scholarly journals Regarding the Utility of Unstructured Data and Natural Language Processing for Identification of Breast Cancer Recurrence

2021 ◽  
pp. 1024-1025
Author(s):  
Debra P. Ritzwoller ◽  
Michael J. Hassett ◽  
Hajime Uno
Keyword(s):  
Breast Cancer ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cancer Recurrence ◽  
Unstructured Data ◽  
Breast Cancer Recurrence

scholarly journals Natural language processing methods for knowledge management—Applying document clustering for fast search and grouping of engineering documents

2021 ◽  
pp. 1063293X2098297
Author(s):  
Ivar Örn Arnarsson ◽  
Otto Frost ◽  
Emil Gustavsson ◽  
Mats Jirstrand ◽  
Johan Malmqvist
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Unstructured Data ◽  
Free Text ◽  
Engineering Change ◽  
Engineering Documents

Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested.

scholarly journals Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing

2020 ◽  
Vol 59 (S 02) ◽  
pp. e64-e78
Author(s):  
Antje Wulff ◽  
Marcel Mast ◽  
Marcus Hassler ◽  
Sara Montag ◽  
Michael Marschollek ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Data ◽  
Expert Knowledge ◽  
Clinical Information ◽  
Structured Data ◽  
Unstructured Data ◽  
Mapping Rules ◽  
Medical Histories

Abstract Background Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly. Objectives The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories. Methods We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School. Results We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall. Conclusion The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission.

scholarly journals Latent Dirichlet Allocation in predicting clinical trial terminations

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Simon Geletta ◽  
Lendie Follett ◽  
Marcia Laugerman
Keyword(s):  
Clinical Trial ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Structured Data ◽  
Unstructured Data ◽  
Future Research ◽  
Funding Agencies ◽  
Dirichlet Allocation

Abstract Background This study used natural language processing (NLP) and machine learning (ML) techniques to identify reliable patterns from within research narrative documents to distinguish studies that complete successfully, from the ones that terminate. Recent research findings have reported that at least 10 % of all studies that are funded by major research funding agencies terminate without yielding useful results. Since it is well-known that scientific studies that receive funding from major funding agencies are carefully planned, and rigorously vetted through the peer-review process, it was somewhat daunting to us that study-terminations are this prevalent. Moreover, our review of the literature about study terminations suggested that the reasons for study terminations are not well understood. We therefore aimed to address that knowledge gap, by seeking to identify the factors that contribute to study failures. Method We used data from the clinicialTrials.gov repository, from which we extracted both structured data (study characteristics), and unstructured data (the narrative description of the studies). We applied natural language processing techniques to the unstructured data to quantify the risk of termination by identifying distinctive topics that are more frequently associated with trials that are terminated and trials that are completed. We used the Latent Dirichlet Allocation (LDA) technique to derive 25 “topics” with corresponding sets of probabilities, which we then used to predict study-termination by utilizing random forest modeling. We fit two distinct models – one using only structured data as predictors and another model with both structured data and the 25 text topics derived from the unstructured data. Results In this paper, we demonstrate the interpretive and predictive value of LDA as it relates to predicting clinical trial failure. The results also demonstrate that the combined modeling approach yields robust predictive probabilities in terms of both sensitivity and specificity, relative to a model that utilizes the structured data alone. Conclusions Our study demonstrated that the use of topic modeling using LDA significantly raises the utility of unstructured data in better predicating the completion vs. termination of studies. This study sets the direction for future research to evaluate the viability of the designs of health studies.

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