Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research

Meystre, Stéphane M.; Savova, Guergana; Kipper-Schuler, Karin; Hurdle, John F.

doi:10.1055/s-0038-1638592

Cited by 371 publications

(49 citation statements)

References 99 publications

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“…Also, these symptoms can manifest in a variety of ways and in many cases, may not be the primary concern of the clinicians writing clinical notes and correspondence. Moreover, OCS need to be clinically distinguishable from schizophrenic mannerisms or posturing or other psychosis related repetitive thoughts or behaviour 10 consequently the algorithm needed to be able to use information presented in the free text to make these distinctions.…”

Section: Discussionmentioning

confidence: 99%

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Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder

Chandran

Robbins

Chang

et al. 2019

Sci Rep

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Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP) applied to Electronic Health Records (EHRs) presents an opportunity to create large datasets to facilitate research in this area. This is a challenging endeavour however, because of the wide range of ways in which these symptoms are recorded, and the overlap of terms used to describe OCS with those used to describe other conditions. We developed an NLP algorithm to extract OCS information from a large mental healthcare EHR data resource at the South London and Maudsley NHS Foundation Trust using its Clinical Record Interactive Search (CRIS) facility. We extracted documents from individuals who had received a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder. These text documents, annotated by human coders, were used for developing and refining the NLP algorithm (600 documents) with an additional set reserved for final validation (300 documents). The developed NLP algorithm utilized a rules-based approach to identify each of symptoms associated with OCS, and then combined them to determine the overall number of instances of OCS. After its implementation, the algorithm was shown to identify OCS with a precision and recall (with 95% confidence intervals) of 0.77 (0.65–0.86) and 0.67 (0.55–0.77) respectively. The development of this application demonstrated the potential to extract complex symptomatic data from mental healthcare EHRs using NLP to facilitate further analyses of these clinical symptoms and their relevance for prognosis and intervention response.

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Section: Discussionmentioning

confidence: 99%

“…Variation in performance of NLP applications may relate in part to the complexity of the task being undertaken 9 . As EHRs continue to be exploited for research, NLP is being applied to increasingly subtle and complex tasks 10 .…”

Section: Introductionmentioning

confidence: 99%

Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder

Chandran

Robbins

Chang

et al. 2019

Sci Rep

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“…Free text is an especially valuable source for information that is not systematically recorded, or difficult to capture in standardized EHR fields, such as social determinants of health (3,4). EHR narratives contain a rich diversity of health information types beyond drugs, diseases, and other well-studied areas (5,6), which have the potential to be unlocked with new natural language processing (NLP) technologies. This article presents a framework for expanding NLP technologies for coding under-studied domains of health information in the EHR, using a case study on physical function.…”

Section: Introductionmentioning

confidence: 99%

Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health

Newman-Griffis¹,

Fosler‐Lussier²

2021

Front. Digit. Health

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Linking clinical narratives to standardized vocabularies and coding systems is a key component of unlocking the information in medical text for analysis. However, many domains of medical concepts, such as functional outcomes and social determinants of health, lack well-developed terminologies that can support effective coding of medical text. We present a framework for developing natural language processing (NLP) technologies for automated coding of medical information in under-studied domains, and demonstrate its applicability through a case study on physical mobility function. Mobility function is a component of many health measures, from post-acute care and surgical outcomes to chronic frailty and disability, and is represented as one domain of human activity in the International Classification of Functioning, Disability, and Health (ICF). However, mobility and other types of functional activity remain under-studied in the medical informatics literature, and neither the ICF nor commonly-used medical terminologies capture functional status terminology in practice. We investigated two data-driven paradigms, classification and candidate selection, to link narrative observations of mobility status to standardized ICF codes, using a dataset of clinical narratives from physical therapy encounters. Recent advances in language modeling and word embedding were used as features for established machine learning models and a novel deep learning approach, achieving a macro-averaged F-1 score of 84% on linking mobility activity reports to ICF codes. Both classification and candidate selection approaches present distinct strengths for automated coding in under-studied domains, and we highlight that the combination of (i) a small annotated data set; (ii) expert definitions of codes of interest; and (iii) a representative text corpus is sufficient to produce high-performing automated coding systems. This research has implications for continued development of language technologies to analyze functional status information, and the ongoing growth of NLP tools for a variety of specialized applications in clinical care and research.

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“…A review conducted by Meystre et al (35) established that much of the available clinical data are in narrative form as a result of transcription of dictations, direct entry by providers, or use of speech recognition applications. Therefore, sentiment analysis in medical context is important since physicians usually give a subjective interpretation in their diagnosis whereas NLP could offer a high-level text understanding by providing a more objective information (36).…”

Section: Nlp Applications In Clinical Contextmentioning

confidence: 99%

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records

et al. 2019

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Clinical practice requires the production of a time-and resource-consuming great amount of notes. They contain relevant information, but their secondary use is almost impossible, due to their unstructured nature. Researchers are trying to address this problems, with traditional and promising novel techniques. Application in real hospital settings seems not to be possible yet, though, both because of relatively small and dirty dataset, and for the lack of language-specific pre-trained models. Aim: Our aim is to demonstrate the potential of the above techniques, but also raise awareness of the still open challenges that the scientific communities of IT and medical practitioners must jointly address to realize the full potential of unstructured content that is daily produced and digitized in hospital settings, both to improve its data quality and leverage the insights from data-driven predictive models. Methods: To this extent, we present a narrative literature review of the most recent and relevant contributions to leverage the application of Natural Language Processing techniques to the free-text content electronic patient records. In particular, we focused on four selected application domains, namely: data quality, information extraction, sentiment analysis and predictive models, and automated patient cohort selection. Then, we will present a few empirical studies that we undertook at a major teaching hospital specializing in musculoskeletal diseases. Results: We provide the reader with some simple and affordable pipelines, which demonstrate the feasibility of reaching literature performance levels with a single institution non-English dataset. In such a way, we bridged literature and real world needs, performing a step further toward the revival of notes fields.

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Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research

Cited by 371 publications

References 99 publications

Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder

Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder

Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records

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