scholarly journals Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Cédric Gil-Jardiné ◽  
Gabrielle Chenais ◽  
Catherine Pradeau ◽  
Eric Tentillier ◽  
Philippe Revel ◽  
...  
Keyword(s):  
Public Health ◽  
Neural Network ◽  
Natural Language ◽  
Alcohol Intoxication ◽  
Automatic Classification ◽  
Epidemiological Surveillance ◽  
Free Text ◽  
Medical Communication ◽  
Health Crisis ◽  
Emergency Medical

Abstract Objectives During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdown. We assessed whether the automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators. Methods We retrieved all 796,209 free-text call reports from the emergency medical communication center of the Gironde department, France, between 2018 and 2020. We trained a natural language processing neural network model with a mixed unsupervised/supervised method to classify all reasons for calls in 2020. Validation and parameter adjustment were performed using a sample of 39,907 manually-coded free-text reports. Results The number of daily calls for flu-like symptoms began to increase from February 21, 2020 and reached an unprecedented level by February 28, 2020 and peaked on March 14, 2020, 3 days before lockdown. It was strongly correlated with daily emergency room admissions, with a delay of 14 days. Calls for chest pain and stress and anxiety, peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. No noticeable trends in relation to lockdown was found for other groups of reasons including gastroenteritis and abdominal pain, stroke, suicide and self-harm, pregnancy and delivery problems. Discussion The first wave of the COVID-19 crisis came along with increased levels of stress and anxiety but no increase in alcohol intoxication and violence. As expected, call related to road traffic crashes sharply decreased. The sharp decrease in the number of calls for malaise was more surprising. Conclusion The content of calls to emergency medical communication centers is an efficient epidemiological surveillance data source that provides insights into the societal upheavals induced by a health crisis. The use of an automatic classification system using artificial intelligence makes it possible to free itself from the context that could influence a human coder, especially in a crisis situation. The COVID-19 crisis and/or lockdown induced deep modifications in the population health profile.

scholarly journals Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using automatic classification.

2020 ◽  
Author(s):  
Cédric Gil-Jardiné ◽  
Gabrielle Chenais ◽  
Catherine Pradeau ◽  
Eric Tentillier ◽  
Philipe Revel ◽  
...  
Keyword(s):  
Public Health ◽  
Language Processing ◽  
Automatic Classification ◽  
Epidemiological Surveillance ◽  
Free Text ◽  
Medical Communication ◽  
Health Crisis ◽  
Preventative Measures ◽  
Emergency Medical ◽  
Human Coder

Abstract Objectives During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators related to the epidemic and to preventative measures such as lockdown. The automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators. Methods We retrieved all 796,209 free-text call reports from the emergency medical communication center of the Gironde department, France, between 2018 and 2020. We trained a natural language processing neural network model with a mixed unsupervised/supervised method to classify all reasons for calls in 2020. Validation and parameter adjustment were performed using a sample of 20,000 manually-coded free-text reports. Results The number of daily calls for flu-like symptoms began to increase from February 21, 2020 and reached an unprecedented level by February 28, 2020 and peaked on March 14, 2020, 3 days before lockdown. It was strongly correlated with daily emergency room admissions, with a delay of 14 days. Calls for chest pain, stress, but also those mentioning dyspnea, ageusia and anosmia peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. Discussion This example of the COVID-19 crisis shows how the availability of reliable and unbiased surveillance platforms can be useful for a timely and relevant monitoring of all events with public health consequences. The use of an automatic classification system using artificial intelligence makes it possible to free itself from the context that could influence a human coder, especially in a crisis situation. Conclusion The content of calls to emergency medical communication centers is an efficient epidemiological surveillance data source that provides insights into the societal upheavals induced by a health crisis.

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

2017 ◽  
Vol 56 (05) ◽  
pp. 377-389 ◽  
Author(s):  
Xingyu Zhang ◽  
Joyce Kim ◽  
Rachel E. Patzer ◽  
Stephen R. Pitts ◽  
Aaron Patzer ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Department ◽  
Logistic Regression ◽  
Natural Language Processing ◽  
Natural Language ◽  
Hospital Admission ◽  
Language Processing ◽  
Predictive Accuracy ◽  
Free Text

SummaryObjective: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements.Methods: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient’s reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model.Results: Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.7310.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN.Conclusions: The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient’s reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.

