Descriptive statistics for: Applying a transformer architecture to intraoperative temporal dynamics improves the prediction of postoperative delirium

Background. Patients who experienced postoperative delirium (POD) are at higher risk of poor outcomes like dementia or death. Previous machine learning models predicting POD mostly relied on time-aggregated features. We aimed to assess the potential of temporal patterns in clinical parameters during surgeries to predict POD.
Methods. Long short-term memory (LSTM) and transformer models, directly consuming time series, were compared to multi-layer perceptrons (MLPs) trained on time-aggregated features. We also fitted hybrid models, fusing either LSTM or transformer models with MLPs. Univariate Spearman’s rank correlations and linear mixed-effect models establish the importance of individual features that we compared to transformers’ attention weights.
Results. We found that best performance is achieved by a transformer architecture ingesting 30 minutes of intraoperative parameter sequences. Systolic invasive blood pressure and given opioids mark the most important input variables, in lin..., We identified promising features due to a literature review and found a potential number of 197 features in the clinical information systems (CIS) across three different hospital sites of our center (see Table B.3 in Supplement B). We selected 148 out of 197 variables due to their availability for at least 1% of patients. Thus, we investigated the influence of rare as well as highly (100%) available features. Details on feature availability (and missingness) are provided in Table B.4 in Supplement B. Table 3 summarizes the feature encoding process. Feature values were either considered as time-static, not changing over the intraoperative phase, or time-dynamic, fluctuating during the surgery. In addition to 148 selected features, we derived four composite features that combined 1. non-invasive and invasive mean blood pressure, 2. set and measured fraction of inspired oxygen (FiO2), 3. invasive and spontaneous urine output, 4. set and measured positive end-expiratory pressure (PEEP). Sin..., , # Applying a transformer architecture to intraoperative temporal dynamics improves the prediction of postoperative delirium

[https://doi.org/10.5061/dryad.bvq83bkhv](https://doi.org/10.5061/dryad.bvq83bkhv)

This dataset contains the supplementary file for the above-mentioned study including table information that could not be displayed in the Appendix. This is a supplementary dataset containing descriptive statistics about the raw data on which prediction models were trained. We could not share raw patient data due to privacy concerns but provide comprehensive summary statistics in Tables 1 and 4 to rebuild our results. Clinical context is provided in Tables 3 and 5 describing clinical codes and data encodings. For analyzing further results regarding model performance and univariate feature importance are provided in 8 and 9.  

## Table Overview

1. Feature Descriptions
2. Missingness Information
3. Clinical Codings
4. Train- and Test Split Statistics
5. Baseline Model Descriptions
6...