The personalized health care service utilizes the relational patient data and big data analytics to tailor the medication recommendations. However, most of the health care data are in unstructured form and it consumes a lot of time and effort to pull them into relational form. This study proposes a novel data lake architecture to reduce the data ingestion time and improve the precision of healthcare analytics. It also removes the data silos and enhances the analytics by allowing the connectivity to the third-party data providers (such as clinical lab results, chemist, insurance company,etc.). The data lake architecture uses the Hadoop Distributed File System (HDFS) to provide the storage for both structured and unstructured data. This study uses K-means clustering algorithm to find the patient clusters with similar health conditions. Subsequently, it employs a support vector machine to find the most successful healthcare recommendations for the each cluster. Our experiment results demonstrate the ability of data lake to reduce the time for ingesting data from various data vendors regardless of its format. Moreover, it is evident that the data lake poses the potential to generate clusters of patients more precisely than the existing approaches. It is obvious that the data lake provides a unified storage location for the data in its native format. It can also improve the personalized healthcare medication recommendations by removing the data silos.
With gigabit networking becoming economically feasible and widely installed at homes, there are new opportunities to revisit in-home, personalized telehealth services. In this paper, we describe a novel telehealth eldercare service that we developed viz., "PhysicalTherapy-as-a-Service" (PTaaS) that connects a remote physical therapist at a clinic to a senior at home. The service leverages a high-speed, low-latency network connection through an interactive interface built on top of Microsoft Kinect motion sensing capabilities. The interface that is built using user-centered design principles for wellness coaching exercises is essentially a 'Synchronous Big Data' application due to its: (i) high data-in-motion velocity (i.e., peak data rate is ? 400 Mbps), (ii) considerable variety (i.e., measurements include 3D sensing, network health, user opinion surveys and video clips of RGB, skelet al and depth data), and (iii) large volume (i.e., several GB of measurement data for a simple exercise activity). The successful PTaaS delivery through this interface is dependent on the veracity analytics needed for correlation of the real-time Big Data streams within a session, in order to assess exercise balance of the senior without any bias due to network quality effects. Our experiments with PTaaS in an actual testbed involving senior homes in Kansas City with Google Fiber connections and our university clinic demonstrate the network configuration and time synchronization related challenges in order to perform online analytics. Our findings provide insights on how to: (a) enable suitable resource calibration and perform network troubleshooting for high user experience for both the therapist and the senior, and (b) realize a Big Data architecture for PTaaS and other similar personalized healthcare services to be remotely delivered at a large-scale in a reliable, secure and cost-effective manner. The exorbitant cost of healthcare and the burden of preventable disease motivate the need for prospective health-care. In this paper we introduce HealthCareND, a user-friendly and interactive personalized disease prediction and information system. HealthCareND is based on our the Collaborative Assessment and Recommendation Engine (CARE) [2]. CARE incorporates innovative methods in collaborative filtering to suggest conditions that a patient may have a high risk of developing in the future, allowing physicians and other healthcare providers to more proactively approach the diagnosis and treatment process. HealthCareND also features an interactive visualization of an undirected complex disease network, which allows users to easily pinpoint disease pairs with a high rate of co-morbidity. Finally, our system also connects users to relevant, credible online med- ical information, making it an excellent 'one-stop-shop' for medical resources The monumental cost of health care, especially for chronic disease treatment, is quickly becoming unmanageable. This crisis has motivated the drive towards preventative medicine, where the primary concern is recognizing disease risk and taking action at the earliest signs. However, universal testing is neither time nor cost efficient. We propose CARE, a Collaborative Assessment and Recommendation Engine, which relies only on a patient's medical history using ICD-9-CM codes in order to predict future diseases risks. CARE uses collaborative filtering to predict each patient's greatest disease risks based on their own medical history and that of similar patients. We also describe an Iterative version, ICARE, which incorporates ensemble concepts for improved performance. These novel systems require no specialized information and provide predictions for medical conditions of all kinds in a single run. We present experimental results on a Medicare dataset, demonstrating that CARE and ICARE perform well at capturing future disease risks. Sooner the Cancer is diagnosed, better the chances of overall survival. Diagnosis and Prognosis are the two major challenging aspects which are to be addressed in treating cancer. Early diagnosis helps treating cancer before they become metastatic; in addition, prognosis will reveal the survival pattern for different attributes i.e., for specific drug, before and after the treatment. Better the diagnosis and prognosis, better the treatment outcome for Cancer. The very high-throughput technologies like NGS generatesexome data of a single cancer patient that ranges from 10 Giga bytes to 15 Giga bytes. This large amount of Omics data can only be analyzed and interpreted using data-sciences techniques and big-data computational models. Here we present a clinical expert system for predicting the outcome of cancer patients from big-data genomics using the inference engine which makes decision from our in-house knowledge base. We propose a cloud based framework that effectively manages the health related Big-data and benefits from the ubiquity of the Internet and social media. The framework facilitates the mobile and desktop users by offering: (a) disease risk assessment service and (b) consultation service with the health experts on Twitter. The disease risk assessment is performed through a collaborative filtering based approach whereas the hubs and authorities based approach is employed to identify the health experts from Twitter. The framework is implemented as Software as a Service (SaaS) to provide the disease risk assessment and expert user interaction services. Experimental results exhibit that the proposed framework achieves high accuracy as compared to the state-of-the-art approaches in terms of disease risk assessment and expert user recommendation.