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Idrobo-Ávila E, Bognár G, Krefting D, Penzel T, Kovács P, Spicher N. Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:250-260. [PMID: 38766543 PMCID: PMC11100950 DOI: 10.1109/ojemb.2024.3379733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/22/2024] [Accepted: 03/12/2024] [Indexed: 05/22/2024] Open
Abstract
Goal: Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis - which involves their joint analysis - can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. Methods: We applied widely known algorithms entitled "signal quality indicators" to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10 s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. Results: 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. Conclusions: The majority of data within VitalDB is suitable for multimodal analysis.
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Affiliation(s)
- Ennio Idrobo-Ávila
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
| | - Gergő Bognár
- Department of Numerical Analysis, Faculty of InformaticsEötvös Loránd University1117BudapestHungary
| | - Dagmar Krefting
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
| | - Thomas Penzel
- Interdisciplinary Center of Sleep MedicineCharité - Universitätsmedizin Berlin10117BerlinGermany
| | - Péter Kovács
- Department of Numerical Analysis, Faculty of InformaticsEötvös Loránd University1117BudapestHungary
| | - Nicolai Spicher
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
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Chiaradonna S, Jevtić P, Lanchier N. Framework for cyber risk loss distribution of hospital infrastructure: Bond percolation on mixed random graphs approach. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023; 43:2450-2485. [PMID: 37038249 DOI: 10.1111/risa.14127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Networks like those of healthcare infrastructure have been a primary target of cyberattacks for over a decade. From just a single cyberattack, a healthcare facility would expect to see millions of dollars in losses from legal fines, business interruption, and loss of revenue. As more medical devices become interconnected, more cyber vulnerabilities emerge, resulting in more potential exploitation that may disrupt patient care and give rise to catastrophic financial losses. In this paper, we propose a structural model of an aggregate loss distribution across multiple cyberattacks on a prototypical hospital network. Modeled as a mixed random graph, the hospital network consists of various patient-monitoring devices and medical imaging equipment as random nodes to account for the variable occupancy of patient rooms and availability of imaging equipment that are connected by bidirectional edges to fixed hospital and radiological information systems. Our framework accounts for the documented cyber vulnerabilities of a hospital's trusted internal network of its major medical assets. To our knowledge, there exist no other models of an aggregate loss distribution for cyber risk in this setting. We contextualize the problem in the probabilistic graph-theoretical framework using a percolation model and combinatorial techniques to compute the mean and variance of the loss distribution for a mixed random network with associated random costs that can be useful for healthcare administrators and cybersecurity professionals to improve cybersecurity management strategies. By characterizing this distribution, we allow for the further utility of pricing cyber risk.
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Affiliation(s)
- Stefano Chiaradonna
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, USA
| | - Petar Jevtić
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, USA
| | - Nicolas Lanchier
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, USA
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Lusk S, Ward CS, Chang A, Twitchell-Heyne A, Fattig S, Allen G, Jankowsky J, Ray R. An automated respiratory data pipeline for waveform characteristic analysis. J Physiol 2023; 601:4767-4806. [PMID: 37786382 PMCID: PMC10841337 DOI: 10.1113/jp284363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 08/11/2023] [Indexed: 10/04/2023] Open
Abstract
Comprehensive and accurate analysis of respiratory and metabolic data is crucial to modelling congenital, pathogenic and degenerative diseases converging on autonomic control failure. A lack of tools for high-throughput analysis of respiratory datasets remains a major challenge. We present Breathe Easy, a novel open-source pipeline for processing raw recordings and associated metadata into operative outcomes, publication-worthy graphs and robust statistical analyses including QQ and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface for uploading data files, setting waveform feature thresholds and defining experimental variables. Breathe Easy was validated against manual selection by experts, which represents the current standard in the field. We demonstrate Breathe Easy's utility by examining a 2-year longitudinal study of an Alzheimer's disease mouse model to assess contributions of forebrain pathology in disordered breathing. Whole body plethysmography has become an important experimental outcome measure for a variety of diseases with primary and secondary respiratory indications. Respiratory dysfunction, while not an initial symptom in many of these disorders, often drives disability or death in patient outcomes. Breathe Easy provides an open-source respiratory analysis tool for all respiratory datasets and represents a necessary improvement upon current analytical methods in the field. KEY POINTS: Respiratory dysfunction is a common endpoint for disability and mortality in many disorders throughout life. Whole body plethysmography in rodents represents a high face-value method for measuring respiratory outcomes in rodent models of these diseases and disorders. Analysis of key respiratory variables remains hindered by manual annotation and analysis that leads to low throughput results that often exclude a majority of the recorded data. Here we present a software suite, Breathe Easy, that automates the process of data selection from raw recordings derived from plethysmography experiments and the analysis of these data into operative outcomes and publication-worthy graphs with statistics. We validate Breathe Easy with a terabyte-scale Alzheimer's dataset that examines the effects of forebrain pathology on respiratory function over 2 years of degeneration.
