Anticipating Postpartum Depression Symptoms Using ML Methods

Gone are the days when machine learning was only used for the automated process of bill making, or for the streamline administrative purposes, these days machine learning methods are used to diagnose illnesses and further the methods are helping the doctors to deliver personalized medicine by retrieving the patient’s medical history. Machine learning, in daily life, has various applications.

 

Using the clinical data and making the present day reports based upon the data of the past is especially helping the doctors to give the best treatment to the patients.

 

A lot of diagnostic centres are focusing on image based screening, and MRI’s to give the patient an insight of the disease further helping the medical professionals to analyze the severity of disease by collocating the data from the past.

 

Postpartum depression is a depression that usually occurs to a woman after a period of one year from childbirth, its common symptoms include anxiety, nervousness and restlessness. It is believed that a drip in hormones estrogen and progesterone are responsible for post -partum depression.

 

An article published in nature reveals that almost 8-15% of women from Sweden every year are diagnosed with postpartum depression within the span of one year.

 

Machine learning seeks to study the pattern behind postpartum depression by studying various factors such as anxiety before childbirth, anxiety during pregnancy, behavioural pattern of spouse/partner the machine learning methods and tools seek to understand the dominant factor behind postpartum depression.

 

A recent study that was conducted within an year of childbirth showed that a lot of women were completely unaware of the symptoms of postpartum depression and put the major symptoms aside as claiming them to be the after-effects of modern day pregnancy. 

 

The XRT model model conducted the research based on specific factors such as medical background,medical history and pregnancy related variables.The research was conducted right after a woman gets discharged from the delivery ward to give most accurate results. 

 

Machine learning is extremely helpful in this regard as it can give the accurate outcomes based on a woman’s medical history and further by studying the behavioural patterns of her close ones.

(Suggested read: AI in healthcare)

 

During the study due to a low positive predictive value a lot of women would be identified as being on the high risk but still having less chances to be diagnosed with postpartum depression. 

 

This XRT study conducted on postpartum depression would further help in the early diagnosis and would in turn mean a blissful and jovial relationship between the mother and the children. 

 

Postpartum depression has been a major reason behind mother’s hesitancy for a second pregnancy the XRT model takes into consideration the major factors including planned pregnancy, sleep during pregnancy, anxiety helping in the early assessments of the depression that further helps the mother receive the treatment on time.

 

(Must check: Big data in healthcare industry)