A natural language processing challenge for clinical records: Research Domains Criteria (RDoC) for psychiatry

Uzuner, Özlem; Stubbs, Amber; Filannino, Michele

doi:10.1016/j.jbi.2017.10.005

Cited by 13 publications

(14 citation statements)

References 27 publications

(20 reference statements)

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“…Consequently, such free-text narratives have been the data source for NLP "challenges" in the health NLP community. [8][9][10][11][12] Symptoms are subjective indications of disease and include phenomena such as pain, fatigue, disturbed sleep, depressed mood, anxiety, nausea, dyspnea, and pruritus. Symptoms are challenging to manage and burden both the patient and healthcare system, 13 so much so that the National Institute of Nursing Research named "symptom science" as 1 of its key themes with the objective of "[providing] a better understanding of the symptoms of chronic illness and [improving] quality of life across diverse populations."…”

mentioning

confidence: 99%

Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review

Koleck

Dreisbach

Bourne

et al. 2019

Journal of the American Medical Informatics Association

306

179

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Objective Natural language processing (NLP) of symptoms from electronic health records (EHRs) could contribute to the advancement of symptom science. We aim to synthesize the literature on the use of NLP to process or analyze symptom information documented in EHR free-text narratives. Materials and Methods Our search of 1964 records from PubMed and EMBASE was narrowed to 27 eligible articles. Data related to the purpose, free-text corpus, patients, symptoms, NLP methodology, evaluation metrics, and quality indicators were extracted for each study. Results Symptom-related information was presented as a primary outcome in 14 studies. EHR narratives represented various inpatient and outpatient clinical specialties, with general, cardiology, and mental health occurring most frequently. Studies encompassed a wide variety of symptoms, including shortness of breath, pain, nausea, dizziness, disturbed sleep, constipation, and depressed mood. NLP approaches included previously developed NLP tools, classification methods, and manually curated rule-based processing. Only one-third (n = 9) of studies reported patient demographic characteristics. Discussion NLP is used to extract information from EHR free-text narratives written by a variety of healthcare providers on an expansive range of symptoms across diverse clinical specialties. The current focus of this field is on the development of methods to extract symptom information and the use of symptom information for disease classification tasks rather than the examination of symptoms themselves. Conclusion Future NLP studies should concentrate on the investigation of symptoms and symptom documentation in EHR free-text narratives. Efforts should be undertaken to examine patient characteristics and make symptom-related NLP algorithms or pipelines and vocabularies openly available.

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confidence: 99%

Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review

Koleck

Dreisbach

Bourne

et al. 2019

Journal of the American Medical Informatics Association

306

179

View full text Add to dashboard Cite

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“…The 2016 CEGS N-GRID shared task used psychiatric data, making it the first ever competition to use psychiatric intake records ; ). The data for the 2016 competition reflected the records "as is" ; Uzuner, Stubbs, and Filannino (2017)): the state at which data was received from the sources. Unlike other medical data, such as that of the 2014 challenge, psychiatric data contains an abundance of information related to the patients such as places lived, jobs held, children's ages, hobbies, traumatic events, patients' relatives' relationship information, and pet names.…”

Section: Overview Of Datasetsmentioning

confidence: 99%

A review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric?

Yogarajan

Pfahringer

Mayo

2020

Applied Artificial Intelligence

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De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data. It is a two-step process where step one is the identification of protected health information (PHI), and step two is replacing such PHI with surrogates. Despite the recent advances in automatic de-identification of EHR, significant obstacles remain if the abundant health data available are to be used to the full potential. Accuracy in de-identification could be considered a necessary, but not sufficient condition for the use of EHR without individual patient consent. We present here a comprehensive review of the progress to date, both the impressive successes in achieving high accuracy and the significant risks and challenges that remain. To best of our knowledge, this is the first paper to present a complete picture of end-to-end automatic deidentification. We review 18 recently published automatic de-identification systems -designed to de-identify EHR in the form of free text-to show the advancements made in improving the overall accuracy of the system, and in identifying individual PHI. We argue that despite the improvements in accuracy there remain challenges in surrogate generation and replacements of identified PHIs, and the risks posed to patient protection and privacy.

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“…Finally, the CEGS-NGRID Shared Tasks and Workshop on Challenges in NLP for Clinical Data made available a corpus of 1,000 manually de-identified psychiatric evaluation records from Partners Healthcare 51 . The organizers extended the HIPAA definition of PHI for better privacy protection.…”

Section: Shared Tasksmentioning

confidence: 99%

Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks

Filannino

Uzuner

2018

Yearb Med Inform

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A natural language processing challenge for clinical records: Research Domains Criteria (RDoC) for psychiatry

Cited by 13 publications

References 27 publications

Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review

Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review

A review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric?

Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks

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