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The COVID-19 pandemic has increased isolation of caregivers making the development of an in-home assessment tool timely. This study examined the usability of the Caregiver Assessment using Serious ...

Sickle cell disease (SCD) is a red blood cell disorder complicated by lifelong issues with pain. Management of SCD related pain is particularly challenging due to its subjective nature. Hence, the development of an objective automatic pain assessment method is critical to pain management in SCD. In this work, we developed a continuous pain assessment model using physiological and body movement sensor signals collected from a wearable wrist-worn device. Specifically, we implemented ensemble feature selection methods to select robust and stable features extracted from wearable data for better understanding of pain. Our experiments showed that the stability of feature selection methods could be substantially increased by using the ensemble approach. Since different ensemble feature selection methods prefer varying feature subsets for pain estimation, we further utilized stacked generalization to maximize the information usage contained in the selected features from different methods. Using this approach, our best performing model obtained the root-mean-square error of 1.526 and the Pearson correlation of 0.618 for continuous pain assessment. This indicates that subjective pain scores can be estimated using objective wearable sensor data with high precision.

Sickle Cell Disease (SCD) is a hereditary disorder of red blood cells in humans. Complications such as pain, stroke, and organ failure occur in SCD as malformed, sickled red blood cells passing through small blood vessels get trapped. Particularly, acute pain is known to be the primary symptom of SCD. The insidious and subjective nature of SCD pain leads to challenges in pain assessment among Medical Practitioners (MPs). Thus, accurate identification of markers of pain in patients with SCD is crucial for pain management. Classifying clinical notes of patients with SCD based on their pain level enables MPs to give appropriate treatment. We propose a binary classification model to predict pain relevance of clinical notes and a multiclass classification model to predict pain level. While our four binary machine learning (ML) classifiers are comparable in their performance, Decision Trees had the best performance for the multiclass classification task achieving 0.70 in F-measure. Our results show the potential clinical text analysis and machine learning offer to pain management in sickle cell patients., The 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Educational programs about vaccination tend to target vaccine acceptance and reduction of hesitancy. Social media provides a promising platform for studying public perception regarding vaccination. In this study, we harvested tweets over a year related to vaccines from February 2018 to January 2019. We present a two-stage classifier to: (1) classify the tweets as relevant or non-relevant and (2) categorize them in terms of pro-vaccination, anti-vaccination, or neutral outlooks. We found that the classifier was able to distinguish clearly between antivaccination and pro-vaccination tweets, but also misclassified many of these as neutral. Using Latent Dirichlet Allocation, we found that two topics were sufficient to describe the corpus of tweets. These dealt with: (1) consequences of vaccination/non- vaccination, and (2) promotion of vaccination/non-vaccination. Finally, using the NRC emotion lexicon, we found practically significant differences in emotions expressed about vaccination between vaccine outlooks, but no practically significant temporal differences by month across a year.

The amount of Internet of Things (IoT) data is growing rapidly. Although there is a growing understanding of the quality of such data at the device and network level, important challenges in interpreting and evaluating the quality at informational and application levels remain to be explored. This article discusses some of these challenges and solutions of IoT systems at the different OSI layers to understand the factors affecting the quality of the overall system. With the help of two IoT-enabled digital health applications, the authors investigate the role of semantics in measuring the data quality of the system, as well as integrating multimodal data for clinical decision support. They also discuss the extension of IoT to the Internet of Everything by including human-in-the-loop to enhance the system accuracy. This paradigm shift through the confluence of sensors and data analytics can lead to accelerated innovation in applications by overcoming the limitations of the current systems, leading to unprecedented opportunities in healthcare.

A feasibility study was conducted to investigate the use of a wearable gait analysis system for classifying gait speed using a low-cost wearable camera in a semi-structured indoor setting. Data were collected from 19 participants who wore the system during indoor walk sequences at varying self-determined speeds (slow, medium, and fast). Gait parameters using this system were compared with parameters obtained from a vest comprising of a single triaxial accelerometer and from a marker-based optical motion-capture system. Computer-vision techniques and signal processing methods were used to generate frequency-domain gait parameters from each gait-recording device, and those parameters were analysed to determine the effectiveness of the different measurement systems in discriminating gait speed. Results indicate that the authors’ low-cost, portable, vision-based system can be effectively used for in-home gait analysis.

