Quantitative Nanoinfrared Spectroscopy of Anisotropic van der Waals Materials.

Anisotropic dielectric tensors of uniaxial van der Waals (vdW) materials are difficult to investigate at infrared frequencies. The small dimensions of high-quality exfoliated crystals prevent the use of diffraction-limited spectroscopies. Near-field microscopes coupled to broadband lasers can function as Fourier transform infrared spectrometers with nanometric spatial resolution (nano-FTIR). Although dielectric functions of isotropic materials can be readily extracted from nano-FTIR spectra, the in- and out-of-plane permittivities of anisotropic vdW crystals cannot be easily distinguished. For thin vdW crystals residing on a substrate, nano-FTIR spectroscopy probes a combination of sample and substrate responses. We exploit the information in the screening of substrate resonances by vdW crystals to demonstrate that both the in- and out-of-plane dielectric permittivities are identifiable for realistic spectra. This novel method for the quantitative nanoresolved characterization of optical anisotropy was used to determine the dielectric tensor of a bulk 2H-WSe2 microcrystal in the mid-infrared.

Readmission prediction via deep contextual embedding of clinical concepts.

OBJECTIVE: Hospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk. However, the complex relationship between readmission and potential risk factors makes readmission prediction a difficult task. The main goal of this paper is to explore deep learning models to distill such complex relationships and make accurate predictions. MATERIALS AND METHODS: We propose CONTENT, a deep model that predicts hospital readmissions via learning interpretable patient representations by capturing both local and global contexts from patient Electronic Health Records (EHR) through a hybrid Topic Recurrent Neural Network (TopicRNN) model. The experiment was conducted using the EHR of a real world Congestive Heart Failure (CHF) cohort of 5,393 patients. RESULTS: The proposed model outperforms state-of-the-art methods in readmission prediction (e.g. 0.6103 ± 0.0130 vs. second best 0.5998 ± 0.0124 in terms of ROC-AUC). The derived patient representations were further utilized for patient phenotyping. The learned phenotypes provide more precise understanding of readmission risks. DISCUSSION: Embedding both local and global context in patient representation not only improves prediction performance, but also brings interpretable insights of understanding readmission risks for heterogeneous chronic clinical conditions. CONCLUSION: This is the first of its kind model that integrates the power of both conventional deep neural network and the probabilistic generative models for highly interpretable deep patient representation learning. Experimental results and case studies demonstrate the improved performance and interpretability of the model.