TopicMachine: Conversion Prediction in Search Advertising Using Latent Topic Models

2014 ◽  
Vol 26 (11) ◽  
pp. 2846-2858 ◽  
Author(s):  
Ahmet Bulut
Keyword(s):  
Topic Models ◽  
Search Advertising ◽  
Latent Topic

Graph Neural Collaborative Topic Model for Citation Recommendation

2022 ◽  
Vol 40 (3) ◽  
pp. 1-30
Author(s):  
Qianqian Xie ◽  
Yutao Zhu ◽  
Jimin Huang ◽  
Pan Du ◽  
Jian-Yun Nie
Keyword(s):  
Topic Model ◽  
Topic Models ◽  
Joint Modeling ◽  
Research Problem ◽  
High Order ◽  
Semantic Structure ◽  
Critical Research ◽  
First Order ◽  
Latent Topic ◽  
Graph Neural Networks

Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on citation prediction via joint modeling of document contents and citations. However, existing RTMs can only capture pairwise or direct (first-order) citation relationships among documents. The indirect (high-order) citation links have been explored in graph neural network–based methods, but these methods suffer from the well-known explainability problem. In this article, we propose a model called Graph Neural Collaborative Topic Model that takes advantage of both relational topic models and graph neural networks to capture high-order citation relationships and to have higher explainability due to the latent topic semantic structure. Experiments on three real-world citation datasets show that our model outperforms several competitive baseline methods on citation recommendation. In addition, we show that our approach can learn better topics than the existing approaches. The recommendation results can be well explained by the underlying topics.

scholarly journals Mining heterogeneous clinical notes by multi-modal latent topic model

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249622
Author(s):  
Zhi Wen ◽  
Pratheeksha Nair ◽  
Chih-Ying Deng ◽  
Xing Han Lu ◽  
Edward Moseley ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Topic Model ◽  
Prolonged Mechanical Ventilation ◽  
Topic Models ◽  
Clinical Notes ◽  
Topic Distribution ◽  
Domain Expertise ◽  
Latent Topic ◽  
Poor Outcomes ◽  
Mimic Iii

Latent knowledge can be extracted from the electronic notes that are recorded during patient encounters with the health system. Using these clinical notes to decipher a patient’s underlying comorbidites, symptom burdens, and treatment courses is an ongoing challenge. Latent topic model as an efficient Bayesian method can be used to model each patient’s clinical notes as “documents” and the words in the notes as “tokens”. However, standard latent topic models assume that all of the notes follow the same topic distribution, regardless of the type of note or the domain expertise of the author (such as doctors or nurses). We propose a novel application of latent topic modeling, using multi-note topic model (MNTM) to jointly infer distinct topic distributions of notes of different types. We applied our model to clinical notes from the MIMIC-III dataset to infer distinct topic distributions over the physician and nursing note types. Based on manual assessments made by clinicians, we observed a significant improvement in topic interpretability using MNTM modeling over the baseline single-note topic models that ignore the note types. Moreover, our MNTM model led to a significantly higher prediction accuracy for prolonged mechanical ventilation and mortality using only the first 48 hours of patient data. By correlating the patients’ topic mixture with hospital mortality and prolonged mechanical ventilation, we identified several diagnostic topics that are associated with poor outcomes. Because of its elegant and intuitive formation, we envision a broad application of our approach in mining multi-modality text-based healthcare information that goes beyond clinical notes. Code available at https://github.com/li-lab-mcgill/heterogeneous_ehr.

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