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

[1]  R. Lipton,et al.  Demographics, Headache Features, and Comorbidity Profiles in Relation to Headache Frequency in People With Migraine: Results of the American Migraine Prevalence and Prevention (AMPP) Study , 2020, Headache.

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

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

[4]  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.

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

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

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

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

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

[10]  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..

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

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

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

[14]  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.

[15]  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..

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

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

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

[19]  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).

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

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

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

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

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

[25]  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.

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

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

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

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

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

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

[32]  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.

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

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

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

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

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

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

[39]  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.

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

[41]  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).

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

[43]  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).

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

[45]  J. Brownstein,et al.  Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance , 2015, Scientific Reports.

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

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

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

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

[50]  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.

[51]  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.

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

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

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

[55]  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.

[56]  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.

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

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

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

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

[61]  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.

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

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

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

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

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

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

[68]  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.

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

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

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

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

[73]  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.

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

[75]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

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

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

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

[79]  Nello Cristianini,et al.  Advances in Kernel Methods - Support Vector Learning , 1999 .

[80]  S. Salzberg C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993 , 1994, Machine Learning.

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