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  1. Article ; Conference proceedings: Auswertung von Blutdruckselbstmessungen in einem Hochrisikokollektiv: Fernüberwachung kann zur Vorhersage von Komplikationen bei Risikoschwangeren beitragen.

    Hackelöer, M / Kaban, N / Rieger, O / Neznansky, M / Henrich, W / Verlohren, S

    Geburtshilfe und Frauenheilkunde

    2022  Volume 82, Issue 10

    Event/congress 64. Kongress der Deutschen Gesellschaft für Gynäkologie und Geburtshilfe e. V., München, 2022-10-12
    Language German
    Publishing date 2022-10-01
    Publisher Georg Thieme Verlag
    Publishing place Stuttgart ; New York
    Document type Article ; Conference proceedings
    ZDB-ID 80111-2
    ISSN 1438-8804 ; 0016-5751 ; 1615-3359
    ISSN (online) 1438-8804
    ISSN 0016-5751 ; 1615-3359
    DOI 10.1055/s-0042-1756835
    Database Thieme publisher's database

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    In stock of ZB MED Cologne/Königswinter

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    Zs.MO 655: Show issues

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  2. Article ; Conference proceedings: [No title information]

    Rieger, O / Hackelöer, M / Schmidt, L / Neznansky, M / Henrich, W / Verlohren, S

    Zeitschrift für Geburtshilfe und Neonatologie

    2021  Volume 225, Issue S 01

    Event/congress 30. Kongress der Deutschen Gesellschaft für Perinatale Medizin - „Wandel als Herausforderung“, digital, 2021-11-24
    Language German
    Publishing date 2021-11-01
    Publisher Georg Thieme Verlag
    Publishing place Stuttgart ; New York
    Document type Article ; Conference proceedings
    ZDB-ID 1226748-x
    ISSN 1439-1651 ; 0948-2393 ; 0300-967X ; 1615-5300
    ISSN (online) 1439-1651
    ISSN 0948-2393 ; 0300-967X ; 1615-5300
    DOI 10.1055/s-0041-1739852
    Database Thieme publisher's database

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  3. Article ; Conference proceedings: [No title information]

    Hackelöer, M / Schmidt, L / Rieger, O / Neznansky, M / Henrich, W / Verlohren, S

    Zeitschrift für Geburtshilfe und Neonatologie

    2021  Volume 225, Issue S 01

    Event/congress 30. Kongress der Deutschen Gesellschaft für Perinatale Medizin - „Wandel als Herausforderung“, digital, 2021-11-24
    Language German
    Publishing date 2021-11-01
    Publisher Georg Thieme Verlag
    Publishing place Stuttgart ; New York
    Document type Article ; Conference proceedings
    ZDB-ID 1226748-x
    ISSN 1439-1651 ; 0948-2393 ; 0300-967X ; 1615-5300
    ISSN (online) 1439-1651
    ISSN 0948-2393 ; 0300-967X ; 1615-5300
    DOI 10.1055/s-0041-1739882
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  4. Article ; Conference proceedings: [No title information]

    Hackeloeer, M / Hoyler, A / Kaban, N / Rieger, O / Neznansky, M / Lorenz-Meier, L / Henrich, W / Verlohren, S

    Zeitschrift für Geburtshilfe und Neonatologie

    2022  Volume 226, Issue S 01

    Event/congress 20. Kongress Deutsche Gesellschaft für Pränatal- und Geburtsmedizin (DGPGM) | Postersession, Aachen, 2022-05-12
    Language German
    Publishing date 2022-05-01
    Publisher Georg Thieme Verlag KG
    Publishing place Stuttgart ; New York
    Document type Article ; Conference proceedings
    ZDB-ID 1226748-x
    ISSN 1439-1651 ; 0948-2393 ; 0300-967X ; 1615-5300
    ISSN (online) 1439-1651
    ISSN 0948-2393 ; 0300-967X ; 1615-5300
    DOI 10.1055/s-0042-1748600
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  5. Article ; Conference proceedings: [No title information]

