Supplemental material, Appendix_B.rjf_online_supp for Use of Enhanced Data Visualization to Improve Patient Judgments about Hypertension Control by Victoria A. Shaffer, Pete Wegier, K. D. Valentine, Jeffery L. Belden, Shannon M. Canfield,... more
Supplemental material, Appendix_B.rjf_online_supp for Use of Enhanced Data Visualization to Improve Patient Judgments about Hypertension Control by Victoria A. Shaffer, Pete Wegier, K. D. Valentine, Jeffery L. Belden, Shannon M. Canfield, Mihail Popescu, Linsey M. Steege, Akshay Jain and Richelle J. Koopman in Medical Decision Making
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In this paper we propose a framework for detecting health patterns based on non-wearable sensor sequence similarity and natural language processing (NLP). In TigerPlace, an aging in place facility from Columbia, MO, we deployed 47 sensor... more
In this paper we propose a framework for detecting health patterns based on non-wearable sensor sequence similarity and natural language processing (NLP). In TigerPlace, an aging in place facility from Columbia, MO, we deployed 47 sensor networks together with a nursing electronic health record (EHR) system to provide early illness recognition. The proposed framework utilizes sensor sequence similarity and NLP on EHR nursing comments to automatically notify the physician when health problems are detected. The reported methodology is inspired by genomic sequence annotation using similarity algorithms such as Smith Waterman (SW). Similarly, for each sensor sequence, we associate health concepts extracted from the nursing notes using Metamap, a NLP tool provided by Unified Medical Language System (UMLS). Since sensor sequences, unlike genomics ones, have an associated time dimension we propose a temporal variant of SW (TSW) to account for time. The main challenges presented by our framework are finding the most suitable time sequence similarity and aggregation of the retrieved UMLS concepts. On a pilot dataset from three Tiger Place residents, with a total of 1685 sensor days and 626 nursing records, we obtained an average precision of 0.64 and a recall of 0.37.
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BACKGROUND Reducing avoidable nursing home (NH)–to-hospital transfers of residents with Alzheimer disease or a related dementia (ADRD) has become a national priority due to the physical and emotional toll it places on residents and the... more
BACKGROUND Reducing avoidable nursing home (NH)–to-hospital transfers of residents with Alzheimer disease or a related dementia (ADRD) has become a national priority due to the physical and emotional toll it places on residents and the high costs to Medicare and Medicaid. Technologies supporting the use of clinical text messages (TMs) could improve communication among health care team members and have considerable impact on reducing avoidable NH-to-hospital transfers. Although text messaging is a widely accepted mechanism of communication, clinical models of care using TMs are sparsely reported in the literature, especially in NHs. Protocols for assessing technologies that integrate TMs into care delivery models would be beneficial for end users of these systems. Without evidence to support clinical models of care using TMs, users are left to design their own methods and protocols for their use, which can create wide variability and potentially increase disparities in resident outcomes. OBJECTIVE Our aim is to describe the protocol of a study designed to understand how members of the multidisciplinary team communicate using TMs and how salient and timely communication can be used to avert poor outcomes for NH residents with ADRD, including hospitalization. METHODS This project is a secondary analysis of data collected from a Centers for Medicare & Medicaid Services (CMS)–funded demonstration project designed to reduce avoidable hospitalizations for long-stay NH residents. We will use two data sources: (1) TMs exchanged among the multidisciplinary team across the 7-year CMS study period (August 2013-September 2020) and (2) an adapted acute care transfer tool completed by advanced practice registered nurses to document retrospective details about NH-to-hospital transfers. The study is guided by an age-friendly model of care called the 4Ms (What Matters, Medications, Mentation, and Mobility) framework. We will use natural language processing, statistical methods, and social network analysis to generate a new ontology and to compare communication patterns found in TMs occurring around the time NH-to-hospital transfer decisions were made about residents with and without ADRD. RESULTS After accounting for inclusion and exclusion criteria, we will analyze over 30,000 TMs pertaining to over 3600 NH-to-hospital transfers. Development of the 4M ontology is in progress, and the 3-year project is expected to run until mid-2025. CONCLUSIONS To our knowledge, this project will be the first to explore the content of TMs exchanged among a multidisciplinary team of care providers as they make decisions about NH-to-hospital resident transfers. Understanding how the presence of evidence-based elements of high-quality care relate to avoidable hospitalizations among NH residents with ADRD will generate knowledge regarding the future scalability of behavioral interventions. Without this knowledge, NHs will continue to rely on ineffective and outdated communication methods that fail to account for evidence-based elements of age-friendly care. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/50231
