IntroductionPostpartum hemorrhage (PPH) is a significant cause of maternal mortality worldwide, particularly in low- and middle-income countries. It is essential to develop effective prediction models to identify women at risk of PPH and implement appropriate interventions to reduce maternal morbidity and mortality. This study aims to predict the occurrence of postpartum hemorrhage using machine learning models based on antenatal, intrapartum, and postnatal visit data obtained from the Kenya Antenatal and Postnatal Care Research Collective cohort.MethodFour machine learning models – logistic regression, naïve Bayes, decision tree, and random forest – were constructed using 67% training data (1,056/1,576). The training data was further split into 67% for model building and 33% cross validation. Once the models are built, the remaining 33% (520/1,576) independent test data was used for external validation to confirm the models' performance. Models were fine-tuned using feature selection through extra tree classifier technique. Model performance was assessed using accuracy, sensitivity, and area under the curve (AUC) of the receiver operating characteristics (ROC) curve.ResultThe naïve Bayes model performed best with 0.95 accuracy, 0.97 specificity, and 0.76 AUC. Seven factors (anemia, limited prenatal care, hemoglobin concentrations, signs of pallor at intrapartum, intrapartum systolic blood pressure, intrapartum diastolic blood pressure, and intrapartum respiratory rate) were associated with PPH prediction in Kenyan population.DiscussionThis study demonstrates the potential of machine learning models in predicting PPH in the Kenyan population. Future studies with larger datasets and more PPH cases should be conducted to improve prediction performance of machine learning model. Such prediction algorithms would immensely help to construct a personalized obstetric path for each pregnant patient, improve resource allocation, and reduce maternal mortality and morbidity.
Recommendation Systems has emerged as an essential component in web-based systems, as their ability to analyze customers' behavior and generate recommendations seeking customers' satisfaction is successfully accomplished. However, the success of these systems depends on amount of customers' personal preference data and content (items') metadata available for harnessing. Therefore, data sparsity poses a major challenge here. To alleviate this problem, data and models from other domains can be leveraged to gain good insight about customers' preferences and content similarities. In specific, this paper proposes the idea of extracting knowledge for transfer learning leveraging pre-trained deep neural networks. Knowledge from pre-trained models is used to efficiently identify similarity and capture customers' preference among the contents. To attain the objective, this paper presented an approach, for generating efficient top-n recommendations using a hybrid recommender model. Performance analysis is performed on the proposed approach and results obtained are promising. Furthermore, extensions for this work are also discussed
In traditional recommender systems, the product recommendations are generally made based on the static behavior or preference of customers. This paper designs a novel production recommendation model that processes customer-product interaction data as a time-based sequential data, and makes personalized product recommendations based on the purchase patterns of customers. Specifically, the model relies on the deep learning technique of recurrent neural network (RNN) to uncover the dynamics in purchase patterns of customers; a bidirectional model with attention mechanism was introduced to personalize the product recommendations. The effectiveness of the proposed model was verified through an experiment on a benchmark dataset called Movie lens. The experimental results show that the RNN-based model can efficiently capture the temporal dynamics of customer preferences, and then generate highly individualized product recommendations.
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