Machine learning approach for early prediction of postpartum depression DOI

S M Morris,

Dipika Rawat

Elsevier eBooks, Год журнала: 2024, Номер unknown, С. 163 - 172

Опубликована: Янв. 1, 2024

Язык: Английский

Triangular fuzzy numbers-based MADM for selecting pregnant mothers at risk of stunting DOI Creative Commons
Wiwien Hadikurniawati, Kristoko Dwi Hartomo, Irwan Sembiring

и другие.

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Год журнала: 2023, Номер 7(3), С. 579 - 585

Опубликована: Июнь 2, 2023

Stunting is caused by a lack of proper nutrition before and after birth. This research paper identifies measures the risk stunting during pregnancy make recommendations for ranking pregnant women at risk. These aims to provide appropriate treatment action reduce mothers giving birth children stunting. To optimal choice, selection procedure stunted considers variety factors, including maternal age, nutrition, arms circumference, hemoglobin, parity, interval, height, baby weight, body mass index (BMI). Decision-maker’s expectation uncertainty imprecision are represented linguistically triangular fuzzy numbers. The numbers arithmetic approach used determine process output. determined from alternative with most parameter values fewest parameters. Based on results calculation, it was that PM (Pregnant Mother) had highest score ranked first. That mother declared as who lowest baby

Язык: Английский

Процитировано

3

Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data DOI Creative Commons

Fumika Kondo,

Jocelyne C. Whitehead,

Fernando Corbalán

и другие.

International Journal of Bipolar Disorders, Год журнала: 2023, Номер 11(1)

Опубликована: Март 25, 2023

Bipolar disorder type-I (BD-I) patients are known to show emotion regulation abnormalities. In a previous fMRI study using an explicit paradigm, we compared responses from 19 BD-I and 17 matched healthy controls (HC). A standard general linear model-based univariate analysis revealed that BD showed increased activations in inferior frontal gyrus when instructed decrease their emotional response as elicited by neutral images. We implemented multivariate pattern recognition analyses on the same data examine if could classify conditions within-group well HC versus BD. reanalyzed approach, PRONTO software. The original experimental paradigm consisted of full 2 × factorial design, with valence (Negative/Neutral) instruction (Look/Decrease) within subject factors. models were able accurately different task analyzed separately (63.24%-75.00%, p = 0.001-0.012). addition, correctly significant accuracy where subjects downregulate felt (59.60%-60.84%, 0.014-0.018). results for classification demonstrated contributions salience network, several occipital regions, parietal lobes, other cortical achieve above-chance classifications. Our successfully reproduced some main obtained analysis, confirming these findings not dependent approach. particular, both types suggest there is difference neural patterns between each group. approach also reappraisal provide most informative activity differentiating BD, irrespective (negative or neutral). current illustrate importance investigating cognitive control propose set candidate regions further

Язык: Английский

Процитировано

1

Optimal risk and diagnosis assessment strategies in perinatal depression: A machine learning approach from the life-ON study cohort DOI Creative Commons
Armando D’Agostino, Corrado Garbazza, Daniele Malpetti

и другие.

Psychiatry Research, Год журнала: 2023, Номер 332, С. 115687 - 115687

Опубликована: Дек. 24, 2023

This study aimed to assess the concordance of various psychometric scales in detecting Perinatal Depression (PND) risk and diagnosis. A cohort 432 women was assessed at 10-15th 23-25th gestational weeks, 33-40 days 180-195 after delivery using Edinburgh Postnatal Scale (EPDS), Visual Analogue (VAS), Hamilton Rating (HDRS), Montgomery-Åsberg (MADRS), Mini International Neuropsychiatric Interview (MINI). Spearman's rank correlation coefficient used agreement across instruments, multivariable classification models were developed predict values a binary scale other scales. Moderate shown between EPDS VAS HDRS MADRS throughout perinatal period. However, decreased postpartum. well-performing model for estimation current depression (EPDS > 9) obtained with MADRS, less robust one major depressive episode (MDE) diagnosis (MINI) HDRS. When is not feasible, may be rapid comprehensive postpartum screening reliability. thorough structured interview or clinical examination remains necessary diagnose MDE.

Язык: Английский

Процитировано

1

The Future of Prediction Modeling in Clinical Practice for Obstetrics and Gynecology DOI
Digna R. Velez Edwards, Todd L. Edwards

Obstetrics and Gynecology, Год журнала: 2024, Номер 143(3), С. 355 - 357

Опубликована: Фев. 15, 2024

Язык: Английский

Процитировано

0

Machine learning approach for early prediction of postpartum depression DOI

S M Morris,

Dipika Rawat

Elsevier eBooks, Год журнала: 2024, Номер unknown, С. 163 - 172

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0