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Affiliation(s)
- Savannah Lusk
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Christopher S. Ward
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andersen Chang
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Shaun Fattig
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Genevera Allen
- Departments of Electrical and Computer Engineering, Statistics, and Computer Science, Rice University, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Joanna Jankowsky
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
- Departments of Neurology, Neurosurgery, and Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Russell Ray
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
- McNair Medical Institute, Houston, TX 77030, USA
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Muñoz-Bonet JI, Posadas-Blázquez V, González-Galindo L, Sánchez-Zahonero J, Vázquez-Martínez JL, Castillo A, Brines J. Exploring the clinical relevance of vital signs statistical calculations from a new-generation clinical information system. Sci Rep 2023; 13:15068. [PMID: 37699960 PMCID: PMC10497571 DOI: 10.1038/s41598-023-40769-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 08/16/2023] [Indexed: 09/14/2023] Open
Abstract
New information on the intensive care applications of new generation 'high-density data clinical information systems' (HDDCIS) is increasingly being published in the academic literature. HDDCIS avoid data loss from bedside equipment and some provide vital signs statistical calculations to promote quick and easy evaluation of patient information. Our objective was to study whether manual records of continuously monitored vital signs in the Paediatric Intensive Care Unit could be replaced by these statistical calculations. Here we conducted a prospective observational clinical study in paediatric patients with severe diabetic ketoacidosis, using a Medlinecare® HDDCIS, which collects information from bedside equipment (1 data point per parameter, every 3-5 s) and automatically provides hourly statistical calculations of the central trend and sample dispersion. These calculations were compared with manual hourly nursing records for patient heart and respiratory rates and oxygen saturation. The central tendency calculations showed identical or remarkably similar values and strong correlations with manual nursing records. The sample dispersion calculations differed from the manual references and showed weaker correlations. We concluded that vital signs calculations of central tendency can replace manual records, thereby reducing the bureaucratic burden of staff. The significant sample dispersion calculations variability revealed that automatic random measurements must be supervised by healthcare personnel, making them inefficient.
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Affiliation(s)
- Juan Ignacio Muñoz-Bonet
- Paediatric Intensive Care Unit, Hospital Clínico Universitario, Av. Blasco Ibáñez 17, 46010, Valencia, Spain.
- Department of Paediatrics, Obstetrics, and Gynaecology, University of Valencia, Valencia, Spain.
