As @Rijnbeek mentioned, we are storing all the continuous waveform signals from the 30 patient monitoring devices in ICU, which constitutes ECG lead 2, SpO2, Respiration, arterial pulse wave, central venous pressure and etCO2 up to 250 Hz sampling rate. By next year, we will collect the signals from 80 patient monitoring devices and ventilators in ICU. Basically the waveform signals are stored in CSV files with associated metadata of it. We are considering how to integrate the waveform signal data together with CDM.
For the radiology image, we are facing similar situations. We are now receiving research collaboration requests using CDM clinical data, radiology image and deep learning algorithms for prediction of disease risk or diagnosis. So we are also in the bottom line how to integrate the waveform signal data and/or radiology image into CDM.
@cfstrand We are applying the deep learning algorithm to the waveform signal and clinical data. For the computing power for deep learning while not sharing the waveform signal or radiology image, we prepared the same deep learning machine and software setting, which the research partner has. Thus if the research partner send their deep leaning training model to us, we can run the model against to our data and can send the resulting topology and parameters of the model.