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Objective:Uncontrolled hypertension is driven by clinical uncertainty around blood pressure data. This research sought to determine whether decision support—in the form of enhanced data visualization—could improve judgments about... more
Objective:Uncontrolled hypertension is driven by clinical uncertainty around blood pressure data. This research sought to determine whether decision support—in the form of enhanced data visualization—could improve judgments about hypertension control.Methods:Participants (Internet sample of patients with hypertension) in three studies (N=209) viewed graphs depicting blood pressure data for fictitious patients. For each graph, participants rated hypertension control, need for medication change, and perceived risk of heart attack and stroke. In Study 3, participants also recalled the percentage of blood pressure measurements outside of the goal range. The graphs varied by systolic blood pressure mean and standard deviation, change in blood pressure values over time, and data visualization type.Results:In all three studies, data visualization type significantly impacted judgments of hypertension control. In Study 1 and 2, perceived hypertension control was lower while perceived need for medication change and subjective perceptions of stroke and heart attack risk were higher for raw data displays compared with enhanced visualization that employed a smoothing function generated by the LOWESS algorithm. In general, perceptions of hypertension control were more closely aligned with clinical guidelines when data visualization included a smoothing function. However, conclusions were mixed when comparing tabular presentations of data to graphical presentations of data in Study 3. Hypertension was perceived to be less well controlled when data was presented in a graph rather than a table, but recall was more accurate.Conclusion:Enhancing data visualization with the use of a smoothing function to minimize the variability present in raw BP data significantly improved judgments about hypertension control. More research is needed to determine the contexts in which graphs are superior to data tables.
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n the last decade, data mining techniques have been applied to sensor data in a wide range of application domains, such as healthcare monitoring systems, manufacturing processes, intrusion detection, database management and others. Many... more
n the last decade, data mining techniques have been applied to sensor data in a wide range of application domains, such as healthcare monitoring systems, manufacturing processes, intrusion detection, database management and others. Many data mining techniques are based on computing the similarity between two sensor data patterns. A variety of representations and similarity measures for multi-attribute time series have been proposed in literature. In this paper, we describe a novel method for computing the similarity of two multi-attribute time series based on a temporal version of Smith-Waterman (SW), a wellknown bioinformatics algorithm. We then apply our method to sensor data from an eldercare application for early illness detection. Our method mitigates difficulties related to data uncertainty and aggregation that often arise when processing sensor data. The experiments take place at an aging-in-place facility, TigerPlace, located in Columbia, MO. To validate our method we used data from nine non-wearable sensor networks placed in TigerPlace apartments, combined with information from an Electronic Health Record (EHR). We provide a set of experiments that investigate temporal version of SW properties, together with experiments on TigerPlace datasets. On a pilot sensor dataset from nine residents, with a total of 1902 days and around 2.1 million sensor hits of collected data, we obtained an average abnormal events prediction F-measure of 0.75.
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Older adults aged 65 and above are at higher risk of falls. Predicting fall risk early can provide caregivers time to provide interventions, which could reduce the risk, potentially avoiding a possible fall. In this paper, we present an... more
Older adults aged 65 and above are at higher risk of falls. Predicting fall risk early can provide caregivers time to provide interventions, which could reduce the risk, potentially avoiding a possible fall. In this paper, we present an analysis of 6-month fall risk prediction in older adults using geriatric assessments, GAITRite measurements, and fall history. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). These geriatric assessments are collected by staff nurses regularly in senior care facilities. From the GAITRite assessments on the residents, we included the Functional Ambulatory Profile (FAP) scores and gait speed to predict fall risk. We used the SHAP (SHapley Additive exPlanations) approach to explain our model predictions to understand which predictor variables contributed to increase or decrease the fal...
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Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based... more
Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-pr...
