Clinical observations indicate that during critical care at the hospitals, patients sleep positioning and motion affect recovery. Unfortunately, there is no formal medical protocol to record, quantify, and analyze patient motion. There is a small number of clinical studies, which use manual analysis of sleep poses and motion recordings to support medical benefits of patient positioning and motion monitoring. Manual processes are not scalable, are prone to human errors, and strain an already taxed healthcare workforce. This study introduces DECU (Deep Eye-CU): an autonomous mulitmodal multiview system, which addresses these issues by autonomously monitoring healthcare environments and enabling the recording and analysis of patient sleep poses and motion. DECU uses three RGB-D cameras to monitor patient motion in a medical Intensive Care Unit (ICU). The algorithms in DECU estimate pose direction at different temporal resolutions and use keyframes to efficiently represent pose transition dynamics. DECU combines deep features computed from the data with a modified version of Hidden Markov Model to more flexibly model sleep pose duration, analyze pose patterns, and summarize patient motion. Extensive experimental results are presented. The performance of DECU is evaluated in ideal (BC: Bright and Clear occlusion-free) and natural (DO: Dark and Occluded) scenarios at two motion resolutions in a mock-up and a real ICU. The results indicate that deep features allow DECU to match the classification performance of engineered features in BC scenes and increase the accuracy by up to 8 in DO scenes. In addition, the overall pose history summarization tracing accuracy shows an average detection rate of 85 in BC and of 76 in DO scenes. The proposed keyframe estimation algorithm allows DECU to reach an average 78 transition classification accuracy.
Multi-sensor and multi-camera systems and methods have been applied to smart environments @cite_16 and @cite_21 . The systems require alterations to existing infrastructure making their deployment in a hospital logistically impossible. The methods are not designed to account for illumination variations and occlusions and do not account for non-sequential, subtle motion. Therefore, these systems and methods cannot be used to analyze patient motion in a real ICU where patients have limited or constrained mobility and the scenes have random occlusions and unpredictable illumination.
@cite_16 @cite_21
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With the latest developments in database technologies, it becomes easier to store the medical records of hospital patients from their first day of admission than was previously possible. In Intensive Care Units (ICU), modern medical information systems can record patient events in relational databases every second. Knowledge mining from these huge volumes of medical data is beneficial to both caregivers and patients. Given a set of electronic patient records, a system that effectively assigns the disease labels can facilitate medical database management and also benefit other researchers, e.g., pathologists. In this paper, we have proposed a framework to achieve that goal. Medical chart and note data of a patient are used to extract distinctive features. To encode patient features, we apply a Bag-of-Words encoding method for both chart and note data. We also propose a model that takes into account both global information and local correlations between diseases. Correlated diseases are characterized by a graph structure that is embedded in our sparsity-based framework. Our algorithm captures the disease relevance when labeling disease codes rather than making individual decision with respect to a specific disease. At the same time, the global optimal values are guaranteed by our proposed convex objective function. Extensive experiments have been conducted on a real-world large-scale ICU database. The evaluation results demonstrate that our method improves multi-label classification results by successfully incorporating disease correlations. Abstract Background Mortality prediction of hospitalized patients is an important problem. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality. By contrast, early mortality prediction for intensive care unit patients remains an open challenge. Most research has focused on severity of illness scoring systems or data mining (DM) models designed for risk estimation at least 24 or 48 h after ICU admission. Objectives This study highlights the main data challenges in early mortality prediction in ICU patients and introduces a new machine learning based framework for Early Mortality Prediction for Intensive Care Unit patients (EMPICU). Materials and methods The proposed method is evaluated on the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database. Mortality prediction models are developed for patients at the age of 16 or above in Medical ICU (MICU), Surgical ICU (SICU) or Cardiac Surgery Recovery Unit (CSRU). We employ the ensemble learning Random Forest (RF), the predictive Decision Trees (DT), the probabilistic Naive Bayes (NB) and the rule-based Projective Adaptive Resonance Theory (PART) models. The primary outcome was hospital mortality. The explanatory variables included demographic, physiological, vital signs and laboratory test variables. Performance measures were calculated using cross-validated area under the receiver operating characteristic curve (AUROC) to minimize bias. 11,722 patients with single ICU stays are considered. Only patients at the age of 16 years old and above in Medical ICU (MICU), Surgical ICU (SICU) or Cardiac Surgery Recovery Unit (CSRU) are considered in this study. Results The proposed EMPICU framework outperformed standard scoring systems (SOFA, SAPS-I, APACHE-II, NEWS and qSOFA) in terms of AUROC and time (i.e. at 6 h compared to 48 h or more after admission). Discussion and conclusion The results show that although there are many values missing in the first few hour of ICU admission, there is enough signal to effectively predict mortality during the first 6 h of admission. The proposed framework, in particular the one that uses the ensemble learning approach – EMPICU Random Forest (EMPICU-RF) offers a base to construct an effective and novel mortality prediction model in the early hours of an ICU patient admission, with an improved performance profile. There are approximately 4 million intensive care unit (ICU) admissions each year in the United States with costs accounting for 4.1 of national health expenditures. Unforeseen adverse events contribute disproportionately to these costs. Thus, there has been substantial research in developing clinical decision support systems to predict and improve ICU outcomes such as ICU mortality, prolonged length of stay, and ICU readmission. However, the data in the ICU is collected at diverse time intervals and includes both static and temporal data. Common methods for static data mining such as Cox and logistic regression and methods for temporal data analysis such as temporal association rule mining do not model the combination of both static and temporal data. This work aims to overcome this challenge to combine static models such as logistic regression and feedforward neural networks with temporal models such as conditional random fields(CRF). We demonstrate the results using adult patient records from a publicly available database called Multi-parameter Intelligent Monitoring in Intensive Care - II (MIMIC-II). We show that the combination models outperformed individual models of logistic regression, feed-forward neural networks and conditional random fields in predicting ICU mortality. The combination models also outperform the static models of logistic regression and feed-forward neural networks for the prediction of 30 day ICU readmissions when tested using Matthews correlation coefficient and accuracy as the metrics. We introduce structured importance sampling, a new technique for efficiently rendering scenes illuminated by distant natural illumination given in an environment map. Our method handles occlusion, high-frequency lighting, and is significantly faster than alternative methods based on Monte Carlo sampling. We achieve this speedup as a result of several ideas. First, we present a new metric for stratifying and sampling an environment map taking into account both the illumination intensity as well as the expected variance due to occlusion within the scene. We then present a novel hierarchical stratification algorithm that uses our metric to automatically stratify the environment map into regular strata. This approach enables a number of rendering optimizations, such as pre-integrating the illumination within each stratum to eliminate noise at the cost of adding bias, and sorting the strata to reduce the number of sample rays. We have rendered several scenes illuminated by natural lighting, and our results indicate that structured importance sampling is better than the best previous Monte Carlo techniques, requiring one to two orders of magnitude fewer samples for the same image quality.