| | - Vicente Posadas-Blázquez
- Paediatric Intensive Care Unit, Hospital Clínico Universitario, Av. Blasco Ibáñez 17, 46010, Valencia, Spain
| | - Laura González-Galindo
- Department of Paediatrics, Obstetrics, and Gynaecology, University of Valencia, Valencia, Spain
| | - Julia Sánchez-Zahonero
- Paediatric Intensive Care Unit, Hospital Clínico Universitario, Av. Blasco Ibáñez 17, 46010, Valencia, Spain
| | | | - Andrés Castillo
- Paediatric Technological Innovation Department, Foundation for Biomedical Research of Hospital Niño Jesús, Madrid, Spain
| | - Juan Brines
- Department of Paediatrics, Obstetrics, and Gynaecology, University of Valencia, Valencia, Spain
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Singhal M, Gupta L, Hirani K. A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia. Cureus 2023; 15:e45038. [PMID: 37829964 PMCID: PMC10566398 DOI: 10.7759/cureus.45038] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2023] [Indexed: 10/14/2023] Open
Abstract
In the field of anaesthesia, artificial intelligence (AI) has become a game-changing technology. Applications of AI include keeping records, monitoring patients, calculating and administering drugs, and carrying out mechanical procedures. This article explores the current uses, challenges, and prospective applications of AI in anaesthesia practices. This review discusses AI-supported systems like anaesthesia information management systems (AIMS), mechanical robots for carrying out procedures, and pharmacological models for drug delivery. AIMS has helped in automated record-keeping, predicting bad events, and monitoring the vital signs of the patient. Their application has a vital role in improving the efficacy of anaesthesia management and patient safety. The application of AI in anaesthesia comes with its own unique difficulties. Noteworthy obstacles include issues with data quantity and quality, technical limitations, and moral and legal dilemmas. The key to overcoming these barriers is to set guidelines for the ethical use of AI in healthcare, improve the reliability and comprehension of AI systems, and certify the health data precision and security. AI has very bright potential. Exciting future directions include developments in AI and machine learning thus development of new applications, and the possible enhancement in training and education. Potential research areas include the application of AI to chronic disease management, pain management, and the reinforcement of anaesthesiologists' education. AI could be used to design authentic lifelike training simulations and individualized student feedback systems, hence transforming anaesthesia education and training methodology. For this review, we conducted a PubMed, Google Scholar, and Cochrane Database search in 2022-2023 and retrieved articles on AI and its uses in anaesthesia. Recommendations for future research and development include strengthening the safety and reliability of health data, building a better understanding of AI systems, and looking into new areas of use. The power of AI can be used to innovate anaesthesia practices by concentrating on these areas.
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Affiliation(s)
- Meghna Singhal
- Department of Anesthesiology and Critical Care, Maulana Azad Medical College, Delhi, IND
| | - Lalit Gupta
- Department of Anesthesiology and Critical Care, Maulana Azad Medical College, Delhi, IND
| | - Kshitiz Hirani
- Department of Anesthesiology and Critical Care, University College of Medical Sciences and Guru Teg Bahadur Hospital, Delhi, IND
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Hwang E, Park HS, Kim HS, Kim JY, Jeong H, Kim J, Kim SH. Development of a Bispectral index score prediction model based on an interpretable deep learning algorithm. Artif Intell Med 2023; 143:102569. [PMID: 37673590 DOI: 10.1016/j.artmed.2023.102569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/24/2023] [Accepted: 04/27/2023] [Indexed: 09/08/2023]
Abstract
BACKGROUND Proper maintenance of hypnosis is crucial for ensuring the safety of patients undergoing surgery. Accordingly, indicators, such as the Bispectral index (BIS), have been developed to monitor hypnotic levels. However, the black-box nature of the algorithm coupled with the hardware makes it challenging to understand the underlying mechanisms of the algorithms and integrate them with other monitoring systems, thereby limiting their use. OBJECTIVE We propose an interpretable deep learning model that forecasts BIS values 25 s in advance using 30 s electroencephalogram (EEG) data. MATERIAL AND METHODS The proposed model utilized EEG data as a predictor, which is then decomposed into amplitude and phase components using fast Fourier Transform. An attention mechanism was applied to interpret the importance of these components in predicting BIS. The predictability of the model was evaluated on both regression and binary classification tasks, where the former involved predicting a continuous BIS value, and the latter involved classifying a dichotomous status at a BIS value of 60. To evaluate the interpretability of the model, we analyzed the attention values expressed in the amplitude and phase components according to five ranges of BIS values. The proposed model was trained and evaluated using datasets collected from two separate medical institutions. RESULTS AND CONCLUSION The proposed model achieved excellent performance on both the internal and external validation datasets. The model achieved a root-mean-square error of 6.614 for the regression task, and an area under the receiver operating characteristic curve of 0.937 for the binary classification task. Interpretability analysis provided insight into the relationship between EEG frequency components and BIS values. Specifically, the attention mechanism revealed that higher BIS values were associated with increased amplitude attention values in high-frequency bands and increased phase attention values in various frequency bands. This finding is expected to facilitate a more profound understanding of the BIS prediction mechanism, thereby contributing to the advancement of anesthesia technologies.
