A Practical Electronic Health Record-Based Dry Weight Supervision Model for Hemodialysis Patients

Bi, Zhaori; Wang, Mengjing; Li, Ni; Ye, Guoxin; Zhou, Dian; Yan, Changhao; Zeng, Xuan; Chen, Jing

doi:10.1109/jtehm.2019.2948604

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…They highlighted impediments to ML processes including need to retrain the neural network with any change in target population, need to ensure a friendly user-interface. Similarly, Bi et al developed a time series-based regression model that predicted weight fluctuations in adult HD patients using Electronic Health Records (EHR) 7 . More recently, Inoue et al took a different approach using a random forest classifier to predict the probability of adjusting DW at each dialysis session 8 .…”

Section: Discussionmentioning

confidence: 99%

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Machine learning models to predict post-dialysis blood pressure in children and young adults on maintenance hemodialysis

Bou-Matar,

Dell,

Bobrowski

2023

Sci Rep

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Hypertension is associated with significant cardiovascular morbidity. Blood pressure (BP) control on maintenance hemodialysis (HD) is strongly impacted by volume status. The objective of this study was to assess whether machine learning (ML) is effective in predicting post-HD BP in children and young adults on HD. We collected data on BP, IDWG, pulse, and weights for patients on maintenance HD (> 3 months). Input features included DW, pre-post weight difference, IDWG and pre-HD BP. Seven models were trained and tuned using open-source libraries. Model performance was evaluated using time-series cross-validation on a rolling basis (3–12 month training, 1-day testing). Various regression scores were compared between models. Data for 35 patients (14,375 HD sessions) were analyzed. Extreme gradient boosting (XGB) and vector autoregression with exogenous regressors (VARX) achieved better accuracy in predicting post-dialysis systolic BP than K-nearest neighbor, support vector regression (SVR) with radial basis function kernel and random forest (p < 0.001 for each). The differences in accuracy between XGB, VARX, SVR with linear kernel, random forest and linear regression were not significant. Using clinical parameters, ML models may be useful in predicting post-HD BP, which may help guide DW adjustment and optimizing BP control for maintenance HD patients.

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Section: Discussionmentioning

confidence: 99%

“…Bi et al proposed a time series-based regression model to predict DW in patients on HD 7 . They achieved remarkable accuracy (> 95%) in predicting DW within a 0.5 kg absolute error margin.…”

Section: Introductionmentioning

confidence: 99%

Machine learning models to predict post-dialysis blood pressure in children and young adults on maintenance hemodialysis

Bou-Matar,

Dell,

Bobrowski

2023

Sci Rep

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“…There have been several studies that utilized the hand-crafted (e.g., the lowest systolic BP) or selected (e.g., pre- or post-dialysis BP) information on the previous sessions to predict events in the subsequent session 30 , 31 . To the best of our knowledge, the present study represents the first attempt to incorporate entire sequences of previous hemodialysis sessions.…”

Section: Discussionmentioning

confidence: 99%

Real-time dual prediction of intradialytic hypotension and hypertension using an explainable deep learning model

Yun,

Yang,

Kim

et al. 2023

Sci Rep

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Both intradialytic hypotension (IDH) and hypertension (IDHTN) are associated with poor outcomes in hemodialysis patients, but a model predicting dual outcomes in real-time has never been developed. Herein, we developed an explainable deep learning model with a sequence-to-sequence-based attention network to predict both of these events simultaneously. We retrieved 302,774 hemodialysis sessions from the electronic health records of 11,110 patients, and these sessions were split into training (70%), validation (10%), and test (20%) datasets through patient randomization. The outcomes were defined when nadir systolic blood pressure (BP) < 90 mmHg (termed IDH-1), a decrease in systolic BP ≥ 20 mmHg and/or a decrease in mean arterial pressure ≥ 10 mmHg (termed IDH-2), or an increase in systolic BP ≥ 10 mmHg (i.e., IDHTN) occurred within 1 h. We developed a temporal fusion transformer (TFT)-based model and compared its performance in the test dataset, including receiver operating characteristic curve (AUROC) and area under the precision-recall curves (AUPRC), with those of other machine learning models, such as recurrent neural network, light gradient boosting machine, random forest, and logistic regression. Among all models, the TFT-based model achieved the highest AUROCs of 0.953 (0.952–0.954), 0.892 (0.891–0.893), and 0.889 (0.888–0.890) in predicting IDH-1, IDH-2, and IDHTN, respectively. The AUPRCs in the TFT-based model for these outcomes were higher than the other models. The factors that contributed the most to the prediction were age and previous session, which were time-invariant variables, as well as systolic BP and elapsed time, which were time-varying variables. The present TFT-based model predicts both IDH and IDHTN in real time and offers explainable variable importance.

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“…A wider adoption of EHRs would reduce health care costs, medical errors [13] , [14] , healthcare disparities, patient complications in hospitals and mortality [15] – [17] . Moreover, sharing EHRs among healthcare professionals will decrease the use of unnecessary services, such as repeated laboratory tests every time the patient changes hospital and office visits [13] , [18] .…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data Sharing

Frontoni

Romeo

Bernardini

et al. 2020

IEEE J. Transl. Eng. Health Med.

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Objective Decision support systems (DSS) have been developed and promoted for their potential to improve quality of health care. However, there is a lack of common clinical strategy and a poor management of clinical resources and erroneous implementation of preventive medicine. Methods To overcome this problem, this work proposed an integrated system that relies on the creation and sharing of a database extracted from GPs’ Electronic Health Records (EHRs) within the Netmedica Italian (NMI) cloud infrastructure. Although the proposed system is a pilot application specifically tailored for improving the chronic Type 2 Diabetes (T2D) care it could be easily targeted to effectively manage different chronic-diseases. The proposed DSS is based on EHR structure used by GPs in their daily activities following the most updated guidelines in data protection and sharing. The DSS is equipped with a Machine Learning (ML) method for analyzing the shared EHRs and thus tackling the high variability of EHRs. A novel set of T2D care-quality indicators are used specifically to determine the economic incentives and the T2D features are presented as predictors of the proposed ML approach. Results The EHRs from 41237 T2D patients were analyzed. No additional data collection, with respect to the standard clinical practice, was required. The DSS exhibited competitive performance (up to an overall accuracy of 98%±2% and macro-recall of 96%±1%) for classifying chronic care quality across the different follow-up phases. The chronic care quality model brought to a significant increase (up to 12%) of the T2D patients without complications. For GPs who agreed to use the proposed system, there was an economic incentive. A further bonus was assigned when performance targets are achieved. Conclusions The quality care evaluation in a clinical use-case scenario demonstrated how the empowerment of the GPs through the use of the platform (integrating the proposed DSS), along with the economic incentives, may speed up the improvement of care.

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A Practical Electronic Health Record-Based Dry Weight Supervision Model for Hemodialysis Patients

Cited by 7 publications

References 30 publications

Machine learning models to predict post-dialysis blood pressure in children and young adults on maintenance hemodialysis

Machine learning models to predict post-dialysis blood pressure in children and young adults on maintenance hemodialysis

Real-time dual prediction of intradialytic hypotension and hypertension using an explainable deep learning model

A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data Sharing

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