Wellbeing Forecasting in Postpartum Anemia Patients
Postpartum anemia is a very common maternal health problem and remains a persistent public health issue globally. It negatively affects maternal mood and could lead to depression, increased fatigue, and decreased cognitive abilities. It can and should be treated by restoring iron stores. However, in...
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MDPI AG
2023-06-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/11/12/1694 |
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| author | David Susič Lea Bombač Tavčar Miha Lučovnik Hana Hrobat Lea Gornik Anton Gradišek |
| author_facet | David Susič Lea Bombač Tavčar Miha Lučovnik Hana Hrobat Lea Gornik Anton Gradišek |
| author_sort | David Susič |
| collection | DOAJ |
| description | Postpartum anemia is a very common maternal health problem and remains a persistent public health issue globally. It negatively affects maternal mood and could lead to depression, increased fatigue, and decreased cognitive abilities. It can and should be treated by restoring iron stores. However, in most health systems, there is typically a six-week gap between birth and the follow-up postpartum visit. Risks of postpartum maternal complications are usually assessed shortly after birth by clinicians intuitively, taking into account psychosocial and physical factors, such as the presence of anemia and the type of iron supplementation. In this paper, we investigate the possibility of using machine-learning algorithms to more reliably forecast three parameters related to patient wellbeing, namely depression (measured by Edinburgh Postnatal Depression Scale—EPDS), overall tiredness, and physical tiredness (both measured by Multidimensional Fatigue Inventory—MFI). Data from 261 patients were used to train the forecasting models for each of the three parameters, and they outperformed the baseline models that always predicted the mean values of the training data. The mean average error of the elastic net regression model for predicting the EPDS score (with values ranging from 0 to 19) was 2.3 and outperformed the baseline, which already hints at the clinical usefulness of using such a model. We further investigated what features are the most important for this prediction, where the EDPS score and both tiredness indexes at birth turned out to be by far the most prominent prediction features. Our study indicates that the machine-learning model approach has the potential for use in clinical practice to predict the onset of depression and severe fatigue in anemic patients postpartum and potentially improve the detection and management of postpartum depression and fatigue. |
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| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-11T02:24:25Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-61336307eabf44ada8836cf02432be182023-11-18T10:37:50ZengMDPI AGHealthcare2227-90322023-06-011112169410.3390/healthcare11121694Wellbeing Forecasting in Postpartum Anemia PatientsDavid Susič0Lea Bombač Tavčar1Miha Lučovnik2Hana Hrobat3Lea Gornik4Anton Gradišek5Department of Intelligent Systems, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, SloveniaDivision of Gynaecology and Obstetrics, University Medical Centre Ljubljana, Šlajmerjeva ulica 3, 1000 Ljubljana, SloveniaDivision of Gynaecology and Obstetrics, University Medical Centre Ljubljana, Šlajmerjeva ulica 3, 1000 Ljubljana, SloveniaDivision of Gynaecology and Obstetrics, University Medical Centre Ljubljana, Šlajmerjeva ulica 3, 1000 Ljubljana, SloveniaDivision of Gynaecology and Obstetrics, University Medical Centre Ljubljana, Šlajmerjeva ulica 3, 1000 Ljubljana, SloveniaDepartment of Intelligent Systems, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, SloveniaPostpartum anemia is a very common maternal health problem and remains a persistent public health issue globally. It negatively affects maternal mood and could lead to depression, increased fatigue, and decreased cognitive abilities. It can and should be treated by restoring iron stores. However, in most health systems, there is typically a six-week gap between birth and the follow-up postpartum visit. Risks of postpartum maternal complications are usually assessed shortly after birth by clinicians intuitively, taking into account psychosocial and physical factors, such as the presence of anemia and the type of iron supplementation. In this paper, we investigate the possibility of using machine-learning algorithms to more reliably forecast three parameters related to patient wellbeing, namely depression (measured by Edinburgh Postnatal Depression Scale—EPDS), overall tiredness, and physical tiredness (both measured by Multidimensional Fatigue Inventory—MFI). Data from 261 patients were used to train the forecasting models for each of the three parameters, and they outperformed the baseline models that always predicted the mean values of the training data. The mean average error of the elastic net regression model for predicting the EPDS score (with values ranging from 0 to 19) was 2.3 and outperformed the baseline, which already hints at the clinical usefulness of using such a model. We further investigated what features are the most important for this prediction, where the EDPS score and both tiredness indexes at birth turned out to be by far the most prominent prediction features. Our study indicates that the machine-learning model approach has the potential for use in clinical practice to predict the onset of depression and severe fatigue in anemic patients postpartum and potentially improve the detection and management of postpartum depression and fatigue.https://www.mdpi.com/2227-9032/11/12/1694wellbeing forecastpostpartum anemiapostpartum depressionfatiguemachine learningEdinburgh Postnatal Depression Scale |
| spellingShingle | David Susič Lea Bombač Tavčar Miha Lučovnik Hana Hrobat Lea Gornik Anton Gradišek Wellbeing Forecasting in Postpartum Anemia Patients Healthcare wellbeing forecast postpartum anemia postpartum depression fatigue machine learning Edinburgh Postnatal Depression Scale |
| title | Wellbeing Forecasting in Postpartum Anemia Patients |
| title_full | Wellbeing Forecasting in Postpartum Anemia Patients |
| title_fullStr | Wellbeing Forecasting in Postpartum Anemia Patients |
| title_full_unstemmed | Wellbeing Forecasting in Postpartum Anemia Patients |
| title_short | Wellbeing Forecasting in Postpartum Anemia Patients |
| title_sort | wellbeing forecasting in postpartum anemia patients |
| topic | wellbeing forecast postpartum anemia postpartum depression fatigue machine learning Edinburgh Postnatal Depression Scale |
| url | https://www.mdpi.com/2227-9032/11/12/1694 |
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