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Affiliation(s)
- Eugene Hwang
- School of Management Engineering, Korea Advanced Institute of Science and Technology, Seoul, Republic of Korea.
| | - Hee-Sun Park
- Biosignal Analysis and Perioperative Outcome Research Laboratory, Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Hyun-Seok Kim
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
| | - Jin-Young Kim
- Biosignal Analysis and Perioperative Outcome Research Laboratory, Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; Department of Medical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hanseok Jeong
- Department of Electrical and Computer Engineering, University of Seoul, Seoul, Republic of Korea
| | - Junetae Kim
- Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea; Healthcare AI Team, Healthcare Platform Center, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea.
| | - Sung-Hoon Kim
- Biosignal Analysis and Perioperative Outcome Research Laboratory, Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Shamaee Z, Mivehchy M. Dominant noise-aided EMD (DEMD): Extending empirical mode decomposition for noise reduction by incorporating dominant noise and deep classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Chen Q, Li R, Lin C, Lai C, Huang Y, Lu W, Li L. SEPRES: Intensive Care Unit Clinical Data Integration System to Predict Sepsis. Appl Clin Inform 2023; 14:65-75. [PMID: 36452980 PMCID: PMC9876660 DOI: 10.1055/a-1990-3037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND The lack of information interoperability between different devices and systems in the intensive care unit (ICU) hinders further utilization of data, especially for early warning of specific diseases in the ICU. OBJECTIVES We aimed to establish a data integration system. Based on this system, the sepsis prediction module was added to compose the Sepsis PREdiction System (SEPRES), where real-time early warning of sepsis can be implemented at the bedside in the ICU. METHODS Data are collected from bedside devices through the integration hub and uploaded to the integration system through the local area network. The data integration system was designed to integrate vital signs data, laboratory data, ventilator data, demographic data, pharmacy data, nursing data, etc. from multiple medical devices and systems. It integrates, standardizes, and stores information, making the real-time inference of the early warning module possible. The built-in sepsis early warning module can detect the onset of sepsis within 5 hours preceding at most. RESULTS Our data integration system has already been deployed in Ruijin Hospital, confirming the feasibility of our system. CONCLUSION We highlight that SEPRES has the potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention.
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Affiliation(s)
- Qiyu Chen
- Division of Applied Mathematics, Fudan University, Shanghai, China
| | - Ranran Li
- Department of Critical Care Medicine, Shanghai Jiaotong University School of Medicine, Ruijin Hospital, Shanghai, China
| | - ChihChe Lin
- Department of Intelligent Medical Products, Shanghai Electric Group Co., Ltd. Central Academe, Shanghai, China
| | - Chiming Lai
- Department of Intelligent Medical Products, Shanghai Electric Group Co., Ltd. Central Academe, Shanghai, China
| | - Yaling Huang
- Department of Intelligent Medical Products, Shanghai Electric Group Co., Ltd. Central Academe, Shanghai, China
| | - Wenlian Lu
- Division of Applied Mathematics, Fudan University, Shanghai, China
| | - Lei Li
- Department of Critical Care Medicine, Shanghai Jiaotong University School of Medicine, Ruijin Hospital, Shanghai, China
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Manrique S, Ruiz-Botella M, Rodríguez A, Gordo F, Guardiola JJ, Bodí M, Gómez J. Secondary use of data extracted from a clinical information system to assess the adherence of tidal volume and its impact on outcomes. Med Intensiva 2022; 46:619-629. [PMID: 36344013 DOI: 10.1016/j.medine.2022.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/09/2022] [Indexed: 06/16/2023]
Abstract
OBJECTIVES To extract data from clinical information systems to automatically calculate high-resolution quality indicators to assess adherence to recommendations for low tidal volume. DESIGN We devised two indicators: the percentage of time under mechanical ventilation with excessive tidal volume (>8mL/kg predicted body weight) and the percentage of patients who received appropriate tidal volume (≤8mL/kg PBW) at least 80% of the time under mechanical ventilation. We developed an algorithm to automatically calculate these indicators from clinical information system data and analyzed associations between them and patients' characteristics and outcomes. SETTINGS This study has been carried out in our 30-bed polyvalent intensive care unit between January 1, 2014 and November 30, 2019. PATIENTS All patients admitted to intensive care unit ventilated >72h were included. INTERVENTION Use data collected automatically from the clinical information systems to assess adherence to tidal volume recommendations and its outcomes. MAIN VARIABLES OF INTEREST Mechanical ventilation days, ICU length of stay and mortality. RESULTS Of all admitted patients, 340 met the inclusion criteria. Median percentage of time under mechanical ventilation with excessive tidal volume was 70% (23%-93%); only 22.3% of patients received appropriate tidal volume at least 80% of the time. Receiving appropriate tidal volume was associated with shorter duration of mechanical ventilation and intensive care unit stay. Patients receiving appropriate tidal volume were mostly male, younger, taller, and less severely ill. Adjusted intensive care unit mortality did not differ according to percentage of time with excessive tidal volume or to receiving appropriate tidal volume at least 80% of the time. CONCLUSIONS Automatic calculation of process-of-care indicators from clinical information systems high-resolution data can provide an accurate and continuous measure of adherence to recommendations. Adherence to tidal volume recommendations was associated with shorter duration of mechanical ventilation and intensive care unit stay.
