Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Hybrid text feature modeling for disease group prediction using unstructured physician notes(Springer Science and Business Media Deutschland GmbH, 2020) S. Krishnan, G.S.; Kamath S․, S.Existing Clinical Decision Support Systems (CDSSs) largely depend on the availability of structured patient data and Electronic Health Records (EHRs) to aid caregivers. However, in case of hospitals in developing countries, structured patient data formats are not widely adopted, where medical professionals still rely on clinical notes in the form of unstructured text. Such unstructured clinical notes recorded by medical personnel can also be a potential source of rich patient-specific information which can be leveraged to build CDSSs, even for hospitals in developing countries. If such unstructured clinical text can be used, the manual and time-consuming process of EHR generation will no longer be required, with huge person-hours and cost savings. In this article, we propose a generic ICD9 disease group prediction CDSS built on unstructured physician notes modeled using hybrid word embeddings. These word embeddings are used to train a deep neural network for effectively predicting ICD9 disease groups. Experimental evaluation showed that the proposed approach outperformed the state-of-the-art disease group prediction model built on structured EHRs by 15% in terms of AUROC and 40% in terms of AUPRC, thus proving our hypothesis and eliminating dependency on availability of structured patient data. © Springer Nature Switzerland AG 2020.Item Predicting Vaccine Hesitancy and Vaccine Sentiment Using Topic Modeling and Evolutionary Optimization(Springer Science and Business Media Deutschland GmbH, 2021) S. Krishnan, G.S.; Kamath S․, S.; Sugumaran, V.The ongoing COVID-19 pandemic has posed serious threats to the world population, affecting over 219 countries with a staggering impact of over 162 million cases and 3.36 million casualties. With the availability of multiple vaccines across the globe, framing vaccination policies for effectively inoculating a country’s population against such diseases is currently a crucial task for public health agencies. Social network users post their views and opinions on vaccines publicly and these posts can be put to good use in identifying vaccine hesitancy. In this paper, a vaccine hesitancy identification approach is proposed, built on novel text feature modeling based on evolutionary computation and topic modeling. The proposed approach was experimentally validated on two standard tweet datasets – the flu vaccine dataset and UK COVID-19 vaccine tweets. On the first dataset, the proposed approach outperformed the state-of-the-art in terms of standard metrics. The proposed model was also evaluated on the UKCOVID dataset and the results are presented in this paper, as our work is the first to benchmark a vaccine hesitancy model on this dataset. © 2021, Springer Nature Switzerland AG.Item Ensemble Neural Models for Depressive Tendency Prediction Based on Social Media Activity of Twitter Users(Springer Science and Business Media Deutschland GmbH, 2022) Saini, G.; Yadav, N.; Kamath S․, S.In view of the ongoing pandemic, Clinical Depression (CD) is a serious health challenge for a large segment of the population. According to recent public surveys, more than 30 million American citizens are the victim of depression each year and depression also causes 30 thousand suicides each year. Early detection of depression can help provide much needed medical intervention and treatment for better mental health. Toward this, the social media posts of users can be a significant source for analyzing their mental health signals, and can also serve as a measure for assessing the prevalence of clinical depression tendencies in the population. In this paper, an approach that leverages the predictive power of supervised and semi-supervised learning algorithms for detecting depressive tendencies in the population using social media activity is presented. Learning models were trained on preprocessed tweet data from the Sentiment140 dataset containing 1.6 million labeled tweets. We also designed a convolution neural network model for the prediction task that outperformed machine learning models by a significant margin with an accuracy of 97.1%. The performance of the proposed models is benchmarked using standard metrics like SMDI (Social Media Depression Index). Crowd-sourcing approaches were adopted for collecting real-time social behavior of users to train the proposed model and demonstrate its potential for real-world applications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Ensemble Learning Approach for Short-term Energy Consumption Prediction(Association for Computing Machinery, 2022) Sujan Reddy, A.; Akashdeep; Harshvardhan; Kamath S․, S.Predicting electricity consumption accurately is crucial for garnering insights and potential trends into energy consumption for effective resource management. Due to the linearity/non-linearity in usage patterns, electricity consumption prediction is challenging and cannot be adequately solved by using a single model. In this paper, we propose ensemble learning based approaches for short-term electricity consumption on an open dataset. The ensemble model is built on the combined predictions of supervised machine learning and deep learning base models. Experimental validation showed that the proposed ensemble model is more accurate and decreases the training time of the second layer of the ensemble by a factor close to ten, compared to the state-of-the-art. We observed a reduction of approximately 34% in the Root mean squared error for the same size of historical window. © 2022 Owner/Author.Item Water Salinity Assessment Using Remotely Sensed Images—A Comprehensive Survey(Springer Science and Business Media Deutschland GmbH, 2023) Priyadarshini, R.; Sudhakara, B.; Kamath S․, S.; Bhattacharjee, S.; Umesh, P.; Gangadharan, K.V.In the past few years, the problem of growing salinity in river estuaries has directly impacted living and health conditions, as well as agricultural activities globally, especially for those rivers which are the sources of daily water consumption for the surrounding community. Key contributing factors include hazardous industrial wastes, residential and urban wastewater, fish hatchery, hospital sewage, and high tidal levels. Conventional survey and sampling-based approaches for water quality assessment are often difficult to undertake on a large-scale basis and are also labor and cost-intensive. On the other hand, remote sensing-based techniques can be a good alternative to cost-prohibitive traditional practices. In this article, an attempt is made to comprehensively assess various approaches, datasets, and models for determining water salinity using remote sensing-based approaches and in situ observations. Our work revealed that remote sensing techniques coupled with other techniques for estimating the salinity of water offer a clear advantage over traditional practices and also is very cost-effective. We also highlight several observations and gaps that can be beneficial for the research community to contribute further in this significant research domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.