ABPImputation is a package for imputing the arterial blood pressure (ABP) waveform using non-invasive physiological waveforms (photoplethysmogram, or PPG, and electrocardiogram, or ECG/EKG).
For more detailed information on the first, please see our paper.
Now we leverage vasopressor drugs and see whether we can get a better prediction than before. Through leveraging medication data in the EHR we present to you our current model.
Hypotension (a sustained decrease in blood pressure) within critical care patients is associated with a higher risk of mortality and other severe complications. It is periodically monitored non-invasively for all ICU patients. Continuous blood pressure monitoring via arterial line catheters has been shown to lead to faster response times but is also associated with complications such as infection. With recent advances in machine learning, there are current models that are able to predict Arterial Blood Pressure (ABP) continuously and non-invasively but lack perfect precision. Therefore, this project aimed to leverage vasopressors medication from the Electronic Health Records (EHR) to make better-informed predictions. By including the administration rate of vasopressor drugs, we retrained the ABP Imputer deep learning model with the electrocardiogram (EKG) and photoplethysmographic (PPG) waveforms to build a prediction system that takes into account clinical interventions. Our results indicated that the precision of the waveforms is on average 5.67 mmHg away from the actual ABP measurement. These results suggest that our potential new method can measure blood pressure continuously and non-invasively for all patients in the ICU setting and beyond, without the need for any additional instrumentation.
clone the current repository
We assume that the input waveform data has been sampled at 100 Hz.
The raw data should be in the following format:
,ekg,ppg,art,pseudo_NIBP_sys_5min,pseudo_NIBP_dias_5min,pseudo_NIBP_mean_5min, epi, dob, dop, phe, nor
2192-01-15 18:05:13.000,0.2942022306679429,0.00014857817077067377,0.01588212121779295,,,,,
2192-01-15 18:05:13.010,0.3923739047014039,-0.000148964018089602,-0.0159184022120733,,,,,
2192-01-15 18:05:13.020,0.36888112077855467,0.00014878046741923878,0.015916127098078936,,,,,
2192-01-15 18:05:13.030,0.39216988733528724,-0.0001497382374610061,-0.0159912403262073,,,,,
2192-01-15 18:05:13.040,0.36341014241462266,0.00014898555592997471,0.015950437232792714,,,,,
2192-01-15 18:05:13.050,0.3878550380346896,-0.00014980310141308973,-0.016016143459373855,,,,,
The first column is the timestamp index, which is assumed to be in sorted order.
Column names for each signal must either be 'ekg' for electrocardiogram,
'ppg' for photoplethysmogram, and
'art' (if available) for arteral line blood pressure, or they should be mapped to such
using the signal_column_names dict in the project_configs.py file.
Non-invasive blood pressure (NIBP) columns should be specified using the nibp_column_names variable
in the project_configs.py file, using the order: systolic, diastolic, mean.
'epi', 'dob', 'dop', 'phe', & 'nor' are simply the vasopressor drugs that were used in training the model
Preprocessing is taken care of within the abp_imputer.ipynb notebook
Generating predictions using the pre-trained model generally requires 3 steps:
- loading waveform data (in the expected format, see Data format section)
- preprocessing the waveform data to add additional features
- load the pre-trained model and generated the imputed waveform(s)
An example code segment can be found in:
abp_imputer.ipynb
Simply change in the 5th cell of abp_imputer.ipynb, the train_or_test = 'train' variable
SIMON LEE1, Ákos Rudas2, & Jeffrey N. Chiang2
1 Bruins In Genomics Summer Program, Institute for Quantitative and Computational Biosciences, UCLA
2 Department of Computational Medicine, University of California, Los Angeles, CA, USA