Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A supervised learning approach for ICU mortality prediction based on unstructured electrocardiogram text reports(Springer Verlag service@springer.de, 2018) S. Krishnan, G.S.; Kamath S․, S.Extracting patient data documented in text-based clinical records into a structured form is a predominantly manual process, both time and cost-intensive. Moreover, structured patient records often fail to effectively capture the nuances of patient-specific observations noted in doctors’ unstructured clinical notes and diagnostic reports. Automated techniques that utilize such unstructured text reports for modeling useful clinical information for supporting predictive analytics applications can thus be highly beneficial. In this paper, we propose a neural network based method for predicting mortality risk of ICU patients using unstructured Electrocardiogram (ECG) text reports. Word2Vec word embedding models were adopted for vectorizing and modeling textual features extracted from the patients’ reports. An unsupervised data cleansing technique for identification and removal of anomalous data/special cases was designed for optimizing the patient data representation. Further, a neural network model based on Extreme Learning Machine architecture was proposed for mortality prediction. ECG text reports available in the MIMIC-III dataset were used for experimental validation. The proposed model when benchmarked against four standard ICU severity scoring methods, outperformed all by 10–13%, in terms of prediction accuracy. © 2018, Springer International Publishing AG, part of Springer Nature.Item Exploring Depression Symptoms through Similarity Methods in Social Media Posts(CEUR-WS, 2023) Recharla, N.; Bolimera, P.; Gupta, Y.; Anand Kumar, M.Regardless of age, gender, or color, depression affects people all over the world. People feel increasingly at ease sharing their opinions on social networking sites practically every day in the present era of communication and technology. Reddit is a social networking site consisting of subreddits, or single-topic communities, created, maintained, and frequented by anonymous users. Users have the ability to post, comment on, and reply to posts within subreddits. Data for this suggested model is gathered from user posts on Reddit. Our approach involves ranking sentences from a collection of Reddit posts according to their relevance to a depression symptom for the 21 symptoms of depression from the BDI-II Questionnaire. © 2023 Copyright for this paper by its authors.Item Depression Severity Detection from Social Media Posts(Springer Science and Business Media Deutschland GmbH, 2024) Recharla, N.; Bolimera, P.; Gupta, Y.; Anand Kumar, M.A.Regardless of age, gender, or color, mental health problems affect people all over the world. People feel increasingly at ease sharing their opinions on social networking sites (SNS) practically every day in the present era of communication and technology. Reddit is a social networking site that consists of subreddits, or single-topic communities, that are created, maintained, and frequented by anonymous users. The dataset used in the paper is, eRisk2021 dataset provided for task 3, which is used for depression severity measurement. It consists posts of Reddit users. In this paper, the approach involves finding user depression severity based on their Reddit history with the help of the BDI-II questionnaire, which is discussed. The paper provides three different approaches in finding the users depression severity from their social media data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Measuring the Severity of the Signs of Eating Disorders Using Machine Learning Techniques(CEUR-WS, 2024) Prasanna, S.; Gulati, A.; Karmakar, S.; Hiranmayi, M.Y.; Anand Kumar, M.The paper presents the results submitted by Team SCaLAR-NITK for task 3 of eRisk Lab at CLEF 2024 [1]. The dataset provided by the task organizers consisted of 74 subjects for training and 18 for testing. We begin by describing the data cleaning and preprocessing steps. Subsequently, we outline various approaches used to address the problem, such as Word2Vec, TF-IDF, Backtranslation and Dimensionality Reduction, among others. Finally, we summarize the results obtained from each approach. Our solutions demonstrated strong performance, achieving the best results in 7 out of the 8 evaluated metrics. © 2024 Copyright for this paper by its authors.