Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks

Ghosh, Suparna; Chakraborty, Prithwish; Nsoesie, Elaine O.; Cohn, Emily; Mekaru, Sumiko R.; Brownstein, John S.; Ramakrishnan, Naren

doi:10.1038/srep40841

Cited by 39 publications

(32 citation statements)

References 46 publications

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“…The association between health care utilization and temporal news trends has been examined previously [ 37 ]. A key finding in earlier studies on search log-characterized patient behavior is the escalation of health-related anxiety in the period leading to a health care utilization episode [ 8 , 21 ].…”

Section: Discussionmentioning

confidence: 99%

Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis

Agarwal¹,

Zhang²,

Zhu³

et al. 2016

J Med Internet Res

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BackgroundBy recent estimates, the steady rise in health care costs has deprived more than 45 million Americans of health care services and has encouraged health care providers to better understand the key drivers of health care utilization from a population health management perspective. Prior studies suggest the feasibility of mining population-level patterns of health care resource utilization from observational analysis of Internet search logs; however, the utility of the endeavor to the various stakeholders in a health ecosystem remains unclear.ObjectiveThe aim was to carry out a closed-loop evaluation of the utility of health care use predictions using the conversion rates of advertisements that were displayed to the predicted future utilizers as a surrogate. The statistical models to predict the probability of user’s future visit to a medical facility were built using effective predictors of health care resource utilization, extracted from a deidentified dataset of geotagged mobile Internet search logs representing searches made by users of the Baidu search engine between March 2015 and May 2015.MethodsWe inferred presence within the geofence of a medical facility from location and duration information from users’ search logs and putatively assigned medical facility visit labels to qualifying search logs. We constructed a matrix of general, semantic, and location-based features from search logs of users that had 42 or more search days preceding a medical facility visit as well as from search logs of users that had no medical visits and trained statistical learners for predicting future medical visits. We then carried out a closed-loop evaluation of the utility of health care use predictions using the show conversion rates of advertisements displayed to the predicted future utilizers. In the context of behaviorally targeted advertising, wherein health care providers are interested in minimizing their cost per conversion, the association between show conversion rate and predicted utilization score, served as a surrogate measure of the model’s utility.ResultsWe obtained the highest area under the curve (0.796) in medical visit prediction with our random forests model and daywise features. Ablating feature categories one at a time showed that the model performance worsened the most when location features were dropped. An online evaluation in which advertisements were served to users who had a high predicted probability of a future medical visit showed a 3.96% increase in the show conversion rate.ConclusionsResults from our experiments done in a research setting suggest that it is possible to accurately predict future patient visits from geotagged mobile search logs. Results from the offline and online experiments on the utility of health utilization predictions suggest that such prediction can have utility for health care providers.

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Section: Discussionmentioning

confidence: 99%

Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis

Agarwal¹,

Zhang²,

Zhu³

et al. 2016

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Disease surveillance and signal detection are among the most commonly cited and revolutionary of the big data use cases in precision public health ( 45 , 60 – 62 ). Precision signal detection or disease surveillance using big data has shown efficacy in air pollution ( 23 , 24 ), antibiotic resistance ( 63 ), cholera ( 64 ), dengue ( 65 , 66 ), drowning ( 67 ), drug safety ( 68 , 69 ), electromagnetic field exposure ( 70 ), Influenza A H1N1 ( 71 ), Lyme disease ( 72 ), monitoring food intake ( 73 ), and whooping cough ( 74 ).…”

Section: Performing Disease Surveillance and Signal Detectionmentioning

confidence: 99%

Big Data’s Role in Precision Public Health

Dolley

2018

Front. Public Health

155

114

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Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

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“…There have been recent attempts in epidemiological research to use semi-supervised (Zhao et al, 2015), supervised (Erraguntla et al, 2010;Santillana et al, 2015;Valdes-Donoso et al, 2017), and unsupervised (Chen et al, 2016;Ghosh et al, 2017;Lim et al, 2017) learning approaches. In AI research, unsupervised ML algorithms such as K-mean are recommended for spatiotemporal profiling, outbreak detection, and surveillance studies.…”

Section: Knowledge Discovery In Databases (Kdd)mentioning

confidence: 99%

“…The data cleaning step of KDD seems to be more commonly practiced in AI modeling studies compared to other preprocessing techniques including data integration, data transformation, and data reduction. Syndromic surveillance studies in social media, for instance, use natural language processing methods such as tokenization, stemming, lemmatization, and stop word removal to clean text data (Lee et al, 2013;Chen et al, 2016;Ghosh et al, 2017).…”

Section: Additional Recommendationsmentioning

confidence: 99%

A review of knowledge discovery process in control and mitigation of avian influenza

Naghani

Dara

Poljak

et al. 2019

Anim. Health. Res. Rev.

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In the last several decades, avian influenza virus has caused numerous outbreaks around the world. These outbreaks pose a significant threat to the poultry industry and also to public health. When an avian influenza (AI) outbreak occurs, it is critical to make informed decisions about the potential risks, impact, and control measures. To this end, many modeling approaches have been proposed to acquire knowledge from different sources of data and perspectives to enhance decision making. Although some of these approaches have shown to be effective, they do not follow the process of knowledge discovery in databases (KDD). KDD is an iterative process, consisting of five steps, that aims at extracting unknown and useful information from the data. The present review attempts to survey AI modeling methods in the context of KDD process. We first divide the modeling techniques used in AI into two main categories: data-intensive modeling and small-data modeling. We then investigate the existing gaps in the literature and suggest several potential directions and techniques for future studies. Overall, this review provides insights into the control of AI in terms of the risk of introduction and spread of the virus.

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Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks

Cited by 39 publications

References 46 publications

Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis

Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis

Big Data’s Role in Precision Public Health

A review of knowledge discovery process in control and mitigation of avian influenza

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