Understanding the impact of lifestyle on the academic performance of middle- and high-school students

This paper presents a path analysis investigating the direct and indirect influence of lifestyle on academic performance for middle-school and high-school students. The correlation between the following sets of variables are studied: (a) lifestyle and stress; (b) stress and obesity; (c) lifestyle and obesity; (d) lifestyle on academic performance; (e) stress on academic performance; and (f) obesity on academic performance. Adolescent students from 18 schools in India participated in this study. While academic performance is estimated using GPA, questionnaires are used to capture the lifestyle habits of students as well as to assess various forms of stressors such as academic, psychological and health-related. Our results suggest that, a healthier lifestyle is positively correlated to academic performance, while high-stress level has a negative influence. A significant negative relationship is observed between lifestyle and stress, and stress and obesity are observed to be positively related. Obesity, surprisingly, is not a significant predictor of student academic performance for the collected survey data. Our investigation further exposes the need to consider lifestyle in future research.Includes bibliographical references

Integrating machine learning algorithms and explainable artificial intelligence approach for predicting patient unpunctuality in psychiatric clinics

This study addresses patient unpunctuality, a major concern affecting patient waiting time, resource utilization, and quality of care. We develop and compare four machine learning models, including multinomial logistic regression, decision tree, random forest, and artificial neural network, to accurately predict patient arrival patterns and aid efficient scheduling. These models are analyzed using the explainable artificial intelligence approach and the Shapley additive explanations model, promoting comprehension and trust in our algorithmic results. Using three years of appointment data from a psychiatric clinic, we identify the travel distance, appointment lead time, patient’s age, Body Mass Index (BMI), and certain mental diagnoses as significant factors affecting the patient’s unpunctuality. Despite the good predictive potential of machine learning algorithms, no single model excels in all performance metrics. The study proposes implementing these machine learning techniques and the explainable artificial intelligence tool into the clinic’s appointment system as a decision support system to minimize patient unpunctuality

Stochastic Inventory Model for Minimizing Blood Shortage and Outdating in a Blood Supply Chain under Supply and Demand Uncertainty

Purpose. Blood, like fresh produce, is a perishable element, with platelets having a limited lifetime of five days and red blood cells lasting 42 days. To manage the blood supply chain more effectively under demand and supply uncertainty, it is of considerable importance to developing a practical blood supply chain model. This paper proposed an essential blood supply chain model under demand and supply uncertainty. Methods. This study focused on how to manage the blood supply chain under demand and supply uncertainty effectively. A stochastic mixed-integer linear programming (MILP) model for the blood supply chain is proposed. Furthermore, this study conducted a sensitivity analysis to examine the impacts of the coefficient of demand and supply variation and the cost parameters on the average total cost and the performance measures (units of shortage, outdated units, inventory holding units, and purchased units) for both the blood center and hospitals. Results. Based on the results, the hospitals and the blood center can choose the optimal ordering policy that works best for them. From the results, we observed that when the coefficient of demand and supply variation is increased, the expected supply chain cost increased with more outdating units, shortages units, and holding units due to the impacts of supply and demand fluctuation. Variation in the inventory holding and expiration costs has an insignificant effect on the total cost. Conclusions. The model developed in this paper can assist managers and pathologists at the blood donation centers and hospitals to determine the most efficient inventory policy with a minimum cost based on the uncertainty of blood supply and demand. The model also performs as a decision support system to help health care professionals manage and control blood inventory more effectively under blood supply and demand uncertainty, thus reducing shortage of blood and expired wastage of blood

A multiple criteria decision-making model for minimizing platelet shortage and outdating in blood supply chains under demand uncertainty

Uncertainty in blood supply and demand has proven to be a massive barrier to developing an efficient blood supply chain system. Recent literature has observed blood units’ profligacy and deficiency of approximately 20% and 14%, respectively, due to a limited donor pool, short shelf life, and emergency demand. Therefore, it is critical to develop inventory models to determine the number of units to order and the time between orders to minimize these criteria. The present study is one of the first to formulate a multi-criteria decision-making (MCDM) model for platelet inventory management along the blood supply chain that minimizes three conflicting measures: total supply chain costs, unit outdated, and unit shortage under demand uncertainty. The developed model is solved using three solution techniques: Preemptive goal programming (PGP), non-preemptive goal programming (NPGP), and weighted objective method (WOM). The results indicate that the desired goal for expected supply chain cost is achieved using the PGP model; however, the unit shortage and outdated exceeded by over 350% for each criterion. In contrast, the target value for outdated units is obtained by WOM and NPGP approaches, with WOM performing slightly better by six units when compared with NPGP. Sensitivity analysis is performed to analyze the impact of order priority and assigned weights on the three performance measures. The decision-makers can choose to implement a suitable inventory policy based on the results obtained from the three models

Resistance Profiles to Second-Line Anti-Tuberculosis Drugs and Their Treatment Outcomes: A Three-Year Retrospective Analysis from South India

Background: Patients with first-line drug resistance (DR) to rifampicin (RIF) or isoniazid (INH) as a first-line (FL) line probe assay (LPA) were subjected to genotypic DST using second-line (SL) LPA to identify SL-DR (including pre-XDR) under the National TB Elimination Program (NTEP), India. SL-DR patients were initiated on different DR-TB treatment regimens and monitored for their outcomes. The objective of this retrospective analysis was to understand the mutation profile and treatment outcomes of SL-DR patients. Materials and Methods: A retrospective analysis of mutation profile, treatment regimen, and treatment outcome was performed for SL-DR patients who were tested at ICMR-NIRT, Supra-National Reference Laboratory, Chennai between the years 2018 and 2020. All information, including patient demographics and treatment outcomes, was extracted from the NTEP Ni-kshay database. Results: Between 2018 and 2020, 217 patients out of 2557 samples tested were identified with SL-DR by SL-LPA. Among them, 158/217 were FQ-resistant, 34/217 were SLID-resistant, and 25/217 were resistant to both. D94G (Mut3C) of gyrA and a1401g of rrs were the most predominant mutations in the FQ and SLID resistance types, respectively. Favorable (cured and treatment complete) and unfavorable outcomes (died, lost to follow up, treatment failed, and treatment regimen changed) were recorded in a total of 82/217 and 68/217 patients in the NTEP Ni-kshay database. Conclusions: As per the testing algorithm, SL- LPA is used for genotypic DST following identification of first-line resistance, for early detection of SL-DR in India. The fluoroquinolone resistance pattern seen in this study population corelates with the global trend. Early detection of fluoroquinolone resistance and monitoring of treatment outcome can help achieve better patient management