Natural sub-bituminous coal as filler enhances mechanical, insulation and flame retardant properties of coir-polypropylene bio-composites.

Additives provide substantial improvement in the properties of composites. Although bio-based composites are preferred over synthetic polymer and metal-based composites, they do not have the requisite properties to meet specific needs. Hence, organic, inorganic and metallic additives are included to improve the properties of bio-based composites. Coal is a readily available resource with high thermal insulation, flame resistance and other properties. This work demonstrates the addition of 20-30% natural sub-bituminous coal as filler for coir-reinforced polypropylene (PP) composites and exhibits an increased tensile strength by 66% and flexural strength by 55% compared to the composites without any filler. Such composites are intended for insulation applications and as a replacement for gypsum-based false ceiling tiles. Various ratios of coal samples were included in the composites and their effect on mechanical, acoustic, thermal insulation, flame and water resistance have been determined. A substantial improvement in both flexural and tensile properties has been observed due to the addition of coal. However, a marginal improvement has been observed in both thermal conductivity (0.65 W/mK) and flame resistance values due to the presence of coal. Adding coal increases the intensity of noise absorption, particularly at a higher frequency, whereas water sorption of the composites tends to decrease with an increase in the coal content. The addition of coal improves and adds unique properties to composites, allowing coir-coal-PP composites to outperform commercially available gypsum-based insulation panels.

MedFuseNet: An attention-based multimodal deep learning model for visual question answering in the medical domain.

Medical images are difficult to comprehend for a person without expertise. The scarcity of medical practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing human errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision maker. Thus, it becomes crucial to have a reliable visual question answering (VQA) system to provide a 'second opinion' on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. Moreover, the VQA system for medical images needs to consider a limited amount of training data available in this domain. In this paper, we develop MedFuseNet, an attention-based multimodal deep learning model, for VQA on medical images taking the associated challenges into account. Our MedFuseNet aims at maximizing the learning with minimal complexity by breaking the problem statement into simpler tasks and predicting the answer. We tackle two types of answer prediction-categorization and generation. We conducted an extensive set of quantitative and qualitative analyses to evaluate the performance of MedFuseNet. Our experiments demonstrate that MedFuseNet outperforms the state-of-the-art VQA methods, and that visualization of the captured attentions showcases the intepretability of our model's predicted results.

Identification of Developmental Delay in Infants Using Wearable Sensors: Full-Day Leg Movement Statistical Feature Analysis.

This paper examines how features extracted from full-day data recorded by wearable sensors are able to differentiate between infants with typical development and those with or at risk for developmental delays. Wearable sensors were used to collect full-day (8-13 h) leg movement data from infants with typical development ([Formula: see text]) and infants at risk for developmental delay ([Formula: see text]). At 24 months, at-risk infants were assessed as having good ([Formula: see text]) or poor ([Formula: see text]) developmental outcomes. With this limited size dataset, our statistical analysis indicated that accelerometer features collected earlier in infancy differentiated between at-risk infants with poor and good outcomes at 24 months, as well as infants with typical development. This paper also tested how these features performed on a subset of the data for which the infant movement was known, i.e., 5-min intervals more representative of clinical observations. Our results on this limited dataset indicated that features for full-day data showed more group differences than similar features for the 5-min intervals, supporting the usefulness of full-day movement monitoring.

A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy.

OBJECTIVES: To predict urinary continence recovery after robot-assisted radical prostatectomy (RARP) using a deep learning (DL) model, which was then used to evaluate surgeon's historical patient outcomes. SUBJECTS AND METHODS: Robotic surgical automated performance metrics (APMs) during RARP, and patient clinicopathological and continence data were captured prospectively from 100 contemporary RARPs. We used a DL model (DeepSurv) to predict postoperative urinary continence. Model features were ranked based on their importance in prediction. We stratified eight surgeons based on the five top-ranked features. The top four surgeons were categorized in 'Group 1/APMs', while the remaining four were categorized in 'Group 2/APMs'. A separate historical cohort of RARPs (January 2015 to August 2016) performed by these two surgeon groups was then used for comparison. Concordance index (C-index) and mean absolute error (MAE) were used to measure the model's prediction performance. Outcomes of historical cases were compared using the Kruskal-Wallis, chi-squared and Fisher's exact tests. RESULTS: Continence was attained in 79 patients (79%) after a median of 126 days. The DL model achieved a C-index of 0.6 and an MAE of 85.9 in predicting continence. APMs were ranked higher by the model than clinicopathological features. In the historical cohort, patients in Group 1/APMs had superior rates of urinary continence at 3 and 6 months postoperatively (47.5 vs 36.7%, P = 0.034, and 68.3 vs 59.2%, P = 0.047, respectively). CONCLUSION: Using APMs and clinicopathological data, the DeepSurv DL model was able to predict continence after RARP. In this feasibility study, surgeons with more efficient APMs achieved higher continence rates at 3 and 6 months after RARP.

Survival outcome prediction in cervical cancer: Cox models vs deep-learning model.

