Corpus ID: 24325278

An innovative solution for breast cancer textual big data analysis

  title={An innovative solution for breast cancer textual big data analysis},
  author={Nicolas Thiebaut and Antoine Simoulin and Karl Neuberger and Issam Ibnouhsein and Nicolas Bousquet and Nathalie Reix and S{\'e}bastien Moli{\`e}re and Carole Mathelin},
The digitalization of stored information in hospitals now allows for the exploitation of medical data in text format, as electronic health records (EHRs), initially gathered for other purposes than epidemiology. Manual search and analysis operations on such data become tedious. In recent years, the use of natural language processing (NLP) tools was highlighted to automatize the extraction of information contained in EHRs, structure it and perform statistical analysis on this structured… Expand
A frame semantic overview of NLP-based information extraction for cancer-related EHR notes
A scoping review of existing clinical NLP literature for cancer identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. Expand
International Journal of Innovative Technology and Exploring Engineering (IJITEE)
Turning a large quantity of fly ash (FA) and bottom ash (BA) into unfired solid bricks is the objective of this study. Five brick mixtures were designed with a constant water-to-binder ratio of 0.35.Expand


Using natural language processing to improve efficiency of manual chart abstraction in research: the case of breast cancer recurrence.
It is hypothesized that natural language processing (NLP) could substantially reduce the burden of manual abstraction in studies examining outcomes, like cancer recurrence, that are documented in unstructured clinical text, such as progress notes, radiology reports, and pathology reports. Expand
The feasibility of using natural language processing to extract clinical information from breast pathology reports
It is demonstrated how a large body of free text medical information such as seen in breast pathology reports, can be converted to a machine readable format using natural language processing, and described the inherent complexities of the task. Expand
Facilitating Cancer Research using Natural Language Processing of Pathology Reports
This paper describes how a preprocessor was integrated with an existing NLP system (MedLEE) in order to reduce modification to the N LP system and to improve performance. Expand
Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction
Results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought. Expand
Natural Language Processing in Oncology: A Review.
An introduction to NLP and its potential applications in oncology, a description of specific tools available, and a review on the state of the current technology with respect to cancer case identification, staging, and outcomes quantification are provided. Expand
Extracting information from textual documents in the electronic health record: a review of recent research.
Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Expand
Getting Started in Text Mining
A surprising phenomenon can be noted in the recent history of biomedical text mining: although several systems have been built and deployed in the past few years—Chilibot, Textpresso, and PreBIND (see Text S1 for these and most other citations), the ones that are seeing high usage rates and are making productive contributions to the working lives of bioscientists have been build not by text mining specialists, but by bioscientism. Expand
The Quaero French Medical Corpus : A Ressource for Medical Entity Recognition and Normalization
A vast amount of information in the biomedical domain is available as natural language free text. An increasing number of documents in the field are written in languages other than English.Expand
Using machine learning to parse breast pathology reports
A machine learning model is trained on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports, which can be used to identify cohorts of patients with characteristics of interest. Expand
Discovering hospital admission patterns using models learnt from electronic hospital records
A novel model for hospital admission-type prediction based on the representation of a patient's medical history in the form of a binary history vector is introduced, shown to outperform previous state-of-the-art in the literature and be vastly superior for long-term prognosis. Expand