5	11067	6875044	2178-10-02 08:21:21	1002086	0.137	N
6	11067	6875044	2178-10-02 08:28:11	1002086	0.052	N
7	38354	5514092	2204-02-21 09:09:50	1001725	100.0	Y
8	38354	5514092	2204-02-21 09:09:50	1001980	5.78	Y

Drugs take effect either immediately or after a couple of minutes, depending on the administration route; furthermore, the effect duration also varies between drugs. From a machine learning point of view, the time when a drug has an effect is at least as important as when the drug is administered. We manually classified the 250 most frequently administered drugs into 'short' (10 minutes) and 'long' (24 hours) duration of effect, and specified intravenously administered drugs as taking effect immediately and all others as taking effect after 30 minutes. Based on this setup, the actual effect time can be determined for every administration from the raw medication data and added as a new feature. We also found that the number of different drugs is far too large for reasonable usage in training machine learning models, so for our specific use cases, we manually distributed them into five groups based on their effect on blood pressure (ranging from 'strongly lowers blood pressure' to 'strongly raises blood pressur e'). Other groupings might be necessary in other use cases.

#### Diagnoses

Diagnoses are typically represented by ICD-10 codes [38], a hierarchical classification list with codes for all sorts of diseases. Indeed, in most diagnoses data we exported for our research, ICD-10 codes are available; in the remaining entries, however, only short free-text descriptions can be found. We, therefore, implemented a mechanism for automatically extracting ICD-10 codes from short German diagnosis texts, which proceeds by f irst 'normalizing' the text under consideration (convert to lower - case, remove special characters, replace frequently used synonyms) and then employing a predefined, automatically generated lookup table from normalized texts to ICD-10 codes to find the matching code. With this simple approach, roughly 15-25% of the missing codes can be imputed. ICD-10 codes consist of a single letter from A to Z, followed by two to four digits. The letter and first two digits specify the disease group, whereas the remaining digits (if any) further specify the particular disease within its group. Due to the large number of different codes appearing in our raw data, we opted to consider only the disease groups represented by the first three characters of each code, decreasing the total number of diagnoses roughly by a factor of four. This way, we reduce the data complexity for subsequent machine learning and facilitate constructing the mapping mentioned above from free-texts to codes.

#### Waveforms

Waveforms and vitals signs are exported as JSON files from the Bedmaster system and are converted into the by far more time- and memory-efficient HDF5 format. Waveforms are split into two-second fs fs 'chunks' in the raw data, which are timestamped lists of 2 numerical values, being the sampling rate (e.g., 240 Hz). After creating the HDF5 files, consecutive chunks that are exactly two seconds apart are merged into contiguous numerical arrays. Temporal distances exceeding two seconds indicate interruptions in the waveform recording (e.g., because the bedside monitoring was turned off for a short period of time), and hence the corresponding chunks cannot be merged; see Supplementary Figure 1 (left) for a graphical depiction. However, we also observed that sometimes the timestamps of two consecutive chunks are either one or three seconds apart, leading to short overlaps and gaps. Visually inspecting these cases revealed a smooth transition of the signals from one chunk to the next. We, therefore, hypothesize that the timestamps are wrong and consequently merge chunks anyway. This is depicted in Supplementary Figure 1 (right).