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Affiliation(s)
- S Manrique
- Intensive Care Unit, Hospital Universitario Joan XXIII, Tarragona, Spain; Instituto de Investigación Sanitaria Pere i Virgili, Rovira i Virgili University, Tarragona, Spain.
| | - M Ruiz-Botella
- Intensive Care Unit, Hospital Universitario Joan XXIII, Tarragona, Spain
| | - A Rodríguez
- Intensive Care Unit, Hospital Universitario Joan XXIII, Tarragona, Spain; Instituto de Investigación Sanitaria Pere i Virgili, Rovira i Virgili University, Tarragona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES). Instituto de Salud Carlos III, Spain
| | - F Gordo
- Servicio de Medicina Intensiva, Hospital Universitario del Henares, Coslada, Madrid, Grupo de Investigación en Patología Crítica, Grado de Medicina, Universidad Francisco de Vitoria, Pozuelo de Alarcón, Madrid, Spain
| | | | - M Bodí
- Intensive Care Unit, Hospital Universitario Joan XXIII, Tarragona, Spain; Instituto de Investigación Sanitaria Pere i Virgili, Rovira i Virgili University, Tarragona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES). Instituto de Salud Carlos III, Spain
| | - J Gómez
- Intensive Care Unit, Hospital Universitario Joan XXIII, Tarragona, Spain; Instituto de Investigación Sanitaria Pere i Virgili, Rovira i Virgili University, Tarragona, Spain
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A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration. Health Inf Sci Syst 2022; 10:22. [PMID: 36039096 PMCID: PMC9417071 DOI: 10.1007/s13755-022-00183-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 07/02/2022] [Indexed: 12/02/2022] Open
Abstract
Industry 4.0 era has witnessed that more and more high-tech and precise devices are applied into medical field to provide better services. Besides EMRs, medical data include a large amount of unstructured data such as X-rays, MRI scans, CT scans and PET scans, which is still continually increasing. These massive, heterogeneous multi-modal data bring the big challenge to finding valuable data sets for healthcare researchers and other users. The traditional data warehouses are able to integrate the data and support interactive data exploration through ETL process. However, they have high cost and are not real-time. Furthermore, they lack of the ability to deal with multi-modal data in two phases—data fusion and data exploration. In the data fusion phase, it is difficult to unify the multi-modal data under one data model. In the data exploration phase, it is challenging to explore the multi-modal data at the same time, which impedes the process of extracting the diverse information underlying multi-modal data. Therefore, in order to solve these problems, we propose a highly efficient data fusion framework supporting data exploration for heterogeneous multi-modal medical data based on data lake. This framework provides a novel and efficient method to fuse the fragmented multi-modal medical data and store their metadata in the data lake. It offers a user-friendly interface supporting hybrid graph queries to explore multi-modal data. Indexes are created to accelerate the hybrid data exploration. One prototype has been implemented and tested in a hospital, which demonstrates the effectiveness of our framework.
