With the increase of data and computation in the healthcare industry, officials are looking for reliable alternatives to compete globally. Cloud technology is a viable option that reduces data and computation overhead at the host side. Due to the fast growth of cloud vendors (CVs), the apt selection of a vendor becomes crucial. Extant CV selection models 1) have reduced scope for minimizing subjective randomness; 2) cannot offer agents with flexible preference window; and 3) have higher human intervention during the decision process. Driven by these lacunae and to counter the same, a novel scientific model is developed in this article. Generalized orthopair fuzzy information is adopted to express preferences. Later, the attitudinal-CRITIC approach is put forward to determine the significance of functional factors. Agents' attitude values are calculated by extending the variance approach. Agent wise personalized prioritization algorithm is developed to rank CVs based on individual perceptions, query vectors, and cumulative rank factors. The scientific model's usefulness is verified by adopting a case study of CV selection in a private medical unit in Trichy. Finally, sensitivity analysis and comparison with extant models help understand the merits and limitations of the proposed work.

Adolescence is a period of emotional instability, during which high risk behavior including experimentation with drugs is common. Clinical manifestations of substance abuse and its withdrawal may mimic common illnesses. We report an adolescent who presented with acute dysentery with unusual course. HEADSS screening revealed psychosocial stressors leading to cannabis addiction. The child was diagnosed to have cannabis withdrawal. He was referred to the de-addiction clinic. HEADSS is a useful tool to identify risk factors and protective factors in adolescents. HEADSS screening during each interaction and early intervention when needed must be an essential component of adolescent health care.

The role of cloud services in the data-intensive industry is indispensable. Cision recently reported that the cloud market would grow to 55 billion USD, with an active contribution of the cloud to healthcare around 2025. Inspired by the report, cloud vendors expand their market and the quality of services to seek growth globally. The rapid growth of the cloud sector in the healthcare industry imposes a challenge: making a rational choice of a cloud vendor (CV) out of a diverse set of vendors. Typically, the healthcare industry 4.0 sees the issue as a large-scale group decision-making problem. Previous studies on a CV selection face certain challenges, such as (i) a lack of the ability to handle multiple users' views, as well as experts'/users' complex linguistic views; (ii) the confidence level associated with a view is not considered; (iii) the transformation of multiple users' views into holistic data is lacking; and (iv) the systematic prioritization of CVs with minimum human intervention is a crucial task. Motivated by these challenges and circumventing them, a new big data-driven decision model is put forward in this paper. Initially, the data in the form of complex expressions are collected from multiple cloud users and are further transformed into a holistic decision matrix by adopting probabilistic linguistic information (PLI). PLI represents complex linguistic expressions along with the associated confidence levels. Later, a holistic decision matrix is formed with the missing values imputed by proposing an imputation algorithm. Furthermore, the criteria weights are determined by using a newly proposed mathematical model and partial information. Finally, the evaluation based on the distance from average solution (EDAS) approach is extended to PLI for the rational ranking of CVs. A real-time example of a CV selection for a healthcare center in India is exemplified so as to demonstrate the usefulness of the model, and the comparison reveals the merits and limitations of the model.

Green supplier selection (GSS) is a crucial issue in green supply chain management. CAPS indicate that industries spend yearly USD 25 million per procurement, which is a huge amount that necessitates a systematic GSS to avoid financial catastrophes. Literatures on GSS reveal that researchers have not addressed the issue of missing preferences, consistency of decision matrices, and repairing inconsistencies. Moreover, handling of complex linguistic expressions is another open challenge in GSS. Motivated by these research gaps, a two-stage decision framework was proposed. From the analysis of different linguistic models, it is clear that a double-hierarchy linguistic model is flexible for handling complex expressions. In the preprocessing stage, preferences were imputed using the case-based method. Later, consistency of matrices was determined using the Cronbach’s coefficient, and inconsistent matrices were repaired using the iterative method. In the next stage, new mathematical models were formulated to calculate weights of experts and criteria. Preferences were sensibly aggregated by using the Maclaurin symmetric mean operator, which captures the criteria interrelationship. Green suppliers were prioritized by using the TODIM method. Finally, the practicality, strengths, and weaknesses of the proposed framework were realized by demonstrating a case study of GSS and comparison with other methods. Results infer that the proposed framework (i) is consistent with the existing models, and the values are 0.60, 0.86, and 0.75, respectively; (ii) is robust with 100% rank-order stability even after adequate weight alterations; and (iii) finally can better discriminate suppliers with a broad deviation range of 0.34–0.35.

