AIMC Topic: Natural Language Processing

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Word Sense Disambiguation of Medical Terms via Recurrent Convolutional Neural Networks.

Studies in health technology and informatics
BACKGROUND: Tagging text data with codes representing biomedical concepts plays an important role in medical data management and analysis. A problem occurs if there are ambiguous words linked to several concepts.

A Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning.

Studies in health technology and informatics
We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)aut...

HTP-NLP: A New NLP System for High Throughput Phenotyping.

Studies in health technology and informatics
Secondary use of clinical data for research requires a method to quickly process the data so that researchers can quickly extract cohorts. We present two advances in the High Throughput Phenotyping NLP system which support the aim of truly high throu...

Personalized Guideline-Based Treatment Recommendations Using Natural Language Processing Techniques.

Studies in health technology and informatics
Clinical guidelines and clinical pathways are accepted and proven instruments for quality assurance and process optimization. Today, electronic representation of clinical guidelines exists as unstructured text, but is not well-integrated with patient...

Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.

Studies in health technology and informatics
Electronic Health Records (EHRs) are now being massively used in hospitals what has motivated current developments of new methods to process clinical narratives (unstructured data) making it possible to perform context-based searches. Current approac...

Medical Text Classification Using Convolutional Neural Networks.

Studies in health technology and informatics
We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health informatio...

Improving Terminology Mapping in Clinical Text with Context-Sensitive Spelling Correction.

Studies in health technology and informatics
The mapping of unstructured clinical text to an ontology facilitates meaningful secondary use of health records but is non-trivial due to lexical variation and the abundance of misspellings in hurriedly produced notes. Here, we apply several spelling...

Prevalence Estimation of Protected Health Information in Swedish Clinical Text.

Studies in health technology and informatics
Obscuring protected health information (PHI) in the clinical text of health records facilitates the secondary use of healthcare data in a privacy-preserving manner. Although automatic de-identification of clinical text using machine learning holds mu...

Developing a Manually Annotated Corpus of Clinical Letters for Breast Cancer Patients on Routine Follow-Up.

Studies in health technology and informatics
This paper introduces the annotation schema and annotation process for a corpus of clinical letters describing the disease course and treatment of oestrogen receptor positive breast cancer patients, after completion of primary surgery and radiotherap...

Development and Evaluation of a Case-Based Retrieval Service.

Studies in health technology and informatics
Identifying similar patients might greatly facilitate the treatment of a given patient, enabling to observe the response and outcome to a particular treatment. Case-based retrieval services dealing with natural language processing are of major import...