Failed labor induction presents a significant challenge, posing considerable risks for maternal health. The development of accurate predictive models is essential for assisting healthcare providers in determining the most suitable delivery methods. This study explores the application of machine learning (ML) models to predict failed labor induction, utilizing data from the Indonesian National Health Insurance (INHI). A retrospective cohort of 9-month period of pregnancy was established using INHI Sample Data, comprising 27,953 pregnancy cases. Failed labor induction was identified through ICD-10 codes (O61). The features considered included demographic data (age, insurance class, region) and binary-encoded diagnoses. Various ML models were assessed for their predictive performance. The ensemble model demonstrated a high area under the curve (AUC) of 0.759 (95% CI, with a balanced trade-off between sensitivity (0.816) and specificity (0.589). SHAP analysis was used to interpret feature contributions, highlighting both clinical and demographic factors influencing model predictions. ML models showed strong potential in predicting failed labor induction using administrative data. While the ensemble model achieved the best overall performance, simpler models such as logistic regression or XGBoost may offer greater practicality for clinical integration in Indonesia.

One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.