Browsing by Author "S. Krishnan, G."

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A novel GA-ELM model for patient-specific mortality prediction over large-scale lab event data
(Elsevier Ltd, 2019) S. Krishnan, G.; Kamath S?, S.
Patient-specific mortality prediction models are an essential component of Clinical Decision Support Systems developed for caregivers in Intensive Care Units (ICUs), that enable timely decisions towards effective patient care and optimized ICU resource management. While high prediction accuracy is a fundamental requirement for any mortality prediction application, being able to so with minimal patient-specific data is a major plus point that can help in improving care delivery and cost optimization. Most existing scoring techniques and prediction models utilize a multitude of lab tests and patient events to predict mortality and also suffer from reduced performance when available patient data is less. In this paper, a Genetic Algorithm based Wrapper Feature Selection technique is proposed for determining most-optimal lab events that contribute predominantly to mortality, even for large-scale patient cohorts. Using this, an Extreme Learning Machine (ELM) based neural network is designed for predicting patient-specific ICU mortality. The proposed GA-ELM model was benchmarked against four popular traditional mortality scores and also state-of-the-art machine learning models for experimental validation. The GA-ELM model achieved promising results as it outperformed the traditional scoring systems by 11%–29% and state-of-the-art models by up to 14%, in terms of AUROC. © 2019 Elsevier B.V.
Coherence-based modeling of clinical concepts inferred from heterogeneous clinical notes for ICU patient risk stratification
(Association for Computational Linguistics, 2019) Gangavarapu, T.; S. Krishnan, G.; Kamath Sâ€¤, S.S.
In hospitals, critical care patients are often susceptible to various complications that adversely affect their morbidity and mortality. Digitized patient data from Electronic Health Records (EHRs) can be utilized to facilitate risk stratification accurately and provide prioritized care. Existing clinical decision support systems are heavily reliant on the structured nature of the EHRs. However, the valuable patient-specific data contained in unstructured clinical notes are often manually transcribed into EHRs. The prolific use of extensive medical jargon, heterogeneity, sparsity, rawness, inconsistent abbreviations, and complex structure of the clinical notes poses significant challenges, and also results in a loss of information during the manual conversion process. In this work, we employ two coherence-based topic modeling approaches to model the free-text in the unstructured clinical nursing notes and capture its semantic textual features with the emphasis on human interpretability. Furthermore, we present FarSight, a long-term aggregation mechanism intended to detect the onset of disease with the earliest recorded symptoms and infections. We utilize the predictive capabilities of deep neural models for the clinical task of risk stratification through ICD-9 code group prediction. Our experimental validation on MIMIC-III (v1.4) database underlined the efficacy of FarSight with coherence-based topic modeling, in extracting discriminative clinical features from the unstructured nursing notes. The proposed approach achieved a superior predictive performance when bench-marked against the structured EHR data based state-of-the-art model, with an improvement of 11.50% in AUPRC and 1.16% in AUROC. Â© 2019 Association for Computational Linguistics.