Predicting the function of transplanted kidney in long-term care processes: Application of a hybrid model

Khazaee, Parviz Rashidi; Bagherzadeh, Mohammad; Niazkhani, Zahra; Pirnejad, Habibollah

doi:10.1016/j.jbi.2019.103116

Cited by 8 publications

(4 citation statements)

References 31 publications

(138 reference statements)

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“…The outpatient transplant clinic of the Urmia Medical Science University (UMSU) has recently implemented a homegrown, renal transplant management system (RTMS). This system has been equipped with a computerized provider order entry (CPOE), a drug-drug interaction CDSS [ 28 ] and a glomerular filtration rate prediction CDSS for upcoming follow up visits [ 29 , 30 ]. Having such a system available in our clinic provided a unique opportunity to develop a DLI-CDSS, alongside others.…”

Section: Methodsmentioning

confidence: 99%

“…To our knowledge, studies of computerized systems being used to manage DLIs are missing in the transplantation field. As part of a longitudinal project to improve the quality of kidney transplantation care (see for example [ 28 , 29 ]), we examined the existing complexities in the context of transplantation care, on the one hand, and the current evidence on DLIs, on the other hand, to develop an evidence-based computerized CDSS to support nephrologists in the safe medication prescribing alongside individual patients’ laboratory values. The processes of this CDSS’s development can be used as a proof-of-concept helping other transplant programs to design and apply more specialized, transplant focused CDSSs.…”

Section: Introductionmentioning

confidence: 99%

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Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system

Niazkhani

Fereidoni

Khazaee

et al. 2020

BMC Med Inform Decis Mak

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Background Drug-laboratory (lab) interactions (DLIs) are a common source of preventable medication errors. Clinical decision support systems (CDSSs) are promising tools to decrease such errors by improving prescription quality in terms of lab values. However, alert fatigue counteracts their impact. We aimed to develop a novel user-friendly, evidence-based, clinical context-aware CDSS to alert nephrologists about DLIs clinically important lab values in prescriptions of kidney recipients . Methods For the most frequently prescribed medications identified by a prospective cross-sectional study in a kidney transplant clinic, DLI-rules were extracted using main pharmacology references and clinical inputs from clinicians. A CDSS was then developed linking a computerized prescription system and lab records. The system performance was tested using data of both fictitious and real patients. The “Questionnaire for User Interface Satisfaction” was used to measure user satisfaction of the human-computer interface. Results Among 27 study medications, 17 needed adjustments regarding renal function, 15 required considerations based on hepatic function, 8 had drug-pregnancy interactions, and 13 required baselines or follow-up lab monitoring. Using IF & THEN rules and the contents of associated alert, a DLI-alerting CDSS was designed. To avoid alert fatigue, the alert appearance was considered as interruptive only when medications with serious risks were contraindicated or needed to be discontinued or adjusted. Other alerts appeared in a non-interruptive mode with visual clues on the prescription window for easy, intuitive notice. When the system was used for real 100 patients, it correctly detected 260 DLIs and displayed 249 monitoring, seven hepatic, four pregnancy, and none renal alerts. The system delivered patient-specific recommendations based on individual lab values in real-time. Clinicians were highly satisfied with the usability of the system. Conclusions To our knowledge, this is the first study of a comprehensive DLI-CDSS for kidney transplant care. By alerting on considerations in renal and hepatic dysfunctions, maternal and fetal toxicity, or required lab monitoring, this system can potentially improve medication safety in kidney recipients. Our experience provides a strong foundation for designing specialized systems to promote individualized transplant follow-up care.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system

Niazkhani

Fereidoni

Khazaee

et al. 2020

BMC Med Inform Decis Mak

Self Cite

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“…They found that the proposed method had a higher secret bit rate and entropy, with a lower bit mismatch rate. In the medical field, Rashidi Khazaee et al (2019) proposed a hybrid prediction model based on multi-layer perceptron, linear regression, and the WEMA method that can predict the function of the transplanted kidney in a long-term care process. They found that the hybrid model could give reliable prediction values for estimated glomerular filtration rate (eGFR) in the routine daily care of kidney transplant patients.…”

Section: Introductionmentioning

confidence: 99%

Hull-WEMA: a novel zero-lag approach in the moving average family, with an application to COVID-19

Hansun¹,

Charles²,

Gherman³

et al. 2022

IJMDM

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The Moving Average (MA) is undeniably one of the most popular forecasting methods in time series analysis. In this study, we consider two variants of MA, namely the Weighted Exponential Moving Average (WEMA) and the Hull Moving Average (HMA). WEMA, which was introduced in 2013, has been widely used in different scenarios but still suffers from lags. To address this shortcoming, we propose a novel zero-lag Hull-WEMA method that combines HMA and WEMA. We apply and compare the proposed approach with HMA and WEMA by using COVID-19 time series data from ten different countries with the highest number of cases on the last observed date. Results show that the new approach achieves a better accuracy level than HMA and WEMA. Overall, the paper advocates a white-box forecasting method, which can be used to predict the number of confirmed COVID-19 cases in the short run more accurately.

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“…They found that the proposed method had a higher secret bit rate and entropy, with a lower bit mismatch rate. In the medical field, Rashidi Khazaee et al (2019) proposed a hybrid prediction model based on multilayer perceptron, linear regression and the WEMA method that can predict the function of the transplanted kidney in a long-term care process. They found that the hybrid model could give reliable prediction values for estimated glomerular filtration rate (eGFR) in the routine daily care of kidney transplant patients.…”

Section: Introductionmentioning

confidence: 99%

Hull-WEMA: A Novel Zero-Lag Approach in the Moving Average Family, with an Application to COVID-19

Varadarajan¹,

Gherman²,

Charles³

et al. 2022

IJMDM

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The moving average (MA) is undeniably one of the most popular forecasting methods in time series analysis. In this study, we consider two variants of MA, namely the weighted exponential moving average (WEMA) and the hull moving average (HMA). WEMA, which was introduced in 2013, has been widely used in different scenarios but still suffers from lags. To address this shortcoming, we propose a novel zero-lag Hull-WEMA method that combines HMA and WEMA. We apply and compare the proposed approach with HMA and WEMA by using COVID-19 time series data from ten different countries with the highest number of cases on the last observed date. Results show that the new approach achieves a better accuracy level than HMA and WEMA. Overall, the paper advocates a white-box forecasting method, which can be used to predict the number of confirmed COVID-19 cases in the short run more accurately.

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Predicting the function of transplanted kidney in long-term care processes: Application of a hybrid model

Cited by 8 publications

References 31 publications

Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system

Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system

Hull-WEMA: a novel zero-lag approach in the moving average family, with an application to COVID-19

Hull-WEMA: A Novel Zero-Lag Approach in the Moving Average Family, with an Application to COVID-19

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