Model appears to outperform ChatGPT and six previously published large text-embedding models
TUESDAY, April 16, 2024 (HealthDay News) — A text-embedding-ada-002 (ADA) machine learning model can identify posttraumatic stress disorder following childbirth (CB-PTSD) from maternal childbirth narratives, according to a study published online April 11 in Scientific Reports.
Alon Bartal, Ph.D., from Bar-Ilan University in Ramat Gan, Israel, and colleagues examined the effectiveness of ChatGPT and the ADA model for detecting CB-PTSD in a sample of 1,295 women who gave birth in the last six months and were 18 years or older by analyzing maternal childbirth narratives. CB-PTSD was assessed using the PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.
The researchers found that CB-PTSD was identified via narrative classification by developing a machine learning model that utilizes numerical vector representation of the ADA model. The model had an F1 score of 0.81 and outperformed ChatGPT and six previously published large text-embedding models trained on mental health or clinical domain data.
“This textual personal narrative-based assessment strategy employing natural language processing analysis has the potential to become an accurate, efficient, low-cost, and patient-friendly strategy for identifying CB-PTSD in the clinic, and facilitating timely interventions to mitigate this maternal mental health disorder,” the authors write.
Model May Help Identify Women at Risk for Overactive Bladder 
Childhood Body Size Linked to Subsequent Reproductive Outcomes .jpg)
Epidural Analgesia in Labor Appears to Be Safe for Newborns 
Homicide, Suicide, and Overdose Leading Causes of Maternal Death 
Higher Parental Vitamin B12 Tied to Lower Prevalence of Birth Defects 
Onset of Mortality, Morbidity Delayed for Offspring of Centenarians 
Antenatal Complications Increased With Maternal Multiple Long-Term Conditions 
Insurance Poses Barrier to Virtual Pulmonary Rehabilitation for COPD 
