We would like to comment on the publication “Artificial-intelligent prediction model
of occurrence of cerebral vasospasms based on machine-learning.”[1] Developing an artificial-intelligence (AI)-based prediction model for symptomatic
cerebral hemorrhage (SAH) subsequent to aneurysm rupture was the goal of this work.
The cerebral hemorrhage or consequences cannot be reliably predicted using the current
rating measures. Using the R environment, the researchers created an AI program to
analyze data from 87 SAH patients using the support vector machines (KSVM) classification
technique. The model's accuracy range for predicting cerebral hemorrhage symptoms
was 61 to 86%. Different incidence rates of cerebral hemorrhage across different dimensions
and types of therapies were identified by subgroup analysis.
The study's accuracy range reveals variability, which could be a result of flaws in
the model or dataset. The reliability of the prediction may be impacted by the small
sample size and restricted number of patients in the validation and testing series.
Furthermore, the study lacked specific details regarding the weighting and interactions
of the AI model's variables. Furthermore, there was no performance benchmarking against
current prediction models. Expanding the sample size will be the main focus of future
research in order to strengthen the prediction model's robustness. The accuracy of
the model might be increased by adding more variables and a larger range of patient
data. An evaluation of the algorithm's performance that is more thorough will be possible
through validation using outside data sets and comparisons with other predictive models.
Longer-term research can evaluate the model's effectiveness in clinical settings and
how it affects patient outcomes.
Predictive accuracy may increase with the advent of sophisticated AI techniques like
deep learning or clustering approaches. Incorporating real-time input data and consistently
gaining knowledge from novel scenarios could enhance the model's flexibility and precision.
It might be easier for physicians to employ predictive models in actual clinical settings
if they have an intuitive user interface. Furthermore, investigating customized predictive
methods based on unique patient profiles can contribute to the provision of more accurate
risk evaluations.
