Prediction of Hospital Associated Infections During Continuous Hospital Stays
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916907951587328 |
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| author | Datta, Rituparna Kamruzzaman, Methun Klein, Eili Y. Madden, Gregory R Deng, Xinwei Vullikanti, Anil Bhattacharya, Parantapa |
| author_facet | Datta, Rituparna Kamruzzaman, Methun Klein, Eili Y. Madden, Gregory R Deng, Xinwei Vullikanti, Anil Bhattacharya, Parantapa |
| contents | The US Centers for Disease Control and Prevention (CDC), in 2019, designated Methicillin-resistant Staphylococcus aureus (MRSA) as a serious antimicrobial resistance threat. The risk of acquiring MRSA and suffering life-threatening consequences due to it remains especially high for hospitalized patients due to a unique combination of factors, including: co-morbid conditions, immuno suppression, antibiotic use, and risk of contact with contaminated hospital workers and equipment. In this paper, we present a novel generative probabilistic model, GenHAI, for modeling sequences of MRSA test results outcomes for patients during a single hospitalization. This model can be used to answer many important questions from the perspectives of hospital administrators for mitigating the risk of MRSA infections. Our model is based on the probabilistic programming paradigm, and can be used to approximately answer a variety of predictive, causal, and counterfactual questions. We demonstrate the efficacy of our model by comparing it against discriminative and generative machine learning models using two real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prediction of Hospital Associated Infections During Continuous Hospital Stays Datta, Rituparna Kamruzzaman, Methun Klein, Eili Y. Madden, Gregory R Deng, Xinwei Vullikanti, Anil Bhattacharya, Parantapa Machine Learning The US Centers for Disease Control and Prevention (CDC), in 2019, designated Methicillin-resistant Staphylococcus aureus (MRSA) as a serious antimicrobial resistance threat. The risk of acquiring MRSA and suffering life-threatening consequences due to it remains especially high for hospitalized patients due to a unique combination of factors, including: co-morbid conditions, immuno suppression, antibiotic use, and risk of contact with contaminated hospital workers and equipment. In this paper, we present a novel generative probabilistic model, GenHAI, for modeling sequences of MRSA test results outcomes for patients during a single hospitalization. This model can be used to answer many important questions from the perspectives of hospital administrators for mitigating the risk of MRSA infections. Our model is based on the probabilistic programming paradigm, and can be used to approximately answer a variety of predictive, causal, and counterfactual questions. We demonstrate the efficacy of our model by comparing it against discriminative and generative machine learning models using two real-world datasets. |
| title | Prediction of Hospital Associated Infections During Continuous Hospital Stays |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.13561 |