Use machine learning to predict on episodes of PostPartum Depression

A few weeks ago I was browsing the internet, looking for sites that specialise on machine learning topics. I found the piece, ‘Machine Learning Methods for Predicting Postpartum Depression: Scoping Review’. I could not carry out a review on this piece because it was a review itself.

I did read the piece and found that the study aims to synthesise the literature on machine learning and big data analytics for maternal mental health, particularly in the prediction of postpartum depression. Fourteen studies were identified, with all of them using the supervised learning methods of support vector machine (SVM) and random forest, being the most commonly used models, followed by Naive Bayes, linear regression, artificial neural networks (ANN), decision trees, ans XGBoost.

Machine learning algorithms can analyse larger datasets and can perform more advanced computations when looking to detect postpartum depression at an early stage. If machine learning techniques are fine tuned, it is hoped that it might become part of evidence based practice in addition to clinical knowledge and existing research evidence. There is no single aetiology for maternal depression, but it is hoped that an effective predictive model can help health care providers in the early detection and effective management of at-risk patients.