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There is increasing excitement in the healthcare field about using behavioral data and healthcare analytics for disease risk prediction, clinical decision support, and overall improvement of personalized medicine. However, this excitement has not effectively translated to improved clinical outcomes due to knowledge gaps, a lack of behavioral risk models, and resistance to evidence-based practice. Reportedly, only 10–20% of clinical decisions are known to be evidence-based and this problem is further highlighted by the fact that the US spends more money on healthcare per person than any other nation, while still wrestling with poor health outcomes. Critics say there are inadequate technological resources and analytical education for clinicians to make behavioral data useful in the medical world. Healthcare technology innovators often neglect important aspects of the reality of integrating clinical data into electronic healthcare solutions. In this study, we developed a decision tree model using R statistical software to predict diabetes since it is among the top causes of death in the US, can be poorly managed, and provides an opportunity for improvement using analytics. This study examined behavioral data and healthcare analytics for use in clinical applications, demonstrating that health information professionals can develop behavioral risk factor prediction models to bridge the gap. Results indicated that decision trees are effective in classifying diabetes in an individual at up to 89.36% accuracy.

The explanation and control of phase transitions remain cornerstones of contemporary physics. Landau provided an invaluable insight into the thermodynamics of complex systems by formulating their phase transitions in terms of an order parameter. Within this formulation, continuous evolution of the order parameter away from zero classifies the phase transition as second-order, whereas a discontinuous change signals a first-order transition. Here we show that the temperature-tuned insulator–metal transition in the prototypical correlated electron system NdNiO3 defies this established binary classification. By harnessing a nanoscale optical probe of the local electronic conductivity, we reveal two physically distinct yet concurrent phase transitions in epitaxial NdNiO3 films. Whereas the sample bulk exhibits a first-order transition between metal and insulator phases, we resolve anomalous nanoscale domain walls in the insulating state that undergo a distinctly continuous insulator–metal transition, with hallmarks of second-order behaviour. We ascribe these domain walls to boundaries between antiferromagnetically ordered domains within the charge ordered bulk. The close correspondence of these observations to predictions from a Landau theory of coupled charge and magnetic orders highlights the importance of coupled order parameters in driving the complex phase transition in NdNiO3. A phase transition between metallic and insulating states is observed to simultaneously happen in two ways at once. The bulk of the sample shows an instantaneous jump in the conductivity, while 1D domain walls show a slow switching into a metallic state.

Plasmon polaritons are hybrid excitations of light and mobile electrons that can confine the energy of long-wavelength radiation at the nanoscale. Plasmon polaritons may enable many enigmatic quantum effects, including lasing 1 , topological protection2,3 and dipole-forbidden absorption 4 . A necessary condition for realizing such phenomena is a long plasmonic lifetime, which is notoriously difficult to achieve for highly confined modes 5 . Plasmon polaritons in graphene—hybrids of Dirac quasiparticles and infrared photons—provide a platform for exploring light–matter interaction at the nanoscale6,7. However, plasmonic dissipation in graphene is substantial 8 and its fundamental limits remain undetermined. Here we use nanometre-scale infrared imaging to investigate propagating plasmon polaritons in high-mobility encapsulated graphene at cryogenic temperatures. In this regime, the propagation of plasmon polaritons is primarily restricted by the dielectric losses of the encapsulated layers, with a minor contribution from electron–phonon interactions. At liquid-nitrogen temperatures, the intrinsic plasmonic propagation length can exceed 10 micrometres, or 50 plasmonic wavelengths, thus setting a record for highly confined and tunable polariton modes. Our nanoscale imaging results reveal the physics of plasmonic dissipation and will be instrumental in mitigating such losses in heterostructure engineering applications. The fundamental limits to plasmon damping in graphene are determined using nanoscale infrared imaging at cryogenic temperatures, and plasmon polaritons are observed to propagate over 10 micrometres in high-mobility encapsulated graphene.

Photonic crystals are commonly implemented in media with periodically varying optical properties. Photonic crystals enable exquisite control of light propagation in integrated optical circuits, and also emulate advanced physical concepts. However, common photonic crystals are unfit for in-operando on/off controls. We overcome this limitation and demonstrate a broadly tunable two-dimensional photonic crystal for surface plasmon polaritons. Our platform consists of a continuous graphene monolayer integrated in a back-gated platform with nano-structured gate insulators. Infrared nano-imaging reveals the formation of a photonic bandgap and strong modulation of the local plasmonic density of states that can be turned on/off or gradually tuned by the applied gate voltage. We also implement an artificial domain wall which supports highly confined one-dimensional plasmonic modes. Our electrostatically-tunable photonic crystals are derived from standard metal oxide semiconductor field effect transistor technology and pave a way for practical on-chip light manipulation., Traditional photonic crystals consist of periodic media with a pre-defined optical response. Here, the authors combine nanostructured back-gate insulators with a continuous layer of graphene to demonstrate an electrically tunable two-dimensional photonic crystal suitable for controlling the propagation of surface plasmon polaritons.

Non-equilibrium photoinduced plasmons in a high-mobility graphene monolayer are investigated at infrared wavelengths. The success of metal-based plasmonics for manipulating light at the nanoscale has been empowered by imaginative designs and advanced nano-fabrication. However, the fundamental optical and electronic properties of elemental metals, the prevailing plasmonic media, are difficult to alter using external stimuli. This limitation is particularly restrictive in applications that require modification of the plasmonic response at sub-picosecond timescales. This handicap has prompted the search for alternative plasmonic media1,2,3, with graphene emerging as one of the most capable candidates for infrared wavelengths. Here we visualize and elucidate the properties of non-equilibrium photo-induced plasmons in a high-mobility graphene monolayer4. We activate plasmons with femtosecond optical pulses in a specimen of graphene that otherwise lacks infrared plasmonic response at equilibrium. In combination with static nano-imaging results on plasmon propagation, our infrared pump–probe nano-spectroscopy investigation reveals new aspects of carrier relaxation in heterostructures based on high-purity graphene.