Natural Language Processing: Applications in Pediatric Research

Savova, Guergana; Pestian, John; Connolly, B.; Miller, Timothy A.; Ni, Yizhao; Dexheimer, Judith W.

doi:10.1007/978-981-10-1104-7_12

Cited by 6 publications

(12 citation statements)

References 69 publications

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“…Keyword searches have identified pediatric cohorts for retrospective studies on complex febrile seizure . Predictive models using machine learning had an accuracy of 90% in distinguishing tractable and intractable epilepsy patients . An NLP system to categorize patients into low‐, high‐, and equivocal‐risk categories for acute appendicitis had a sensitivity of 89.7% and positive predicted value of 95.2% .…”

Section: Discussionmentioning

confidence: 99%

Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes

Doan

Maehara

Chaparro

et al. 2016

Academic Emergency Medicine

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Objective Delayed diagnosis of Kawasaki disease (KD) may lead to serious cardiac complications. We sought to create and test the performance of a natural language processing (NLP) tool, the KD-NLP, in the identification of emergency department (ED) patients for whom the diagnosis of KD should be considered. Methods We developed an NLP tool that recognizes the KD diagnostic criteria based on standard clinical terms and medical word usage using 22 pediatric ED notes augmented by Unified Medical Language System vocabulary. With high suspicion for KD defined as fever and three or more KD clinical signs, KD-NLP was applied to 253 ED notes from children ultimately diagnosed with either KD or another febrile illness. We evaluated KD-NLP performance against ED notes manually reviewed by clinicians and compared the results to a simple keyword search. Results KD-NLP identified high-suspicion patients with a sensitivity of 93.6% and specificity of 77.5% compared to notes manually reviewed by clinicians. The tool outperformed a simple keyword search (sensitivity = 41.0%; specificity = 76.3%). Conclusions KD-NLP showed comparable performance to clinician manual chart review for identification of pediatric ED patients with a high suspicion for KD. This tool could be incorporated into the ED electronic health record system to alert providers to consider the diagnosis of KD. KD-NLP could serve as a model for decision support for other conditions in the ED.

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Section: Discussionmentioning

confidence: 99%

Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes

Doan

Maehara

Chaparro

et al. 2016

Academic Emergency Medicine

View full text Add to dashboard Cite

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“…12 To extract information from such notes into structured data, techniques like natural-language processing may provide a great opportunity for answering pediatric research questions. 14,15 These techniques enable analyses that would be impossible to perform Figure 1 Innovative pediatric data collection beyond the site-centric randomized clinical trial (RCT). Data from these pediatric clinical studies can be supplemented by increased use of real-world pediatric data from electronic health records.…”

Section: Real-world Data Collectionmentioning

confidence: 99%

“…17 Finally, the data extracted using natural-language processing might be required to identify patients eligible for inclusion in cohorts for observational research. 15 Although effectiveness research with real-world data can be problematic due to the difficulty in controlling for confounding variables and nonrandomized treatment decisions, real-world data offer many other opportunities. 12,18,19 First, real-world data might be used to generate or select hypotheses on the most effective treatment that can then be tested in an RCT.…”

Section: • Limited Control • Complex Data Analysis • No Expert Supervmentioning

confidence: 99%

“…To extract information from such notes into structured data, techniques like natural‐language processing may provide a great opportunity for answering pediatric research questions . These techniques enable analyses that would be impossible to perform on the text data itself when it would be too time‐consuming to do a manual extraction of the relevant features from the text data.…”

Section: Advances In Pediatric Data Collectionmentioning

confidence: 99%

“…In another example, Liang and others used natural‐language processing to allow the use of unstructured information from EHRs for the development of a deep‐learning model for automatic pediatric diagnoses that surpassed the accuracy of junior, but not senior, physicians . Finally, the data extracted using natural‐language processing might be required to identify patients eligible for inclusion in cohorts for observational research …”

Section: Advances In Pediatric Data Collectionmentioning

confidence: 99%

See 2 more Smart Citations

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

Goulooze

Zwep

Vogt

et al. 2020

Clin Pharma and Therapeutics

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Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real‐world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable and may demand the use of artificial intelligence and machine learning to allow the data to be analyzed for decision making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric end points and biomarkers, the prediction of treatment nonresponders, and the prediction of placebo‐responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.

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Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes

Schoene

Turner

Mel³

et al. 2023

IEEE Trans. Affective Comput.

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Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furthermore, psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. In this paper, we investigate whether it is possible to automatically identify suicide notes from other types of social media blogs in two document-level classification tasks. The first task aims to identify suicide notes from depressed and blog posts in a balanced dataset, whilst the second experiment looks at how well suicide notes can be classified when there is a vast amount of neutral text data, which makes the task more applicable to real-world scenarios. Furthermore we perform a linguistic analysis using LIWC (Linguistic Inquiry and Word Count). We present a learning model for modelling long sequences in two experiment series. We achieve an f1-score of 88.26% over the baselines of 0.60 in experiment 1 and 96.1% over the baseline in experiment 2. Finally, we show through visualisations which features the learning model identifies, these include emotions such as love and personal pronouns.

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Natural Language Processing: Applications in Pediatric Research

Cited by 6 publications

References 69 publications

Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes

Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes

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