Rishikesan Kamaleswaran

• PhD
• Professor (Associate) at Duke University

Objective: Atrial fibrillation (AF) is a major cause of hospitalization and death in the United States. Moreover, as the average age of individuals increases around the world, early detection and diagnosis of AF become even more pressing. In this paper, we introduce a novel deep learning architecture for detection of normal sinus rhythm, AF, other...

Objectives: We used artificial intelligence to develop a novel algorithm using physiomarkers to predict the onset of severe sepsis in critically ill children. Design: Observational cohort study. Setting: PICU. Patients: Children age between 6 and 18 years old. Interventions: None. Measurements and main results: Continuous minute-by-minut...

Background: Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a minimal set of streaming physiological data in real-time. Methods and findings: A total of 29,552...

A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the predictors of complicated courses and subsequent m...

We present an interpretable machine learning algorithm called ‘eARDS’ for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Heal...

Background Interest in integrating robotics within intensive care units (ICUs) has been propelled by technological advancements, workforce challenges, and heightened clinical demands, including during the COVID-19 pandemic. The integration of robotics in ICUs could potentially enhance patient care and operational efficiency amid existing challenges...

Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The adoption of the Phoenix sepsis criteria eliminated the Systemic Inflammatory Response Syndrome criteria from the definition of pediatric sepsis. The objective of this study is to derive and validate mac...

Importance Understanding whether there are racial and ethnic and residential disparities in prenatal telehealth uptake is necessary for ensuring equitable access and guiding implementation of future hybrid (ie, both telehealth and in-person) prenatal care. Objective To assess temporal changes in individuals using hybrid prenatal care before and du...

Acute respiratory distress syndrome (ARDS) is a severe organ dysfunction associated with significant mortality and morbidity among critically ill patients admitted to the Intensive Care Unit (ICU). The etiology related to ARDS can be highly heterogeneous, with infection or trauma as the most common associations. The Berlin criteria, the current gol...

Background Patients with sepsis-induced hypotension are generally treated with a combination of intravenous fluids and vasopressors. The attributes of patients receiving a liberal compared to a restrictive fluid strategy have not been fully characterized. We use machine learning (ML) techniques to identify key predictors of restrictive versus liber...

Background: Blood transfusions, crucial in managing anemia and coagulopathy in intensive care unit (ICU) settings, require accurate prediction for effective resource allocation and patient risk assessment. However, existing clinical decision support systems have primarily targeted a particular patient demographic with unique medical conditions and...

OBJECTIVES Monocytes are plastic cells that assume different polarization states that can either promote inflammation or tissue repair and inflammation resolution. Polarized monocytes are partially defined by their transcriptional profiles that are influenced by environmental stimuli. The airway monocyte response in pediatric acute respiratory dist...

Identifying critically ill patients who are likely to improve their respiratory physiology following RBC blood transfusion is dynamic and difficult. Current decision tools are over-reliant on hemoglobin transfusion thresholds, without considering respiratory measures that may reflect physiologic effects of anemia and functional responses to RBC tra...

Background Septic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of these patients may reveal insights about the broader heterogeneity in the clinical course of sepsis, co...

Importance Intravenous fluids are an essential part of treatment in sepsis, but there remains clinical equipoise on which type of crystalloid fluids to use in sepsis. A previously reported sepsis subphenotype (ie, group D) has demonstrated a substantial mortality benefit from balanced crystalloids compared with normal saline. Objective To test the...

As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model’s capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learning approaches, such as insufficient number of samples, domain shifts, and generalization. The...

Background Sepsis poses a grave threat, especially among children, but treatments are limited owing to heterogeneity among patients. We sought to test the clinical and biological relevance of pediatric septic shock subclasses identified using reproducible approaches. Methods We performed latent profile analyses using clinical, laboratory, and biom...

Accurate lung segmentation in chest x-ray images plays a pivotal role in early disease detection and clinical decision-making. In this study, we introduce an innovative approach to enhance the precision of lung segmentation using the Segment Anything Model (SAM). Despite its versatility, SAM faces the challenge of prompt decoupling, often resulting...

Preschool children with recurrent wheezing are a heterogeneous population with many underlying biological pathways that contribute to clinical presentations. Although the morbidity of recurrent wheezing in preschool children is significant, biological studies in this population remain quite limited. To address this gap, this study performed untarge...

BACKGROUND Interest in integrating robotics within intensive care units (ICUs) has been propelled by technological advancements, workforce challenges, and heightened clinical demands, including during the COVID-19 pandemic. The integration of robotics in ICUs could potentially enhance patient care and operational efficiency amid existing challenges...

Sepsis is a major public health emergency and one of the leading causes of morbidity and mortality in critically ill patients. For each hour treatment is delayed, shock-related mortality increases, so early diagnosis and intervention is of utmost importance. However, earlier recognition of shock requires active monitoring, which may be delayed due...

Objective Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future I...

Background: Septic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of these patients may reveal insights about the broader heterogeneity in the clinical course of sepsis. W...

