Automatic summarization and visualisation of healthcare tweets

Lavanya, P. G.; Suresha, M

doi:10.1109/icacci.2017.8126063

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…The reason might be that the Poisson's ratio varies very small for different soil types, within the range of 0.25~0.3 in this study case. Low variance features are believed to not significantly affect the changing of output [48].…”

Section: Discussionmentioning

confidence: 99%

Physics-Informed Ensemble Machine Learning Framework for Improved Prediction of Tunneling-Induced Short- and Long-Term Ground Settlement

2023

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Machine learning (ML), one of the AI techniques, has been used in geotechnical engineering for over three decades, resulting in more than 600 peer-reviewed papers. However, AI applications in geotechnical engineering are significantly lagging compared with other fields. One of the reasons for the lagging is that hyperparameters used in many AI techniques need physical meaning in geotechnical applications. This paper focuses on widening the applications of ML in predicting tunneling-induced short- and long-term ground settlement and optimizing ML architectures considering their interpretability and ability to provide physically consistent results. Informed by the underlying physics knowledge, tunneling-induced ground settlement is divided into long-term and short-term settlements since different mechanisms and influencing parameters contribute to these two deformation types. Based on the above considerations, this paper introduces a physics-informed ensemble machine learning (PIML) framework to strengthen the connection between ML techniques and physics theories, followed by identifying/utilizing different sets of parameters for effectively predicting short- and long-term tunneling-induced settlements, respectively. Together with in situ observations and experimental lab results, parameters obtained from physics equations are set as inputs for the ML models. Results show that the proposed PIML framework effectively predicts tunneling-induced ground movements, with a predicting accuracy above 0.8. Additionally, parametric studies of variable significance and comparisons among different ML designs reveal that in situ observed dynamic parameters, for instance tunnel face and monitoring points (DTM), gap parameter, and tunnel depth, are essential in predicting tunneling-induced short-term settlement, while predicting long-term settlements largely depends on features, such as tunnel depth, volume compressibility, and excess pore pressure, derived from physics theories.

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Section: Discussionmentioning

confidence: 99%

Physics-Informed Ensemble Machine Learning Framework for Improved Prediction of Tunneling-Induced Short- and Long-Term Ground Settlement

2023

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“…Das et al (2017) defined feature selection and reduction as an effort to pick the important features and remove the unnecessary ones from the dataset for machine learning. For instance, variance threshold is a feature selection technique, which excludes the features with the same values across all samples from the dataset (Lavanya & Mallappa, 2017). Laplacian score is another feature selection technique, which picks the features with most relevance using Eigen maps and locality preserving maps (Lavanya & Mallappa, 2017).…”

Section: Data Miningmentioning

confidence: 99%

“…For instance, variance threshold is a feature selection technique, which excludes the features with the same values across all samples from the dataset (Lavanya & Mallappa, 2017). Laplacian score is another feature selection technique, which picks the features with most relevance using Eigen maps and locality preserving maps (Lavanya & Mallappa, 2017). For data extraction using text characteristics, the traditional Term Frequency-Inverse Document Frequency (TF-IDF) algorithm works by sorting the text-characteristic words per weights in the descending order (Yang, 2017).…”

Section: Data Miningmentioning

confidence: 99%

“…Another study by Jung and Tonguz (2017) was conducted to analyze the sentiments of users reflected from tweets to highlight their health situation, especially weight loss. Lavanya and Mallappa (2017) collected tweets that mentioned certain types of cancer and identified the tweets that were most frequently mentioned using the k-means algorithm (which is a simple centroid-based algorithm).…”

Section: Sentiment Analysis In Health Carementioning

confidence: 99%

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Machine Learning-based Flu Forecasting Study Using the Official Data from the Centers for Disease Control and Prevention and Twitter Data

Wahid

Munkeby²,

Sambasivam

2021

IISIT

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Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practice's on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendations for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area.

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