Towards the second order adaptation in the next generation remote patient management systems

Remote Patient Management (RPM) systems are expected to be increasingly important for chronic disease management as they facilitate monitoring vital signs of patients at their home, alerting the care givers in case of worsening. They also provide patients with educational content. RPM systems collect a lot of (different types of) data about patients, providing an opportunity for personalizing information services. In our recent work we highlighted the importance of using available information for personalization and presented a possible next generation RPM system that enables personalization of educational content and its delivery to patients. We introduced a generic methodology for personalization and emphasized the role of knowledge discovery (KDD). In this paper we focus on the necessity of the second-order adaptation mechanisms in the RPM systems to address the challenge of continuous on-line (re)learning of actionable patterns from the patient data

Heart failure hospitalization prediction in remote patient management systems

Healthcare systems are shifting from patient care in hospitals to monitored care at home. It is expected to improve the quality of care without exploding the costs. Remote patient management (RPM) systems offer a great potential in monitoring patients with chronic diseases, like heart failure or diabetes. Patient modeling in RPM systems opens opportunities in two broad directions: personalizing information services, and alerting medical personnel about the changing conditions of a patient. In this study we focus on heart failure hospitalization (HFH) prediction, which is a particular problem of patient modeling for alerting. We formulate a short term HFH prediction problem and show how to address it with a data mining approach. We emphasize challenges related to the heterogeneity, different types and periodicity of the data available in RPM systems. We present an experimental study on HFH prediction using, which results lay a foundation for further studies and implementation of alerting and personalization services in RPM systems

Spin Fluctuation Dynamics and Multiband Superconductivity in Iron Pnictides

Multiband superconductivity, involving resonant pair scattering between different bands, has emerged as a possible explanation of some of the main characteristics of the recently discovered iron pnictides. A key feature of such interband pairing mechanism is that it can generate or enhance superconducting pairing irrespective of whether it is attractive or repulsive. The latter case typically leads to the superconducting gap switching its sign among different sections of the Fermi surface. In iron pnictides, the natural scenario is that the gap changes sign between the hole and the electron Fermi surfaces. However, the macroscopic symmetry of such an extended s'-wave state still belongs to the general s-wave category, raising the question of how to distinguish it from an ordinary s-wave. In such a quest, it is essential to use experimental techniques that have a momentum space resolution and can probe momenta of order M, the wavevector that separates the hole and the electron Fermi surfaces in the Brillouin zone. Here we study experimental signatures in the spin fluctuation dynamics of the fully-gapped s- and s'-wave superconducting states, as well as those of the nodal d- and p-wave. The coupling between spin fluctuations of the incipient nearly-nested spin density-wave (SDW) and the Bogoliubov-deGennes quasiparticles of the superconducting state leads to the Landau-type damping of the former. The intrinsic structure of the superconducting gap leaves a distinctive signature in the form of this damping, allowing it to be used to diagnose the nature of iron-based superconductivity in neutron scattering and other experiments sensitive to spin fluctuations in momentum space. We also discuss the coexistence between superconductivity and SDW order.Comment: 10 pages, 4 figure