1,185 research outputs found
Early hospital mortality prediction using vital signals
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.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE
2018 and published in Smart Health journa
Activity segmentation with special emphasis on sit-to-stand analysis
The entire thesis text is included in the research.pf file; the official abstract appears in the short.pf file; a non-technical public abstract appears in the public.pf file.Title from PDF of title page (University of Missouri--Columbia, viewed on July 8, 2010.)Thesis advisor: Dr. Marjorie Skubic.M.S. University of Missouri-Columbia 2010.In this study, we present algorithms to segment the activities of sitting and standing, and identify the regions of sit-to-stand transitions in a given image sequence. As a means of fall risk assessment, we propose methods to measure sit-to-stand time using the three dimensional modeling of a human body in vowel space as well as ellipse fitting algorithms and image features to capture orientation of the body. Fuzzy clustering methods such as the Gustafson vessel algorithm are also investigated. The proposed algorithms were tested on 9 subjects with ages ranging from 18 to 88. The classification results were the best for the vowel height with the ellipse fit algorithm at 96.6%; using the vowel height alone gave a classification rate of 86.7%. The comparison was done with the marker-based V icon motion capture system as ground truth as well as a manually controlled stop watch. The average error in sit-to-stand time measurement was the best for vowel voxel height with the ellipse fit technique at 270 ms and worst for vowel voxel height alone at 380 ms. This application can be used as a part of a continuous video monitoring system in the homes of older adults and can provide valuable information which could help detect fall risk and enable them to lead an independent life style for a longer time.Includes bibliographical references
Caregiver Assessment Using Smart Gaming Technology: A Preliminary Approach
As pre-diagnostic technologies are becoming increasingly accessible, using
them to improve the quality of care available to dementia patients and their
caregivers is of increasing interest. Specifically, we aim to develop a tool
for non-invasively assessing task performance in a simple gaming application.
To address this, we have developed Caregiver Assessment using Smart Gaming
Technology (CAST), a mobile application that personalizes a traditional word
scramble game. Its core functionality uses a Fuzzy Inference System (FIS)
optimized via a Genetic Algorithm (GA) to provide customized performance
measures for each user of the system. With CAST, we match the relative level of
difficulty of play using the individual's ability to solve the word scramble
tasks. We provide an analysis of the preliminary results for determining task
difficulty, with respect to our current participant cohort.Comment: 7 pages, 1 figures, 6 table
Resident Identification using Kinect Depth Image Data and Fuzzy Clustering Techniques
As a part of our passive fall risk assessment research in home environments, we present a method to identify older residents using features extracted from their gait information from a single depth camera. Depth images have been collected continuously for about eight months from several apartments at a senior housing facility. Shape descriptors such as bounding box information and image moments were extracted from silhouettes of the depth images. The features were then clustered using Possibilistic C Means for resident identification. This technology will allow researchers and health professionals to gather more information on the individual residents by filtering out data belonging to non-residents. Gait related information belonging exclusively to the older residents can then be gathered. The data can potentially help detect changes in gait patterns which can be used to analyze fall risk for elderly residents by passively observing them in their home environments
A Knowledge Graph Framework for Detecting Traffic Events Using Stationary Cameras
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
Toward Mental Effort Measurement Using Electrodermal Activity Features
The ability to monitor mental effort during a task using a wearable sensor may improve productivity for both work and study. The use of the electrodermal activity (EDA) signal for tracking mental effort is an emerging area of research. Through analysis of over 92 h of data collected with the Empatica E4 on a single participant across 91 different activities, we report on the efficacy of using EDA features getting at signal intensity, signal dispersion, and peak intensity for prediction of the participant\u27s self-reported mental effort. We implemented the logistic regression algorithm as an interpretable machine learning approach and found that features related to signal intensity and peak intensity were most useful for the prediction of whether the participant was in a self-reported high mental effort state; increased signal and peak intensity were indicative of high mental effort. When cross-validated by activity moderate predictive efficacy was achieved (AUC = 0.63, F1 = 0.63, precision = 0.64, recall = 0.63) which was significantly stronger than using the model bias alone. Predicting mental effort using physiological data is a complex problem, and our findings add to research from other contexts showing that EDA may be a promising physiological indicator to use for sensor-based self-monitoring of mental effort throughout the day. Integration of other physiological features related to heart rate, respiration, and circulation may be necessary to obtain more accurate predictions
Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization
While there has been recent progress in abstractive summarization as applied
to different domains including news articles, scientific articles, and blog
posts, the application of these techniques to clinical text summarization has
been limited. This is primarily due to the lack of large-scale training data
and the messy/unstructured nature of clinical notes as opposed to other domains
where massive training data come in structured or semi-structured form.
Further, one of the least explored and critical components of clinical text
summarization is factual accuracy of clinical summaries. This is specifically
crucial in the healthcare domain, cardiology in particular, where an accurate
summary generation that preserves the facts in the source notes is critical to
the well-being of a patient. In this study, we propose a framework for
improving the factual accuracy of abstractive summarization of clinical text
using knowledge-guided multi-objective optimization. We propose to jointly
optimize three cost functions in our proposed architecture during training:
generative loss, entity loss and knowledge loss and evaluate the proposed
architecture on 1) clinical notes of patients with heart failure (HF), which we
collect for this study; and 2) two benchmark datasets, Indiana University Chest
X-ray collection (IU X-Ray), and MIMIC-CXR, that are publicly available. We
experiment with three transformer encoder-decoder architectures and demonstrate
that optimizing different loss functions leads to improved performance in terms
of entity-level factual accuracy.Comment: Accepted to EMBC 202
COVID-19 and Mental Health/Substance Use Disorders on Reddit: A Longitudinal Study
COVID-19 pandemic has adversely and disproportionately impacted people
suffering from mental health issues and substance use problems. This has been
exacerbated by social isolation during the pandemic and the social stigma
associated with mental health and substance use disorders, making people
reluctant to share their struggles and seek help. Due to the anonymity and
privacy they provide, social media emerged as a convenient medium for people to
share their experiences about their day to day struggles. Reddit is a
well-recognized social media platform that provides focused and structured
forums called subreddits, that users subscribe to and discuss their experiences
with others. Temporal assessment of the topical correlation between social
media postings about mental health/substance use and postings about Coronavirus
is crucial to better understand public sentiment on the pandemic and its
evolving impact, especially related to vulnerable populations. In this study,
we conduct a longitudinal topical analysis of postings between subreddits
r/depression, r/Anxiety, r/SuicideWatch, and r/Coronavirus, and postings
between subreddits r/opiates, r/OpiatesRecovery, r/addiction, and r/Coronavirus
from January 2020 - October 2020. Our results show a high topical correlation
between postings in r/depression and r/Coronavirus in September 2020. Further,
the topical correlation between postings on substance use disorders and
Coronavirus fluctuates, showing the highest correlation in August 2020. By
monitoring these trends from platforms such as Reddit, epidemiologists, and
mental health professionals can gain insights into the challenges faced by
communities for targeted interventions.Comment: First workshop on computational & affective intelligence in
healthcare applications in conjunction with ICPR 202
Sit-to-Stand Detection using Fuzzy Clustering Techniques
The ability to rise from a chair is an important parameter to assess the balance deficits of a person. In particular, this can be an indication of risk for falling in elderly persons. Our goal is automated assessment of fall risk using video data. Towards this goal, we present a simple yet effective method of detecting transition, i.e. sit-to-stand and stand-to-sit, from image frames using fuzzy clustering methods on image moments. The technique described in this paper is shown to be robust even in the presence of noise and has been tested on several data sequences using different subjects yielding promising results
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