Imaging Screening of Catastrophic Neurological Events Using a Software Tool: Preliminary Results

Fernandes, Adriana; Gomes, António; Veiga, João Paulo Cândia; Ermida, D.; Vardasca, T.

doi:10.1016/j.transproceed.2015.03.021

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…There are many factors that influence the number of potential donors but, regardless of that, it is known that the main source of all deceased donations come from Brain Dead (BD) donors, implying that most potential deceased donors must be localized in the Intensive Care Units (ICUs) and Emergency Departments (EDs) in patients who are victims of stroke or other severe brain events. [1,4] A comprehensive review of organ donation considers BD patients as the ideal multiorgan donors. As a result,…”

Section: Overviewmentioning

confidence: 99%

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Real-Time Healthcare Intelligence in Organ Transplantation

Fernandes

Coimbra

Abelha

2018

Next-Generation Mobile and Pervasive Healthcare Solutions

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Organ transplantation is the best and often the only treatment for patients with end-stage organ failure. However, the universal shortage of deceased donors results in a worrying situation that must be addressed. Brain dead donors constitute the largest share of organ donors, but identifying a patient that may progress to brain death can be a complex task. Therefore, the urgent need of intelligent solutions to support the decision-making process is crucial in critical areas as the organ transplantation is. This work aims at acquiring knowledge on the potential organ donor criteria for further detection and implementing a platform to assist the process of identification of potential organ donors at Centro Hospitalar do Porto – Hospital de Santo António. The developed system is currently implemented and displays a steady and competent behavior providing consequently a way to have more control of the information needed for the decision-making process

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Section: Overviewmentioning

confidence: 99%

“…A predictive risk modeling for early detection of patients with the highest probability to evolve to brain death can have a significant impact on BD donation. [1,4]…”

Section: Introductionmentioning

confidence: 99%

Real-Time Healthcare Intelligence in Organ Transplantation

Fernandes

Coimbra

Abelha

2018

Next-Generation Mobile and Pervasive Healthcare Solutions

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Automated screening of potential organ donors using a temporal machine learning model

Sauthier

Bouchakri

Carrier

et al. 2023

Sci Rep

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Organ donation is not meeting demand, and yet 30–60% of potential donors are potentially not identified. Current systems rely on manual identification and referral to an Organ Donation Organization (ODO). We hypothesized that developing an automated screening system based on machine learning could reduce the proportion of missed potentially eligible organ donors. Using routine clinical data and laboratory time-series, we retrospectively developed and tested a neural network model to automatically identify potential organ donors. We first trained a convolutive autoencoder that learned from the longitudinal changes of over 100 types of laboratory results. We then added a deep neural network classifier. This model was compared to a simpler logistic regression model. We observed an AUROC of 0.966 (CI 0.949–0.981) for the neural network and 0.940 (0.908–0.969) for the logistic regression model. At a prespecified cutoff, sensitivity and specificity were similar between both models at 84% and 93%. Accuracy of the neural network model was robust across donor subgroups and remained stable in a prospective simulation, while the logistic regression model performance declined when applied to rarer subgroups and in the prospective simulation. Our findings support using machine learning models to help with the identification of potential organ donors using routinely collected clinical and laboratory data.

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Using clinical signs of neglect to identify elder neglect cases

Friedman

Avila

Liu

et al. 2017

Journal of Elder Abuse & Neglect

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Elder neglect is the one of the most pervasive forms of mistreatment, and often the only place outside of the individual's residence to identify and assist neglected individuals is in a medical setting. However, elder neglect cases treated in hospitals do not present with a single diagnosis or clinical sign, but rather involve a complex constellation of clinical signs. Currently, there is a lack of comprehensive guidelines on which clinical signs to use in screening tools for neglect among patients treated in hospitals. Using the DELPHI method, a group of experts developed and tested a scale to be used as a pre-screener that conceptually could be integrated into electronic health record systems so that it could identify potential neglect cases in an automated manner. By applying the scale as a pre-screener for neglect, the tool would reduce the pool of at-risk patients who would benefit from in-depth screening for elder neglect by 95%.

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Imaging Screening of Catastrophic Neurological Events Using a Software Tool: Preliminary Results

Cited by 3 publications

References 5 publications

Real-Time Healthcare Intelligence in Organ Transplantation

Real-Time Healthcare Intelligence in Organ Transplantation

Automated screening of potential organ donors using a temporal machine learning model

Using clinical signs of neglect to identify elder neglect cases

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