Deep neuro‐fuzzy approach for risk and severity prediction using recommendation systems in connected health care

Internet of Things (IoT) and Data science have revolutionized the entire technological landscape across the globe. Because of it, the health care ecosystems are adopting the cutting‐edge technologies to provide assistive and personalized care to the patients. But, this vision is incomplete without the adoption of data‐focused mechanisms (like machine learning, big data analytics) that can act as enablers to provide early detection and treatment of patients even without admission in the hospitals. Recently, there has been an increasing trend of providing assistive recommendation and timely alerts regarding the severity of the disease to the patients. Even, remote monitoring of the present day health situation of the patient is possible these days though the analysis of the data generated using IoT devices by doctors. Motivated from these facts, we design a health care recommendation system that provides a multilevel decision‐making related to the risk and severity of the patient diseases. The proposed systems use an all‐disease classification mechanism based on convolutional neural networks to segregate different diseases on the basis of the vital parameters of a patient. After classification, a fuzzy inference system is used to compute the risk levels for the patients. In the last step, based on the information provided by the risk analysis, the patients are provided with the potential recommendation about the severity staging of the associated diseases for timely and suitable treatment. The proposed work has been evaluated using different datasets related to the diseases and the outcomes seem to be promising

WASPSS: A Clinical Decision Support System for Antimicrobial Stewardship

The increase of infections caused by resistant bacteria has become one of the major health-care problems worldwide. The creation of multidisciplinary teams dedicated to the implementation of antimicrobial stewardship programmes (ASPs) is encouraged by all clinical institutions to cope with this problem. In this chapter, we describe the Wise Antimicrobial Stewardship Program Support System (WASPSS), a CDSS focused on providing support for ASP teams. WASPSS gathers the required information from other hospital systems in order to provide decision support in antimicrobial stewardship from both patient-centered and global perspectives. To achieve this, it combines business intelligence techniques with a rule-based inference engine to integrate the data and knowledge required in this scenario. The system provides functions such as alerts, recommendations, antimicrobial prescription support and global surveillance. Furthermore, it includes experimental modules for improving the adoption of clinical guidelines and applying prediction models related with antimicrobial resistance. All these functionalities are provided through a multi-user web interface, personalized for each role of the ASP team

Big Data, Patents, and the Future of Medicine

Big data has tremendous potential to improve health care. Unfortunately, intellectual property law isn’t ready to support that leap. In the next wave of data- driven medicine, black-box medicine, researchers use sophisticated algorithms to examine huge troves of health data, finding complex, implicit relationships and making individualized assessments for patients. Black-box medicine offers potentially immense benefits, but also requires substantial high investment. Firms must develop new datasets, models, and validations, which are all nonrivalrous information goods with significant spillovers, requiring incentives for welfare-optimizing investment. Current intellectual property law fails to provide adequate incentives for black- box medicine. The Supreme Court has sharply restricted patentable subject matter in the recent Prometheus, Myriad, and Alice cases, and what might still be patentable is limited by the statutory requirements of written description and enablement. Other incentives for investment, such as trade secrecy or prizes, fail to fill the gaps. These limits push firms away from using big data in medicine to solve big problems, and push firms toward small-scale incremental innovation. Small tweaks to doctrine will help, but are not enough. Instead, the big data needed to support transformative medical innovation should be considered as infrastructure for innovation and should be the focus of substantial public effort

Making sense of big data in health research: Towards an EU action plan.

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans