Conference Papers

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

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Subject: search.filters.subject.Clinical decision support systems

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    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.
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    A Supervised Approach for Patient-Specific ICU Mortality Prediction Using Feature Modeling
    (Springer Verlag service@springer.de, 2019) S. Krishnan, G.S.; Kamath S․, S.K.
    Intensive Care Units (ICUs) are one of the most essential, but expensive healthcare services provided in hospitals. Modern monitoring machines in critical care units continuously generate huge amount of data, which can be used for intelligent decision-making. Prediction of mortality risk of patients is one such predictive analytics application, which can assist hospitals and healthcare personnel in making informed decisions. Traditional scoring systems currently in use are parametric scoring methods which often suffer from low accuracy. In this paper, an empirical study on the effect of feature selection on the feature set of traditional scoring methods for modeling an optimal feature set to represent each patient’s profile along with a supervised learning approach for ICU mortality prediction have been presented. Experimental evaluation of the proposed approach in comparison to standard severity scores like SAPS-II, SOFA and OASIS showed that the proposed model outperformed them by a margin of 12–16% in terms of prediction accuracy. © 2019, Springer Nature Singapore Pte Ltd.
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    Diagnostic Code Group Prediction by Integrating Structured and Unstructured Clinical Data
    (Springer Science and Business Media Deutschland GmbH, 2021) Prabhakar, A.; Shidharth, S.; S. Krishnan, G.S.; Kamath S․, S.
    Diagnostic coding is a process by which written, verbal and other patient-case related documentation are used for enabling disease prediction, accurate documentation, and insurance settlements. It is a prevalently manual process even in countries that have successfully adopted Electronic Health Record (EHR) systems. The problem is exacerbated in developing countries where widespread adoption of EHR systems is still not at par with Western counterparts. EHRs contain a wealth of patient information embedded in numerical, text, and image formats. A disease prediction model that exploits all this information, enabling accurate and faster diagnosis would be quite beneficial. We address this challenging task by proposing mixed ensemble models consisting of boosting and deep learning architectures for the task of diagnostic code group prediction. The models are trained on a dataset created by integrating features from structured (lab test reports) as well as unstructured (clinical text) data. We analyze the proposed model’s performance on MIMIC-III, an open dataset of clinical data using standard multi-label metrics. Empirical evaluations underscored the significant performance of our approach for this task, compared to state-of-the-art works which rely on a single data source. Our novelty lies in effectively integrating relevant information from both data sources thereby ensuring larger ICD-9 code coverage, handling the inherent class imbalance, and adopting a novel approach to form the ensemble models. © 2021, Springer Nature Switzerland AG.