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  <doc>
    <id>1652</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>18</issue>
    <volume>22</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2022-09-17</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data</title>
    <abstract language="eng">Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world’s population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-16521</identifier>
    <enrichment key="opus.import.date">2022-10-06T15:26:43+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
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    <enrichment key="opus.import.checksum">de80cdbe4d807a542a7f92bf669d5f04</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.3390/s22187033</enrichment>
    <enrichment key="SourceTitle">Chumachenko, D.; Butkevych, M.; Lode, D.; Frohme, M.; Schmailzl, K.J.G.; Nechyporenko, A. Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data. Sensors 2022, 22, 7033. https://doi.org/10.3390/s22187033</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Dmytro Chumachenko</author>
    <author>Mykola Butkevych</author>
    <author>Daniel Lode</author>
    <author>Marcus Frohme</author>
    <author>Kurt J. G. Schmailzl</author>
    <author>Alina Nechyporenko</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>myocardial infraction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>heart rate variability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>10-second heart rate variability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>diagnostics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>k-nearest neighbor classifier</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>radial basis function</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>decision tree</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>random forest</value>
    </subject>
    <collection role="ddc" number="570">Biowissenschaften; Biologie</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="green_open_access" number="1">Gold Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1652/sensors-22-07033-v2.pdf</file>
  </doc>
  <doc>
    <id>1580</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>23</volume>
    <type>article</type>
    <publisherName>BioMed Central</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Examination of blood samples using deep learning and mobile microscopy</title>
    <abstract language="eng">Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards.</abstract>
    <parentTitle language="eng">BMC Bioinformatics</parentTitle>
    <identifier type="issn">1471-2105</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-15802</identifier>
    <enrichment key="opus.import.date">2022-02-14T09:00:12+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="opus.import.file">filename=phpFXy8uO</enrichment>
    <enrichment key="opus.import.checksum">011f6eec170f6aa4094c35fc8e058106</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.1186/s12859-022-04602-4</enrichment>
    <enrichment key="SourceTitle">Pfeil, J., Nechyporenko, A., Frohme, M. et al. Examination of blood samples using deep learning and mobile microscopy. BMC Bioinformatics 23, 65 (2022). https://doi.org/10.1186/s12859-022-04602-4</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Juliane Pfeil</author>
    <author>Alina Nechyporenko</author>
    <author>Marcus Frohme</author>
    <author>Frank T. Hufert</author>
    <author>Katja Schulze</author>
    <collection role="ddc" number="570">Biowissenschaften; Biologie</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="Funding" number="">Projekt DEAL</collection>
    <collection role="green_open_access" number="1">Gold Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1580/s12859-022-04602-4.pdf</file>
  </doc>
  <doc>
    <id>1902</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>108</pageFirst>
    <pageLast>119</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>3302</volume>
    <type>conferenceobject</type>
    <publisherName>RWTH Aachen</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automatic Creation of Masks for Marking Histological Images of the Epithelium of the Paranasal Sinuses</title>
    <abstract language="eng">The article discusses the approach to solving the problem of reducing time spent on the preparation of medical images for teaching neural networks, by reducing the time of creating masks for images. The task is considered on the example of processing images of the mucous membrane of the paranasal sinus. The specifics of the task did not allow effectively using existing software solutions. During the study, a software solution was proposed, which made it possible to radically reduce the time of creating masks for images. The article also analyzes the shortcomings of the automated creation of masks, as well as the directions of their solution. The loss of time due to the adjustment of the color palette can be reduced even more to 1-2 minutes, the average deviation is 7.61%.</abstract>
    <parentTitle language="eng">Proceedings of the 5th International Conference on Informatics &amp; Data-Driven Medicine, Lyon, France, November 18 - 20, 2022</parentTitle>
    <identifier type="url">https://ceur-ws.org/Vol-3302/paper6.pdf</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-19024</identifier>