A Public Health Enforcement Initiative to Combat Underage Drinking Using Emergency Medical Services Call Data

2012 ◽  
Vol 27 (2) ◽  
pp. 167-171
Author(s):  
Daniel L. Lemkin ◽  
Michael C. Bond ◽  
Donald W. Alves ◽  
Richard A. Bissell
Keyword(s):  
Public Health ◽  
Emergency Medical Services ◽  
Alcohol Intoxication ◽  
Underage Drinking ◽  
Medical Services ◽  
Government Officials ◽  
Societal Costs ◽  
Response Data ◽  
Emergency Medical ◽  
Disproportionate Number

AbstractObjectiveThe objective of this study was to determine whether Emergency Medical Services (EMS) records can identify bars that serve a disproportionate number of minors, and if government officials will use this data to direct underage drinker enforcement efforts.MethodsEmergency Medical Services call logs to all bars in the study area were cross-referenced with a local hospital's records. The records of patients with alcohol-related complaints were analyzed. Outlier bars were identified, and presented to government officials who completed a survey to assess if this information would prompt new enforcement efforts.ResultsEmergency Medical Services responded to 149 establishments during the study period. Eighty-four responses were distributed across six bars, and 78 were matched with the hospital's records. Fifty-one patients, 18 (35%) of whom were underage, were treated for alcohol intoxication, with 46% of the cases originating from four bars. Government officials found the information useful, and planned to initiate new operations based on the information.ConclusionsAlcohol consumption by minors can lead to life-long abuse, with high personal, financial, and societal costs. Emergency Medical Services response data and hospital records can be used to identify bars that allow underage drinking, which is useful in directing law enforcement efforts.Lemkin DL, Bond MC, Alves DW, Bissell RA. A public health enforcement initiative to combat underage drinking using emergency medical services call data. Prehosp Disaster Med. 2012;27(2):1-5.

scholarly journals Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI

2020 ◽  
Vol 10 (4) ◽  
pp. 286
Author(s):  
Tak Sung Heo ◽  
Yu Seop Kim ◽  
Jeong Myeong Choi ◽  
Yeong Seok Jeong ◽  
Soo Young Seo ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Brain Mri ◽  
Free Text ◽  
Word Level ◽  
Poor Outcomes ◽  
Document Level

Brain magnetic resonance imaging (MRI) is useful for predicting the outcome of patients with acute ischemic stroke (AIS). Although deep learning (DL) using brain MRI with certain image biomarkers has shown satisfactory results in predicting poor outcomes, no study has assessed the usefulness of natural language processing (NLP)-based machine learning (ML) algorithms using brain MRI free-text reports of AIS patients. Therefore, we aimed to assess whether NLP-based ML algorithms using brain MRI text reports could predict poor outcomes in AIS patients. This study included only English text reports of brain MRIs examined during admission of AIS patients. Poor outcome was defined as a modified Rankin Scale score of 3–6, and the data were captured by trained nurses and physicians. We only included MRI text report of the first MRI scan during the admission. The text dataset was randomly divided into a training and test dataset with a 7:3 ratio. Text was vectorized to word, sentence, and document levels. In the word level approach, which did not consider the sequence of words, and the “bag-of-words” model was used to reflect the number of repetitions of text token. The “sent2vec” method was used in the sensation-level approach considering the sequence of words, and the word embedding was used in the document level approach. In addition to conventional ML algorithms, DL algorithms such as the convolutional neural network (CNN), long short-term memory, and multilayer perceptron were used to predict poor outcomes using 5-fold cross-validation and grid search techniques. The performance of each ML classifier was compared with the area under the receiver operating characteristic (AUROC) curve. Among 1840 subjects with AIS, 645 patients (35.1%) had a poor outcome 3 months after the stroke onset. Random forest was the best classifier (0.782 of AUROC) using a word-level approach. Overall, the document-level approach exhibited better performance than did the word- or sentence-level approaches. Among all the ML classifiers, the multi-CNN algorithm demonstrated the best classification performance (0.805), followed by the CNN (0.799) algorithm. When predicting future clinical outcomes using NLP-based ML of radiology free-text reports of brain MRI, DL algorithms showed superior performance over the other ML algorithms. In particular, the prediction of poor outcomes in document-level NLP DL was improved more by multi-CNN and CNN than by recurrent neural network-based algorithms. NLP-based DL algorithms can be used as an important digital marker for unstructured electronic health record data DL prediction.

