# Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset
## 3 Results
### Feature Analysis  
To facilitate data preparation, we have predefined specific ranges for temporal features with extreme outliers (see Table 2). These ranges were derived from the literature as well as from data-driven observations. However, it is not trivial to determine them because each feature may have several types of outliers. For example, feature values that fall outside a certain normal physiological range due to the patient's condition, such as a significantly high heart rate in severe cardiovascular disease [61], versus those that are physiologically irrelevant, such as a body temperature above 70 \({}^{\circ}\)C (158 \({}^{\circ}\)F). Figure 3 illustrates the statistics of the vital signs as well as some laboratory parameters. For illustrative purposes, the extreme outliers of some features (e.g., body temperature) are not shown in the figure. The respiratory rate (the number of breaths per minute) is an important vital sign that provides a good indication of a patient's health condition. The mean respiratory rate in the MIMIC-IV dataset is 20.46 brmm with a range of 0-2,355,560. The lower and upper limits of this range are extremely abnormal, i.e., physiologically infeasible for a living human. The range we established in Table 2 is only based on the feature values observed in the dataset and may still be ambiguous; however, it does capture the reported (normal) range of 14-36 brmm for adults [62]. The extreme values of heart rate, commonly measured in any hospitalization, can indicate serious illness in a patient [63]. By examining the minimum and maximum heart rates of all patients in the MIMIC-IV dataset, we identified three heart rates below zero. The predefined upper limit of 225 brmm for the heart rate is also extremely deviated by 106 measurements. This results in a total of only 109 invalid outliers out of 6,463,819 measurements, i.e., the majority (>99.95%) of the heart rate data in the MIMIC-IV dataset remained within the range given in Table 2, which also contains the heart rates of fit people [64] and healthy people [65]. Oxygen saturation, another important vital sign, is well captured in the MIMIC-IV dataset according to the range specified in Table 2 (>99.96% of all data). There are 2346 (114) data points below (above) the lower (upper) limit of the specified range that remain physiologically impossible. The range of fluctuating oxygen saturation has been set much broader than normal because it is quite difficult to determine practical limits when considering various factors such as diseases or other medical conditions [45]. Figure 3: Distribution (box plot) of several selected features, namely body temperature, chloride, creatinine, heart rate, MCV, O2 saturation, and respiratory rate before data cleaning. The box plots show the wide range of variation for few features, with some extreme outliers not shown for visualization purposes (e.g., for body temperature). Body temperature has been shown to be an important vital sign, with an elevated body temperature independently associated with prolonged LOS [66]. The normal adult body temperature is reported to be approximately 36.5 \({}^{\circ}\)C (97.7 \({}^{\circ}\)F), regardless of the measurement method or location. However, in the MIMIC-IV dataset, the average patient body temperature is 37.1 \({}^{\circ}\)C (98.8 \({}^{\circ}\)F) because MIMIC contains data from patients in severe conditions. The increased body temperature may be due to a specific disease, or conversely, an elevated body temperature may cause a certain medical condition [66]. In the MIMIC-IV dataset, we identified invalid measurements for body temperature, e.g., a minimum of \(-99.9\)\({}^{\circ}\)F (\(-73.3\)\({}^{\circ}\)C), which is obviously an erroneous record. To explore the influence of features on LOS, we compute the correlation matrix of all features and LOS shown in Figure 4. The GCS has the highest correlation with LOS at \(-0.32\).