Early Rehospitalization Post–Kidney Transplant Due to Infectious Complications: Can We Predict the Patients at Risk?

Leal, Rita; Pinto, Helena; Galvão, Ana; Rodrigues, Luís; Santos, Lídia; Romãozinho, Catarina; Macário, Fernando; Alves, Rui; Campos, Mário; Mota, A.; Figueiredo, Arnaldo

doi:10.1016/j.transproceed.2017.01.062

Cited by 16 publications

(12 citation statements)

References 20 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Infections after transplantation are influenced by surgical procedure, immunosuppressive therapies, and other invasive interventions . Both HSV and CMV infections are a common cause of mild‐to‐moderate illness in immunosuppressed patients, especially within the 3 months following renal transplantation, because this is the period of higher risk for early complications . We aimed to describe the salivary shedding of HHV in a group of renal transplant patients.…”

Section: Discussionmentioning

confidence: 99%

“…Recognizing modifiable risk factors is of major importance for graft and outcome. Kidney transplantation guidelines have already taken into account the prevention of the most common infections in the early post‐transplantation period, including prophylaxis of CMV infection . The evaluation of salivary shedding of herpesviruses might be potentially helpful in determining the infection risk in renal transplant recipients.…”

Section: Discussionmentioning

confidence: 99%

“…Herpesvirus infections have a high incidence in renal transplant recipients, especially cytomegalovirus (CMV) infections. CMV is the most common viral infection during the post‐transplant period, also being one of the major causes of morbidity and mortality in kidney transplantation …”

Section: Introductionmentioning

confidence: 99%

“…Some studies are concerned with the impact of herpesvirus infections on renal transplant patients, although there are no strategies to predict active ones . Antiviral prophylaxis has been found to be more protective against CMV infection than the pre‐emptive approach .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Salivary shedding of herpesviruses in renal transplant recipients

Sarmento

Caliento

Souza

et al. 2018

J of Invest & Clin Dent

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Salivary shedding of herpesviruses in renal transplant recipients

Sarmento

Caliento

Souza

et al. 2018

J of Invest & Clin Dent

View full text Add to dashboard Cite

show abstract

“…According to Jones et al [3], about a half of surgical readmissions may be preventable, indicating potential positive effects of the prediction model on both patients and medical institutes. There have been many approaches to predict a readmission using electronic health records (EHRs), the majority of which are based only on structured data, such as demographic information, lab test values, and vital signs [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission

Shin

Hogan

Adams

et al. 2019

2019 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

View full text Add to dashboard Cite

Electronic Health Records (EHRs) have been heavily used to predict various downstream clinical tasks such as readmission or mortality. One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature. Although recent advances in deep learning (DL) enables models to extract interpretable features from unstructured data, they often require a large amount of training data. However, many tasks in medical domains inherently consist of small sample data with lengthy documents; for a kidney transplant as an example, data from only a few thousand of patients are available and each patient's document consists of a couple of millions of words in major hospitals. Thus, complex DL methods cannot be applied to these kind of domains. In this paper, we present a comprehensive ensemble model using vector space modeling and topic modeling. Our proposed model is evaluated on the readmission task of kidney transplant patients, and improves 0.0211 in terms of c-statistics from the previous stateof-the-art approach using structured data, while typical DL methods fails to beat this approach. The proposed architecture provides the interpretable score for each feature from both modalities, structured and unstructured data, which is shown to be meaningful through a physician's evaluation.

show abstract