    <enrichment key="opus.import.date">2024-04-12T06:02:18+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="SourceTitle">Nechyporenko, A., Hubarenko, Y., Hubarenko, M., Kalnytska, V., Alekseeva, V., &amp; Gargin, V. (2022). Automatic Creation of Masks for Marking Histological Images of the Epithelium of the Paranasal Sinuses. (N. Shakhovska, S. Chrétien, I. Izonin, &amp; J. Campos), Proceedings of the 5th International Conference on Informatics &amp; Data-Driven Medicine, Lyon, France, November 18 - 20, 2022. Aachen: RWTH Aachen. Retrieved from https://ceur-ws.org/Vol-3302/paper6.pdf</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alina Nechyporenko</author>
    <author>Yevhen Hubarenko</author>
    <author>Maryna Hubarenko</author>
    <author>Violeta Kalnytska</author>
    <author>Victoriia Alekseeva</author>
    <author>Vitaliy Gargin</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neural network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mask</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>microscopic image</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>epithelium</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>inflammatory change</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="ddc" number="610">Medizin und Gesundheit</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1902/paper6.pdf</file>
  </doc>
  <doc>
    <id>1907</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>78</pageFirst>
    <pageLast>85</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>3302</volume>
    <type>conferenceobject</type>
    <publisherName>RWTH Aachen</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Impact of Russian War on COVID-19 Dynamics in Germany: the Simulation Study by Statistical Machine Learning</title>
    <abstract language="eng">The new coronavirus COVID-19 has been spreading worldwide for almost three years. The global community has developed effective measures to contain and control the pandemic. However, new factors are emerging that are driving the dynamics of COVID-19. One of these factors was the escalation of Russia's war in Ukraine. This study aims to test the hypothesis of the influence of migration flows caused by the Russian war in Ukraine on the dynamics of the epidemic process in Germany. For this, a model of the COVID-19 epidemic process was built based on the polynomial regression method. The model's adequacy was tested 30 days before the start of the escalation of the Russian war in Ukraine. To assess the impact of the war on the dynamics of COVID-19, the model was used to calculate the forecast of cumulative new and fatal cases of COVID-19 in Germany in the first 30 days after the start of the escalation of the Russian war in Ukraine. Modeling showed that migration flows from Ukraine are not a critical factor in the growth of the dynamics of the incidence of COVID-19 in Germany, but they influenced the number of cases. The next stage of the study is the development of more complex models for a detailed analysis of population dynamics, identifying factors influencing the epidemic process in the context of the Russian war in Ukraine, and assessing their information content.</abstract>
    <parentTitle language="eng">Proceedings of the 5th International Conference on Informatics &amp; Data-Driven Medicine, Lyon, France, November 18 - 20, 2022</parentTitle>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-19074</identifier>
    <identifier type="url">https://ceur-ws.org/Vol-3302/short4.pdf</identifier>
    <enrichment key="opus.import.data">@inproceedingsDBLP:conf/iddm/ChumachenkoNF22, author = Dmytro Chumachenko and Alina S. Nechyporenko and Marcus Frohme, editor = Natalia Shakhovska and Stéphane Chrétien and Ivan Izonin and Jaime Campos, title = Impact of Russian War on COVID-19 Dynamics in Germany: the Simulation Study by Statistical Machine Learning, booktitle = Proceedings of the 5th International Conference on Informatics &amp; Data-Driven Medicine, Lyon, France, November 18 - 20, 2022, series = CEUR Workshop Proceedings, volume = 3302, pages = 78–85, publisher = CEUR-WS.org, year = 2022, url = https://ceur-ws.org/Vol-3302/short4.pdf, timestamp = Fri, 10 Mar 2023 16:22:42 +0100, biburl = https://dblp.org/rec/conf/iddm/ChumachenkoNF22.bib, bibsource = dblp computer science bibliography, https://dblp.org</enrichment>
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    <enrichment key="opus.import.date">2024-04-12T07:01:59+00:00</enrichment>
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    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">6618dc67d43d75.15210651</enrichment>
    <enrichment key="SourceTitle">Chumachenko, D., Nechyporenko, A., &amp; Frohme, M. (2022). Impact of Russian War on COVID-19 Dynamics in Germany: the Simulation Study by Statistical Machine Learning. (N. Shakhovska, S. Chrétien, I. Izonin, &amp; J. Campos), Proceedings of the 5th International Conference on Informatics &amp; Data-Driven Medicine, Lyon, France, November 18 - 20, 2022. Aachen: CEUR-WS.org. Retrieved from https://ceur-ws.org/Vol-3302/short4.pdf</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Dmytro Chumachenko</author>
    <author>Alina Nechyporenko</author>
    <author>Marcus Frohme</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>epidemic model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>polynomial regression</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>war</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>COVID-19</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>infectious disease simulation</value>
    </subject>
    <collection role="ddc" number="006">Spezielle Computerverfahren</collection>
    <collection role="ddc" number="610">Medizin und Gesundheit</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1907/short4.pdf</file>
  </doc>
</export-example>
