Faculty Publications
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Publications by NITK Faculty
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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 Ontology-driven Text Feature Modeling for Disease Prediction using Unstructured Radiological Notes(Instituto Politecnico Nacional revista@cic.ipn.mx, 2019) S. Krishnan, G.S.; Kamath S?, S.Clinical Decision Support Systems (CDSSs) support medical personnel by offering aid in decision-making and timely interventions in patient care. Typically such systems are built on structured Electronic Health Records (EHRs), which, unfortunately have a very low adoption rate in developing countries at present. In such situations, clinical notes recorded by medical personnel, though unstructured, can be a significant source for rich patient related information. However, conversion of unstructured clinical notes to a structured EHR form is a manual and time consuming task, underscoring a critical need for more efficient, automated methods. In this paper, a generic disease prediction CDSS built on unstructured radiology text reports is proposed. We incorporate word embeddings and clinical ontologies to model the textual features of the patient data for training a feed-forward neural network for ICD9 disease group prediction. The proposed model built on unstructured text outperformed the state-of-the-art model built on structured data by 9% in terms of AUROC and 23% in terms of AUPRC, thus eliminating the dependency on the availability of structured clinical data. © 2019 Instituto Politecnico Nacional. All rights reserved.