Introduction Drug-related problem is an undesirable event experienced by a patient associated with drug therapy that interferes with achieving the specified goals of therapy. Drug-related problems are usually seen in chronic disease patients who are taking a greater number of medicines as a part of their therapy. Thus it is necessary to assess these problems.Objective To collect information regarding drug-related problems occurring in chronic disease patients in different wards of the hospital and to identify its potential causes.Methodology A prospective observational study was conducted for six months in Navodaya Medical College Hospital amp Research Centre involving 160 participants. Data were collected from the case sheets using data entry forms from all the hospitalized patients who were diagnosed with chronic diseases in the hospital during the study period.Results In this study most of the problems were observed in patients aged above 60 years. The antihypertensive agents were producing 31 of the problems during drug therapy. Thirty six percent of the study population was experiencing untreated symptoms. In this study 38 of the study population experienced incomplete drug treatment despite existing indications.Conclusion The study points the necessity for improved practices in appropriate prescribing to scale back the drug therapy problems in chronic disease patients. The study will help in reducing the issues in chronic disease patients.nbsp

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Researchers using Electroencephalograms ("EEGs") to diagnose clinical outcomes often run into computational complexity problems. In particular, extracting complex, sometimes nonlinear, features from a large number of time-series often require large amounts of processing time. In this paper we describe a distributed system that leverages modern cloud-based technologies and tools and demonstrate that it can effectively, and efficiently, undertake clinical research. Specifically we compare three types of clusters, showing their relative costs (in both time and money) to develop a distributed machine learning pipeline for predicting gestation time based on features extracted from these EEGs.