Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Sensor Data

Sujay Nagaraj, Andrew J Goodwin, Dmytro Lopushanskyy, Sebastian David Goodfellow, Danny Eytan, Hadrian Balaci, Robert Greer, Anand Jayarajan, Azadeh Assadi, Mjaye Leslie Mazwi, Anna Goldenberg
Proceedings of the 9th Machine Learning for Healthcare Conference, PMLR 252, 2024.

Abstract

Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely used in the Critical Care Unit (CCU) for blood sampling, medication administration, and high-frequency blood pressure measurement. Judiciously accessing these lines is important, as over-utilization is associated with significant in-hospital morbidity and mortality. Documenting the frequency of line-access is an important step in reducing these adverse outcomes. Unfortunately, the current gold-standard for documentation is manual and subject to error, omission, and bias. The high-frequency blood pressure waveform data from sensors in these lines are often noisy and full of artifacts. Standard approaches in signal processing remove noise artifacts before meaningful analysis. However, from bedside observations, we characterized a *distinct* artifact that occurs during each instance of C-Line or A-Line use. These artifacts are buried amongst physiological waveform and extraneous noise. We focus on Machine Learning (ML) models that can detect these artifacts from waveform data in real-time - finding needles in needle stacks, in order to automate the documentation of line-access. We built and evaluated ML classifiers running in real-time at a major children’s hospital to achieve this goal. We demonstrate the utility of these tools for reducing documentation burden, increasing available information for bedside clinicians, and informing unit-level initiatives to improve patient safety.

Cite this Paper

BibTeX
@InProceedings{pmlr-v252-nagaraj24a, title = {Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Sensor Data}, author = {Nagaraj, Sujay and Goodwin, Andrew J and Lopushanskyy, Dmytro and Goodfellow, Sebastian David and Eytan, Danny and Balaci, Hadrian and Greer, Robert and Jayarajan, Anand and Assadi, Azadeh and Mazwi, Mjaye Leslie and Goldenberg, Anna}, booktitle = {Proceedings of the 9th Machine Learning for Healthcare Conference}, year = {2024}, editor = {Deshpande, Kaivalya and Fiterau, Madalina and Joshi, Shalmali and Lipton, Zachary and Ranganath, Rajesh and Urteaga, Iñigo}, volume = {252}, series = {Proceedings of Machine Learning Research}, month = {16--17 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v252/main/assets/nagaraj24a/nagaraj24a.pdf}, url = {https://proceedings.mlr.press/v252/nagaraj24a.html}, abstract = {Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely used in the Critical Care Unit (CCU) for blood sampling, medication administration, and high-frequency blood pressure measurement. Judiciously accessing these lines is important, as over-utilization is associated with significant in-hospital morbidity and mortality. Documenting the frequency of line-access is an important step in reducing these adverse outcomes. Unfortunately, the current gold-standard for documentation is manual and subject to error, omission, and bias. The high-frequency blood pressure waveform data from sensors in these lines are often noisy and full of artifacts. Standard approaches in signal processing remove noise artifacts before meaningful analysis. However, from bedside observations, we characterized a *distinct* artifact that occurs during each instance of C-Line or A-Line use. These artifacts are buried amongst physiological waveform and extraneous noise. We focus on Machine Learning (ML) models that can detect these artifacts from waveform data in real-time - finding needles in needle stacks, in order to automate the documentation of line-access. We built and evaluated ML classifiers running in real-time at a major children’s hospital to achieve this goal. We demonstrate the utility of these tools for reducing documentation burden, increasing available information for bedside clinicians, and informing unit-level initiatives to improve patient safety.} }
Endnote
%0 Conference Paper %T Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Sensor Data %A Sujay Nagaraj %A Andrew J Goodwin %A Dmytro Lopushanskyy %A Sebastian David Goodfellow %A Danny Eytan %A Hadrian Balaci %A Robert Greer %A Anand Jayarajan %A Azadeh Assadi %A Mjaye Leslie Mazwi %A Anna Goldenberg %B Proceedings of the 9th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2024 %E Kaivalya Deshpande %E Madalina Fiterau %E Shalmali Joshi %E Zachary Lipton %E Rajesh Ranganath %E Iñigo Urteaga %F pmlr-v252-nagaraj24a %I PMLR %U https://proceedings.mlr.press/v252/nagaraj24a.html %V 252 %X Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely used in the Critical Care Unit (CCU) for blood sampling, medication administration, and high-frequency blood pressure measurement. Judiciously accessing these lines is important, as over-utilization is associated with significant in-hospital morbidity and mortality. Documenting the frequency of line-access is an important step in reducing these adverse outcomes. Unfortunately, the current gold-standard for documentation is manual and subject to error, omission, and bias. The high-frequency blood pressure waveform data from sensors in these lines are often noisy and full of artifacts. Standard approaches in signal processing remove noise artifacts before meaningful analysis. However, from bedside observations, we characterized a *distinct* artifact that occurs during each instance of C-Line or A-Line use. These artifacts are buried amongst physiological waveform and extraneous noise. We focus on Machine Learning (ML) models that can detect these artifacts from waveform data in real-time - finding needles in needle stacks, in order to automate the documentation of line-access. We built and evaluated ML classifiers running in real-time at a major children’s hospital to achieve this goal. We demonstrate the utility of these tools for reducing documentation burden, increasing available information for bedside clinicians, and informing unit-level initiatives to improve patient safety.
APA
Nagaraj, S., Goodwin, A.J., Lopushanskyy, D., Goodfellow, S.D., Eytan, D., Balaci, H., Greer, R., Jayarajan, A., Assadi, A., Mazwi, M.L. & Goldenberg, A.. (2024). Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Sensor Data. Proceedings of the 9th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 252 Available from https://proceedings.mlr.press/v252/nagaraj24a.html.

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