The use of natural language processing on narrative medication schedules to compute average weekly dose

Lü, Chao; Leng, Jianwei; Cannon, Grant W.; Zhou, Xi; Egger, Marlene J.; South, Brett R.; Burningham, Zachary; Zeng, Qing; Sauer, Brian C.

doi:10.1002/pds.4086

Cited by 5 publications

(6 citation statements)

References 11 publications

(32 reference statements)

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“…Handwritten rules are still a technique employed despite the time-consuming task of writing them [14]. This is because of some particular entities such as duration, and reason exhibits a high variability which is difficult to capture with machine learning using a few amounts of data.…”

Section: Related Workmentioning

confidence: 99%

Towards Spoken Medical Prescription Understanding

Kocabiyikoglu¹,

Portet

Blanchon

et al. 2019

2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)

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Prescription Management Systems (PMS) have appeared in health institutions to reduce medication errors which affect several million people worldwide each year. However, practitioners must enter information manually into PMS which decreases the time devoted to care. In this paper, we propose to provide a Natural Language interface to the PMS so that practitioners can record their prescriptions orally through mobile devices at the point of care. We briefly describe the overall approach and focus on the Natural Language Understanding process which was approached through slot-filling. To deal with the paucity of data and the imbalanced class problem, we present a method to artificially generate medical prescriptions. Experiments on the artificial and a realistic dataset with several state-of-the-art NLU systems show that the method makes it possible to learn competitive NLU models and opens the way to experiments on speech corpora.

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Section: Related Workmentioning

confidence: 99%

Towards Spoken Medical Prescription Understanding

Kocabiyikoglu¹,

Portet

Blanchon

et al. 2019

2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)

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“…An additional nine articles were identified through the reference lists of other articles ( n = 8) and through key stakeholder engagement ( n = 1). In total, 38 articles were included in the literature review, among which 33 were primary articles, 1–4,11–39 three were narrative reviews, 6,7,9 and two were systematic reviews 5,8 . Herein, we focus on results from the primary articles.…”

Section: Resultsmentioning

confidence: 99%

“…Information captured in NEPIs can be used to address unique research questions in pharmacoepidemiology. For example, the content of NEPIs can be used to examine whether clinicians are prescribing medications for specific indications, adhering to regulatory agency labeling, 2 or to quantify or evaluate the impact of dosing instructions, including their specificity or complexity, on medication adherence 2,3 . NEPIs can also be used to better define timing of drug exposure.…”

Section: Introductionmentioning

confidence: 99%

“…As with any unstructured data, extracting and operationalizing information from NEPIs can be challenging. With smaller datasets, text can be extracted and coded manually; however, use of larger datasets may require semi‐automated methods, such as natural language processing (NLP), to efficiently use these data 3,4 . Information within NEPIs, however, can be highly variable and the data extracted are only as good as the quality of data entered.…”

Section: Introductionmentioning

confidence: 99%

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The use of narrative electronic prescribing instructions in pharmacoepidemiology: A scoping review for the International Society for Pharmacoepidemiology

Romanelli

Schwartz

Dixon

et al. 2021

Pharmacoepidemiology and Drug

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Narrative electronic prescribing instructions (NEPIs) are text that convey information on the administration or co‐administration of a drug as directed by a prescriber. For researchers, NEPIs have the potential to advance our understanding of the risks and benefits of medications in populations; however, due to their unstructured nature, they are not often utilized. The goal of this scoping review was to evaluate how NEPIs are currently employed in research, identify opportunities and challenges for their broader application, and provide recommendations on their future use. The scoping review comprised a comprehensive literature review and a survey of key stakeholders. From the literature review, we identified 33 primary articles that described the use of NEPIs. The majority of articles (n = 19) identified issues with the quality of information in NEPIs compared with structured prescribing information; nine articles described the development of novel algorithms that performed well in extracting information from NEPIs, and five described the used of manual or simpler algorithms to extract prescribing information from NEPIs. A survey of 19 stakeholders indicated concerns for the quality of information in NEPIs and called for standardization of NEPIs to reduce data variability/errors. Nevertheless, stakeholders believed NEPIs present an opportunity to identify prescriber's intent for the prescription and to study temporal treatment patterns. In summary, NEPIs hold much promise for advancing the field of pharmacoepidemiology. Researchers should take advantage of addressing important questions that can be uniquely answered with NEPIs, but exercise caution when using this information and carefully consider the quality of the data.

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“…Compared to text categorization, information extraction focuses on specific information elements found in clinical text. This includes cancer stage in patients with lung cancer [62], average weekly doses of drugs [63], adverse events in robotic surgery [64], indwelling urinary catheter and urinary symptoms [65], liver tumor characteristics from radiology reports [66], left ventricular ejection fraction from echocardiography reports [67], wound information (wound type, pressure ulcer stage, wound size, anatomic location, and wound treatment) from free text clinical notes [39], and congestive heart failure medication information from Veteran Administration EHRs [68]. Clinical trials attract much attention for tasks that require specific information extraction for public health research, including comparative effectiveness research [69], mapping of disease research [70], categorizing adverse events by age and type [71], or characterizing cancer drug toxicity [72].…”

Section: Text Classification and Information Extraction Remain Strongmentioning

confidence: 99%

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

Névéol¹,

Zweigenbaum²

2017

Yearb Med Inform

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SummaryObjectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The five clinical NLP best papers provide a contribution that ranges from emerging original foundational methods to transitioning solid established research results to a practical clinical setting. They offer a framework for abbreviation disambiguation and coreference resolution, a classification method to identify clinically useful sentences, an analysis of counseling conversations to improve support to patients with mental disorder and grounding of gradable adjectives. Conclusions: Clinical NLP continued to thrive in 2016, with an increasing number of contributions towards applications compared to fundamental methods. Fundamental work addresses increasingly complex problems such as lexical semantics, coreference resolution, and discourse analysis. Research results translate into freely available tools, mainly for English.

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The use of natural language processing on narrative medication schedules to compute average weekly dose

Cited by 5 publications

References 11 publications

Towards Spoken Medical Prescription Understanding

Towards Spoken Medical Prescription Understanding

The use of narrative electronic prescribing instructions in pharmacoepidemiology: A scoping review for the International Society for Pharmacoepidemiology

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

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