Predicting Mental Health of Best Human Capital for Sustainable Organization through Psychological and Personality Health Issues: Shift from Traditional to Novel Machine Learning-Supervised Technique Approach.

Researchers in the past discussed the psychological issue like stress, anxiety, depression, phobias on various forms, and cognitive issues (e.g., positive thinking) together with personality traits on traditional research methodologies. These psychological issues vary from one human to other human based on different personality traits. In this paper, we discussed both psychological issues together with personality traits for predicting the best human capital that is mentally healthy and strong. In this research, we replace the traditional methods of research used in the past for judging the mental health of the society, with the latest artificial intelligence techniques to predict these components for attaining the best human capital. In the past, researchers have point out major flaws in predicting psychological issue and addressing a right solution to the human resource working in organizations of the world. In order to give solution to these issues, we used five different psychological issues pertinent to human beings for accurate prediction of human resource personality that effect the overall performance of the employee. In this regard, a sample of 500 data has been collected to train and test on computer through python for selecting the best model that will outperform all the other models. We used supervised AI techniques like support vector machine linear, support vector machine radial basis function, decision tree model, logistic regression, and neural networks. Results proved that psychological issue data from employee of different organizations are better means for predicting the overall performance based on personality traits than using either of them alone. Overall, the novel traditional techniques predicted that sustainable organization is always subject to the control of psychological illness and polishing the personality traits of their human capital.

Broadening of the red blood cell distribution width is associated with increased severity of illness in patients with sepsis.

BACKGROUND: Sepsis is a pro-inflammatory state caused by systemic infection. As sepsis progresses, multiple organ systems become affected with subsequent increase in mortality. Elevated red cell distribution width (RDW) has been seen with changes of other inflammatory markers and thus could potentially serve as a means of assessing sepsis severity. In this study, we examine the association of RDW with APACHE II score and in-hospital mortality. METERIALS AND METHODS: We conducted a retrospective study involving a cohort of patients with sepsis. The study period spanned 2 years with a cohort of 349 patients. Data were collected to determine if RDW is associated with APACHE II scores and in-hospital mortality in this cohort. RESULTS: RDW correlated weakly (r s = 0.27), but significantly (P < 0.0001) with APACHE II scores; coefficient of determination (r (2) = 0.09). The odds ratios for the association of RDW with APACHE II were calculated over the RDW range 12-20% at a dichotomized level of APACHE II, i.e., <15 and ≥15. At a RDW ≥16%, multivariate analysis including all potential confounders indicated that RDW was independently associated with an APACHE II score of ≥15. Similarly, mortality was associated with RDW ≥16%. CONCLUSION: A prognostic biomarker for sepsis in the form of a routine blood test may be of considerable clinical utility. The results of our study suggest that RDW may have value in differentiating between more severe and less severe cases of sepsis. Future studies with larger samples are needed to confirm these findings.