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Abstract ObjectivesThe predictive intensive care unit (ICU) scoring system is crucial for predicting patient outcomes, particularly mortality. Traditional scoring systems rely mainly on structured clinical data from electronic health records, which can overlook important clinical information in narratives and images. Materials and MethodsIn this work, we build a deep learning-based survival prediction model that utilizes multimodality data for ICU mortality prediction. Four sets of features are investigated: (1) physiological measurements of Simplified Acute Physiology Score (SAPS) II, (2) common thorax diseases predefined by radiologists, (3) bidirectional encoder representations from transformers-based text representations, and (4) chest X-ray image features. The model was evaluated using the Medical Information Mart for Intensive Care IV dataset. ResultsOur model achieves an average C-index of 0.7829 (95% CI, 0.7620-0.8038), surpassing the baseline using only SAPS-II features, which had a C-index of 0.7470 (95% CI: 0.7263-0.7676). Ablation studies further demonstrate the contributions of incorporating predefined labels (2.00% improvement), text features (2.44% improvement), and image features (2.82% improvement). Discussion and ConclusionThe deep learning model demonstrated superior performance to traditional machine learning methods under the same feature fusion setting for ICU mortality prediction. This study highlights the potential of integrating multimodal data into deep learning models to enhance the accuracy of ICU mortality prediction. 

Abstract Vapor sensors with both high sensitivity and broad detection range are technically challenging yet highly desirable for widespread chemical sensing applications in diverse environments. Generally, an increased surface‐to‐volume ratio can effectively enhance the sensitivity to low concentrations, but often with the trade‐off of a constrained sensing range. Here, an approach is demonstrated for NH₃sensor arrays with an unprecedentedly broad sensing range by introducing controllable steps on the surface of an n‐type single crystal. Step edges, serving as adsorption sites with electron‐deficient properties, are well‐defined, discrete, and electronically active. NH₃molecules selectively adsorb at the step edges and nearly eliminate known trap‐like character, which is demonstrated by surface potential imaging. Consequently, the strategy can significantly boost the sensitivity of two‐terminal NH₃resistance sensors on thin crystals with a few steps while simultaneously enhancing the tolerance on thick crystals with dense steps. Incorporation of these crystals into parallel sensor arrays results in ppb–to–% level detection range and a convenient linear relation between sheet conductance and semi‐log NH₃concentration, allowing for the precise localization of vapor leakage. In general, the results suggest new opportunities for defect engineering of organic semiconductor crystal surfaces for purposeful vapor or chemical sensing. 

The predictive Intensive Care Unit (ICU) scoring system plays an important role in ICU management for its capability of predicting important outcomes, especially mortality. There are many scoring systems that have been developed and used in the ICU. These scoring systems are primarily based on the structured clinical data contained in the electronic health record (EHR), which may suffer the loss of the important clinical information contained in the narratives and images. In this work, we build a deep learning based survival prediction model with multimodality data to predict ICU-mortality. Four sets of features are investigated: (1) physiological measurements of Simplified Acute Physiology Score (SAPS) II, (2) common thorax diseases predefined by radiologists, (3) BERT-based text representations, and (4) chest X-ray image features. We use the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset to evaluate the proposed model. Our model achieves the average C-index of 0.7847 (95% confidence interval, 0.7625–0.8068), which substantially exceeds that of the baseline with SAPS-II features (0.7477 (0.7238–0.7716)). Ablation studies further demonstrate the contributions of pre-defined labels (2.12%), text features (2.68%), and image features (2.96%). Our model achieves a higher average C-index than the traditional machine learning methods under the same feature fusion setting, which suggests that the deep learning methods can outperform the traditional machine learning methods in ICU-mortality prediction. These results highlight the potential of deep learning models with multimodal information to enhance ICU-mortality prediction. We make our work publicly available at https://github.com/bionlplab/mimic-icu-mortality.