A real-time biosurveillance mechanism for early-stage disease detection from microblogs: a case study of interconnection between emotional and climatic factors related to migraine disease

For many years, certain climatic factors have been used to predict potential disease outcomes of relevance to humans. This is because early discovery of disease (or its symptoms) would help people or healthcare professionals to take the necessary precautions. Since microblogs can be used to create new connections and maintain existing relationships, disease detection in microblogs is still considered a serious problem for many healthcare systems, especially for establishing a successful epidemic recognition procedure. To tackle this issue, this study proposed a novel tracking approach to diagnose illnesses in microblogs. It is based on the interconnection between certain emotional type and climatic factors associated with a specific disease (e.g., migraine). In this study, detailed migraine data were collected from Twitter. We used K-means and Apriori algorithms to extract migraine-related emotions and investigate the potential associations between migraine symptoms and climatic factors. The results showed that sad emotions were highly interrelated with migraine symptoms. The classification results showed that Sequential Minimal Optimization (SMO) was efficient (95.53% accuracy) in detecting the migraine symptoms from Twitter. The proposed mechanism can be used efficiently in biosurveillance systems due to its capability in identifying the hidden symptoms of a sickness on microblogs. This study paves the way to discover disease-related features using both emotional and climatic factors.

[1]  Mizuki Morita,et al.  Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter , 2011, EMNLP.

[2]  Michael J. Paul,et al.  National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic , 2013, PloS one.

[3]  Robert C. Holte,et al.  Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.

[4]  K. Widnell,et al.  Measuring the impact of migraine for evaluating outcomes of preventive treatments for migraine headaches , 2016, Health and Quality of Life Outcomes.

[5]  Björn Eskofier,et al.  An approximation of the Gaussian RBF kernel for efficient classification with SVMs , 2016, Pattern Recognit. Lett..

[6]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[7]  Parsa Ghaffari,et al.  Opinion Mining and Sentiment Polarity on Twitter and Correlation between Events and Sentiment , 2016, 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService).

[8]  D. Trichopoulos,et al.  A Role of Sunshine in the Triggering of Suicide , 2002, Epidemiology.

[9]  S. Vollaro,et al.  Effect of weather on temporal pain patterns in patients with temporomandibular disorders and migraine , 2017, Journal of oral rehabilitation.

[10]  Hadi Kharrazi,et al.  Characterizing Diabetes, Diet, Exercise, and Obesity Comments on Twitter , 2017, Int. J. Inf. Manag..

[11]  Chunhua Weng,et al.  Advancing Clinical Research Through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning , 2019, Health Informatics.

[12]  M. Lanteri-Minet,et al.  Quality of life impairment, disability and economic burden associated with chronic daily headache, focusing on chronic migraine with or without medication overuse: A systematic review , 2011, Cephalalgia : an international journal of headache.

[13]  Wael Khreich,et al.  A Survey of Techniques for Event Detection in Twitter , 2015, Comput. Intell..

[14]  Eamonn J. Keogh.  Instance-Based Learning , 2010, Encyclopedia of Machine Learning and Data Mining.

[15]  Ramesh Sharda,et al.  Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis , 2019, Information Systems Frontiers.

[16]  J S Brownstein,et al.  Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance , 2016, Scientific reports.

[17]  R. Lipton,et al.  Sex Differences in the Prevalence, Symptoms, and Associated Features of Migraine, Probable Migraine and Other Severe Headache: Results of the American Migraine Prevalence and Prevention (AMPP) Study , 2013, Headache.

[18]  Devanshi D. Dave,et al.  3D mathematical modeling of calcium signaling in Alzheimer’s disease , 2019, Network Modeling Analysis in Health Informatics and Bioinformatics.

[19]  M. Mutz,et al.  On the Sunny Side of Life: Sunshine Effects on Life Satisfaction , 2013 .

[20]  H. Parsa,et al.  It’s Raining Complaints! How Weather Factors Drive Consumer Comments and Word-of-Mouth , 2019, Journal of Hospitality & Tourism Research.

[21]  Olga Baysal,et al.  Mining Twitter Data for Influenza Detection and Surveillance , 2016, 2016 IEEE/ACM International Workshop on Software Engineering in Healthcare Systems (SEHS).

[22]  L. Ekselius,et al.  Serotonergic medication enhances the association between suicide and sunshine. , 2016, Journal of affective disorders.

