Forecasting tourist arrivals with machine learning and internet search index

2017 ◽  
Author(s):  
Shaolong Sun ◽  
Shouyang Wang ◽  
Yunjie Wei ◽  
Xianduan Yang ◽  
Kwok-Leung Tsui
Keyword(s):  
Machine Learning ◽  
Internet Search ◽  
Search Index

Forecasting tourist arrivals with machine learning and internet search index

2019 ◽  
Vol 70 ◽  
pp. 1-10 ◽  
Author(s):  
Shaolong Sun ◽  
Yunjie Wei ◽  
Kwok-Leung Tsui ◽  
Shouyang Wang
Keyword(s):  
Machine Learning ◽  
Internet Search ◽  
Search Index

Applicability of internet search index for asthma admission forecast using machine learning

2018 ◽  
Vol 33 (3) ◽  
pp. 723-732 ◽  
Author(s):  
Li Luo ◽  
Chengcheng Liao ◽  
Fengyi Zhang ◽  
Wei Zhang ◽  
Chunyang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Search ◽  
Search Index

GETESS: Constructing a Linguistic Search Index for an Internet Search Engine

2001 ◽  
pp. 227-238 ◽  
Author(s):  
Ilvio Bruder ◽  
Antje Düsterhöft ◽  
Markus Becker ◽  
Jochen Bedersdorfer ◽  
Günter Neumann
Keyword(s):  
Search Engine ◽  
Internet Search ◽  
Internet Search Engine ◽  
Search Index

Composite leading search index: a preprocessing method of internet search data for stock trends prediction

2015 ◽  
Vol 234 (1) ◽  
pp. 77-94 ◽  
Author(s):  
Ying Liu ◽  
Yibing Chen ◽  
Sheng Wu ◽  
Geng Peng ◽  
Benfu Lv
Keyword(s):  
Internet Search ◽  
Preprocessing Method ◽  
Search Data ◽  
Search Index

scholarly journals Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study

10.2196/19348 ◽  
2020 ◽  
Vol 7 (9) ◽  
pp. e19348
Author(s):  
Michael Leo Birnbaum ◽  
Prathamesh "Param" Kulkarni ◽  
Anna Van Meter ◽  
Victor Chen ◽  
Asra F Rizvi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Digital Data ◽  
Diagnostic Process ◽  
Schizophrenia Spectrum Disorders ◽  
Internet Search ◽  
Online Search ◽  
Search Activity ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders

Background Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions. Objective We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders. Methods We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity. Results Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were less likely to use words related to health. Conclusions Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring.

scholarly journals Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study (Preprint)

2020 ◽  
Author(s):  
Michael Leo Birnbaum ◽  
Prathamesh "Param" Kulkarni ◽  
Anna Van Meter ◽  
Victor Chen ◽  
Asra F Rizvi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Digital Data ◽  
Diagnostic Process ◽  
Schizophrenia Spectrum Disorders ◽  
Internet Search ◽  
Online Search ◽  
Search Activity ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders

BACKGROUND Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions. OBJECTIVE We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders. METHODS We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity. RESULTS Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were less likely to use words related to health. CONCLUSIONS Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring.

scholarly journals Toward the use of neural networks for influenza prediction at multiple spatial resolutions

2021 ◽  
Vol 7 (25) ◽  
pp. eabb1237
Author(s):  
Emily L. Aiken ◽  
Andre T. Nguyen ◽  
Cecile Viboud ◽  
Mauricio Santillana
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Real Time ◽  
The United States ◽  
Network Approach ◽  
Internet Search ◽  
Learning Methods ◽  
Neural Network Approach ◽  
Machine Learning Methods ◽  
Search Data

Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care–based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal “data gap,” but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.

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