Intradialytic blood pressure pattern recognition based on density peak clustering

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.

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

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.

Online fault diagnosis for sucker rod pumping well by optimized density peak clustering

Online diagnosis for sucker rod pumping well has great significances for rapidly grasping operations of the oil well. Feature extraction of the working condition and determination of the online diagnostic algorithm are two indispensable parts. In this paper, five feature vectors are extracted using Freeman chain codes. Then, an optimized density peak clustering (DPC) method is proposed to realize online diagnosis solved by an improved brain storm optimization (BSO) algorithm, in which the cloud model is adopted to generate new solutions in the searching space. During the online diagnosis process, a new cluster updating strategy is used to update the cluster centers online. According to the proposed online diagnostic method, various samples are automatically classified into different classifications by the unsupervised learning. The simulation results verify that the proposed online diagnosis method is satisfactory, which can give a higher and more stable diagnostic accuracy.

Time-series deep survival prediction for hemodialysis patients using an attention-based Bi-GRU network

BACKGROUND AND OBJECTIVE

The number of end-stage renal disease (ESRD) patients treated with hemodialysis (HD) has significantly increased, but the prognosis remains poor. Time-series features have been included in only a few studies to predict HD patient survival, and how to utilize such features effectively remains unclear. This article aims to develop a more accurate, interpretable, and clinically practical personalized survival prediction model for HD patients.

METHODS

This study proposed and evaluated an attention-based Bi-GRU network using time-series features for survival prediction. A distance-based loss function was proposed to improve performance. We used data from 1232 ESRD patients who received regular hemodialysis treatment for ≥ 3 months from 2007 to 2016 at the First Affiliated Hospital of Zhejiang University. The proposed model was compared with representative sequence modeling deep learning architectures and existing survival analysis methods in terms of the C-index and IBS value. Post hoc tests were used to test statistical significance. The attention map was used to assess feature importance over time. The impact of time-series changes on survival was investigated after controlling initial values (using BMI as an example).

RESULTS

The proposed method outperformed other sequence modeling architectures and the state-of-the-art survival analysis approaches in terms of the C-index and the integrated Brier score (IBS) value. Our method achieved a C-index of 0.7680 (95% confidence intervals [CI]: 0.7645, 0.7716) and an IBS of 0.1302 (95% confidence intervals [CI]: 0.1292, 0.1313), showing an improvement of up to 5.4% in terms of the C-index and a decrease of 3.2% in terms of the IBS value. The addition of the distance-based loss function improved the performance. The predicted risk and actual risk levels closely agreed. This study also found that even after controlling the initial body mass index (BMI) values, different 3-month BMI trends could produce different survival outcomes.

CONCLUSIONS

This study proposed a more effective and interpretable method to use time-series information in survival analysis. The proposed method may help promote personalized medicine and improve patient prognosis.

A maintenance hemodialysis mortality prediction model based on anomaly detection using longitudinal hemodialysis data

BACKGROUND

Most end-stage renal disease patients rely on hemodialysis (HD) to maintain their life, and they face a serious financial burden and high risk of mortality. Due to the current situation of the health care system in China, a large number of patients on HD are lost to follow-up, making the identification of patients with high mortality risks an intractable problem.

OBJECTIVE

This paper aims to propose a maintenance HD mortality prediction approach using longitudinal HD data under the situation of data imbalance caused by follow-up losses.

METHODS

A long short-term memory autoencoder (LSTM AE) based model is proposed to capture the physical condition changes of HD patients and distinguish between surviving and nonsurviving patients. The approach adopts anomaly detection theory, using only the surviving samples in the model training and identifying dead samples based on autoencoder reconstruction errors. The data are from a Chinese hospital electronic health record system between July 30, 2007, and August 25, 2016, and 36/72/108 continuous HD sessions were used to predict mortality within prediction windows of 90/180/365 days. Furthermore, the model performance is compared to that of logistic regression, support vector machine, random forest, LSTM classifier, isolation forest, and stacked autoencoder models.

RESULTS

Data for 1200 patients (survival: 1055, death: 145) were used to predict mortality during the next 90 days using 36 continuous HD sessions. The area under the PR curve for the LSTM AE was 0.57, the Recall_macro was 0.86, and the F1-score_macro was 0.87, outperforming the other models. Upon varying the observation window or prediction window length, LSTM AE continued to outperform the other models. According to the variable importance analysis, the dialysis session length was the feature that contributed the most to the prediction model.

CONCLUSIONS

The proposed approach was able to detect patients on maintenance HD with high mortality risk from an imbalanced dataset using anomaly detection theory and leveraging longitudinal HD data.