Journal of Health & Medical Informatics

In India, a very high prevalence of sickle cell trait (SCT) has been reported from central, southern and western states, the frequency ranges from 0 to 48% with sporadic cases in eastern and north-western states. Of the total 6675 screened individuals for haemoglobin S (HbS) from Maharashtra, Kerala and Orissa, 748 samples of eight tribal populations were considered for present study. A very high frequency of 20.3% has been observed for HbS among the Pardhan followed by the Gond (15.7%) and the Gowari (7.3%). The Banjara and the Halba show a similar pattern of HbS distribution being 5.9% and 5.04% respectively. The gene is found to be completely absent among the Mana of the same region. The Khutia khond of Orissa state show a lowest frequency for HbS gene (0.9%) of all the studied tribal groups. The Mullukuruman exhibits moderate frequency of 10.8% as compared to other tribal groups in southern India. The tribal people of central and southern had a geographical unicentric origin and had unicentric origin of the mutated gene when these tribal populations were in direct contact and underwent panmixia or gene flow. But now they dispersed and live distantly isolating themselves and maintain strict endogamy leading to high frequency for HbS gene

Adverse events undeniably compromise patient safety. Inadvertent complications attributable to preventable error rather than patient illness or disease suggests a fragmented and dire system of care. Adverse events by definition refer to diagnostic errors, and make up a substantial fraction of all medical errors leading to unnecessary morbidity, deaths, and healthcare costs. The Canadian Adverse Events Study published in 2004 and conducted by the Canadian Institute of Health Information found that the overall rate of adverse events in 2000 was seven point five per one hundred patients admitted to Canadian hospitals, one point six of which were associated with causing death

By the emergence and rapid spread of 2009 pandemic influenza A (H1N1) virus through the world, several methods have been developed to predict and prevent this lethal disease. Although many efforts have been made by statistical and traditional intelligent methods to anticipate this disease, but none of them could satisfy the expectations of specialists. This paper aims to present an efficient hybrid method to predict H1N1 with a reliable sensitivity. In this way, three methods including Gaussian mixture model (GMM), neural network (NN), and fuzzy rule-based system (FRBS) have been fused in order to provide an accurate and reliable prediction scheme to anticipate presence of H1N1 influenza. In this study, 230 individuals were participated and their clinical data were collected. The proposed hybrid scheme was implicated and the results showed to be superior to using each of the decision components containing NN, FRBS, and GMM classifiers. The achieved results produced 95.83% sensitivity and 80.95% specificity on unseen data which support the effectiveness of the hybrid method to predict the influenza in its golden time.

When people hear the word "robot" their first association is science fiction as a source of visionary fascinating technology that is going to be part of our daily life. When the Czech Capek brothers wrote their science fiction stories and coined the term robot, they probably based it on the 16th century Jewish-Czech folklore of the Golem of Prague, who was created by the rabbi of Prague (the Maharal) to protect his community from the hostile anti-Semites. In Hebrew, the world Golem is used as a metaphor for a brainless creature or entity that serves man under controlled conditions. The most significant boost to public recognition of the robotic concept was done by Isaac Asimov, who published a series of short stories about robots and contributed the famous three laws of robotic behavior or function. The robots in his stories, like the Golem, were independent machines that replaced human beings for various activities. In the last 4 decades, robotics has penetrated into every industrialized field, from assembling cars to medicine, mainly as part of automatism and manufacturing, replacing human workers in many tasks.