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11
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Kim S, Yun D, Kwon S, Lee SR, Kim K, Kim YC, Kim DK, Oh KH, Joo KW, Lee HC, Jung CW, Kim YS, Han SS. System of integrating biosignals during hemodialysis: the CONTINUAL (Continuous mOnitoriNg viTal sIgN dUring hemodiALysis registry. Kidney Res Clin Pract 2022; 41:363-371. [PMID: 35698753 PMCID: PMC9184839 DOI: 10.23876/j.krcp.21.157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 10/01/2021] [Indexed: 12/04/2022] Open
Abstract
Background Appropriate monitoring of intradialytic biosignals is essential to minimize adverse outcomes because intradialytic hypotension and arrhythmia are associated with cardiovascular risk in hemodialysis patients. However, a continuous monitoring system for intradialytic biosignals has not yet been developed. Methods This study investigated a cloud system that hosted a prospective, open-source registry to monitor and collect intradialytic biosignals, which was named the CONTINUAL (Continuous mOnitoriNg viTal sIgN dUring hemodiALysis) registry. This registry was based on real-time multimodal data acquisition, such as blood pressure, heart rate, electrocardiogram, and photoplethysmogram results. Results We analyzed session information from this system for the initial 8 months, including data for some cases with hemodynamic complications such as intradialytic hypotension and arrhythmia. Conclusion This biosignal registry provides valuable data that can be applied to conduct epidemiological surveys on hemodynamic complications during hemodialysis and develop artificial intelligence models that predict biosignal changes which can improve patient outcomes.
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Affiliation(s)
- Seonmi Kim
- Division of Nephrology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Donghwan Yun
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Soonil Kwon
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - So-Ryoung Lee
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Kwangsoo Kim
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yong Chul Kim
- Division of Nephrology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dong Ki Kim
- Division of Nephrology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kook-Hwan Oh
- Division of Nephrology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kwon Wook Joo
- Division of Nephrology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chul-Woo Jung
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yon Su Kim
- Division of Nephrology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seung Seok Han
- Division of Nephrology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
- Correspondence: Seung Seok Han Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea. E-mail:
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Secondary use of data extracted from a clinical information system to assess the adherence of tidal volume and its impact on outcomes. Med Intensiva 2022. [DOI: 10.1016/j.medin.2022.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Optimal Long Distance ECG Signal Data Delivery Using LoRa Technology. JOURNAL OF BIOMIMETICS BIOMATERIALS AND BIOMEDICAL ENGINEERING 2022. [DOI: 10.4028/p-6z381m] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Cardiovascular disease is the leading cause of death in the world and the number one killer in Indonesia, with a mortality rate of 17.05%. The target of this research is to increase the range of electrocardiograph (ECG) equipment using LoRa Technology. With LoRa Technology, it is expected that the data transmission process can run effectively and produce an accurate ECG signal and minimal noise. The research method is by sending a heart signal from the ECG simulator by the microcontroller via LoRa Technology which is received by the PC (Personal Computer) and the ECG signal is displayed on the PC display. The most optimal setting will be obtained from the sender-receiver distance and baudrate by measuring data loss and delay. In this study, the simulated cardiac signal from the phantom ECG is fed to an analog signal processing circuit, then the signal is converted to digital and digitally filtered on the microcontroller, then the signal is sent via the LoRa HC-12 Transceiver to a PC with baudrate, distance and barrier settings. The results obtained are that data transmission can be carried out at a distance of 175 meters without a barrier and a distance of 50 meters with a barrier. This remote ECG equipment can detect heart signals and the results can be sent to a PC using LoRa Technology. The implication is that the transmission of ECG signal data via the Lora HC-12 Transceiver media can be carried out optimally at the 9600 baudrate setting.
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Chalumuri YR, Kimball JP, Mousavi A, Zia JS, Rolfes C, Parreira JD, Inan OT, Hahn JO. Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:1336. [PMID: 35214238 PMCID: PMC8963055 DOI: 10.3390/s22041336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 01/14/2022] [Accepted: 01/25/2022] [Indexed: 12/15/2022]
Abstract
This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only discriminate normovolemia from hypovolemia but also classify hypovolemia into absolute hypovolemia and relative hypovolemia. We realized our blood volume classification algorithm by (i) extracting a multitude of features from multi-modal physiological signals including the electrocardiogram (ECG), the seismocardiogram (SCG), the ballistocardiogram (BCG), and the photoplethysmogram (PPG), (ii) constructing two ML classifiers using the features, one to classify normovolemia vs. hypovolemia and the other to classify hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) sequentially integrating the two to enable multi-class classification (normovolemia, absolute hypovolemia, and relative hypovolemia). We developed the blood volume decompensation state classification algorithm using the experimental data collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia challenges. Leave-one-subject-out analysis showed that our classification algorithm achieved an F1 score and accuracy of (i) 0.93 and 0.89 in classifying normovolemia vs. hypovolemia, (ii) 0.88 and 0.89 in classifying hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) 0.77 and 0.81 in classifying the overall blood volume decompensation state. The analysis of the features embedded in the ML classifiers indicated that many features are physiologically plausible, and that multi-modal SCG-BCG fusion may play an important role in achieving good blood volume classification efficacy. Our work may complement existing computational algorithms to estimate blood volume compensatory reserve as a potential decision-support tool to provide guidance on context-sensitive hypovolemia therapeutic strategy.