Patients with sickle cell disease (SCD) experience lifelong struggles with both chronic and acute pain, often requiring medical interventMaion. Pain can be managed with medications, but dosages must balance the goal of pain mitigation against the risks of tolerance, addiction and other adverse effects. Setting appropriate dosages requires knowledge of a patient’s subjective pain, but collecting pain reports from patients can be difficult for clinicians and disruptive for patients, and is only possible when patients are awake and communicative. Here we investigate methods for estimating SCD patients’ pain levels indirectly using vital signs that are routinely collected and documented in medical records. Using machine learning, we develop both sequential and non-sequential probabilistic models that can be used to infer pain levels or changes in pain from sequences of these physiological measures. We demonstrate that these models outperform null models and that objective physiological data can be used to inform estimates for subjective pain., Author summary Understanding subjective human pain remains a major challenge. If objective data could be used in place of reported pain levels, it could reduce patient burdens and enable the collection of much larger data sets that could deepen our understanding of causes of pain and allow for accurate forecasting and more effective pain management. Here we apply two machine learning approaches to data from patients with sickle cell disease, who often experience debilitating pain crises. Using vital sign data routinely collected in hospital settings including respiratory rate, heart rate, and blood pressure and amidst the real-world challenges of irregular timing, missing data, and inter-patient variation, we demonstrate that these models outperform baseline models in estimating subjective pain, distinguishing between typical and atypical pain levels, and detecting changes in pain. Once trained, these types of models could be used to improve pain estimates in real time in the absence of direct pain reports.

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information., 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE 2018 and published in Smart Health journal

With the introduction of the large number of fitness devices on the market, there are numerous possibilities for their use in managing chronic diseases in older adults. For example, monitoring people with dementia using commercially available devices that measure heart rate, breathing rate, lung volume, step count, and activity level could be used to predict episodic behavioral and psychological symptoms before they become distressing or disruptive. However, since these devices are designed primarily for fitness assessment, validation of the sensors in a controlled environment with the target cohort population is needed. In this study, we present validation results using a commercial fitness tracker, the Hexoskin sensor vest, with thirty-one participants aged 65 and older. Estimated physiological measures investigated in this study are heart rate, breathing rate, lung volume, step count, and activity level of the participants. Findings indicate that while the processed step count, heart rate, and breathing rate show strong correlations to the clinically accepted gold standard values, lung volume and activity level do not. This indicates the need to proceed cautiously when making clinical decisions using such sensors, and suggests that users should focus on the three strongly correlated parameters for further analysis, at least in the older population. The use of physiological measurement devices such as the Hexoskin may eventually become a non-intrusive way to continuously assess physiological measures in older adults with dementia who are at risk for distressing behavioral and psychological symptoms.

Zika virus has caught the worlds attention, and has led people to share their opinions and concerns on social media like Twitter. Using text-based features, extracted with the help of Parts of Speech (POS) taggers and N-gram, a classifier was built to detect Zika related tweets from Twitter. With a simple logistic classifier, the system was successful in detecting Zika related tweets from Twitter with a 92% accuracy. Moreover, key features were identified that provide deeper insights on the content of tweets relevant to Zika. This system can be leveraged by domain experts to perform sentiment analysis, and understand the temporal and spatial spread of Zika.

In this chapter, we will describe our comprehensive literature survey on using vision technologies for in-home activity monitoring using computer vision techniques, as well as computational intelligence (CI) approaches. Specifically, through our survey of the body of work, we will address the following questions: I. What are the challenges of using standard RGB cameras for activity analysis and how are they solved? II. Why do most existing algorithms perform so poorly in real-world settings? III. Which the design choices should be considered when deciding between wearable cameras or stationary cameras for activity analysis? IV. What does CI bring to the vision world as compared to computer vision techniques in the activity analysis domain?

