Clinical NLP

Melax Technologies, Inc. Announces Ability to Extract COVID-19 Data from Textual Documents | PR Newswire | 7/23/2020

… helped clients develop NLP solutions for computer-assisted coding, predictive modeling, quality measurement, precision medicine, and more. The company’s featured product, the CLAMP toolkit, was developed based on over ten years of award-winning clinical NLP innovations by Dr. Hua Xu’s team at The University of Texas Health Science Center at Houston . CLAMP is currently used by hundreds of health systems and HIT companies. The software is highly configurable as …

Clinical Text Data in Machine Learning: Systematic Review | 3/31/2020

… NLP tools by leveraging large amounts of text data. Objective: The main aim of this study was to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigated the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. Methods: Our methodology was based on the guidelines for performing …

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Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction | 3/30/2020

… and to standardize the definition of phenotypes for large-scale studies of disease onset and treatment outcomes in routine clinical care [ 31 , 32 , 33 , 34 ], however, unstructured data still remains a challenge. In the clinical NLP community, efforts have been made to standardize corpus development including building and sharing annotated lexical resources, normalizing data elements, and developing an ontology-based web tool [ 13 , 35 , 36 , 37 ]. However, to the best …

Selected articles from the BioCreative/OHNLP challenge 2018 | 12/27/2019

… observed in the general NLP domain. Towards this goal, we organized the BioCreative/OHNLP Challenge 2018 workshop ( https://sites.google.com/view/ohnlp2018/home ) to promote community efforts on methodological advancements and data curation mechanisms in clinical NLP. The challenge consists of two independent clinical NLP tasks: 1) Family History Extraction; and 2) Clinical Semantic Textual Similarity. The top performing teams were invited to present their solutions during the BioCreative/OHNLP Challenge …

Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text | 12/5/2019

… all three medical concept-attribute detection tasks. Conclusions This study demonstrates the efficacy of our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications. Background Clinical narratives are rich with patients’ clinical information such as disorders, medications, procedures and lab tests, which are critical for clinical and translational research using Electronic Health Records (EHRs). Clinical Natural Language …

Editorial: The second international workshop on health natural language processing (HealthNLP 2019) | 12/5/2019

… electronic health record (EHR) systems has made massive clinical narrative data available electronically. Natural language processing (NLP) technologies that can unlock information from narrative text have received great attention in the medical domain. Many clinical NLP methods and systems have been developed and showed promising results in various tasks. These methods and tools have also been successfully applied to facilitate clinical research, as well as to support healthcare applications. Recent …

Natural Language Processing Improves Care for Liver Failure Patients | HealthIT Analytics | 11/12/2019

… transplants,” said study co-author and computational linguist Masoud Rouhizadeh, MSc, PhD, one of the NLP leads at the Johns Hopkins Institute for Clinical and Translational Research and co-founder of its Center for Clinical NLP . “It was like they were searching for words to make themselves understood.” After transplant surgery, the majority of patients returned to normal or near-normal language patterns, with more concise sentences and longer words …

Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes | 11/12/2019

… inventories from the biomedical literature and contain fewer short forms. Wu et al. [ 43 ] highlight that “ accurate identification of clinical abbreviations is a challenging task and advanced abbreviation recognition modules are needed for existing clinical NLP systems ”. Dealing with short forms is therefore a challenge that requires an approach to deal with terms appearing in both biomedical and clinical documents. The paper is organised as follows. In the next section …

Natural language processing helps evaluate electronic messages from ESLD patients | 11/7/2019

… transplants,” says study co-author and computational linguist Masoud Rouhizadeh, M.Sc., Ph.D., one of the NLP leads at the Johns Hopkins Institute for Clinical and Translational Research and co-founder of its Center for Clinical NLP . “It was like they were searching for words to make themselves understood.” Following transplant surgery, the majority of patients returned to normal or near-normal language patterns – more concise sentences with longer words, including …

A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems | 8/15/2019

Natural language processing (NLP) has become essential for secondary use of clinical data. Over the last two decades, many clinical NLP systems were developed in both academia and industry. However, nearly all existing systems are restricted to specific clinical settings mainly because they were developed for and tested with specific datasets, and they often fail to scale up. Therefore, using existing NLP systems for one’s own clinical purposes requires substantial …

Looking for Clinical NLP dev (experience working with UMLS and its lexicon tools) | 4/25/2019

Java Looking for Clinical NLP dev (experience working with UMLS and its lexicon tools) We are working on a clinical NLP project that uses the UMLS vocabulary and tools - and are looking to grow the solution beyond simple concept matching. The first core requirement is to utilize UMLS lexicon tools to improve the accuracy/sensitive of our extractions, but this project will be long term. We are ideally looking for …

UMLS

Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text | 12/5/2019

… our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications. Background Clinical narratives are rich with patients’ clinical information such as disorders … encode them into standard concepts in ontologies such as the UMLS (Unified Medical Language System). However, downstream clinical applications, such as clinical decision support systems, often require additional attribute information of medical concepts. For example …

Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes | 11/12/2019

… datasets, resulting in 880 term pairs (target term, candidate term). Each concept, represented by an n-gram, is mapped to UMLS using MetaMap; we also developed a bespoke method for mapping short forms (e.g. abbreviations … task and advanced abbreviation recognition modules are needed for existing clinical NLP systems ”. Dealing with short forms is therefore a challenge that requires an approach to deal with terms appearing in both biomedical and clinical …