Background: In critically ill patients, complex relationships exist among patient disease factors, medication management, and mortality. Considering the potential for nonlinear relationships and the high dimensionality of medication data, machine learning and advanced regression methods may offer advantages over traditional regression techniques. T...

OBJECTIVES To develop and validate machine learning (ML) models to predict high-flow nasal cannula (HFNC) failure in COVID-19, compare their performance to the respiratory rate-oxygenation (ROX) index, and evaluate model accuracy by self-reported race. DESIGN Retrospective cohort study. SETTING Four Emory University Hospitals in Atlanta, GA. PAT...

Background Metabolic dysfunction–associated steatotic liver disease (MASLD), formerly known as NAFLD, is the most common liver disease in children. Liver biopsy remains the gold standard for diagnosis, although more efficient screening methods are needed. We previously developed a novel NAFLD screening panel in youth using machine learning applied...

Background The innate mechanisms associated with viral exacerbations in preschool children with recurrent wheezing are not understood. Objective We sought to assess differential gene expression in blood neutrophils from preschool children with recurrent wheezing, stratified by aeroallergen sensitization, at baseline and after exposure to polyinosi...

Background: Multiple organ dysfunction syndrome (MODS) disproportionately drives morbidity and mortality among critically ill patients. However, we lack a comprehensive understanding of its pathobiology. Identification of genes associated with a persistent MODS trajectory may shed light on underlying biology and allow for accurate prediction of tho...

OBJECTIVE Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predictive models in pediatric critical care. DESIGN Scoping review and expert opinion. SETTING We q...

Background Sepsis poses a grave threat, especially among children, but treatments are limited due to clinical and biological heterogeneity among patients. Thus, there is an urgent need for precise subclassification of patients to guide therapeutic interventions. Methods We used clinical, laboratory, and biomarker data from a prospective multi-cent...

Introduction Accurate identification of venous thromboembolism (VTE) is critical to develop replicable epidemiological studies and rigorous predictions models. Traditionally, VTE studies have relied on international classification of diseases (ICD) codes which are inaccurate – leading to misclassification bias. Here, we developed ClotCatcher, a nov...

Objectives To develop and externally validate an intubation prediction model for children admitted to a PICU using objective and routinely available data from the electronic medical records (EMRs). Design Retrospective observational cohort study. Setting Two PICUs within the same healthcare system: an academic, quaternary care center (36 beds) an...

Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approac...

p>Chest radiography is a commonly utilized imaging technique for acquiring Chest X-Ray (CXR) images due to its cost-effectiveness and its role in diagnosing lung?related disorders. Nevertheless, interpreting CXR images can be challenging, and the process of separating the lung field from CXR images can be a valuable tool for assessing and diagnosin...

p>Chest radiography is a commonly utilized imaging technique for acquiring Chest X-Ray (CXR) images due to its cost-effectiveness and its role in diagnosing lung?related disorders. Nevertheless, interpreting CXR images can be challenging, and the process of separating the lung field from CXR images can be a valuable tool for assessing and diagnosin...

Chronic respiratory diseases affect millions and are leading causes of death in the US and worldwide. Pulmonary auscultation provides clinicians with critical respiratory health information through the study of Lung Sounds (LS) and the context of the breathing-phase and chest location in which they are measured. Existing auscultation technologies,...

Sepsis is associated with significant mortality and morbidity among critically ill patients admitted to intensive care units (ICU) and represents a major health challenge globally. Given the significant clinical and biological heterogeneity among patients and the dynamic nature of the host immune response, identifying those at high risk of poor out...

Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profiles of patients managed in the ICU to explore pharmacophenotypes that correlated with ICU compli...

Objective: Common Data Models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a Common Data Model (CDM) for ICU medications to standardize the medication features needed to support future...

Objective : To examine whether Heart rate Interval based Rapid Alert (HIRA) score derived from a combination model of Heart rate variability (HRV) and Modified Early Warning Score (MEWS) is a surrogate for the detection of Acute Respiratory Failure (ARF) in critically ill sepsis patients. Approach : Retrospective HRV analysis of sepsis patients ad...

Machine learning (ML) models are increasingly pivotal in automating clinical decisions. Yet, a glaring oversight in prior research has been the lack of proper processing of Electronic Medical Record (EMR) data in the clinical context for errors and outliers. Addressing this oversight, we introduce an innovative projections-based method that seamles...

Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patien...

p>Acute respiratory distress syndrome (ARDS) is a severe organ dysfunction that is associated with significant mortality and morbidity among critically ill patients admitted to the Intensive Care Unit (ICU). The etiology associated with ARDS can be highly heterogeneous, with most cases being associated with infection or trauma. ARDS is often descri...

As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model's capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learning approaches, such as insufficient number of samples, domain shifts, and generalization. The...

Background: Preschool children with recurrent wheezing are heterogeneous, with differing responses to respiratory viral infections. Although neutrophils are crucial for host defense, their function has not been studied in this population. Objective: We performed functional immunophenotyping on isolated blood neutrophils from 52 preschool childre...

