932 research outputs found
Transfer learning for unsupervised influenza-like illness models from online search data
A considerable body of research has demonstrated that online
search data can be used to complement current syndromic surveillance systems. The vast majority of previous work proposes solutions that are based on supervised learning paradigms, in which
historical disease rates are required for training a model. However,
for many geographical regions this information is either sparse or
not available due to a poor health infrastructure. It is these regions
that have the most to benefit from inferring population health statistics from online user search activity. To address this issue, we
propose a statistical framework in which we first learn a supervised model for a region with adequate historical disease rates, and
then transfer it to a target region, where no syndromic surveillance
data exists. This transfer learning solution consists of three steps:
(i) learn a regularized regression model for a source country, (ii)
map the source queries to target ones using semantic and temporal similarity metrics, and (iii) re-adjust the weights of the target
queries. It is evaluated on the task of estimating influenza-like illness (ILI) rates. We learn a source model for the United States, and
subsequently transfer it to three other countries, namely France,
Spain and Australia. Overall, the transferred (unsupervised) models
achieve strong performance in terms of Pearson correlation with
the ground truth (> .92 on average), and their mean absolute error
does not deviate greatly from a fully supervised baseline
Tracking COVID-19 using online search
Previous research has demonstrated that various properties of infectious
diseases can be inferred from online search behaviour. In this work we use time
series of online search query frequencies to gain insights about the prevalence
of COVID-19 in multiple countries. We first develop unsupervised modelling
techniques based on associated symptom categories identified by the United
Kingdom's National Health Service and Public Health England. We then attempt to
minimise an expected bias in these signals caused by public interest -- as
opposed to infections -- using the proportion of news media coverage devoted to
COVID-19 as a proxy indicator. Our analysis indicates that models based on
online searches precede the reported confirmed cases and deaths by 16.7 (10.2 -
23.2) and 22.1 (17.4 - 26.9) days, respectively. We also investigate transfer
learning techniques for mapping supervised models from countries where the
spread of disease has progressed extensively to countries that are in earlier
phases of their respective epidemic curves. Furthermore, we compare time series
of online search activity against confirmed COVID-19 cases or deaths jointly
across multiple countries, uncovering interesting querying patterns, including
the finding that rarer symptoms are better predictors than common ones.
Finally, we show that web searches improve the short-term forecasting accuracy
of autoregressive models for COVID-19 deaths. Our work provides evidence that
online search data can be used to develop complementary public health
surveillance methods to help inform the COVID-19 response in conjunction with
more established approaches.Comment: Published in Nature Digital Medicine. Please note that the published
version differs from this preprin
Machine learning in drug supply chain management during disease outbreaks: a systematic review
The drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks
Data-Centric Epidemic Forecasting: A Survey
The COVID-19 pandemic has brought forth the importance of epidemic
forecasting for decision makers in multiple domains, ranging from public health
to the economy as a whole. While forecasting epidemic progression is frequently
conceptualized as being analogous to weather forecasting, however it has some
key differences and remains a non-trivial task. The spread of diseases is
subject to multiple confounding factors spanning human behavior, pathogen
dynamics, weather and environmental conditions. Research interest has been
fueled by the increased availability of rich data sources capturing previously
unobservable facets and also due to initiatives from government public health
and funding agencies. This has resulted, in particular, in a spate of work on
'data-centered' solutions which have shown potential in enhancing our
forecasting capabilities by leveraging non-traditional data sources as well as
recent innovations in AI and machine learning. This survey delves into various
data-driven methodological and practical advancements and introduces a
conceptual framework to navigate through them. First, we enumerate the large
number of epidemiological datasets and novel data streams that are relevant to
epidemic forecasting, capturing various factors like symptomatic online
surveys, retail and commerce, mobility, genomics data and more. Next, we
discuss methods and modeling paradigms focusing on the recent data-driven
statistical and deep-learning based methods as well as on the novel class of
hybrid models that combine domain knowledge of mechanistic models with the
effectiveness and flexibility of statistical approaches. We also discuss
experiences and challenges that arise in real-world deployment of these
forecasting systems including decision-making informed by forecasts. Finally,
we highlight some challenges and open problems found across the forecasting
pipeline.Comment: 67 pages, 12 figure
Analysis of Tweets for Social Media Health Applications
abstract: Social networking sites like Twitter have provided people a platform to connect
with each other, to discuss and share information and news or to entertain themselves. As the number of users continues to grow there has been explosive growth in the data generated by these users. Such a vast data source has provided researchers a way to study and monitor public health.
Accurately analyzing tweets is a difficult task mainly because of their short length, the inventive spellings and creative language expressions. Instead of focusing at the topic level, identifying tweets that have personal health experience mentions would be more helpful to researchers, governments and other organizations. Another important limitation in the current systems for social media health applications is the use of a disease-specific model and dataset to study a particular disease. Identifying adverse drug reactions is an important part of the drug development process. Detecting and extracting adverse drug mentions in tweets can supplement the list of adverse drug reactions that result from the drug trials and can help in the improvement of the drugs.
This thesis aims to address these two challenges and proposes three systems. A generalizable system to identify personal health experience mentions across different disease domains, a system for automatic classifications of adverse effects mentions in tweets and a system to extract adverse drug mentions from tweets. The proposed systems use the transfer learning from language models to achieve notable scores on Social Media Mining for Health Applications(SMM4H) 2019 (Weissenbacher et al. 2019) shared tasks.Dissertation/ThesisMasters Thesis Computer Science 201
Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature
The booming adoption of Artificial Intelligence (AI) likewise poses benefits and challenges. In this paper, we particularly focus on the bright side of AI and its promising potential to face our society’s grand challenges. Given this potential, different studies have already conducted valuable work by conceptualizing specific facets of AI and sustainability, including reviews on AI and Information Systems (IS) research or AI and business values. Nonetheless, there is still little holistic knowledge at the intersection of IS, AI, and sustainability. This is problematic because the IS discipline, with its socio-technical nature, has the ability to integrate perspectives beyond the currently dominant technological one as well as can advance both theory and the development of purposeful artifacts. To bridge this gap, we disclose how IS research currently makes use of AI to boost sustainable development. Based on a systematically collected corpus of 95 articles, we examine sustainability goals, data inputs, technologies and algorithms, and evaluation approaches that coin the current state of the art within the IS discipline. This comprehensive overview enables us to make more informed investments (e.g., policy and practice) as well as to discuss blind spots and possible directions for future research
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