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.
Systems like CARE (Collaborative Assessment and Recommendation Engine), ICARE predicts the future disease risk of the patient by analysing patient's previous history @cite_13 . HealthCare ND is also a system to predict the future disease risk of the patient @cite_17 . However, it is an interactive system, which will get the health-related information from the patient and process the data with ICD-9-CM codes then return the results of the patient's future prediction. proposed a system called Collaborative Filtering based Disease Risk Assessment (CFDRA) @cite_11 . It is a cloud computing based risk assessment prediction system. In a system anticipated by Veeresh , genomics and clinical data from patients EHR will be utilised to predict the cancer risk @cite_12 . Physical therapy-as-a-service (PTaaS) application is a model designed to connect the sensors available in the patient’s home and the therapist office computer @cite_21 .
@cite_21 @cite_17 @cite_13 @cite_12 @cite_11
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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. 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 inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods. In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process. MDNet includes an image model and a language model. The image model is proposed to enhance multi-scale feature ensembles and utilization efficiency. The language model, integrated with our improved attention mechanism, aims to read and explore discriminative image feature descriptions from reports to learn a direct mapping from sentence words to image pixels. The overall network is trained end-to-end by using our developed optimization strategy. Based on a pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we conduct sufficient experiments to demonstrate that MDNet outperforms comparative baselines. The proposed image model obtains state-of-the-art performance on two CIFAR datasets as well. The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods. In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process. MDNet includes an image model and a language model. The image model is proposed to enhance multi-scale feature ensembles and utilization efficiency. The language model, integrated with our improved attention mechanism, aims to read and explore discriminative image feature descriptions from reports to learn a direct mapping from sentence words to image pixels. The overall network is trained end-to-end by using our developed optimization strategy. Based on a pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we conduct sufficient experiments to demonstrate that MDNet outperforms comparative baselines. The proposed image model obtains state-of-the-art performance on two CIFAR datasets as well.