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Affiliation(s)
- Yekanth Ram Chalumuri
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
| | - Jacob P. Kimball
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA; (J.P.K.); (J.S.Z.); (O.T.I.)
| | - Azin Mousavi
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
| | - Jonathan S. Zia
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA; (J.P.K.); (J.S.Z.); (O.T.I.)
| | - Christopher Rolfes
- Global Center for Medical Innovation, Translational Training and Testing Laboratories, Inc. (T3 Labs), Atlanta, GA 30313, USA;
| | - Jesse D. Parreira
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
| | - Omer T. Inan
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA; (J.P.K.); (J.S.Z.); (O.T.I.)
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
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15
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Afshar AS, Li Y, Chen Z, Chen Y, Lee JH, Irani D, Crank A, Singh D, Kanter M, Faraday N, Kharrazi H. An exploratory data quality analysis of time series physiologic signals using a large-scale intensive care unit database. JAMIA Open 2021; 4:ooab057. [PMID: 34350392 PMCID: PMC8327372 DOI: 10.1093/jamiaopen/ooab057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 06/04/2021] [Accepted: 07/02/2021] [Indexed: 11/14/2022] Open
Abstract
Physiological data, such as heart rate and blood pressure, are critical to clinical decision-making in the intensive care unit (ICU). Vital signs data, which are available from electronic health records, can be used to diagnose and predict important clinical outcomes; While there have been some reports on the data quality of nurse-verified vital sign data, little has been reported on the data quality of higher frequency time-series vital signs acquired in ICUs, that would enable such predictive modeling. In this study, we assessed the data quality issues, defined as the completeness, accuracy, and timeliness, of minute-by-minute time series vital signs data within the MIMIC-III data set, captured from 16009 patient-ICU stays and corresponding to 9410 unique adult patients. We measured data quality of four time-series vital signs data streams in the MIMIC-III data set: heart rate (HR), respiratory rate (RR), blood oxygen saturation (SpO2), and arterial blood pressure (ABP). Approximately, 30% of patient-ICU stays did not have at least 1 min of data during the time-frame of the ICU stay for HR, RR, and SpO2. The percentage of patient-ICU stays that did not have at least 1 min of ABP data was ∼56%. We observed ∼80% coverage of the total duration of the ICU stay for HR, RR, and SpO2. Finally, only 12.5%%, 9.9%, 7.5%, and 4.4% of ICU lengths of stay had ≥ 99% data available for HR, RR, SpO2, and ABP, respectively, that would meet the three data quality requirements we looked into in this study. Our findings on data completeness, accuracy, and timeliness have important implications for data scientists and informatics researchers who use time series vital signs data to develop predictive models of ICU outcomes.
