[PDF][PDF] Combining structured and unstructured data in electronic health record for readmission prediction via deep learning
S Qu - The Ohio State University, 2020 - kb.osu.edu
With the aid of statistical learning tools nowadays, a variety of clinical prediction tasks can be
examined and modeled in a quantitative way. Predicting hospital readmission probability is …
examined and modeled in a quantitative way. Predicting hospital readmission probability is …
Prediction of hospital readmission for heart disease: A deep learning approach
Hospital readmissions consume large amounts of medical resources and negatively impact
the healthcare system. Predicting the readmission rate early one can alleviate the financial …
the healthcare system. Predicting the readmission rate early one can alleviate the financial …
[HTML][HTML] Readmission prediction using deep learning on electronic health records
Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF)
patients that pose significant health risks and escalate care cost. In order to reduce …
patients that pose significant health risks and escalate care cost. In order to reduce …
Readmission prediction using trajectory-based deep learning approach
Hospital readmission refers to the situation where a patient is re-hospitalized with the same
primary diagnosis after discharge. It causes $26 billion preventable expense to the US …
primary diagnosis after discharge. It causes $26 billion preventable expense to the US …
Readmission prediction for patients with heterogeneous medical history: A trajectory-based deep learning approach
Hospital readmission refers to the situation where a patient is re-hospitalized with the same
primary diagnosis within a specific time interval after discharge. Hospital readmission …
primary diagnosis within a specific time interval after discharge. Hospital readmission …
An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records
F Azzalini, T Dolci, M Vagaggini - arXiv preprint arXiv:2310.10187, 2023 - arxiv.org
With the increasing availability of patients' data, modern medicine is shifting towards
prospective healthcare. Electronic health records contain a variety of information useful for …
prospective healthcare. Electronic health records contain a variety of information useful for …
Predicting hospital readmission via cost-sensitive deep learning
With increased use of electronic medical records (EMRs), data mining on medical data has
great potential to improve the quality of hospital treatment and increase the survival rate of …
great potential to improve the quality of hospital treatment and increase the survival rate of …
[PDF][PDF] Cost-sensitive deep learning for early readmission prediction at a major hospital
With increased use of electronic medical records (EMRs), data mining on medical data has
great potential to improve the quality of hospital treatment and increase the survival rate of …
great potential to improve the quality of hospital treatment and increase the survival rate of …
Hospital readmission prediction using discriminative patterns
Avoidable hospital readmission is problematic as it increases the burden on healthcare
systems, leads to a shortage of hospital beds and impacts on the costs of healthcare …
systems, leads to a shortage of hospital beds and impacts on the costs of healthcare …
[PDF][PDF] Readmission risk prediction for patients with heterogeneous hazard: A trajectory-aware deep learning Approach
Hospital readmission refers to the situation where a patient is re-hospitalized with the same
primary diagnosis after discharge. Hospital readmission causes $26 billion preventable …
primary diagnosis after discharge. Hospital readmission causes $26 billion preventable …