Timezone: »
For fluid resuscitation of critically ill to be effective, it must be well calibrated in terms of timing and dosages of treatments. Both under-resuscitation due to delayed or inadequate treatment and over-resuscitation can lead to unfavorable patient outcomes. In current practice, sufficiency of resuscitation is determined using primarily invasively measured vital signs, including Arterial Pressure and SvO2. These measurements may not be available in non-acute care settings and outside of hospitals, in particular in the field when treating subjects injured in traffic accidents or wounded in combat, where only non-invasive monitoring is available to drive care. We propose a Machine Learning (ML) approach to estimate the sufficiency of fluid resuscitation utilizing only non-invasively measured vital signs. We also aim at addressing another challenge known from literature: the impact of inter-patient diversity on the ability of ML models to generalize well to previously unseen subjects. The reference to a stable personal baseline, though an effective remedy for the inter-patient diversity, is usually not available for e.g. trauma patients rushed in for care and presenting in already acute states. We propose a novel framework to address those challenges. It uses only non-invasively measured vital signs to predict sufficiency of resuscitation, and compensates for the lack of personal baselines by leveraging reference data collected from previous patients. Through comprehensive evaluation on the physiological data collected in laboratory animal experiments, we demonstrate that the proposed approach can achieve competitive performance on new patients using only non-invasive measurements without access to their personal baselines. These characteristics enable effective monitoring of fluid resuscitation in real-world acute settings with limited monitoring resources, and can help facilitate broader adoption of ML in this important subfield of healthcare.
Author Information
Xinyu Li (Carnegie Mellon University)
Michael Pinsky (University of Pittsburgh)
Artur Dubrawski (Carnegie Mellon University)
More from the Same Authors
-
2021 : Robust Interpretable Rule Learning to Identify Expertise Transfer Opportunities in Healthcare »
Willa Potosnak · Sebastian Caldas Rivera · Gilles Clermont · Kyle Miller · Artur Dubrawski -
2020 : ML4D Townhall »
Artur Dubrawski -
2020 Session: Orals & Spotlights Track 33: Health/AutoML/(Soft|Hard)ware »
Dustin Tran · Artur Dubrawski -
2020 Poster: Preference-based Reinforcement Learning with Finite-Time Guarantees »
Yichong Xu · Ruosong Wang · Lin Yang · Aarti Singh · Artur Dubrawski -
2020 Spotlight: Preference-based Reinforcement Learning with Finite-Time Guarantees »
Yichong Xu · Ruosong Wang · Lin Yang · Aarti Singh · Artur Dubrawski -
2019 : Poster Session I »
Shuangjia Zheng · Arnav Kapur · Umar Asif · Eyal Rozenberg · Cyprien Gilet · Oleksii Sidorov · Yogesh Kumar · Tom Van Steenkiste · William Boag · David Ouyang · Paul Jaeger · Sheng Liu · Aparna Balagopalan · Deepta Rajan · Marta Skreta · Nikhil Pattisapu · Jann Goschenhofer · Viraj Prabhu · Di Jin · Laura-Jayne Gardiner · Irene Li · sriram kumar · Qiyuan Hu · Mehul Motani · Justin Lovelace · Usman Roshan · Lucy Lu Wang · Ilya Valmianski · Hyeonwoo Lee · Sunil Mallya · Elias Chaibub Neto · Jonas Kemp · Marie Charpignon · Amber Nigam · Wei-Hung Weng · Sabri Boughorbel · Alexis Bellot · Lovedeep Gondara · Haoran Zhang · Taha Bahadori · John Zech · Rulin Shao · Edward Choi · Laleh Seyyed-Kalantari · Emily Aiken · Ioana Bica · Yiqiu Shen · Kieran Chin-Cheong · Subhrajit Roy · Ioana Baldini · So Yeon Min · Dirk Deschrijver · Pekka Marttinen · Damian Pascual Ortiz · Supriya Nagesh · Niklas Rindtorff · Andriy Mulyar · Katharina Hoebel · Martha Shaka · Pierre Machart · Leon Gatys · Nathan Ng · Matthias Hüser · Devin Taylor · Dennis Barbour · Natalia Martinez · Clara McCreery · Benjamin Eyre · Vivek Natarajan · Ren Yi · Ruibin Ma · Chirag Nagpal · Nan Du · Chufan Gao · Anup Tuladhar · Sam Shleifer · Jason Ren · Pouria Mashouri · Ming Yang Lu · Farideh Bagherzadeh-Khiabani · Olivia Choudhury · Maithra Raghu · Scott Fleming · Mika Jain · GUO YANG · Alena Harley · Stephen Pfohl · Elisabeth Rumetshofer · Alex Fedorov · Saloni Dash · Jacob Pfau · Sabina Tomkins · Colin Targonski · Michael Brudno · Xinyu Li · Yiyang Yu · Nisarg Patel -
2019 : Spotlight Paper Talks »
Arnav Kapur · Maithra Raghu · Xinyu Li -
2019 Poster: Mutually Regressive Point Processes »
Ifigeneia Apostolopoulou · Scott Linderman · Kyle Miller · Artur Dubrawski -
2018 : Poster Session I »
Aniruddh Raghu · Daniel Jarrett · Kathleen Lewis · Elias Chaibub Neto · Nicholas Mastronarde · Shazia Akbar · Chun-Hung Chao · Henghui Zhu · Seth Stafford · Luna Zhang · Jen-Tang Lu · Changhee Lee · Adityanarayanan Radhakrishnan · Fabian Falck · Liyue Shen · Daniel Neil · Yusuf Roohani · Aparna Balagopalan · Brett Marinelli · Hagai Rossman · Sven Giesselbach · Jose Javier Gonzalez Ortiz · Edward De Brouwer · Byung-Hoon Kim · Rafid Mahmood · Tzu Ming Hsu · Antonio Ribeiro · Rumi Chunara · Agni Orfanoudaki · Kristen Severson · Mingjie Mai · Sonali Parbhoo · Albert Haque · Viraj Prabhu · Di Jin · Alena Harley · Geoffroy Dubourg-Felonneau · Xiaodan Hu · Maithra Raghu · Jonathan Warrell · Nelson Johansen · Wenyuan Li · Marko Järvenpää · Satya Narayan Shukla · Sarah Tan · Vincent Fortuin · Beau Norgeot · Yi-Te Hsu · Joel H Saltz · Veronica Tozzo · Andrew Miller · Guillaume Ausset · Azin Asgarian · Francesco Paolo Casale · Antoine Neuraz · Bhanu Pratap Singh Rawat · Turgay Ayer · Xinyu Li · Mehul Motani · Nathaniel Braman · Laetitia M Shao · Adrian Dalca · Hyunkwang Lee · Emma Pierson · Sandesh Ghimire · Yuji Kawai · Owen Lahav · Anna Goldenberg · Denny Wu · Pavitra Krishnaswamy · Colin Pawlowski · Arijit Ukil · Yuhui Zhang -
2018 : Introductory remarks »
Artur Dubrawski -
2017 : Introductory remarks »
Artur Dubrawski -
2017 Poster: Noise-Tolerant Interactive Learning Using Pairwise Comparisons »
Yichong Xu · Hongyang Zhang · Aarti Singh · Artur Dubrawski · Kyle Miller -
2015 Demonstration: An interactive system for the extraction of meaningful visualizations from high-dimensional data »
Madalina Fiterau · Artur Dubrawski · Donghan Wang -
2012 Poster: Projection Retrieval for Classification »
Madalina Fiterau · Artur Dubrawski -
2008 Poster: Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text »
Yi Zhang · Jeff Schneider · Artur Dubrawski