scholarly journals Novel AI to avert the mental health crisis in COVID-19: Novel application of GPT2 in Cognitive Behaviour Therapy

2022 ◽  
Author(s):  
Rajagopal A
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Natural Language ◽  
Three Dimensions ◽  
Fine Tuning ◽  
World Health ◽  
Mental Health Crisis ◽  
Health Crisis ◽  
Cognitive Behaviour ◽  
Time In Literature

Abstract The effect of the COVID-19 pandemic on mental health is substantial. The World Health Organization has called for action to avert an impending mental health crisis. To respond to this call, this paper contributes a novel application of Deep Learning in Natural Language Generation (NLG) to seed healthy thoughts for mental health therapy. For the 1st time in literature, a transfer learning capable large neural network with more than 100 million parameters for a NLG based mental health therapy application is proposed & demonstrated. This AI is designed to address scalable impact for millions of families with a timely health intervention in a privacy-safe approach. To the best of our knowledge, this is the first research paper to apply GPT2 (Generative Pretrained Transformer) for Cognitive Behavior therapy (CBT). Further, the paper demonstrates the proposed neural network architecture with a lab prototype implementation with reproducible results. This paper demonstrates this AI’s ability to generate conditional synthetic human-like text intended to seed a healthy mental outlook. This is accomplished by fine tuning a pre-trained GPT2 language model. The source code and video demonstration is contributed at https://sites.google.com/view/ai-in-mental-health.Also, for the 1st time in literature, a novel idea of NLU (Natural Language Understanding) activated NLG therapy is demonstrated with reproducible results using a BERT based classifier to activate the GPT2 based therapy. Performance of GPT2 models of three different sizes (124, 355, 774 million parameters) was the same for a very small dataset, thus a small GPT2 model is suggested for on-device AI inference. This AI is a step forward in responding to WHO’s call for action to avert the crisis. Towards addressing all the three dimensions of the monumental challenge, the paper designed a novel AI architecture by taking advantage of both BERT & GPT2. It also demonstrated the feasibility of Transformers-based AI for developing a mental health therapy solution. Further, this paper contributed an open-source AI prototype to support research communities to transform global mental wellness.

scholarly journals COVID-19 Pandemic: What Can the West Learn From the East?

2020 ◽  
Author(s):  
Mostafa Shokoohi ◽  
Mehdi Osooli ◽  
Saverio Stranges
Keyword(s):  
Public Health ◽  
Control Strategies ◽  
Health Impact ◽  
Epidemiological Surveillance ◽  
The West ◽  
Health Crisis ◽  
The Public ◽  
Western Countries ◽  
Incidence And Mortality ◽  
And Control

Differences in public health approaches to control the coronavirus disease 2019 (COVID-19) pandemic could largely explain substantial variations in epidemiological indicators (such as incidence and mortality) between the West and the East. COVID-19 revealed vulnerabilities of most western countries’ healthcare systems in their response to the ongoing public health crisis. Hence, western countries can possibly learn from practices from several East Asian countries regarding infrastructures, epidemiological surveillance and control strategies to mitigate the public health impact of the pandemic. In this paper, we discuss that the lack of rapid and timely community-centered approaches, and most importantly weak public health infrastructures, might have resulted in a high number of infected cases and fatalities in many western countries.

scholarly journals Novel AI to avert the mental health crisis in COVID-19: Novel application of GPT2 in Cognitive Behaviour Therapy

2021 ◽  
Author(s):  
Rajagopal A ◽  
Nirmala V ◽  
Andrew J ◽  
Arun M
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Natural Language ◽  
Three Dimensions ◽  
Fine Tuning ◽  
World Health ◽  
Mental Health Crisis ◽  
Health Crisis ◽  
Cognitive Behaviour ◽  
Time In Literature

