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Artificial Intelligence (AI) and machine learning have advanced healthcare by defining relationships in complex conditions. Out-of-hospital cardiac arrest (OHCA) is a medically complex condition with several etiologies. Survival for OHCA has remained static at 10% for decades in the United States. Treatment of OHCA requires the coordination of numerous interventions, including the delivery of multiple medications. Current resuscitation algorithms follow a single strict pathway, regardless of fluctuating cardiac physiology. OHCA resuscitation requires a real-time biomarker that can guide interventions to improve outcomes. End tidal capnography (ETCO2) is commonly implemented by emergency medical services professionals in resuscitation and can serve as an ideal biomarker for resuscitation. However, there are no effective conceptual frameworks utilizing the continuous ETCO2 data. In this manuscript, we detail a conceptual framework using AI and machine learning techniques to leverage ETCO2 in guided resuscitation. 

The availability of large-scale electronic health record datasets has led to the development of artificial intel- ligence (AI) methods for clinical risk prediction that help improve patient care. However, existing studies have shown that AI models suffer from severe performance decay after several years of deployment, which might be caused by various temporal dataset shifts. When the shift occurs, we have access to large-scale pre-shift data and small-scale post-shift data that are not enough to train new models in the post-shift environment. In this study, we propose a new method to address the issue. We reweight patients from the pre-shift environ- ment to mitigate the distribution shift between pre- and post-shift environments. Moreover, we adopt a Kullback-Leibler divergence loss to force the models to learn similar patient representations in pre- and post-shift environments. Our experimental results show that our model efficiently mitigates temporal shifts, improving prediction performance. 

Ultrawideband (UWB) radar sensors are an emerging biosensing modality that can be used to assess the dielectric properties of internal tissues. Antenna effects, including antenna body interactions limit the sensors ability to isolate the weak returns from the internal tissues. In this paper we develop a data driven calibration method for recovering Green’s function of the multilayered media model of the tissue profiles using an Invertible Neural Network (INN). The proposed INN structure is trained to invert the antenna transfer function to form estimates of the Green’s function modeling returns from internal tissues. We use simulation experiments to assess the effectiveness of the trained INN in antenna transfer function inversion. 

The wavelength used for illumination dictates the scale of the mechanisms that interact with the incident electromagnetic (EM) energy. We model the synthetic Aperture Radar Image of a target as a superposition of the returns from scattering mechanisms that depend on the wavelength of the illuminating waveform and the viewing angle. In this work, we present a method to jointly model the scattering responses of the target over a wide aperture of measurements and a wide swath of frequencies spanning the C to X Band. Specifically, we estimate the location of the scattering centers and their azimuth-dependent responses normalized by the wavelength, jointly for low and high bands. We verify the validity of the proposed model using simulated data from a backhoe and Civilian vehicle data domes dataset over two non-overlapping frequency bands centered at 7GHz and 12 GHz. 

Generative models learned from training using deep learning methods can be used as priors in under-determined inverse problems, including imaging from sparse set of measurements. In this paper, we present a novel hierarchical deep-generative model MrSARP for SAR imagery that can synthesize SAR images of a target at different resolutions jointly. MrSARP is trained in conjunction with a critic that scores multi resolution images jointly to decide if they are realistic images of a target at different resolutions. We show how this deep generative model can be used to retrieve the high spatial resolution image from low resolution images of the same target. The cost function of the generator is modified to improve its capability to retrieve the input parameters for a given set of resolution images. We evaluate the model's performance using three standard error metrics used for evaluating super-resolution performance on simulated data and compare it to upsampling and sparsity based image super-resolution approaches. 

Abstract—Sleep staging is a key challenge in diagnosing and treating sleep-related diseases due to its labor-intensive, time- consuming, costly, and error-prone. With the availability of large- scale sleep signal data, many deep learning methods are proposed for automatic sleep staging. However, these existing methods face several challenges including the heterogeneity of patients’ underlying health conditions and the difficulty modeling complex interactions between sleep stages. In this paper, we propose a neural network architecture named DREAM to tackle these is- sues for automatic sleep staging. DREAM consists of (i) a feature representation network that generates robust representations for sleep signals via the variational auto-encoder framework and contrastive learning and (ii) a sleep stage classification network that explicitly models the interactions between sleep stages in the sequential context at both feature representation and label classification levels via Transformer and conditional random field architectures. Our experimental results indicate that DREAM significantly outperforms existing methods for automatic sleep staging on three sleep signal datasets. 

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in developed countries. Identifying patients at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Recently, deep-learning-based models have been developed and achieved superior performance for late AMD pre- diction. However, most existing methods are limited to the color fundus photography (CFP) from the last ophthalmic visit and do not include the longitudinal CFP history and AMD progression during the previous years’ visits. Patients in different AMD subphenotypes might have various speeds of progression in different stages of AMD disease. Capturing the progression information during the previous years’ visits might be useful for the prediction of AMD pro- gression. In this work, we propose a Contrastive-Attention-based Time-aware Long Short-Term Memory network (CAT-LSTM) to predict AMD progression. First, we adopt a convolutional neural network (CNN) model with a contrastive attention module (CA) to extract abnormal features from CFPs. Then we utilize a time-aware LSTM (T-LSTM) to model the patients’ history and consider the AMD progression information. The combination of disease pro- gression, genotype information, demographics, and CFP features are sent to T-LSTM. Moreover, we leverage an auto-encoder to represent temporal CFP sequences as fixed-size vectors and adopt k-means to cluster them into subphenotypes. We evaluate the pro- posed model based on real-world datasets, and the results show that the proposed model could achieve 0.925 on area under the receiver operating characteristic (AUROC) for 5-year late-AMD prediction and outperforms the state-of-the-art methods by more than 3%, which demonstrates the effectiveness of the proposed CAT-LSTM. After analyzing patient representation learned by an auto-encoder, we identify 3 novel subphenotypes of AMD patients with different characteristics and progression rates to late AMD, paving the way for improved personalization of AMD management. The code of CAT-LSTM can be found at GitHub .