Prescription Based Recommender System for Diabetic Patients Using Efficient Map Reduce

Healthcare sector has been deprived of leveraging knowledge gained through data insights, due to manual processes and legacy record-keeping methods. Outdated methods for maintaining healthcare records have not been proven sufficient for treating chronic diseases like diabetes. Data analysis methods such as Recommendation System (RS) can serve as a boon for treating diabetes. RS leverages predictive analysis and provides clinicians with information needed to determine the treatments to patients. Prescription-based Health Recommender System (HRS) is proposed in this paper which aids in recommending treatments by learning from the treatments prescribed to other patients diagnosed with diabetes. An Advanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering is also proposed to cluster the data for deriving recommendations by using winnowing algorithm as a similarity measure. A parallel processing of data is applied using map-reduce to increase the efficiency & scalability of clustering process for effective treatment of diabetes. This paper provides a good picture of how the Map Reduce can benefit in increasing the efficiency and scalability of the HRS using clustering

Integration of EHR and PHR leveraging cloud services for approving treatments

Historically clinicians have been prescribing treatment to patients based on their visit to the hospital without referring to previous health records of the patients. This is primarily because clinicians do not have access to patient’s medical records. Though, with digital revolution in healthcare domain, patient’s medical history is available via Patient Health Records using Electronic Health Records (EHR) and Personal Health Records (PHR) systems. However, as EHR and PHR are maintained as separate systems in isolated manner, efficient accessibility of these systems is still very limited. To provide a unified view of patient data to the providers and the patients, integrated data resulting from such system not only allow to access past health records of patients, but further leverage PHRs data to provide quality treatment by examining the effect of treatment on patient’s health. Data integration techniques such as ETL and Cloud based technologies can be used to develop a system which provides real-time integration of healthcare data sourced & streamed from various healthcare systems. It will leverage the recommendations of quality treatments to the patients as well as the clinicians from cohesive view of integrated EHR and PHR data.&nbsp