Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Predictive analytics

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    LATA – Label attention transformer architectures for ICD-10 coding of unstructured clinical notes
    (Institute of Electrical and Electronics Engineers Inc., 2021) Mayya, V.; Kamath S․, S.S.; Sugumaran, V.
    Effective code assignment for patient clinical records in a hospital plays a significant role in the process of standardizing medical records, mainly for streamlining clinical care delivery, billing, and managing insurance claims. The current practice employed is manual coding, usually carried out by trained medical coders, making the process subjective, error-prone, inexact, and time-consuming. To alleviate this cost-intensive process, intelligent coding systems built on patients’ structured electronic medical records are critical. Classification of medical diagnostic codes, like ICD-10, is widely employed to categorize patients’ clinical conditions and associated diagnoses. In this work, we present a neural model LATA, built on Label Attention Transformer Architectures for automatic assignment of ICD-10 codes. Our work is benchmarked on the CodiEsp dataset, a dataset for automatic clinical coding systems for multilingual medical documents, used in the eHealth CLEF 2020-Multilingual Information Extraction Shared Task. The experimental results reveal that the proposed LATA variants outperform their basic BERT counterparts by 33-49% in terms of standard metrics like precision, recall, F1-score and mean average precision. The label attention mechanism also enables direct extraction of textual evidence in medical documents that map to the clinical ICD-10 diagnostic codes. © 2021 IEEE.
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    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.
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    Using Stacking Ensemble Method for Rental Bike Prediction
    (Springer Science and Business Media Deutschland GmbH, 2025) Akashdeep, S.; Mahalinga, A.N.; Harshvardhan, R.; Chinnahalli KomariGowda, S.; Patil, N.
    Rental bike platforms that improve mobility comfort are on the rise in major cities worldwide. One of the essential requirements for these rental bike systems is that bikes are available to end users at the specified time, reducing waiting time. Increased waiting time indicates that movement has been halted, implying that more efficiency can be gained. As a result, the city’s main priority is ensuring a steady supply of bicycles. It’s crucial to be able to forecast the number of bikes needed at each hour for this. This work look at alternative models for forecasting the bike count per hour needed to maintain a steady supply of bikes. Weather data (Temperature, Humidity, Wind speed, Dew point), the quantity of bikes hired every hour, and time information are all used to train the models. Filtering can also be used to exclude non-predictive parameters and rank features based on how well they predict outcomes. The effectiveness of the regression model was assessed using a testing set after they had been trained using repeated cross-validation. For the model Gradient Boosting Machine, the optimum R2 value is 0.96. The most significant predictors are also determined, as well as their relationships. Bike-sharing demand, data mining, predictive analytics, public bikes, regression. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.