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The rapid aging of the population worldwide requires increased attention from healthcare providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling,... more
The rapid aging of the population worldwide requires increased attention from healthcare providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling, need increased attention and monitoring. When monitoring daily routines for older adults, it is desirable to detect the early signs of health changes before serious health events, such as hospitalizations, happen so that timely and adequate preventive care may be provided. By deploying multi-sensor systems in homes of the elderly, we can track trajectories of daily behaviors in a feature space defined using the sensor data. In this article, we investigate a methodology for tracking the evolution of the behavior trajectories over long periods (years) using high-dimensional streaming clustering and provide very early indicators of changes in health. If we assume that habitual behaviors correspond to clusters in feature space and diseases produce a cha...
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Social network analysis (SNA) uses quantitative methods to analyze relationships between people. In the current study, SNA was applied in two nursing homes (NHs) to describe how health care teams interact via text messages. Two data... more
Social network analysis (SNA) uses quantitative methods to analyze relationships between people. In the current study, SNA was applied in two nursing homes (NHs) to describe how health care teams interact via text messages. Two data sources were used: (a) a Qualtrics ® survey completed by advanced practice RNs containing resident transfer data, and (b) text messages from a secure platform called Mediprocity™. SNA software was used to generate a visual representation of the social networks and calculate quantitative measures of network structure, including density, clustering coefficient, hierarchy, and centralization. Differences were found in the low and high transfer rate NHs for all SNA measures. Staff in the NH with low transfer rate had greater decision-making interactions, higher information exchange rates, and more individuals communicating with each other compared to the high transfer rate NH. SNA can be applied to examine communication patterns found in text messages occurr...
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Background Nearly half of US adults with diagnosed hypertension have uncontrolled blood pressure. Clinical inertia may contribute, including patient-physician uncertainty about how variability in blood pressures impacts overall control.... more
Background Nearly half of US adults with diagnosed hypertension have uncontrolled blood pressure. Clinical inertia may contribute, including patient-physician uncertainty about how variability in blood pressures impacts overall control. Better information display may support clinician-patient hypertension decision making through reduced cognitive load and improved situational awareness. Methods A multidisciplinary team employed iterative user-centered design to create a blood pressure visualization EHR prototype that included patient-generated blood pressure data. An attitude and behavior survey and 10 focus groups with patients (N = 16) and physicians (N = 24) guided iterative design and confirmation phases. Thematic analysis of qualitative data yielded insights into patient and physician needs for hypertension management. Results Most patients indicated measuring home blood pressure, only half share data with physicians. When receiving home blood pressure data, 88% of physicians i...
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Research supports that the use of alarm intervention technology alone does not conclusively reduce fall incidents. In an effort to address the need for broader use of intervention technology, a study was conducted at a large Missouri... more
Research supports that the use of alarm intervention technology alone does not conclusively reduce fall incidents. In an effort to address the need for broader use of intervention technology, a study was conducted at a large Missouri hospital. The purpose of the study was to determine hospital personnel perceptions toward the utilization of depth imaging technology in hospital rooms and to explore expanded use of video surveillance systems in the patient room.
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Living alone in their own residence, older adults are at risk for late assessment of physical or cognitive changes due to many factors such as their impression that such changes are simply a normal part of aging or their reluctance to... more
Living alone in their own residence, older adults are at risk for late assessment of physical or cognitive changes due to many factors such as their impression that such changes are simply a normal part of aging or their reluctance to admit to a problem. This paper describes an early illness recognition framework using sensor network technology to identify the health trajectory of older adults reflected in patterns of day-today activities. Describing the behavior of older adults could help clinicians to identify those at the greatest risk for functional decline and adverse events. The proposed framework, denoted as Abnormal Frequent Activity Pattern (AFAP), is based on the identification of known past abnormal frequent activities in current sensor data. More specifically, AFAP declares a day abnormal when past frequent abnormal behavior patterns, not found during normal days, are discovered in the current activity data. While AFAP requires the labeling of past days as normal/abnormal, it doesn't need specific activity identification. Frequent activity patterns (FAP) are found using MEME, a bioinformatics motif detection algorithm. To validate our approach, we used data obtained from TigerPlace, an aging in place community situated in Columbia, MO, where apartments are equipped with sensor networks (motion, bed and depth sensors). A retrospective multiple case study (N=3) design was used to quantify the in-home older adult's daily routines, over a period of two weeks. Within-person variability of routine activities may be used as a new predictor in the study of health trajectories of older adults.
