Dear Dr. Ghaderi @hamidghaderi ,
My name is Andy Wang, and I am a student researcher at the Laboratory of Computational Intensive Care Medicine at Johns Hopkins University School of Medicine, working under the supervision of Dr. Robert Stevens. Our lab focuses on discovering novel methods to treat critical illness through machine learning and statistical analysis of clinical data. We are currently investigating the use of heart-rate variability (HRV) features to identify distinct phenotypes in patients with Traumatic Brain Injury to predict clinical outcomes and personalize treatment strategies.
We were very impressed by your recent paper, "Discovery of generalizable TBI phenotypes using multivariate time series clustering", published in Computers in Biology and Medicine. Your SLAC-Time-SingleMode algorithm is highly relevant to our work, and we hope to adapt and extend your methodologies to HRV data. We are particularly interested in the data preprocessing steps you employed, as understanding the initial structure of your data and the transformations applied throughout model training would be extremely valuable for our project.
I emailed Dr. Subbian @vsubbian requesting access to any portion of your SLAC-Time-SingleMode preprocessing code, which would greatly assist us in building a robust HRV-based phenotyping model. He recommended that I raise an issue here and speak with you. We understand that sharing code may be time-consuming and that privacy regulations may pose challenges. If sharing the code directly is not possible, we would be grateful for any guidance you could offer regarding the structure of your data after preprocessing or any additional resources you could recommend. Thank you for your time and consideration.
Sincerely,
Andy
Dear Dr. Ghaderi @hamidghaderi ,
My name is Andy Wang, and I am a student researcher at the Laboratory of Computational Intensive Care Medicine at Johns Hopkins University School of Medicine, working under the supervision of Dr. Robert Stevens. Our lab focuses on discovering novel methods to treat critical illness through machine learning and statistical analysis of clinical data. We are currently investigating the use of heart-rate variability (HRV) features to identify distinct phenotypes in patients with Traumatic Brain Injury to predict clinical outcomes and personalize treatment strategies.
We were very impressed by your recent paper, "Discovery of generalizable TBI phenotypes using multivariate time series clustering", published in Computers in Biology and Medicine. Your SLAC-Time-SingleMode algorithm is highly relevant to our work, and we hope to adapt and extend your methodologies to HRV data. We are particularly interested in the data preprocessing steps you employed, as understanding the initial structure of your data and the transformations applied throughout model training would be extremely valuable for our project.
I emailed Dr. Subbian @vsubbian requesting access to any portion of your SLAC-Time-SingleMode preprocessing code, which would greatly assist us in building a robust HRV-based phenotyping model. He recommended that I raise an issue here and speak with you. We understand that sharing code may be time-consuming and that privacy regulations may pose challenges. If sharing the code directly is not possible, we would be grateful for any guidance you could offer regarding the structure of your data after preprocessing or any additional resources you could recommend. Thank you for your time and consideration.
Sincerely,
Andy