AIMC Topic: Pregnancy Complications

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Image Enhancement Model Based on Deep Learning Applied to the Ureteroscopic Diagnosis of Ureteral Stones during Pregnancy.

Computational and mathematical methods in medicine
OBJECTIVE: To explore the image enhancement model based on deep learning on the effect of ureteroscopy with double J tube placement and drainage on ureteral stones during pregnancy. We compare the clinical effect of ureteroscopy with double J tube pl...

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis.

Scientific reports
The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed model...

Intrahepatic cholestasis of pregnancy: machine-learning algorithm to predict elevated bile acid based on clinical and laboratory data.

Archives of gynecology and obstetrics
PURPOSE: Applying machine-learning models to clinical and laboratory features of women with intrahepatic cholestasis of pregnancy (ICP) and creating algorithm to identify these patients without bile acid measurements.

Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease.

Journal of the American Heart Association
Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women...

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence.

Journal of pharmacokinetics and pharmacodynamics
The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and S...

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015.

Scientific reports
Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric histo...

Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem.

European journal of epidemiology
We developed algorithms to identify pregnant women with suicidal behavior using information extracted from clinical notes by natural language processing (NLP) in electronic medical records. Using both codified data and NLP applied to unstructured cli...

Non-surgical management of primary hyperparathyroidism.

Best practice & research. Clinical endocrinology & metabolism
The purpose of this chapter is to discuss the options available for patients with primary hyperparathyrodism (PHPT) not undergoing parathyroidectomy (PTx). Adequate hydration should be recommended in all patients. Calcium intake should not be restric...

Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing.

BMC medical informatics and decision making
BACKGROUND: We examined the comparative performance of structured, diagnostic codes vs. natural language processing (NLP) of unstructured text for screening suicidal behavior among pregnant women in electronic medical records (EMRs).