4. Discussion
This study established a
prediction model based on RF, GBDT, decision tree, Naïve Bayes, and KNN
algorithms to predict the in-patient mortality of ICH patients, and
compared the effects of the five models. The results showed that the
AUROC and precision of the RF model were better than other
models. RF feature importance and
LASSO method were used to screen out fewer variables, so that the model
was simplified and the performance is not affected. This had important
implications for practical applications of the model, because
researchers can predict mortality rates from just 18 data sets of
patients without using more than 100 variables.
This study adds temporal
information into the prediction of mortality model of ICH patients. We
took the maximum and minimum values of each indicator in the first,
second and third days after admission of ICH patients as the model input
variables, which can make the model learn more information automatically
and make the prediction effect better. Compared with previous studies[23-26], the AUROC obtained by the GBDT model in
this study reached 0.93, which was higher than the previous results,
although the data sources and methods used were different.
From the results, the indicators of the first, second and third days all
had an important impact on the death of ICH patients. This has not been
considered in previous studies. The most important influencing factor is
GCS score, which is consistent with literature reports[27]. The lower the GCS score, the higher the
mortality rate (Fig. 3). Another important factor affecting ICH
mortality was hematoma volume [27], but this index
was not included in this study, because only 405 patients (35.43% of
the total number) had a record of this index, which was seriously
missing. It has been reported that the larger the size of hematoma, the
higher the mortality rate of patients with ICH.
This study is also the first time
to use the MIMIC-III database as the data source to establish ICH
patient’s mortality prediction model. The dataset spanned more than a
decade and details of patient care. The integrity and normality of the
data were assured, and it was the basis for our models to incorporate
temporal information. Therefore, there was no problem in our data that
the population age was relatively small [13] or the number of
participants was small [23,26,28]. But the
MIMIC-III data was based on a single medical center, which meant the
generality of our results deserves consideration.
Machine learning has great potential in the field of health and even in
the field of critical care [29]. Specifically, the
ICH mortality prediction model constructed by using machine learning
methods (such as RF, GBDT, etc.) is far better than the traditional
scoring system ICH score [30]. Since ICH score
involved hematoma size, ICH score was not compared with other machine
learning models in this paper. However, from the existing research
results, there is no doubt that the machine learning model is better
than the ICH score.