Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances

Velupillai, Sumithra; Suominen, Hanna; Liakata, Maria; Roberts, Angus; Shah, Anoop D; Morley, Katherine I.; Osborn, David; Hayes, Joseph; Stewart, Robert; Downs, Johnny; Chapman, Wendy W.; Dutta, Rina

doi:10.1016/j.jbi.2018.10.005

Cited by 171 publications

(118 citation statements)

References 60 publications

Supporting

Mentioning

116

Contrasting

Unclassified

Order By: Relevance

“…The last two Yearbook surveys of the NLP section most closely related to medical IE were published in 2015 [26] and 2008 [27]. The survey by Velupillai et al [28] dealt with opportunities and challenges of medical NLP for health outcomes research, with particular emphasis on evaluation criteria and protocols.…”

Section: (Medical Information Martmentioning

confidence: 99%

Medical Information Extraction in the Age of Deep Learning

Hahn

Oleynik

2020

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: We survey recent developments in medical Information Extraction (IE) as reported in the literature from the past three years. Our focus is on the fundamental methodological paradigm shift from standard Machine Learning (ML) techniques to Deep Neural Networks (DNNs). We describe applications of this new paradigm concentrating on two basic IE tasks, named entity recognition and relation extraction, for two selected semantic classes—diseases and drugs (or medications)—and relations between them. Methods: For the time period from 2017 to early 2020, we searched for relevant publications from three major scientific communities: medicine and medical informatics, natural language processing, as well as neural networks and artificial intelligence. Results: In the past decade, the field of Natural Language Processing (NLP) has undergone a profound methodological shift from symbolic to distributed representations based on the paradigm of Deep Learning (DL). Meanwhile, this trend is, although with some delay, also reflected in the medical NLP community. In the reporting period, overwhelming experimental evidence has been gathered, as illustrated in this survey for medical IE, that DL-based approaches outperform non-DL ones by often large margins. Still, small-sized and access-limited corpora create intrinsic problems for data-greedy DL as do special linguistic phenomena of medical sublanguages that have to be overcome by adaptive learning strategies. Conclusions: The paradigm shift from (feature-engineered) ML to DNNs changes the fundamental methodological rules of the game for medical NLP. This change is by no means restricted to medical IE but should also deeply influence other areas of medical informatics, either NLP- or non-NLP-based.

show abstract

Section: (Medical Information Martmentioning

confidence: 99%

Medical Information Extraction in the Age of Deep Learning

Hahn

Oleynik

2020

Yearb Med Inform

View full text Add to dashboard Cite

show abstract

“…Concept extraction is the most common clinical natural language processing (NLP) task (Tang et al, 2013;Kundeti et al, 2016;Unanue et al, 2017;Wang et al, 2018b), and a precursor to downstream tasks such as relations (Rink et al, 2011), frame parsing (Gupta et al, 2018;Si and Roberts, 2018), co-reference (Lee et al, 2011), and phenotyping (Xu et al, 2011;Velupillai et al, 2018). Corpora such as those from i2b2 (Uzuner et al, 2011;Sun et al, 2013;Stubbs et al, 2015), ShARe/CLEF (Suominen et al, 2013;Kelly et al, 2014), and SemEval (Pradhan et al, 2014;Elhadad et al, 2015;Bethard et al, 2016) act as evaluation benchmarks and datasets for training machine learning (ML) models.…”

Section: Introductionmentioning

confidence: 99%

Enhancing clinical concept extraction with contextual embeddings

Wang

et al. 2019

Journal of the American Medical Informatics Association

233

178

View full text Add to dashboard Cite

Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (e.g., ELMo, BERT) have further pushed the stateof-the-art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (word2vec, GloVe, fastText). Both offthe-shelf, open-domain embeddings and pretrained clinical embeddings from MIMIC-III are evaluated. We explore a battery of embedding methods consisting of traditional word embeddings and contextual embeddings, and compare these on four concept extraction corpora: i2b2 2010, i2b2 2012, SemEval 2014, and SemEval 2015. We also analyze the impact of the pre-training time of a large language model like ELMo or BERT on the extraction performance. Last, we present an intuitive way to understand the semantic information encoded by contextual embeddings. Contextual embeddings pre-trained on a large clinical corpus achieves new state-of-the-art performances across all concept extraction tasks. The best-performing model outperforms all state-of-the-art methods with respective F1measures of 90. 25, 93.18 (partial), 80.74, and 81.65. We demonstrate the potential of contextual embeddings through the state-of-the-art performance these methods achieve on clinical concept extraction. Additionally, we demonstrate that contextual embeddings encode valuable semantic information not accounted for in traditional word representations.

show abstract

“…The free text within EHRs, especially mental health records, contain a large proportion [1] and variety of information about clinical encounters that might go unnoticed if not explored. This is especially true for mental health records where many presentations, contextual factors, interventions and outcomes are not captured in structured fields (such as symptoms, behaviours, and self-reported experiences) so that the extraction of such information from free text generates a more accurate picture [2]. The potential for data linkages between the hospital EHRs and clinical practices in the community as well as linkages across other population databases [3] provides a more complete picture of the care received by patients.…”

Section: Health Informaticsmentioning

confidence: 99%

From Learning About Machines to Machine Learning: Applications for Mental Health Rehabilitation

Chaturvedi

2020

J. Psychosoc. Rehabil. Ment. Health

View full text Add to dashboard Cite

Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances

Cited by 171 publications

References 60 publications

Medical Information Extraction in the Age of Deep Learning

Medical Information Extraction in the Age of Deep Learning

Enhancing clinical concept extraction with contextual embeddings

From Learning About Machines to Machine Learning: Applications for Mental Health Rehabilitation

Contact Info

Product

Resources

About