BACKGROUND: Historically, the Cox proportional hazard regression model has been the mainstay for survival analyses in oncologic research. The Cox proportional hazard regression model generally is used based on an assumption of linear association. However, it is likely that, in reality, there are many clinicopathologic features that exhibit a nonlinear association in biomedicine. OBJECTIVE: The purpose of this study was to compare the deep-learning neural network model and the Cox proportional hazard regression model in the prediction of survival in women with cervical cancer. STUDY DESIGN: This was a retrospective pilot study of consecutive cases of newly diagnosed stage I-IV cervical cancer from 2000-2014. A total of 40 features that included patient demographics, vital signs, laboratory test results, tumor characteristics, and treatment types were assessed for analysis and grouped into 3 feature sets. The deep-learning neural network model was compared with the Cox proportional hazard regression model and 3 other survival analysis models for progression-free survival and overall survival. Mean absolute error and concordance index were used to assess the performance of these 5 models. RESULTS: There were 768 women included in the analysis. The median age was 49 years, and the majority were Hispanic (71.7%). The majority of tumors were squamous (75.3%) and stage I (48.7%). The median follow-up time was 40.2 months; there were 241 events for recurrence and progression and 170 deaths during the follow-up period. The deep-learning model showed promising results in the prediction of progression-free survival when compared with the Cox proportional hazard regression model (mean absolute error, 29.3 vs 316.2). The deep-learning model also outperformed all the other models, including the Cox proportional hazard regression model, for overall survival (mean absolute error, Cox proportional hazard regression vs deep-learning, 43.6 vs 30.7). The performance of the deep-learning model further improved when more features were included (concordance index for progression-free survival: 0.695 for 20 features, 0.787 for 36 features, and 0.795 for 40 features). There were 10 features for progression-free survival and 3 features for overall survival that demonstrated significance only in the deep-learning model, but not in the Cox proportional hazard regression model. There were no features for progression-free survival and 3 features for overall survival that demonstrated significance only in the Cox proportional hazard regression model, but not in the deep-learning model. CONCLUSION: Our study suggests that the deep-learning neural network model may be a useful analytic tool for survival prediction in women with cervical cancer because it exhibited superior performance compared with the Cox proportional hazard regression model. This novel analytic approach may provide clinicians with meaningful survival information that potentially could be integrated into treatment decision-making and planning. Further validation studies are necessary to support this pilot study.

Association of tumor differentiation grade and survival of women with squamous cell carcinoma of the uterine cervix.

OBJECTIVE: To examine the association between tumor grade and survival for women with squamous cervical cancer. METHODS: This retrospective observational study utilized the Surveillance, Epidemiology, and End Result program data between 1983 and 2013 to examine women with squamous cervical cancer with known tumor differentiation grade. Multivariable analyses were performed to assess independent associations between tumor differentiation grade and survival. RESULTS: A total of 31,536 women were identified including 15,175 (48.1%) with grade 3 tumors, 14,084 (44.7%) with grade 2 neoplasms and 2,277 (7.2%) with grade 1 tumors. Higher tumor grade was significantly associated with older age, higher stage disease, larger tumor size, and lymph node metastasis (all, p<0.001). In a multivariable analysis, grade 2 tumors (adjusted-hazard ratio [HR]=1.21; p<0.001) and grade 3 tumors (adjusted-HR=1.45; p<0.001) were independently associated with decreased cause-specific survival (CSS) compared to grade 1 tumors. Among the 7,429 women with stage II-III disease who received radiotherapy without surgical treatment, grade 3 tumors were independently associated with decreased CSS compared to grade 2 tumors (adjusted-HR=1.16; p<0.001). Among 4,045 women with node-negative stage I disease and tumor size ≤4 cm who underwent surgical treatment without radiotherapy, grade 2 tumors (adjusted-HR=2.54; p=0.028) and grade 3 tumors (adjusted-HR=4.48; p<0.001) were independently associated with decreased CSS compared to grade 1 tumors. CONCLUSION: Our study suggests that tumor differentiation grade may be a prognostic factor in women with squamous cervical cancer, particularly in early-stage disease. Higher tumor grade was associated with poorer survival.

Benchmarking deep learning models on large healthcare datasets.

Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the 'raw' clinical time series data is used as input features to the models.

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

Measuring and Predicting Tag Importance for Image Retrieval.

Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems. However, all tags are treated as equally important in these systems, which may result in misalignment between visual and textual modalities during MIR training. This will further lead to degenerated retrieval performance at query time. To address this issue, we investigate the problem of tag importance prediction, where the goal is to automatically predict the tag importance and use it in image retrieval. To achieve this, we first propose a method to measure the relative importance of object and scene tags from image sentence descriptions. Using this as the ground truth, we present a tag importance prediction model to jointly exploit visual, semantic and context cues. The Structural Support Vector Machine (SSVM) formulation is adopted to ensure efficient training of the prediction model. Then, the Canonical Correlation Analysis (CCA) is employed to learn the relation between the image visual feature and tag importance to obtain robust retrieval performance. Experimental results on three real-world datasets show a significant performance improvement of the proposed MIR with Tag Importance Prediction (MIR/TIP) system over other MIR systems.

Interpretable Deep Models for ICU Outcome Prediction.

Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns ofdiseases. Recently, deep learning models have been employedfor many computational phenotyping and healthcare prediction tasks to achieve state-of-the-art performance. However, deep models lack interpretability which is crucial for wide adoption in medical research and clinical decision-making. In this paper, we introduce a simple yet powerful knowledge-distillation approach called interpretable mimic learning, which uses gradient boosting trees to learn interpretable models and at the same time achieves strong prediction performance as deep learning models. Experiment results on Pediatric ICU dataset for acute lung injury (ALI) show that our proposed method not only outperforms state-of-the-art approaches for morality and ventilator free days prediction tasks but can also provide interpretable models to clinicians.