An explainable machine learning framework for lung cancer hospital length of stay prediction

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

The Prognostic Utility of Lymphocyte-Based Measures and Ratios in Chemotherapy-Induced Febrile Neutropenia Patients following Granulocyte Colony-Stimulating Factor Therapy

Background and Objectives: Chemotherapy-induced febrile neutropenia is the most widespread oncologic emergency with high morbidity and mortality rates. Herein we present a retrospective risk factor identification study to evaluate the prognostic role of lymphocyte-based measures and ratios in a cohort of chemotherapy-induced febrile neutropenia patients following granulocyte colony-stimulating factor (G-CSF) therapy. Materials and Methods: The electronic medical records at our center were utilized to identify patients with a first attack of chemotherapy-induced febrile neutropenia and were treated accordingly with G-CSF between January 2010 to December 2020. Patients’ demographics and disease characteristics along with laboratory tests data were extracted. Prognosis-related indicators were the absolute neutrophil count (ANC) at admission and the following 6 days besides the length of stay and mortality rate. Results: A total of 80 patients were enrolled, which were divided according to the absolute lymphocyte count at admission into two groups, the first includes lymphopenia patients (n = 55) and the other is the non-lymphopenia group (n = 25) with a cutoff point of 700 lymphocytes/μL. Demographics and baseline characteristics were generally insignificant among the two groups but the white blood cell count was higher in the non-lymphopenia group. ANC, neutrophils percentage and ANC difference in reference to admission among the two study groups were totally insignificant. The same insignificant pattern was observed in the length of stay and the mortality rate. Univariate analysis utilizing the ANC difference compared to the admission day as the dependent variable, revealed no predictability role in the first three days of follow up for any of the variables included. However, during the fourth day of follow up, both WBC (OR = 0.261; 95% CI: 0.075, 0.908; p = 0.035) and lymphocyte percentage (OR = 1.074; 95% CI: 1.012, 1.141; p = 0.019) were marginally significant, in which increasing WBC was associated with a reduction in the likelihood of ANC count increase, compared to the lymphocyte percentage which exhibited an increase in the likelihood. In comparison, sequential ANC difference models demonstrated lymphocyte percentage (OR = 0.961; 95% CI: 0.932, 0.991; p = 0.011) and monocyte-to-lymphocyte ratio (OR = 7.436; 95% CI: 1.024, 54.020; p = 0.047) reduction and increment in the enhancement of ANC levels, respectively. The fifth day had WBC (OR = 0.790; 95% CI: 0.675, 0.925; p = 0.003) to be significantly decreasing the likelihood of ANC increment. Conclusions: we were unable to determine any concrete prognostic role of lymphocyte-related measures and ratios. It is plausible that several limitations could have influenced the results obtained, but as far as our analysis is concerned ALC role as a predictive factor for ANC changes remains questionable