[23]  Steven L. Salzberg,et al.  Book Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993 , 1994, Machine Learning.

[24]  Iana Sabatovych.  Use of Sentiment Analysis for Predicting Public Opinion on Referendum: A Feasibility Study , 2019, The Reference Librarian.

[25]  Zion Tsz Ho Tse,et al.  Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response , 2018, Data.

[26]  Jaishree Singh,et al.  Improving Efficiency of Apriori Algorithm Using Transaction Reduction , 2013 .

[27]  James Boit,et al.  Topical Mining of Malaria Using Social Media. A Text Mining Approach , 2020, HICSS.

[28]  W. Brannath,et al.  Migraine and weather: A prospective diary-based analysis , 2011, Cephalalgia : an international journal of headache.

[29]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[30]  Prabhat Kumar,et al.  Performance evaluation of classification methods with PCA and PSO for diabetes , 2020 .

[31]  Madhav Erraguntla,et al.  Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management , 2019, Health Informatics J..

[32]  Bu-Sung Lee,et al.  Event Detection in Twitter , 2011, ICWSM.

[33]  KhreichWael,et al.  A Survey of Techniques for Event Detection in Twitter , 2015, CI 2015.

[34]  Taghi M. Khoshgoftaar,et al.  Sample size determination for biomedical big data with limited labels , 2020, Network Modeling Analysis in Health Informatics and Bioinformatics.

[35]  Mehdi Hosseinzadeh,et al.  Using Twitter to raise the profile of childhood cancer awareness month , 2019, Network Modeling Analysis in Health Informatics and Bioinformatics.

[36]  Y. Lim,et al.  Long-Term Fine Particulate Matter Exposure and Major Depressive Disorder in a Community-Based Urban Cohort , 2016, Environmental health perspectives.

[37]  Maunendra Sankar Desarkar,et al.  Term Specific TF-IDF Boosting for Detection of Rumours in Social Networks , 2019, 2019 11th International Conference on Communication Systems & Networks (COMSNETS).

[38]  Laura Schweitzer,et al.  Advances In Kernel Methods Support Vector Learning , 2016 .

[39]  Huilong Duan,et al.  A probabilistic topic model for clinical risk stratification from electronic health records , 2015, J. Biomed. Informatics.

[40]  Naoaki Okazaki,et al.  Disease Event Detection based on Deep Modality Analysis , 2015, ACL.

[41]  Z. Spasova,et al.  The effect of weather and its changes on emotional state – individual characteristics that make us vulnerable , 2012 .

[42]  K. Suzanne Barber,et al.  Predicting Disease Outbreaks Using Social Media: Finding Trustworthy Users , 2018, Proceedings of the Future Technologies Conference (FTC) 2018.

[43]  J S Brownstein,et al.  An overview of internet biosurveillance. , 2013, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[44]  Michael J. Paul,et al.  Session Introduction , 2016, PSB.

[45]  Sung Hoon Lim,et al.  An unsupervised machine learning model for discovering latent infectious diseases using social media data , 2017, J. Biomed. Informatics.

[46]  Iana Sabatovych.  Do social media create revolutions? Using Twitter sentiment analysis for predicting the Maidan Revolution in Ukraine , 2019 .

[47]  M. Borro,et al.  Pharmacogenetic considerations for migraine therapies , 2018, Expert opinion on drug metabolism & toxicology.

[48]  R. Dales,et al.  Air Pollution and Hospitalization for Headache in Chile , 2009, American journal of epidemiology.

[49]  Paul Jen-Hwa Hu,et al.  Managing Emerging Infectious Diseases with Information Systems: Reconceptualizing Outbreak Management Through the Lens of Loose Coupling , 2011 .

[50]  Tommy Gärling,et al.  Season and Weather Effects on Travel-Related Mood and Travel Satisfaction , 2017, Front. Psychol..

[51]  P. Martus,et al.  The influence of weather on migraine – are migraine attacks predictable? , 2014, Annals of clinical and translational neurology.

[52]  Sandra Bringay,et al.  Detection of suicide-related posts in Twitter data streams , 2018, IBM J. Res. Dev..

[53]  Bernard Kamsu-Foguem,et al.  Mining association rules for the quality improvement of the production process , 2013, Expert Syst. Appl..

[54]  Yun Kang,et al.  Regional Influenza Prediction with Sampling Twitter Data and PDE Model , 2020, International journal of environmental research and public health.

