Journal of Health & Medical Informatics

Abstract The current epidemic of Ebola virus disease (EVD) is caused by Zaire Ebola virus-Makona variant. Results are presented indicating that 88% of the information entropy (H) in the ZEBOV-Makona glycoprotein (GP1,2) was distributed to amino acids residing in random coil structures. In contrast, only 12% of the total H was due to mutations of amino acids in helical and extended sheet secondary structures. It is proposed that some of the H in random coils may represent mutational escape from host defense while the paucity of H in helical and extended sheet structures may reflect conformational constraints on mutation. By relating GP1,2 secondary structure and H in regions of GP1,2, this research helps to computationally identify potential targets for the design of anti-Ebola vaccines and drugs.

Background: Many of the reports on HIV/AIDS shows that the number of ART registered patients are increasing from time to time. Despite those reports show increasing of patients’ number, they did not try to make prediction of attributes based on the given attributes more than statistical explanation. This study concerned to use data mining techniques on ART data base. The main objective of the study is to apply data mining techniques for predicting CD4 status of patients on ART in Jimma and Bonga Hospitals. Methodology: The study followed the CRISP-DM data mining methodology which has six phases: business understanding, data understanding, data preparation, model building, evaluation and deployment. For this study, data was taken from two hospitals of the south west of Ethiopia; Jimma and Bonga hospitals. Classification algorithm was used to predict CD4 status of the patients those who are following ART therapy. J48 is a technique used for building classification and PART is used to compare the result of J48 algorithm. Results: The best performance achieved by J48 decision tree algorithm is a generalized decision tree pruning with reduced attributes. The model classifies instances correctly (88.79%) and incorrectly (11.21%). The weighted average precision of the model is 0.88 with recall of 0.89 and ROC area of 0.85. The model has 760 numbers of leaves and 916 tree size. The time taken to build the model is 0.05 seconds. The analysis of this model shows that the model is quit efficient to predict CD4 status of patients those who are following ART. Conclusion: Classification done using J48 decision tree is the best model as compared to PART rule algorithm and that can be used for prediction. From the model built it is possible to conclude that attributes like: Eligible reason, ART status, ART start year, OA weight, OAWHO stage, Current regimen, Family planning, Functional status, Marital status, Past ARV are the most determining factors of CD4 status.

Abstract Objectives: Due to the escalating healthcare expenditure and the number of hospitalizations, it is becoming increasingly important for healthcare organizations to evaluate the cost and improve the quality and efficiency of treatment. Method: We deploy neural networks to examine the strategic association between hospitalization experience and treatment results. The healthcare data for the years 2009-2012 is downloaded from the Statewide Planning and Research Cooperative System (SPARCS) of the New York State Department of Health (NYSDOH). We operationalize the hospitalization experience using the indicators facility ID, procedure description, type of admission, patient disposition upon discharge, APR severity of illness, source of payment, and age group; and the treatment result using indicators hospital length of stay and APR risk of mortality Results: Our findings show that there are significant differences in length of stay and mortality rates depending on the treatment procedure. Treatment result shows a strong association with procedure and with the patients’ disposition upon discharge. Interestingly, under similar health conditions, patients who are under the public healthcare system tend to have longer length of hospital stays than others. Conclusions: We offer a portfolio of factors to be considered in evaluating patient health outcomes from hospitalization. We emphasize the need for efficient utilization of investment in healthcare, be it public or private.

Abstract
Medical errors in the United States are estimated to claim anywhere between 210,000 and 400,000 human lives every year and the numbers have skyrocketed almost five times higher than the 1999 estimates published by the Institute of Medicine (IOM). With these latest revelations, it is no surprise that medical errors are the third leading cause of deaths in the United States, overshadowing auto accidents, strokes, Alzheimer’s, diabetes, and everything else besides cancer and heart diseases. With hundreds of thousands of people dying from preventable medical errors every year, the issue has long been a reality and has not really received the attention it merits. The digital revolution to move paper records on digital space is in uptick and the track records have backed Electronic Health Records in curtailing medication and communication related errors but haven’t shown certainty and promise in curtailing diagnostic and technology related errors. That said, the rising death toll of preventable medical errors have however, not been put to a stop. At the outset, this paper centres on evaluating the success rate of health-IT in curtailing medical error rates in the United States and asserts on the need to implement effective strategies and improve diligence on revamping systems to reduce the incidence of medical errors and make it a national priority! Outcome of the paper would help consumers perceive an understanding of health-IT’s potential in reducing preventable medical errors. Is health-IT knight, knave or a pawn?

Complete androgen insensitivity syndrome (CAIS) is an X linked disorder characterized by lack of mullerian derivatives, absent uterus, normal breast development and sparse or absent axillary and pubic hairs. Mutation in the androgen receptor is the cause for this rare disorder. Classically diagnosis is made when evaluating for primary amenorrhea in a young girl. These patients are at higher risk for development of gonadal malignancy the risk of which increases with increasing age. Bilateral gonadectomy after puberty is recommended. We are presenting a case of CAIS who was diagnosed at the age of 60 years during surgery for giant inguinal hernia.

Abstract It is believed that herbal medicines act in a holistic way, but been derived from nature they can be specific in response. Herein, we review herbal medicines which are used for the treatment of cardiovascular diseases (CVDs) and their interactions with other drugs. CVDs have a high mortality rate and morbidity in the world, therefore, prevention and reduction of risk factors, which are associated with CVD, are the major tasks of healthcare professionals and scientists. Modern medicines despite having promising effects are unable to bring to a standstill the CVDs, consequently people are are paying attention to CAM (complementary and alternative medicines) -the herbal medicines.