baseline System ◽  
Scale Data

Obtaining medical information has a beneficial influence on patients' treatment and QOL (quality of life). The authors aim to make a system that helps patients to collect narrative information. Extracting information from data written by patients will allow the acquisition of information which is easy to understand and provides encouragement. Additionally, by using large-scale data, the system can be utilized for discovering unknown effects or patterns. As the first step, the purpose of this paper is to extract descriptions of the effects caused by taking drugs as a triplet of expressions from illness survival blogs' snippets. This paper proposes a method to extract the triplets using specific clue words and parsing the results in order to extract from blogs written in free natural language. Moreover, recall was improved by combining their proposed method and a baseline system, and precision was improved by filtering using dictionaries we created from existing medical documents.

scholarly journals Clinical trial cohort selection based on multi-level rule-based natural language processing system

2019 ◽  
Vol 26 (11) ◽  
pp. 1218-1226 ◽  
Author(s):  
Long Chen ◽  
Yu Gu ◽  
Xin Ji ◽  
Chao Lou ◽  
Zhiyong Sun ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Rule Based ◽  
Language System ◽  
Unified Medical Language System ◽  
Rule Based System ◽  
Medical Language ◽  
Cohort Selection

Abstract Objective Identifying patients who meet selection criteria for clinical trials is typically challenging and time-consuming. In this article, we describe our clinical natural language processing (NLP) system to automatically assess patients’ eligibility based on their longitudinal medical records. This work was part of the 2018 National NLP Clinical Challenges (n2c2) Shared-Task and Workshop on Cohort Selection for Clinical Trials. Materials and Methods The authors developed an integrated rule-based clinical NLP system which employs a generic rule-based framework plugged in with lexical-, syntactic- and meta-level, task-specific knowledge inputs. In addition, the authors also implemented and evaluated a general clinical NLP (cNLP) system which is built with the Unified Medical Language System and Unstructured Information Management Architecture. Results and Discussion The systems were evaluated as part of the 2018 n2c2-1 challenge, and authors’ rule-based system obtained an F-measure of 0.9028, ranking fourth at the challenge and had less than 1% difference from the best system. While the general cNLP system didn’t achieve performance as good as the rule-based system, it did establish its own advantages and potential in extracting clinical concepts. Conclusion Our results indicate that a well-designed rule-based clinical NLP system is capable of achieving good performance on cohort selection even with a small training data set. In addition, the investigation of a Unified Medical Language System-based general cNLP system suggests that a hybrid system combining these 2 approaches is promising to surpass the state-of-the-art performance.

A natural language system to aid simulation model formulation

1989 ◽  
Vol 16 (4) ◽  
pp. 535-543 ◽  
Author(s):  
Horng-Ming Su ◽  
Voratas Kachitvichyanukul
Keyword(s):  
Natural Language ◽  
Simulation Model ◽  
Model Formulation ◽  
Language System

HAM-ANS (Hamburg application-oriented natural-language system)

1982 ◽  
pp. 57-57
Author(s):  
Walther von Hahn ◽  
Wolfgang Wahlster ◽  
Wolfgang Hoeppner
Keyword(s):  
Natural Language ◽  
Language System

scholarly journals СТРУКТУРИЗАЦІЯ МЕДИЧНОЇ ІНФОРМАЦІЇ ЗА ДОПОМОГОЮ ОНТОЛОГІЇ

2016 ◽  
Author(s):  
V. V. Prykhodnyuk
Keyword(s):  
Natural Language ◽  
Medical Information ◽  
Large Sets

This article describes methodology of structuring of medical information, which than can be used to create ontologies. Structuring is possible for unstructured (natural-language texts) or ill-structured (large sets of non-consistent tables) information.

scholarly journals Using of Natural Language Processing Techniques in Suicide Research

2017 ◽  
Vol 1 (2) ◽  
pp. 89 ◽  
Author(s):  
Azam Orooji ◽  
Mostafa Langarizadeh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Medical Information ◽  
Inclusion Criteria ◽  
Data Set ◽  
Completed Suicide ◽  
Teenagers And Young Adults ◽  
Processing Techniques ◽  
The Web

It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. Since, more medical information is available in text, Preventing the growing trend of suicide in communities requires analyzing various textual resources, such as patient records, information on the web or questionnaires. For this purpose, this study systematically reviews recent studies related to the use of natural language processing techniques in the area of people’s health who have completed suicide or are at risk. After electronically searching for the PubMed and ScienceDirect databases and studying articles by two reviewers, 21 articles matched the inclusion criteria. This study revealed that, if a suitable data set is available, natural language processing techniques are well suited for various types of suicide related research.

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