Natural Language Processing for Biosurveillance

Chapman, Wendy W.; Gundlapalli, Adi V.; South, Brett R.; Dowling, John

doi:10.1007/978-1-4419-6892-0_13

Cited by 13 publications

(9 citation statements)

References 47 publications

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“…Symbolic (or grammatical) techniques use the characteristics of the language (i.e., semantics, syntax, and the relationships among sentences) to interpret a narrative document to the extent necessary for encoding it into one of a set of discrete categories. 22 Only a few studies have used symbolic NLP techniques to identify adverse events such as VTEs. While the results of these studies are promising, symbolic NLP techniques were found to have low to moderate sensitivity and PPV for identifying DVT and PE.…”

Section: Introductionmentioning

confidence: 99%

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data

Rochefort

Verma

Eguale

et al. 2014

Journal of the American Medical Informatics Association

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Background Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures, accurate and efficient methods for monitoring VTE rates are needed. Therefore, we sought to determine the accuracy of statistical natural language processing (NLP) for identifying DVT and PE from electronic health record data.Methods We randomly sampled 2000 narrative radiology reports from patients with a suspected DVT/PE in Montreal (Canada) between 2008 and 2012. We manually identified DVT/PE within each report, which served as our reference standard. Using a bag-of-words approach, we trained 10 alternative support vector machine (SVM) models predicting DVT, and 10 predicting PE. SVM training and testing was performed with nested 10-fold cross-validation, and the average accuracy of each model was measured and compared.Results On manual review, 324 (16.2%) reports were DVT-positive and 154 (7.7%) were PE-positive. The best DVT model achieved an average sensitivity of 0.80 (95% CI 0.76 to 0.85), specificity of 0.98 (98% CI 0.97 to 0.99), positive predictive value (PPV) of 0.89 (95% CI 0.85 to 0.93), and an area under the curve (AUC) of 0.98 (95% CI 0.97 to 0.99). The best PE model achieved sensitivity of 0.79 (95% CI 0.73 to 0.85), specificity of 0.99 (95% CI 0.98 to 0.99), PPV of 0.84 (95% CI 0.75 to 0.92), and AUC of 0.99 (95% CI 0.98 to 1.00).Conclusions Statistical NLP can accurately identify VTE from narrative radiology reports.

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Section: Introductionmentioning

confidence: 99%

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data

Rochefort

Verma

Eguale

et al. 2014

Journal of the American Medical Informatics Association

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“…6 Several implementation models have been developed for resource-poor settings. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] AI model concepts are applicable in: (1) creating intelligent Electronic Health Records (EHR), (2) performing bio-surveillance, (3) diagnosing disease, (4) assisting in clinical decision making, and (5) optimizing planning and scheduling processes.…”

Section: Ai and Global Healthmentioning

confidence: 99%

Artificial Intelligence in Global Health —A Framework and Strategy for Adoption and Sustainability

Hadley¹,

Pettit²,

Malik³

et al. 2020

Int J MCH AIDS

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Artificial Intelligence (AI) applications in medicine have grown considerably in recent years. AI in the forms of Machine Learning, Natural Language Processing, Expert Systems, Planning and Logistics methods, and Image Processing networks provide great analytical aptitude. While AI methods were first conceptualized for radiology, investigations today are established across all medical specialties. The necessity for proper infrastructure, skilled labor, and access to large, well-organized data sets has kept the majority of medical AI applications in higher-income countries. However, critical technological improvements, such as cloud computing and the near-ubiquity of smartphones, have paved the way for use of medical AI applications in resource-poor areas. Global health initiatives (GHI) have already begun to explore ways to leverage medical AI technologies to detect and mitigate public health inequities. For example, AI tools can help optimize vaccine delivery and community healthcare worker routes, thus enabling limited resources to have a maximal impact. Other promising AI tools have demonstrated an ability to: predict burn healing time from smartphone photos; track regions of socioeconomic disparity combined with environmental trends to predict communicable disease outbreaks; and accurately predict pregnancy complications such as birth asphyxia in low resource settings with limited patient clinical data. In this commentary, we discuss the current state of AI-driven GHI and explore relevant lessons from past technology-centered GHI. Additionally, we propose a conceptual framework to guide the development of sustainable strategies for AI-driven GHI, and we outline areas for future research. Keywords: • Artificial Intelligence • AI Framework • Global Health • Implementation • Sustainability • AI Strategy Copyright © 2020 Hadley et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…Recent work has shown that diagnosis can be inferred from clinical notes [14][15][16][17][18][19][20]. Natural language processing (NLP) is the essential component used to extract relevant clinical signs and symptoms from unstructured clinical notes that help to identify the presenting syndrome in real-time surveillance systems [21,22].…”

Section: Background and Significancementioning

confidence: 99%

The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance

Ferraro

Gesteland³

et al. 2017

Appl Clin Inform

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Natural Language Processing for Biosurveillance

Cited by 13 publications

References 47 publications

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data

Artificial Intelligence in Global Health —A Framework and Strategy for Adoption and Sustainability

The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance

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