With the rapid increase in urban development, it is critical to utilize dynamic sensor streams for traffic understanding, especially in larger cities where route planning or infrastructure planning is more critical. This creates a strong need to understand traffic patterns using ubiquitous sensors to allow city officials to be better informed when planning urban construction and to provide an understanding of the traffic dynamics in the city. In this study, we propose our framework ITSKG (Imagery-based Traffic Sensing Knowledge Graph) which utilizes the stationary traffic camera information as sensors to understand the traffic patterns. The proposed system extracts image-based features from traffic camera images, adds a semantic layer to the sensor data for traffic information, and then labels traffic imagery with semantic labels such as congestion. We share a prototype example to highlight the novelty of our system and provide an online demo to enable users to gain a better understanding of our system. This framework adds a new dimension to existing traffic modeling systems by incorporating dynamic image-based features as well as creating a knowledge graph to add a layer of abstraction to understand and interpret concepts like congestion to the traffic event detection system.

Background: In order to harness what people are tweeting about Zika, there needs to be a computational framework that leverages machine learning techniques to recognize relevant Zika tweets and, further, categorize these into disease-specific categories to address specific societal concerns related to the prevention, transmission, symptoms, and treatment of Zika virus. Objective: The purpose of this study was to determine the relevancy of the tweets and what people were tweeting about the 4 disease characteristics of Zika: symptoms, transmission, prevention, and treatment. Methods: A combination of natural language processing and machine learning techniques was used to determine what people were tweeting about Zika. Specifically, a two-stage classifier system was built to find relevant tweets about Zika, and then the tweets were categorized into 4 disease categories. Tweets in each disease category were then examined using latent Dirichlet allocation (LDA) to determine the 5 main tweet topics for each disease characteristic. Results: Over 4 months, 1,234,605 tweets were collected. The number of tweets by males and females was similar (28.47% [351,453/1,234,605] and 23.02% [284,207/1,234,605], respectively). The classifier performed well on the training and test data for relevancy (F1 score=0.87 and 0.99, respectively) and disease characteristics (F1 score=0.79 and 0.90, respectively). Five topics for each category were found and discussed, with a focus on the symptoms category. Conclusions: We demonstrate how categories of discussion on Twitter about an epidemic can be discovered so that public health officials can understand specific societal concerns within the disease-specific categories. Our two-stage classifier was able to identify relevant tweets to enable more specific analysis, including the specific aspects of Zika that were being discussed as well as misinformation being expressed. Future studies can capture sentiments and opinions on epidemic outbreaks like Zika virus in real time, which will likely inform efforts to educate the public at large. [JMIR Public Health Surveill 2017;3(2):e38]

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%., Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM), 2017 IEEE/ACM International Conference

We present the results of analyzing gait motion in first-person video taken from a commercially available wearable camera embedded in a pair of glasses. The video is analyzed with three different computer vision methods to extract motion vectors from different gait sequences from four individuals for comparison against a manually annotated ground truth dataset. Using a combination of signal processing and computer vision techniques, gait features are extracted to identify the walking pace of the individual wearing the camera as well as validated using the ground truth dataset. Our preliminary results indicate that the extraction of activity from the video in a controlled setting shows strong promise of being utilized in different activity monitoring applications such as in the eldercare environment, as well as for monitoring chronic healthcare conditions.

Humanitarian and public institutions are increasingly relying on data from social media sites to measure public attitude, and provide timely public engagement. Such engagement supports the exploration of public views on important social issues such as gender-based violence (GBV). In this study, we examine Big (Social) Data consisting of nearly fourteen million tweets collected from the Twitter platform over a period of ten months to analyze public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. The exploitation of Big Data requires the techniques of Computational Social Science to mine insight from the corpus while accounting for the influence of both transient events and sociocultural factors. We reveal public awareness regarding GBV tolerance and suggest opportunities for intervention and the measurement of intervention effectiveness assisting both governmental and non-governmental organizations in policy development