    Hackeloeer, M / Hoyler, A / Rana, S / Rieger, O / Neznansky, M / Karumanchi, A / Henrich, W / Verlohren, S

    Zeitschrift für Geburtshilfe und Neonatologie

    2022  Volume 226, Issue S 01

    Event/congress 20. Kongress Deutsche Gesellschaft für Pränatal- und Geburtsmedizin (DGPGM) | Postersession, Aachen, 2022-05-12
    Language German
    Publishing date 2022-05-01
    Publisher Georg Thieme Verlag KG
    Publishing place Stuttgart ; New York
    Document type Article ; Conference proceedings
    ZDB-ID 1226748-x
    ISSN 1439-1651 ; 0948-2393 ; 0300-967X ; 1615-5300
    ISSN (online) 1439-1651
    ISSN 0948-2393 ; 0300-967X ; 1615-5300
    DOI 10.1055/s-0042-1748601
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  6. Article ; Conference proceedings: Nachweis der Generalisierbarkeit eines prädiktiven Algorithmus zur Vorhersage Präeklampsie-assoziierter Komplikationen

    Hackelöer, M / Hoyler, A / Rana, S / Rieger, O / Neznansky, M / Karumanchi, A / Henrich, W / Verlohren, S

    Geburtshilfe und Frauenheilkunde

    2022  Volume 82, Issue 10

    Event/congress 64. Kongress der Deutschen Gesellschaft für Gynäkologie und Geburtshilfe e. V., München, 2022-10-12
    Language German
    Publishing date 2022-10-01
    Publisher Georg Thieme Verlag
    Publishing place Stuttgart ; New York
    Document type Article ; Conference proceedings
    ZDB-ID 80111-2
    ISSN 1438-8804 ; 0016-5751 ; 1615-3359
    ISSN (online) 1438-8804
    ISSN 0016-5751 ; 1615-3359
    DOI 10.1055/s-0042-1756834
    Database Thieme publisher's database

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  7. Article ; Online: A machine-learning-based algorithm improves prediction of preeclampsia-associated adverse outcomes.

    Schmidt, Leon J / Rieger, Oliver / Neznansky, Mark / Hackelöer, Max / Dröge, Lisa A / Henrich, Wolfgang / Higgins, David / Verlohren, Stefan

    American journal of obstetrics and gynecology

    2022  Volume 227, Issue 1, Page(s) 77.e1–77.e30

    Abstract: Background: Preeclampsia presents a highly prevalent burden on pregnant women with an estimated incidence of 2% to 5%. Preeclampsia increases the maternal risk of death 20-fold and is one of the main causes of perinatal morbidity and mortality. Novel ... ...

