Using the MIMIC-IV clinical database and, specifically, the related preliminary MIMIC-IV waveform data release, we endeavored to create a machine learning tool in the form of a gated recurrent unit (GRU) neural network. Our goal was to study the possibility that intrusive measurements of vital signs, such as blood pressure, might be reliably predicted from parallel time series data sets of non-intrusive measurements of other vital signs, such as heart and respiratory rates. Relying on non-intrusive measurements to alert health care professionals of potentially life-threatening conditions in a timely manner can be life-saving in certain situations. To this end we developed and implemented a custom GRU solution in the form of software that can be run in a Web browser to analyze the high cadence numerical data sets of the MIMIC-IV waveform release. Despite the limitations of this data set, such as the small number of records, inconsistencies in what has been recorded, and often, noisy or incomplete data, we were able to obtain potentially promising results.

The authors have declared no competing interest.

The research reported in this paper was supported by CRISP (Center for Research on Integrated Systems Platform) of the Department of Systems and Computer Engineering at Carleton University.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study used the de-identified MIMIC-IV waveform database (PhysioNet). Because the dataset is publicly available in de-identified form, the study was exempt from institutional review board approval.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

This study used the publicly available MIMIC-IV Clinical Database and MIMIC-IV Waveform Database, which are hosted on PhysioNet (https://physionet.org/). Access to the data requires credentialed approval through PhysioNet's data use agreement. No new patient data were generated in this study. The analysis code developed for this work is available on GitHub at https://github.com/vttoth/MIMIC-WF.

https://github.com/vttoth/MIMIC-WF