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Rishikesan Kamaleswaran
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2020 – today
- 2024
- [j20]Moamen Soliman, Curtis Marshall, Jacob P. Kimball
, Tilendra Choudhary, Gilles Clermont, Michael R. Pinsky, Timothy G. Buchman
Parsimonious waveform-derived features consisting of pulse arrival time and heart rate variability predicts the onset of septic shock. Biomed. Signal Process. Control. 92: 105974 (2024) - [j19]Alireza Rafiei, Milad Ghiasi Rad
Improving mixed-integer temporal modeling by generating synthetic data using conditional generative adversarial networks: A case study of fluid overload prediction in the intensive care unit. Comput. Biol. Medicine 168: 107749 (2024) - [j18]Jiafeng Song
Riemannian manifold-based geometric clustering of continuous glucose monitoring to improve personalized diabetes management. Comput. Biol. Medicine 183: 109255 (2024) - [j17]Alireza Rafiei, Ronald Moore, Sina Jahromi, Farshid Hajati, Rishikesan Kamaleswaran:
Meta-learning in Healthcare: A Survey. SN Comput. Sci. 5(6): 791 (2024) - [c21]Seibi Kobara
Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing. AIME (1) 2024: 345-354 - [i11]Alireza Rafiei, Ronald Moore, Tilendra Choudhary, Curtis Marshall, Geoffrey Smith, John D. Roback, Ravi M. Patel
Robust Meta-Model for Predicting the Need for Blood Transfusion in Non-traumatic ICU Patients. CoRR abs/2401.00972 (2024) - [i10]Seibi Kobara, Alireza Rafiei, Masoud Nateghi, Selen Bozkurt, Rishikesan Kamaleswaran, Abeed Sarker:
Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing. CoRR abs/2403.00821 (2024) - [i9]Alan Wu, Tilendra Choudhary, Pulakesh Upadhyaya, Ayman Ali, Philip Yang, Rishikesan Kamaleswaran:
Deep Representation Learning-Based Dynamic Trajectory Phenotyping for Acute Respiratory Failure in Medical Intensive Care Units. CoRR abs/2405.02563 (2024) - 2023
- [j16]Mohammad Nikbakht
Synthetic seismocardiogram generation using a transformer-based neural network. J. Am. Medical Informatics Assoc. 30(7): 1266-1273 (2023) - [j15]Song Wei
Causal Graph Discovery From Self and Mutually Exciting Time Series. IEEE J. Sel. Areas Inf. Theory 4: 747-761 (2023) - [j14]Jeffrey Wang, Joao Souza de Vale, Saransh Gupta, Pulakesh Upadhyaya, Felipe A. Lisboa, Seth A. Schobel
ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports. BMC Medical Informatics Decis. Mak. 23(1): 262 (2023) - [j13]Jesus Antonio Sanchez-Perez
Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study. IEEE J. Biomed. Health Informatics 27(12): 5734-5744 (2023) - [c20]Song Wei
Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes. KDD 2023: 2536-2546 - [i8]Song Wei, Ronald Moore, Hanyu Zhang, Yao Xie, Rishikesan Kamaleswaran:
Transfer Causal Learning: Causal Effect Estimation with Knowledge Transfer. CoRR abs/2305.09126 (2023) - [i7]Alireza Rafiei, Ronald Moore, Sina Jahromi, Farshid Hajati, Rishikesan Kamaleswaran:
Meta-learning in healthcare: A survey. CoRR abs/2308.02877 (2023) - [i6]Mehak Arora, Hassan Mortagy, Nathan Dwarshius, Swati Gupta, Andre L. Holder, Rishikesan Kamaleswaran:
Mixed-Integer Projections for Automated Data Correction of EMRs Improve Predictions of Sepsis among Hospitalized Patients. CoRR abs/2308.10781 (2023) - [i5]Jeffrey Smith, Andre L. Holder, Rishikesan Kamaleswaran, Yao Xie:
Detecting algorithmic bias in medical AI-models. CoRR abs/2312.02959 (2023) - 2022
- [j12]Zeyu Liu
A Machine Learning-Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction. INFORMS J. Comput. 34(4): 2039-2057 (2022) - [j11]Jesus Antonio Sanchez-Perez
A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers. Sensors 22(3): 1130 (2022) - [j10]Anni Zhou
OnAI-Comp: An Online AI Experts Competing Framework for Early Sepsis Detection. IEEE ACM Trans. Comput. Biol. Bioinform. 19(6): 3595-3603 (2022) - [c19]Jonathan M. Beus, Edwin Ray, Sarah Thompson, Ryan Birmingham, Brad Cundiff, Mike Malto, Kevin Duncan, Dileep Gunda, Rishikesan Kamaleswaran, Evan Orenstein:
Challenges in Custom Machine Learning Model Implementation in a Vendor System: Use of a Deep Learning Model for Pediatric CLABSI Prediction. AMIA 2022 - [c18]Jeffrey Smith, Christopher S. Josef, Yao Xie, Rishikesan Kamaleswaran:
Online Critical-State Detection of Sepsis Among ICU Patients using Jensen-Shannon Divergence. AMIA 2022 - [c17]Jesus Antonio Sanchez-Perez
Initial Validation of Multi-Frequency Patch-Based Impedance Pneumography in Hospital Settings. IEEE SENSORS 2022: 1-4 - [c16]Jie Wang, Ronald Moore, Yao Xie, Rishikesan Kamaleswaran:
Improving Sepsis Prediction Model Generalization With Optimal Transport. ML4H@NeurIPS 2022: 474-488 - [c15]Yanbo Xu, Alind Khare, Glenn Matlin, Monish Ramadoss, Rishikesan Kamaleswaran, Chao Zhang, Alexey Tumanov:
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification. NeurIPS 2022 - [i4]Yanbo Xu, Alind Khare, Glenn Matlin
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification. CoRR abs/2210.15056 (2022) - [i3]Ronald Moore, Rishikesan Kamaleswaran:
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data. CoRR abs/2212.06364 (2022) - 2021
- [j9]Zeyu Liu
HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study. Comput. Biol. Medicine 131: 104255 (2021) - [j8]Azade Tabaie, Evan W. Orenstein, Shamim Nemati, Rajit K. Basu, Swaminathan Kandaswamy, Gari D. Clifford, Rishikesan Kamaleswaran:
Predicting presumed serious infection among hospitalized children on central venous lines with machine learning. Comput. Biol. Medicine 132: 104289 (2021) - [c14]Kushal Kodnad, Azade Tabaie, Joshua M. Rosenblum, Rishikesan Kamaleswaran:
Predicting Same Hospital Readmission following Fontan Cavopulmonary Anastomosis using Machine Learning. BIBM 2021: 933-938 - [i2]Song Wei, Yao Xie, Christopher S. Josef, Rishikesan Kamaleswaran:
Inferring Granger Causality from Irregularly Sampled Time Series. CoRR abs/2106.02600 (2021) - 2020
- [j7]An-Kwok Ian Wong, Patricia C. Cheung, Rishikesan Kamaleswaran, Greg S. Martin, Andre L. Holder:
Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome. Frontiers Big Data 3: 579774 (2020) - [c13]Rishikesan Kamaleswaran, Jiaoying Lian, Dong-Lien Lin, Himasagar Molakapuri, Sri Manikanth Nunna, Parth Shah, Shiv Dua, Rema Padman:
Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms. AMIA 2020 - [i1]Aditya Singh, Akram Mohammed, Lokesh Chinthala, Rishikesan Kamaleswaran:
Machine learning predicts early onset of fever from continuous physiological data of critically ill patients. CoRR abs/2009.07103 (2020)
2010 – 2019
- 2019
- [j6]Franco van Wyk, Anahita Khojandi, Akram Mohammed
A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier. Int. J. Medical Informatics 122: 55-62 (2019) - [j5]Franco van Wyk, Anahita Khojandi
A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling. J. Heal. Informatics Res. 3(2): 245-263 (2019) - [j4]Franco van Wyk, Anahita Khojandi
Correction to: A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling. J. Heal. Informatics Res. 3(2): 264-265 (2019) - [j3]Jacob R. Sutton, Ruhi Mahajan
PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform. IEEE J. Biomed. Health Informatics 23(1): 59-65 (2019) - [j2]Franco van Wyk, Anahita Khojandi
Improving Prediction Performance Using Hierarchical Analysis of Real-Time Data: A Sepsis Case Study. IEEE J. Biomed. Health Informatics 23(3): 978-986 (2019) - 2018
- [c12]Ruhi Mahajan, Rishikesan Kamaleswaran
A hybrid feature extraction method to detect Atrial Fibrillation from single lead ECG recording. BHI 2018: 116-119 - 2017
- [c11]Ruhi Mahajan, Rishikesan Kamaleswaran
Cardiac Rhythm Classification from a Short Single Lead ECG Recording via Random Forest. CinC 2017 - [c10]Ruhi Mahajan, Rishikesan Kamaleswaran
Effects of Varying Sampling Frequency on the Analysis of Continuous ECG Data Streams. DMAH@VLDB 2017: 73-87 - 2016
- [j1]Rishikesan Kamaleswaran
PhysioEx: Visual Analysis of Physiological Event Streams. Comput. Graph. Forum 35(3): 331-340 (2016) - [c9]Rishikesan Kamaleswaran
CoRAD: Visual Analytics for Cohort Analysis. ICHI 2016: 517-526 - 2014
- [c8]Rishikesan Kamaleswaran
A Real-Time Multi-dimensional Visualization Framework for Critical and Complex Environments. CBMS 2014: 325-328 - 2013
- [c7]Rishikesan Kamaleswaran
Cloud framework for real-time synchronous physiological streams to support rural and remote Critical Care. CBMS 2013: 473-476 - 2012
- [c6]Rishikesan Kamaleswaran
A novel framework for event stream processing of clinical practice guidelines. BHI 2012: 933-936 - [c5]Rishikesan Kamaleswaran
CBPsp: Complex Business Processes for Stream Processing. CCECE 2012: 1-4 - [c4]Rishikesan Kamaleswaran
Integrating complex business processes for knowledge-driven clinical decision support systems. EMBC 2012: 1306-1309 - 2011
- [c3]Rishikesan Kamaleswaran
A method for interactive hypothesis testing for clinical decision support systems using Ptolemy II. CCECE 2011: 1278-1281 - 2010
- [c2]Jennifer Percival, Carolyn McGregor
A framework for nursing documentation enabling integration with HER and real-time patient monitoring. CBMS 2010: 468-473 - [c1]Marion Blount, Carolyn McGregor
On the integration of an artifact system and a real-time healthcare analytics system. IHI 2010: 647-655
Coauthor Index
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last updated on 2024-11-07 20:30 CET by the dblp team
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