    Abstract Background: Preeclampsia presents a highly prevalent burden on pregnant women with an estimated incidence of 2% to 5%. Preeclampsia increases the maternal risk of death 20-fold and is one of the main causes of perinatal morbidity and mortality. Novel biomarkers, such as soluble fms-like tyrosine kinase-1 and placental growth factor in addition to a wide span of conventional clinical data (medical history, physical symptoms, laboratory parameters, etc.), present an excellent basis for the application of early-detection machine-learning models.
    Objective: This study aimed to develop, train, and test an automated machine-learning model for the prediction of adverse outcomes in patients with suspected preeclampsia.
    Study design: Our real-world dataset of 1647 (2472 samples) women was retrospectively recruited from women who presented to the Department of Obstetrics at the Charité - Universitätsmedizin Berlin, Berlin, Germany, between July 2010 and March 2019. After standardization and data cleaning, we calculated additional features regarding the biomarkers soluble fms-like tyrosine kinase-1 and placental growth factor and sonography data (umbilical artery pulsatility index, middle cerebral artery pulsatility index, mean uterine artery pulsatility index), resulting in a total of 114 features. The target metric was the occurrence of adverse outcomes throughout the remaining pregnancy and 2 weeks after delivery. We trained 2 different models, a gradient-boosted tree and a random forest classifier. Hyperparameter training was performed using a grid search approach. All results were evaluated via a 10 × 10-fold cross-validation regimen.
    Results: We obtained metrics for the 2 naive machine-learning models. A gradient-boosted tree model was performed with a positive predictive value of 88%±6%, a negative predictive value of 89%±3%, a sensitivity of 66%±5%, a specificity of 97%±2%, an overall accuracy of 89%±3%, an area under the receiver operating characteristic curve of 0.82±0.03, an F1 score of 0.76±0.04, and a threat score of 0.61±0.05. The random forest classifier returned an equal positive predictive value (88%±6%) and specificity (97%±1%) while performing slightly inferior on the other available metrics. Applying differential cutoffs instead of a naive cutoff for positive prediction at ≥0.5 for the classifier's results yielded additional increases in performance.
    Conclusion: Machine-learning techniques were a valid approach to improve the prediction of adverse outcomes in pregnant women at high risk of preeclampsia vs current clinical standard techniques. Furthermore, we presented an automated system that did not rely on manual tuning or adjustments.
    MeSH term(s) Biomarkers ; Female ; Humans ; Machine Learning ; Placenta Growth Factor ; Pre-Eclampsia/diagnosis ; Pre-Eclampsia/epidemiology ; Pregnancy ; Retrospective Studies ; Vascular Endothelial Growth Factor Receptor-1/metabolism
    Chemical Substances Biomarkers ; Placenta Growth Factor (144589-93-5) ; Vascular Endothelial Growth Factor Receptor-1 (EC 2.7.10.1)
    Language English
    Publishing date 2022-02-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 80016-8
    ISSN 1097-6868 ; 0002-9378
    ISSN (online) 1097-6868
    ISSN 0002-9378
    DOI 10.1016/j.ajog.2022.01.026
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Conference proceedings: [No title information]

    Schmidt, L / Rieger, O / Neznansky, M / Hackelöer, M / Dröge, L / Henrich, W / Higgins, D / Verlohren, S

    Zeitschrift für Geburtshilfe und Neonatologie

    2021  Volume 225, Issue S 01

    Event/congress 30. Kongress der Deutschen Gesellschaft für Perinatale Medizin - „Wandel als Herausforderung“, digital, 2021-11-24
    Language German
    Publishing date 2021-11-01
    Publisher Georg Thieme Verlag
    Publishing place Stuttgart ; New York
    Document type Article ; Conference proceedings
    ZDB-ID 1226748-x
    ISSN 1439-1651 ; 0948-2393 ; 0300-967X ; 1615-5300
    ISSN (online) 1439-1651
    ISSN 0948-2393 ; 0300-967X ; 1615-5300
    DOI 10.1055/s-0041-1739883
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  9. Article: Agricultural economics data on the Internet: USDA Economics and Statistics System

    Koltay, Z / Rieger, O.Y

    Journal of agricultural & food information. 1996. v. 3 (4)

    1996  

    Keywords statistics ; agricultural economics ; microcomputers ; telecommunications ; USDA ; databases ; information services ; Economic Research Service ; United States
    Language English
    Size p. 27-49.
    Document type Article
    ZDB-ID 1207352-0
    ISSN 1540-4722 ; 1049-6505
    ISSN (online) 1540-4722
    ISSN 1049-6505
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Bonn / Germany

    Z 4592: Show issues

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  10. Book ; Thesis: Versuche über die physiologischen Wirkungen des Chinolins

    Rieger, Ottmar

    1888  

    Author's details Ottmar Rieger
    Language German
    Size 30 Seiten, 8°
    Publisher Junge & Sohn
    Publishing place Erlangen
    Publishing country XA-DXDE
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Dissertation, Universität Erlangen, 1888
    HBZ-ID HT021281492
    Database Catalogue ZB MED Medicine, Health

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    Shelf markLoan statusLocation
    Eu. Diss. Erlangen 1888: Rieger order to use in reading room Magazin

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