Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section
The correlation between the kind of cesarean section and post-traumatic stress disorder (PTSD) in Greek women after a traumatic birth experience has been recognized in previous studies along with other risk factors, such as perinatal conditions and traumatic life events. Data from early studies have...
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/15/7492 |
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| author | Christos Orovas Eirini Orovou Maria Dagla Alexandros Daponte Nikolaos Rigas Stefanos Ougiaroglou Georgios Iatrakis Evangelia Antoniou |
| author_facet | Christos Orovas Eirini Orovou Maria Dagla Alexandros Daponte Nikolaos Rigas Stefanos Ougiaroglou Georgios Iatrakis Evangelia Antoniou |
| author_sort | Christos Orovas |
| collection | DOAJ |
| description | The correlation between the kind of cesarean section and post-traumatic stress disorder (PTSD) in Greek women after a traumatic birth experience has been recognized in previous studies along with other risk factors, such as perinatal conditions and traumatic life events. Data from early studies have suggested some possible links between some vulnerable factors and the potential development of postpartum PTSD. The classification of each case in three possible states (PTSD, profile PTSD, and free of symptoms) is typically performed using the guidelines and the metrics of the version V of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) which requires the completion of several questionnaires during the postpartum period. The motivation in the present work is the need for a model that can detect possible PTSD cases using a minimum amount of information and produce an early diagnosis. The early PTSD diagnosis is critical since it allows the medical personnel to take the proper measures as soon as possible. Our sample consists of 469 women who underwent emergent or elective cesarean delivery in a university hospital in Greece. The methodology which is followed is the application of random decision forests (RDF) to detect the most suitable and easily accessible information which is then used by an artificial neural network (ANN) for the classification. As is demonstrated from the results, the derived decision model can reach high levels of accuracy even when only partial and quickly available information is provided. |
| first_indexed | 2024-03-09T12:49:18Z |
| format | Article |
| id | doaj.art-aed160a6ef8a4118a8d5b627175d6741 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:49:18Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-aed160a6ef8a4118a8d5b627175d67412023-11-30T22:09:00ZengMDPI AGApplied Sciences2076-34172022-07-011215749210.3390/app12157492Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean SectionChristos Orovas0Eirini Orovou1Maria Dagla2Alexandros Daponte3Nikolaos Rigas4Stefanos Ougiaroglou5Georgios Iatrakis6Evangelia Antoniou7Department of Product and Systems Design Engineering, Faculty of Engineering, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Midwifery, School of Health, Sciences, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Midwifery, University of West Attica, 12243 Egaleo, GreeceSchool of Health and Science, Faculty of Medicine, University of Thessaly, 41500 Larisa, GreeceDepartment of Midwifery, University of West Attica, 12243 Egaleo, GreeceDepartment of Digital Systems, School of Economics and Technology, University of the Peloponnese, Kladas, 23100 Sparta, GreeceDepartment of Midwifery, University of West Attica, 12243 Egaleo, GreeceDepartment of Midwifery, University of West Attica, 12243 Egaleo, GreeceThe correlation between the kind of cesarean section and post-traumatic stress disorder (PTSD) in Greek women after a traumatic birth experience has been recognized in previous studies along with other risk factors, such as perinatal conditions and traumatic life events. Data from early studies have suggested some possible links between some vulnerable factors and the potential development of postpartum PTSD. The classification of each case in three possible states (PTSD, profile PTSD, and free of symptoms) is typically performed using the guidelines and the metrics of the version V of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) which requires the completion of several questionnaires during the postpartum period. The motivation in the present work is the need for a model that can detect possible PTSD cases using a minimum amount of information and produce an early diagnosis. The early PTSD diagnosis is critical since it allows the medical personnel to take the proper measures as soon as possible. Our sample consists of 469 women who underwent emergent or elective cesarean delivery in a university hospital in Greece. The methodology which is followed is the application of random decision forests (RDF) to detect the most suitable and easily accessible information which is then used by an artificial neural network (ANN) for the classification. As is demonstrated from the results, the derived decision model can reach high levels of accuracy even when only partial and quickly available information is provided.https://www.mdpi.com/2076-3417/12/15/7492artificial neural networksrandom decision forestsposttraumatic stress disorderDSM-Vemergency cesarean sectionelective cesarean section |
| spellingShingle | Christos Orovas Eirini Orovou Maria Dagla Alexandros Daponte Nikolaos Rigas Stefanos Ougiaroglou Georgios Iatrakis Evangelia Antoniou Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section Applied Sciences artificial neural networks random decision forests posttraumatic stress disorder DSM-V emergency cesarean section elective cesarean section |
| title | Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section |
| title_full | Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section |
| title_fullStr | Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section |
| title_full_unstemmed | Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section |
| title_short | Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section |
| title_sort | neural networks for early diagnosis of postpartum ptsd in women after cesarean section |
| topic | artificial neural networks random decision forests posttraumatic stress disorder DSM-V emergency cesarean section elective cesarean section |
| url | https://www.mdpi.com/2076-3417/12/15/7492 |
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