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BACKGROUND Reducing avoidable nursing home (NH)–to-hospital transfers of residents with Alzheimer disease or a related dementia (ADRD) has become a national priority due to the physical and emotional toll it places on residents and the... more
BACKGROUND Reducing avoidable nursing home (NH)–to-hospital transfers of residents with Alzheimer disease or a related dementia (ADRD) has become a national priority due to the physical and emotional toll it places on residents and the high costs to Medicare and Medicaid. Technologies supporting the use of clinical text messages (TMs) could improve communication among health care team members and have considerable impact on reducing avoidable NH-to-hospital transfers. Although text messaging is a widely accepted mechanism of communication, clinical models of care using TMs are sparsely reported in the literature, especially in NHs. Protocols for assessing technologies that integrate TMs into care delivery models would be beneficial for end users of these systems. Without evidence to support clinical models of care using TMs, users are left to design their own methods and protocols for their use, which can create wide variability and potentially increase disparities in resident outco...
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As the number of features in pattern recognition applications continuously grows, new algorithms are necessary to reduce the dimensionality of the feature space while producing comparable results. For example, a dynamic area of research,... more
As the number of features in pattern recognition applications continuously grows, new algorithms are necessary to reduce the dimensionality of the feature space while producing comparable results. For example, a dynamic area of research, activity recognition, produces large quantities of high-velocity, high-dimensionality data that require real time classification. While dimensionality reduction approaches such as principle component analysis (PCA) and feature selection work well for datasets of reasonable size and dimensionality, they fail on big data. A possible approach to classification of high-dimensionality datasets is to combine a typical classifier, fuzzy k-nearest neighbor in our case (FKNN), with feature reduction by random projection (RP). As opposed to PCA where one projection matrix is computed based on least square optimality, in RP, a projection matrix is chosen at random multiple times. As the random projection procedure is repeated many times, the question is how to aggregate the values of the classifier obtained in each projection. In this paper we present a fusion strategy for RP FKNN, denoted as RPFKNN. The fusion strategy is based on the class membership values produced by FKNN and classification accuracy in each projection. We test RPFKNN on several synthetic and activity recognition datasets.
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One of the most important objects in bioinformatics is a gene product (a protein or an RNA). Besides the gene sequence and expression values found following a microarray experiment, for many gene products, additional functional... more
One of the most important objects in bioinformatics is a gene product (a protein or an RNA). Besides the gene sequence and expression values found following a microarray experiment, for many gene products, additional functional information comes from the set of gene ontology (GO) annotations and the set of journal abstracts related to the gene product. For these genes, it
Research Interests: Bioinformatics, Mathematics, Measure Theory, Computer Science, Ontology, and 15 moreFuzzy Logic, Engineering Management, Fuzzy set theory, Biomedical informatics, Fuzzy Systems, Gene expression, Genetic Engineering, DNA, Ontologies, Classification, Gene ontology, Case Based Reasoning, Choquet Integral, Metric, and Fuzzy Measure
Falling represents a major health concern for the elderly. To address this concern we proposed in a previous paper an acoustic fall detection system, FADE, composed of a microphone array and a motion detector. FADE may help the elderly... more
Falling represents a major health concern for the elderly. To address this concern we proposed in a previous paper an acoustic fall detection system, FADE, composed of a microphone array and a motion detector. FADE may help the elderly living alone by alerting a caregiver as soon as a fall is detected. A crucial component of FADE is the classification software that labels an event as a fall or part of the daily routine based on its sound signature. A major challenge in the design of the classifier is that it is almost impossible to obtain realistic fall sound signatures for training purposes. To address this problem we investigate a type of classifier, one-class classifier, that requires only examples from one class (i.e., non-fall sounds) for training. In our experiments we used three one-class (OC) classifiers: nearest neighbor (OCNN), SVM (OCSVM) and Gaussian mixture (OCGM). We compared the results of OC to the regular (two-class) classifiers on two datasets.