The paper aims to present an integrated approach to solve the decision-making problem under the probabilistic hesitant fuzzy information (PHFI) features, which is an extension of the hesitant fuzzy set. The considered PHFI not only allows multiple opinions, but also associates occurrence probability to each opinion, which increases the reliability of the information. Motivated by these features of PHFI, an approach is presented to solve the decision problem with partial known information about the attribute and expert weights. In addition, an algorithm for finding some missing values in the preference information is presented and stated their properties. Afterward, the Hamy mean operator has been used to aggregate the different collective information into a single one. Also, we presented a COPRAS method to the PHFI for ranking the given alternatives. The presented algorithm has been demonstrated through a case study of cloud vendor selection and its validity has been revealed by comparing the approach results with the several existing algorithm results.

Background: Neonatal deaths account for 47% of all deaths in children younger than 5 years globally. More than a third of deaths are due to preterm related complications. Understanding the factors contributing to preterm deaths and pattern of mortality is needed to implement interventions that are essential in improving neonatal survival.Methods: This was a retrospective study done in neonatal intensive care unit, Institute of Child Health (ICH) and Hospital for Children, Chennai, a tertiary care regional center. All preterm (<32 weeks) deaths registered in the neonatal medical records from 1st of January 2018 to 31st of December 2018 were analysed. Primary causes of deaths were analysed by two consultants. When there were more than one cause contributing to responsible for death the most significant problem was taken as the cause of death.Results: Overall neonatal mortality was 312 (14.2%) of 2189 neonates. Out of 148 admissions in the study population mortality was 74.2% (26 of 35) for extreme preterm (<28 weeks) infants and 42.7% (48 of 113) for very preterm (28 to 32 weeks) infants. Predominant causes of death were sepsis 44.5% (33), prematurity-related complications 37.8% (28) followed by congenital anomalies 12.1% (9) and miscellaneous causes 2.7% (2). Respiratory Distress Syndrome (RDS) and Intra Ventricular Haemorrhage (IVH) attributed to 75% (21/ 28) of deaths among prematurity-related complications.Conclusions: This study identified sepsis; prematurity related complications were the predominant causes of mortality in the extreme to very preterm population. Understanding the specific causes of preterm mortality would help to implement interventions to promote their survival.

Background: Polycystic ovary syndrome (PCOS) and thyroid disorders are two of the most common endocrine disorders in the general population. Both of these endocrine disorders share common predisposing factors, gynaecological features and have profound effect on reproductive function in women. The aim of this study is to study the prevalence of thyroid dysfunction in patients with polycystic ovarian syndrome and to evaluate the relationship between polycystic ovarian syndrome and thyroid dysfunction.Methods: This is a cross sectional observational study done on 100 patients with Poly Cystic Ovarian Syndrome based on Rotterdam’s criteria. The exclusion criteria was hyperprolactinemia, congenital adrenal hyperplasia and virilising tumour. Thyroid function was evaluated by measurement of fasting serum thyroid stimulating hormone (TSH), free thyroxine levels (free T3 and free T4).Results: The mean age of the study patients was 26±4.2 years. Among the study patients, 11% of them had goitre. 18% of the patients with presented with subclinical hypothyroidism. The mean TSH levels in the study patients was 4.62±2.12 mIU/ml. The overall prevalence of thyroid dysfunction was 33% in the study patients with PCOS.Conclusions: This study concludes that the prevalence of hypothyroidism is increased in women with PCOS patients.