[55]  Jong-Ling Fuh,et al.  Patients with migraine are right about their perception of temperature as a trigger: time series analysis of headache diary data , 2015, The Journal of Headache and Pain.

[56]  Sujan Kumar Saha,et al.  Web Information Extraction for Finding Remedy Based on a Patient-Authored Text: A Study on Homeopathy , 2020, Network Modeling Analysis in Health Informatics and Bioinformatics.

[57]  A. Peters,et al.  Weather-induced ischemia and arrhythmia in patients undergoing cardiac rehabilitation: another difference between men and women , 2008, International journal of biometeorology.

[58]  Jenine K. Harris,et al.  Using Twitter to Identify and Respond to Food Poisoning: The Food Safety STL Project , 2017, Journal of public health management and practice : JPHMP.

[59]  Cecile Paris,et al.  Harnessing Tweets for Early Detection of an Acute Disease Event , 2019, Epidemiology.

[60]  Hosam Al-Samarraie,et al.  A First Look at the Effectiveness of Personality Dimensions in Promoting Users’ Satisfaction With the System , 2018 .

[61]  Hosam Al-Samarraie,et al.  Geo-spatial-based Emotions: A Mechanism for Event Detection in Microblogs , 2019, ICSCA.

[62]  Dhruba K. Bhattacharyya,et al.  Developing an effective biclustering technique using an enhanced proximity measure , 2020, Network Modeling Analysis in Health Informatics and Bioinformatics.

[63]  Jing Tian,et al.  Predicting consumer variety-seeking through weather data analytics , 2018, Electron. Commer. Res. Appl..

[64]  Mark Dredze,et al.  Separating Fact from Fear: Tracking Flu Infections on Twitter , 2013, NAACL.

[65]  Chris Hankin,et al.  Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification , 2019, Inf. Process. Manag..

[66]  L. Appel,et al.  The effect of ambient temperature and barometric pressure on ambulatory blood pressure variability. , 2001, American journal of hypertension.

[67]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[68]  Aron Culotta,et al.  Towards detecting influenza epidemics by analyzing Twitter messages , 2010, SOMA '10.

[69]  Nigam H. Shah,et al.  An unsupervised learning method to identify reference intervals from a clinical database , 2016, J. Biomed. Informatics.

[70]  Massimiliano Orsini,et al.  A Web Geographic Information System to share data and explorative analysis tools: The application to West Nile disease in the Mediterranean basin , 2018, PloS one.

[71]  Michael D. Barnes,et al.  "Right Time, Right Place" Health Communication on Twitter: Value and Accuracy of Location Information , 2012, Journal of medical Internet research.

[72]  J. Unützer,et al.  Exploring opportunities to support mental health care using social media: A survey of social media users with mental illness , 2019, Early Intervention in Psychiatry.

[73]  J. Y. Park,et al.  Long-term exposure to ambient air pollutants and mental health status: A nationwide population-based cross-sectional study , 2018, PloS one.

[74]  Paul N Zivich,et al.  Social Media- and Internet-Based Disease Surveillance for Public Health. , 2020, Annual review of public health.

[75]  N. Chafekar,et al.  Clinical Profile of Primary Headaches and Awareness of Trigger factors in Migraine patients , 2018, MVP Journal of Medical Sciences.

[76]  Jaime E Hart,et al.  The relation between past exposure to fine particulate air pollution and prevalent anxiety: observational cohort study , 2015, BMJ : British Medical Journal.

[77]  I. Wing,et al.  Increasing ambient temperature reduces emotional well-being. , 2016, Environmental research.

[78]  Chien Chin Chen,et al.  A novel trend surveillance system using the information from web search engines , 2016, Decis. Support Syst..

[79]  J. Mercante,et al.  Anxiety and depression symptoms and migraine: a symptom-based approach research , 2017, The Journal of Headache and Pain.

[80]  Hadi Veisi,et al.  Predicting the spread of influenza epidemics by analyzing twitter messages , 2019, Health and Technology.

[81]  R. Shapiro,et al.  Sex and Gender Differences in Migraine-Evaluating Knowledge Gaps. , 2018, Journal of women's health.

[82]  Christopher M. Danforth,et al.  A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter , 2018, ArXiv.

[83]  J. Allik,et al.  The influence of the weather on affective experience: An experience sampling study. , 2011 .

[84]  N. Chai,et al.  The epidemiology and comorbidities of migraine and tension-type headache , 2012 .