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ABSTRACT
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ABSTRACT In this paper, we propose a reinforcement random forest algorithm as a novel approach to detect unpaved road regions at stand-off distances. A random forest classifier is used to differentiate between road and non-road... more
ABSTRACT In this paper, we propose a reinforcement random forest algorithm as a novel approach to detect unpaved road regions at stand-off distances. A random forest classifier is used to differentiate between road and non-road pixels/patches without over fitting the training data. Utilizing a reinforcement technique, the algorithm can handle foreign objects that we encounter in real world driving. Furthermore, classifying road patches at different distances generates multiple levels of road agreement for each pixel within the image. Using different threshold values of this agreement level provides adaptability to the road finding results. The selection of low threshold values produces better detection rates but also increases false alarms. On the other hand, high threshold values lower the detection rate and decreases false detections. In our experiments, the proposed algorithm is tested on color video of unpaved road in an arid environment.
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... be computed! Page 9. 454 DT Anderson et al. Acknowledgments Derek Anderson is a pre-doctoral biomedical informatics research fellow funded by the National Library of Medicine (T15 LM07089). References 1. Bezdek, JC ...
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Page 1. Functional Summarization of Gene Product Clusters Using Gene Ontology Similarity Measures Mihail Popescu1, James M. Keller2, Joyce A. Mitchell1, James C. Bezdek3 1Health Management and Informatics Dept., University ...
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In this chapter the author presents a fuzzy clustering methodology that can be employed for large relational datasets. Relational data is an N×N matrix that consists of pair-wise dissimilarities among N objects. Large relational datasets... more
In this chapter the author presents a fuzzy clustering methodology that can be employed for large relational datasets. Relational data is an N×N matrix that consists of pair-wise dissimilarities among N objects. Large relational datasets are encountered in many domains such as psychology or medical informatics, but they are abundant in bioinformatics where gene products are compared to each other based on various characteristics such as DNA or amino acid sequence. The fuzzy clustering methodology is exemplified on a set of about 30,000 human gene products.
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This paper addresses the problem of constructing a summarization of groups of patients that are found by clustering a hospital database where diagnoses are encoded in a controlled medical vocabulary, called ICD-9. Our method finds the... more
This paper addresses the problem of constructing a summarization of groups of patients that are found by clustering a hospital database where diagnoses are encoded in a controlled medical vocabulary, called ICD-9. Our method finds the "most representative terms" (MRTs) for a patient cluster by using weights from a fuzzy partition matrix generated by fuzzy clustering the patient similarity matrix. We present a novel approach to computing patient similarity by using OWA operators. Finally, we apply our method to a set of 2077 cardiology patients.
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Solid-desiccant dehumidifiers are increasingly becoming an integral part of desiccant based air-conditioning systems because of their effective handling of latent heat loads compared to conventional vapor compression units. In these... more
Solid-desiccant dehumidifiers are increasingly becoming an integral part of desiccant based air-conditioning systems because of their effective handling of latent heat loads compared to conventional vapor compression units. In these units, either a silica gel or a molecular sieve is used for dehumidification of air. Both of them have the capability to co-adsorb various chemical pollutants during dehumidification of air. However, the shape of the isotherm for water vapor on these materials is not favorable for desiccant cooling applications. A mixture (IM desiccant) containing a silica gel, a molecular sieve, and a hydrophobic molecular sieve that was coated on an aluminum foil was studied for its capability for simultaneous removal of moisture and some selected pollutants from air. Experimental data were obtained in a fixed bed adsorber that simulated the operation of a rotary desiccant wheel. Air to be dehumidified and cleaned and the hot regeneration air were cycled in a specific ...
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Research Interests: Mathematics, Applied Mathematics, Computer Science, Artificial Intelligence, Computational Linguistics, and 13 moreFuzzy Logic, Pattern Recognition, Fuzzy set theory, Sport, Hidden Markov Models, hidden Markov model, Fuzzy Classification, Spatial Pattern, Degeneration, Electrical And Electronic Engineering, Fuzzy relation, Fuzzy Number, and Extension Principle
... Each document is represented as a graph (network) of ontology terms. A method for clustering scale-free networks nodes is also described. ... Such ontologies do not necessarily have the machine enforceable, declarative constraints... more
... Each document is represented as a graph (network) of ontology terms. A method for clustering scale-free networks nodes is also described. ... Such ontologies do not necessarily have the machine enforceable, declarative constraints of Gruber's description of the ontology that ...