scholarly journals Best Paper Selection

2020 ◽  
Vol 29 (01) ◽  
pp. 234-234
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Health Services ◽  
Language Processing ◽  
Satellite Images ◽  
Access To Health Services ◽  
Remote Communities ◽  
Access To Health ◽  
Article Abstract

Bruzelius E, Le M, Kenny A, Downey J, Danieletto M, Baum A, Doupe P, Silva B, Landrigan PJ, Singh P. Satellite images and machine learning can identify remote communities to facilitate access to health services. J Am Med Inform Assoc 2019;26(8-9):806-12 https://academic.oup.com/jamia/article/26/8-9/806/5549818 Feldman J, Thomas-Bachli A, Forsyth J, Patel ZH, Khan K. Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise. J Am Med Inform Assoc 2019;26(11):1355-9 https://academic.oup.com/jamia/article-abstract/26/11/1355/5541002?redirectedFrom=fulltext

scholarly journals Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management

2017 ◽  
Vol 25 (4) ◽  
pp. 1170-1187 ◽  
Author(s):  
Madhav Erraguntla ◽  
Josef Zapletal ◽  
Mark Lawley
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Support Vector ◽  
Human Populations ◽  
Illustrative Case ◽  
The Impact ◽  
Disease Analysis

The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

scholarly journals Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise

2019 ◽  
Vol 26 (11) ◽  
pp. 1355-1359 ◽  
Author(s):  
Joshua Feldman ◽  
Andrea Thomas-Bachli ◽  
Jack Forsyth ◽  
Zaki Hasnain Patel ◽  
Kamran Khan
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Disease Activity ◽  
Language Processing ◽  
World Health ◽  
Online Media ◽  
Data Set ◽  
Media Reports

Abstract Objective We assessed whether machine learning can be utilized to allow efficient extraction of infectious disease activity information from online media reports. Materials and Methods We curated a data set of labeled media reports (n = 8322) indicating which articles contain updates about disease activity. We trained a classifier on this data set. To validate our system, we used a held out test set and compared our articles to the World Health Organization Disease Outbreak News reports. Results Our classifier achieved a recall and precision of 88.8% and 86.1%, respectively. The overall surveillance system detected 94% of the outbreaks identified by the WHO covered by online media (89%) and did so 43.4 (IQR: 9.5–61) days earlier on average. Discussion We constructed a global real-time disease activity database surveilling 114 illnesses and syndromes. We must further assess our system for bias, representativeness, granularity, and accuracy. Conclusion Machine learning, natural language processing, and human expertise can be used to efficiently identify disease activity from digital media reports.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

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