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Affiliation(s)
- Ali S Afshar
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland USA
| | - Yijun Li
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Zixu Chen
- Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA
| | - Yuxuan Chen
- Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA
| | - Jae Hun Lee
- Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA
| | - Darius Irani
- Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA
| | - Aidan Crank
- Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA
| | - Digvijay Singh
- Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA
| | - Michael Kanter
- Department of Clinical Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California, USA
| | - Nauder Faraday
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland USA
| | - Hadi Kharrazi
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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16
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Park J, Rhim S, Han K, Ko J. Disentangling the clinical data chaos: User-centered interface system design for trauma centers. PLoS One 2021; 16:e0251140. [PMID: 33979368 PMCID: PMC8115807 DOI: 10.1371/journal.pone.0251140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 04/20/2021] [Indexed: 11/24/2022] Open
Abstract
This paper presents a year-long study of our project, aiming at (1) understanding the work practices of clinical staff in trauma intensive care units (TICUs) at a trauma center, with respect to their usage of clinical data interface systems, and (2) developing and evaluating an intuitive and user-centered clinical data interface system for their TICU environments. Based on a long-term field study in an urban trauma center that involved observation-, interview-, and survey-based studies to understand our target users and their working environment, we designed and implemented MediSenseView as a working prototype. MediSenseView is a clinical-data interface system, which was developed through the identification of three core challenges of existing interface system use in a trauma care unit-device separation, usage inefficiency, and system immobility-from the perspectives of three staff groups in our target environment (i.e., doctors, clinical nurses and research nurses), and through an iterative design study. The results from our pilot deployment of MediSenseView and a user study performed with 28 trauma center staff members highlight their work efficiency and satisfaction with MediSenseView compared to existing clinical data interface systems in the hospital.
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Affiliation(s)
- JaeYeon Park
- School of Integrated Technology, Yonsei University, Incheon, South Korea
| | - Soyoung Rhim
- Department of Computer Engineering, Ajou University, Suwon, South Korea
| | - Kyungsik Han
- Department of Computer Engineering, Ajou University, Suwon, South Korea
| | - JeongGil Ko
- School of Integrated Technology, Yonsei University, Incheon, South Korea
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17
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Schenck EJ, Hoffman KL, Cusick M, Kabariti J, Sholle ET, Campion TR. Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data. J Biomed Inform 2021; 118:103789. [PMID: 33862230 DOI: 10.1016/j.jbi.2021.103789] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/12/2021] [Accepted: 04/10/2021] [Indexed: 12/28/2022]
Abstract
Patients treated in an intensive care unit (ICU) are critically ill and require life-sustaining organ failure support. Existing critical care data resources are limited to a select number of institutions, contain only ICU data, and do not enable the study of local changes in care patterns. To address these limitations, we developed the Critical carE Database for Advanced Research (CEDAR), a method for automating extraction and transformation of data from an electronic health record (EHR) system. Compared to an existing gold standard of manually collected data at our institution, CEDAR was statistically similar in most measures, including patient demographics and sepsis-related organ failure assessment (SOFA) scores. Additionally, CEDAR automated data extraction obviated the need for manual collection of 550 variables. Critically, during the spring 2020 COVID-19 surge in New York City, a modified version of CEDAR supported pandemic response efforts, including clinical operations and research. Other academic medical centers may find value in using the CEDAR method to automate data extraction from EHR systems to support ICU activities.
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Affiliation(s)
- Edward J Schenck
- Weill Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Katherine L Hoffman
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Marika Cusick
- Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States
| | - Joseph Kabariti
- Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States
| | - Evan T Sholle
- Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States
| | - Thomas R Campion
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States; Department of Pediatrics, Weill Cornell Medicine, New York, NY, United States; Clinical & Translational Science Center, Weill Cornell Medicine, New York, NY, United States
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18
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HCI for biomedical decision-making: From diagnosis to therapy. J Biomed Inform 2020; 111:103593. [PMID: 33069887 DOI: 10.1016/j.jbi.2020.103593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 10/06/2020] [Indexed: 01/08/2023]
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19
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Sun Y, Kolacinski R, Loparo K. Transitive Topic Modeling with Conversational Structure Context: Discovering Topics that are Most Popular in Online Discussions. INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2020. [DOI: 10.1142/s1793351x20400103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
With the explosive growth of online discussions published everyday on social media platforms, comprehension and discovery of the most popular topics have become a challenging problem. Conventional topic models have had limited success in online discussions because the corpus is extremely sparse and noisy. To overcome their limitations, we use the discussion thread tree structure and propose a “popularity” metric to quantify the number of replies to a comment to extend the frequency of word occurrences, and the “transitivity” concept to characterize topic dependency among nodes in a nested discussion thread. We build a Conversational Structure Aware Topic Model (CSATM) based on popularity and transitivity to infer topics and their assignments to comments. Experiments on real forum datasets are used to demonstrate improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.
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Affiliation(s)
- Yingcheng Sun
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Richard Kolacinski
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Kenneth Loparo
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
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