Machine Learning for the Prediction of Depression Progression from Inflammation Markers.

Major depressive disorder is one of the major contributors to disability worldwide with an estimated prevalence of 4%. Depression is a heterogeneous disease often characterized by an undefined pathogenesis and multifactorial phenotype that complicate diagnosis and follow-up. Translational research and identification of objective biomarkers including inflammation can assist clinicians in diagnosing depression and disease progression. Investigating inflammation markers using machine learning methods combines recent understanding of the pathogenesis of depression associated with inflammatory changes as part of chronic disease progression that aims to highlight complex interactions. In this paper, 721 patients attending a diabetes health screening clinic (DiabHealth) were classified into no depression (none) to minimal depression (none-minimal), mild depression, and moderate to severe depression (moderate-severe) based on the Patient Health Questionnaire (PHQ-9). Logistic Regression, K-nearest Neighbors, Support Vector Machine, Random Forest, Multi-layer Perceptron, and Extreme Gradient Boosting were applied and compared to predict depression level from inflammatory marker data that included C-reactive protein (CRP), Interleukin (IL)-6, IL-1ß, IL-10, Complement Component 5a (C5a), D-Dimer, Monocyte Chemoattractant Protein (MCP)-1, and Insulin-like Growth Factor (IGF)-1. MCP-1 and IL-1ß were the most significant inflammatory markers for the classification performance of depression level. Extreme Gradient Boosting outperformed the models achieving the highest accuracy and Area Under the Receiver Operator Curve (AUC) of 0.89 and 0.95, respectively.Clinical Relevance- The findings of this study show the potential of machine learning models to aid in clinical practice, leading to a more objective assessment of depression level based on the involvement of MCP-1 and IL-1ß inflammatory markers with disease progression.

Identifying Patient Satisfaction Determinants in Hemodialysis Settings: A Systematic Review.

Purpose: Patient satisfaction is a measure of care quality that assists providers in determining the effectiveness of their services while meeting patients' expectations. This study aimed to review existing studies that have focused on patients' satisfaction determinants in Hemodialysis (HD) settings. Methods: Electronic databases (PubMed, ScienceDirect, Scopus, and Google Scholar) were searched from 2000 onwards to identify studies using search terms related to patient satisfaction and hemodialysis centers. Article review was limited to studies written in English. A total of 19 articles were included by following the PRISMA statement. Data were extracted using a structured form and summarized in a tabular format to identify different determinants that showed a relationship with patient satisfaction. Determinants were classified into provider-related determinants and patient-related characteristics. Results: Provider-related determinants of patient satisfaction in HD centers include staff, facility, service, and treatment. Patient-related characteristics associated with satisfaction include demographics and health status history. Based on this systematic review, key correlates of patient satisfaction in hemodialysis centers include: staff, facility, service, treatment, patient's demographics, and health status. Conclusion: The findings of this study can help healthcare facilities in taking measures in line with the specified determinants to enhance patient satisfaction and improve the organizational performance of the healthcare centers. It is important to constantly study and improve these determinants based on patient feedback to improve patient satisfaction and quality of care.