Abstract
This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3–100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.3–100%, and 97%, CI 95%: 93.7–100% SMOTE-Tomek, and SMOTE-ENN respectively). Under-sampling methods reported the least important AUC results (50%, with CI 95%: 40.2–59.8%) for both (ENN and Tomek- Links). Using ML explainable technique called SHAP, we explained the outcome of the predictive model (RF) with SMOTE class balancing technique to understand the most significant clinical features that contributed to predicting lung cancer LOS with the RF model. Our promising framework allows us to employ ML techniques in-hospital clinical information systems to predict lung cancer admissions into ICU.
Highlights
This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models
Our study represents the potential of machine learning to predict the Length of Stay of ICU cancer-based hospitalization in particular lung cancer patients efficiently
We have evaluated suitable class balancing methods to deal with the imbalanced class problem, primarily challenging to the predictive modeling task because of the severely skewed class distribution in clinical health records data
Summary
This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. Our promising framework allows us to employ ML techniques in-hospital clinical information systems to predict lung cancer admissions into ICU. Traditional LOS calculation methods are currently in use, such as ICU APACHE versions (I, II, III, IV), SAPS6–9, and S OFA10 These methods use patients’ features or ICU features to estimate the inpatient LOS during hospital admission. Whether in the ICU or otherwise, Hospital LOS is one of such important outcomes, whose prediction relies on such techniques as per recent literature These techniques are broadly generalizable, and scientists can build ensembles based on these algorithms to predict many other clinical outcomes. Most of these studies are focused on emergency departments (ED) or cardiovascular-related admission to ICU units or patients who stayed in ICU after the surgical or medical intervention using classification approaches such as those indicated in this s tudy[24]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
