Browsing by Author "Bhattacharya, Sakyajit"

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    A dual boundary classifier for predicting acute hypotensive episodes in critical care
    Bhattacharya, Sakyajit; Huddar, Vijay; Rajan, Vaibhav; Reddy, Chandan K. (PLOS, 2018-02-23)
    An Acute Hypotensive Episode (AHE) is the sudden onset of a sustained period of low blood pressure and is one among the most critical conditions in Intensive Care Units (ICU). Without timely medical care, it can lead to an irreversible organ damage and death. By identifying patients at risk for AHE early, adequate medical intervention can save lives and improve patient outcomes. In this paper, we design a novel dual-boundary classification based approach for identifying patients at risk for AHE. Our algorithm uses only simple summary statistics of past Blood Pressure measurements and can be used in an online environment facilitating real-time updates and prediction. We perform extensive experiments with more than 4,500 patient records and demonstrate that our method outperforms the previous best approaches of AHE prediction. Our method can identify AHE patients two hours in advance of the onset, giving sufficient time for appropriate clinical intervention with nearly 80% sensitivity and at 95% specificity, thus having very few false positives.
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    Predicting Complications in Critical Care Using Heterogeneous Clinical Data
    Huddar, Vijay; Desiraju, Bapu Koundinya; Rajan, Vaibhav; Bhattacharya, Sakyajit; Roy, Shourya; Reddy, Chandan K. (IEEE, 2016-10-19)
    Patients in hospitals, particularly in critical care, are susceptible to many complications affecting morbidity and mortality. Digitized clinical data in electronic medical records can be effectively used to develop machine learning models to identify patients at risk of complications early and provide prioritized care to prevent complications. However, clinical data from heterogeneous sources within hospitals pose signi ficant modeling challenges. In particular, unstructured clinical notes are a valuable source of information containing regular assessments of the patient's condition but contain inconsistent abbreviations and lack the structure of formal documents. Our contributions in this paper are twofold. First, we present a new preprocessing technique for extracting features from informal clinical notes that can be used in a classifi cation model to identify patients at risk of developing complications. Second, we explore the use of collective matrix factorization, a multi-view learning technique, to model heterogeneous clinical data text-based features in combination with other measurements, such as clinical investigations, comorbidities, and demographic data. We present a detailed case study on postoperative respiratory failure using more than 700 patient records from the MIMIC II database. Our experiments demonstrate the ef ficacy of our preprocessing technique in extracting discriminatory features from clinical notes as well as the bene fits of multi-view learning to combine clinical measurements with text data for predicting complications.