Little is known about electrocardiogram (ECG) markers of Parkinson’s disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case–control study included samples f...

p>Acute respiratory distress syndrome (ARDS) is a severe organ dysfunction that is associated with significant mortality and morbidity among critically ill patients admitted to the Intensive Care Unit (ICU). The etiology associated with ARDS can be highly heterogeneous, with most cases being associated with infection or trauma. ARDS is often descri...

Objective: The challenge of irregular temporal data, which is particularly prominent for medication use in the critically ill, limits the performance of predictive models. The purpose of this evaluation was to pilot test integrating synthetic data within an existing dataset of complex medication data to improve machine learning model prediction of...

Unlabelled: CD4+ T cells contribute to lung inflammation in acute respiratory distress syndrome. The CD4+ T-cell response in pediatric acute respiratory distress syndrome (PARDS) is unknown. Objectives: To identify differentially expressed genes and networks using a novel transcriptomic reporter assay with donor CD4+ T cells exposed to the airwa...

Background Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based model...

A novel problem of improving causal effect estimation accuracy with the help of knowledge transfer under the same covariate (or feature) space setting, i.e., homogeneous transfer learning (TL), is studied, referred to as the Transfer Causal Learning (TCL) problem. While most recent efforts in adapting TL techniques to estimate average causal effect...

p> Abstract — The Segment Anything Model (SAM) is a state-of-the-art deep learning architecture for image seg- mentation that allows users to segment arbitrary objects in images using points, or bounding boxes. SAM uses a novel two-stage approach that first encodes the input image into a high-dimensional embedding, and then generates object masks u...

p> Abstract — The Segment Anything Model (SAM) is a state-of-the-art deep learning architecture for image seg- mentation that allows users to segment arbitrary objects in images using points, or bounding boxes. SAM uses a novel two-stage approach that first encodes the input image into a high-dimensional embedding, and then generates object masks u...

Background Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may pr...

Background: Asthma exacerbations are highly prevalent in children, but only a few studies have examined the biologic mechanisms underlying exacerbations in this population. Objective: High-resolution metabolomics analyses were performed to understand the differences in metabolites in children with exacerbating asthma who were hospitalized in a p...

Sepsis is a severe medical condition caused by a dysregulated host response to infection that has a high incidence and mortality rate. Even with such a high-level occurrence rate, the detection and diagnosis of sepsis continues to pose a challenge. There is a crucial need to accurately forecast the onset of sepsis promptly while also identifying th...

Objective: To design and validate a novel deep generative model for seismocardiogram (SCG) dataset augmentation. SCG is a noninvasively acquired cardiomechanical signal used in a wide range of cardivascular monitoring tasks; however, these approaches are limited due to the scarcity of SCG data. Methods: A deep generative model based on transform...

Exogenous estrogen is associated with reduced COVID mortality in non-immunosuppressed/immunocompromised (non-ISC) post-menopausal females. Here, we examined the association of estrogen or testosterone hormone replacement therapy (HRT) with COVID outcomes in solid organ transplant recipients (SOTR) compared to non-ISC individuals, given known differ...

Background: There is no generalizable transcriptomics signature of pediatric acute respiratory distress syndrome. Our goal was to identify a whole blood differential gene expression signature for pediatric acute hypoxemic respiratory failure (AHRF) using transcriptomic microarrays within twenty-four hours of diagnosis. We used publicly available h...

Children with life-threatening asthma exacerbations who are admitted to a pediatric intensive care unit (PICU) are a heterogeneous group with poorly studied inflammatory features. We hypothesized that distinct clusters of children with asthma in a PICU would be identified based on differences in plasma cytokine levels and that these clusters would...

We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By leveraging a recently developed stochastic monotone Variational Inequality (VI) formulation, we cast the causal discovery problem as a general convex optimization. F...

Unlabelled: Progressive hypoxemia is the predominant mode of deterioration in COVID-19. Among hypoxemia measures, the ratio of the Pao2 to the Fio2 (P/F ratio) has optimal construct validity but poor availability because it requires arterial blood sampling. Pulse oximetry reports oxygenation continuously (ratio of the Spo2 to the Fio2 [S/F ratio])...

We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By leveraging a recently developed stochastic monotone Variational Inequality (VI) formulation, we cast the causal discovery problem as a general convex optimization. F...

Background/objectives: Eosinophilic esophagitis (EoE) is an inflammatory disease of unclear etiology. The aim of this study was to use untargeted plasma metabolomics to identify metabolic pathway alterations associated with EoE to better understand the pathophysiology. Methods: This prospective, case-control study included 72 children, aged 1-17...

Commentary on https://ccforum.biomedcentral.com/articles/10.1186/s13054-022-03977-3

Unlabelled: To investigate the relationship between ICU-acquired weakness (ICUAW) signatures and sepsis-related mortality using gene expression from the blood within 24 hours of sepsis onset. Design: Observational study using differential gene expression analysis. Setting: Publicly available gene expression profile GSE54514, single-center medi...