Abstract The effect of the COVID-19 pandemic on mental health is substantial. The World Health Organization has called for action to avert an impending mental health crisis. To respond to this call, this paper contributes a novel application of Deep Learning in Natural Language Generation (NLG) to seed healthy thoughts for mental health therapy. For the 1st time in literature, a transfer learning capable large neural network with more than 100 million parameters for a NLG based mental health therapy application is proposed & demonstrated. This AI is designed to address scalable impact for millions of families with a timely health intervention in a privacy-safe approach. To the best of our knowledge, this is the first research paper to apply GPT2 (Generative Pretrained Transformer) for Cognitive Behavior therapy (CBT). Further, the paper demonstrates the proposed neural network architecture with a lab prototype implementation with reproducible results. This paper demonstrates this AI’s ability to generate conditional synthetic human-like text intended to seed a healthy mental outlook. This is accomplished by fine tuning a pre-trained GPT2 language model. The source code and video demonstration is contributed at https://sites.google.com/view/ai-in-mental-health.Also, for the 1st time in literature, a novel idea of NLU (Natural Language Understanding) activated NLG therapy is demonstrated with reproducible results using a BERT based classifier to activate the GPT2 based therapy. Performance of GPT2 models of three different sizes (124, 355, 774 million parameters) was the same for a very small dataset, thus a small GPT2 model is suggested for on-device AI inference. This AI is a step forward in responding to WHO’s call for action to avert the crisis. Towards addressing all the three dimensions of the monumental challenge, the paper designed a novel AI architecture by taking advantage of both BERT & GPT2. It also demonstrated the feasibility of Transformers-based AI for developing a mental health therapy solution. Further, this paper contributed an open-source AI prototype to support research communities to transform global mental wellness.

DARR: A Free-text Analysis System for the Automatic Documentation of Radiological Reports

1977 ◽  
Vol 16 (03) ◽  
pp. 144-153 ◽  
Author(s):  
E. Vaccari ◽  
W. Delaney ◽  
A. Chiesa
Keyword(s):  
Natural Language ◽  
Text Analysis ◽  
Automatic Documentation ◽  
Free Text ◽  
Software System ◽  
Automatic Translation ◽  
Radiological Report ◽  
Analysis System ◽  
Content Processing

A software system for the automatic free-text analysis and retrieval of radiological reports is presented. Such software involves: (1) automatic translation of the specific natural language in a formalized metalanguage in order to transform the radiological report in a »normalized report« analyzable by computer; (2) content processing of the normalized report to select desired information. The approach used to accomplish point (1) is described in detail referring to a specific application.

Eating NAFTA

2018 ◽  
Author(s):  
Alyshia Gálvez
Keyword(s):  
Public Health ◽  
Chronic Disease ◽  
Low Income ◽  
Food System ◽  
Dietary Habits ◽  
Trade Agreement ◽  
Well Being ◽  
Health Crisis ◽  
The North ◽  
The World

In the two decades since the North American Free Trade Agreement (NAFTA) went into effect, Mexico has seen an epidemic of diet-related illness. While globalization has been associated with an increase in chronic disease around the world, in Mexico, the speed and scope of the rise has been called a public health emergency. The shift in Mexican foodways is happening at a moment when the country’s ancestral cuisine is now more popular and appreciated around the world than ever. What does it mean for their health and well-being when many Mexicans eat fewer tortillas and more instant noodles, while global elites demand tacos made with handmade corn tortillas? This book examines the transformation of the Mexican food system since NAFTA and how it has made it harder for people to eat as they once did. The book contextualizes NAFTA within Mexico’s approach to economic development since the Revolution, noticing the role envisioned for rural and low-income people in the path to modernization. Examination of anti-poverty and public health policies in Mexico reveal how it has become easier for people to consume processed foods and beverages, even when to do so can be harmful to health. The book critiques Mexico’s strategy for addressing the public health crisis generated by rising rates of chronic disease for blaming the dietary habits of those whose lives have been upended by the economic and political shifts of NAFTA.

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