# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 4 Materials and Methods
### Analysis of Selected Papers
Incorporating the insights from this study, this article can enrich the discussion by providing a real-world example of predictive modeling in healthcare. By showcasing the effectiveness of multiple linear regression models in estimating hospital LoSs for femur -fracture patients, this article underscores the practical applications of ML in healthcare management. This study highlights the importance of considering not only algorithmic performance, but also the contextual factors and interventions that may affect predictive modeling outcomes in healthcare settings. The study in [38] presents a solution to predict hospital LoS after total hip arthroplasty, a critical factor influencing patient recovery and healthcare costs. Traditional prediction methods lack accuracy and generalization, prompting the adoption of the XGBoost algorithm, an ML technique. By analyzing patient data and optimizing model parameters, this study demonstrates that XGBoost outperforms traditional methods, with an average prediction error of 2.03 days. Integrating these findings into this article for hospital LoS predictions in hip arthroplasty patients could highlight the potential of advanced algorithms to improve patient care and resource allocation in orthopedic surgery. Article [39] presents an ML-based data-mining model aimed at analyzing and predicting patient admissions, particularly in emergency departments. The authors highlight the challenges of emergency department crowding and emphasize the need for innovative approaches to improve patient flow and prevent overcrowding. They discuss the various ML algorithms employed, including naive Bayes, random forest, and support vector machine, to predict patient admissions based on features such as age, gender, blood pressure, diabetes, and previous medical records. This study also compares the performance of these algorithms and identifies the support vector machine as the most accurate predictor among them. In the Results Section, we concluded that the support vector machine model outperforms the naive Bayes and random forest classifier when predicting patient admissions accurately. They suggest that these ML models can significantly benefit hospitals by addressing the challenge of patient overcrowding in emergency departments and improving overall patient flow. In article [39], although the values are specified, the author concludes that the support vector machine was the most accurate model. Study [40] focuses on evaluating the accuracy of predicting hospital LoS following neurosurgery using the GPT-3 language model in comparison to predictions made by both doctors and patients. Building on previous research, the study employed unstructured textual data from electronic health records to train the neural network language model. The researchers then compared the hospital LoS predictions generated by the model with the expectations of doctors and patients to assess its efficacy. The findings revealed that there was no significant disparity (\(p=0.109\)) between hospital LoS predictions made by doctors, patients, and the GPT-3 language model. However, neurosurgeons exhibited a tendency to offer more accurate prognoses compared to both the model and patients. Despite this, this study concluded that modern neural network language models, like GPT-3, show promise for hospital LoS predictions for neurosurgery patients.