Sensitivity and Specificity of an Ngram Method for Classifying Emergency Department Visits into the Respiratory Syndrome in the Turkish Language

Brown, Peter C.; Oktay, Cem; Çevik, Arif Alper; Kılıçaslan, İsa; Goodall, Colin; Halász, Sylvia; Cochrane, Dennis G.; Jacobson, George; Tse, Sandy

doi:10.1197/j.aem.2007.03.1230

Cited by 2 publications

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“…After establishing a type of intervention, the appropriateness of individual AI model types may be considered and targeted to optimize model fit. In Figure 1, we chose well-regarded papers per intervention category (intelligent electronic records, 10 biosurveillance, 8,9,[11][12][13][14]15,16,25 diagnostics, [26][27][28][29][30] clinical decision assistance, 7,19,20,[31][32][33][34][35] and automated planning and scheduling, 36 ) and investigated each paper to determine which AI application was chosen to meet the intervention's goal. After an AI model is selected, sustainability considerations must be made.…”

Section: A Synergistic Approach To Ai Based Global Health Initiativesmentioning

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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Section: A Synergistic Approach To Ai Based Global Health Initiativesmentioning

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

View full text Add to dashboard Cite

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“…While manual data collection has been utilized in the past, an automated n-gram classifier has been described that uses data from a set of ED visits for which both ICD diagnosis code and CCs are available. 12 Text fragments (3 to 6 characters long) are found, which are then associated with syndromic ICD code groupings. This method was found to be efficient and feasible for epidemiological, large scale assessments in English 11 but theoretically would be language-independent and could be implemented anywhere, in any language, as long as its characters are able to be processed by the program.…”

Section: Introductionmentioning

confidence: 99%

Using n-Grams for Syndromic Surveillance in a Turkish Emergency Department without English Translation: A Feasibility Study

Halász

Brown

Oktay

et al. 2013

Biomed Inform Insights

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IntroductionSyndromic surveillance is designed for early detection of disease outbreaks. An important data source for syndromic surveillance is free-text chief complaints (CCs), which are generally recorded in the local language. For automated syndromic surveillance, CCs must be classified into predefined syndromic categories. The n-gram classifier is created by using text fragments to measure associations between chief complaints (CC) and a syndromic grouping of ICD codes.ObjectivesThe objective was to create a Turkish n-gram CC classifier for the respiratory syndrome and then compare daily volumes between the n-gram CC classifier and a respiratory ICD-10 code grouping on a test set of data.MethodsThe design was a feasibility study based on retrospective cohort data. The setting was a university hospital emergency department (ED) in Turkey. Included were all ED visits in the 2002 database of this hospital. Two of the authors created a respiratory grouping of International Classification of Diseases, 10th Revision ICD-10-CM codes by consensus, chosen to be similar to a standard respiratory (RESP) grouping of ICD codes created by the Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE), a project of the Centers for Disease Control and Prevention. An n-gram method adapted from AT&T Labs’ technologies was applied to the first 10 months of data as a training set to create a Turkish CC RESP classifier. The classifier was then tested on the subsequent 2 months of visits to generate a time series graph and determine the correlation with daily volumes measured by the CC classifier versus the RESP ICD-10 grouping.ResultsThe Turkish ED database contained 30,157 visits. The correlation (R2) of n-gram versus ICD-10 for the test set was 0.78.ConclusionThe n-gram method automatically created a CC RESP classifier of the Turkish CCs that performed similarly to the ICD-10 RESP grouping. The n-gram technique has the advantage of systematic, consistent, and rapid deployment as well as language independence.

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Sensitivity and Specificity of an Ngram Method for Classifying Emergency Department Visits into the Respiratory Syndrome in the Turkish Language

Cited by 2 publications

References 0 publications

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

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

Using n-Grams for Syndromic Surveillance in a Turkish Emergency Department without English Translation: A Feasibility Study

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