We leveraged a large data set from the Medical Information Mart for Intensive Care III (MIMIC-III) [24] and its matched physiological waveform database (recorded with monitors) [36] to develop the TOP-Net model (codes available [37]). The pretrained model was transferred to a relatively small data set, from patients who were continuously monitored with a medical-grade wearable embedded system (SensEcho, Beijing SensEcho Science & Technology Co Ltd) in a real clinical environment [38]. The process is presented in Figure 1.

We combined 2 types of data to develop TOP-Net: (1) information from biological sensors (wearable), including heart rate, respiratory rate and SpO₂; (2) patients’ personal information from electronic health records, which represents their individual health status when admitted to the hospital, including age, gender, admission type, first care unit, and history of cardiovascular disease.

MIMIC III is a large, publicly available critical care database (version 1.4 [24]), with 38,557 adult patients’ (52,955 ICU admissions) detailed hospital information such as demographic information, laboratory test results, and diagnosis codes. Patients’ multiple physiological signals (waveforms) and corresponding numeric format of vital signs are stored in the MIMIC III Waveform Database, which contains 10,282 patients’ time alignment information and 22,247 numeric records that can be matched to the clinical database [36]. The basic information is stored in the tables of admissions, patients’ hospital admission information; icustays, ICU transfer (in and out) information; patients, individual birth and death dates; and diagnoses_icd, diagnosis codes during hospitalization. All of the tables can be associated with subject_id, a unique identity of patients. The waveform database includes the header files (name, unit, and recording frequency) and segments of recordings (numeric signals). Figure 3 presents the method used to link tables of information with the temporal waveforms.

The use of general ward data was approved by the ethics committee of the General Hospital of PLA (S2018-095-01). In the general ward, we utilized a SensEcho medical-grade monitoring system, which can monitor patients anytime and anywhere. SensEcho contains 3 parts (Figure 4): a wearable multisensor system unit, a wireless network and data transmission unit, and a central monitoring system [35,38]. The multisensors include a single-lead ECG sensor (200 Hz), a sensor for respiratory inductive plethysmography (25 Hz), a noninvasive photoplethysmogram sensor for SpO₂ monitoring (1 Hz) based on near-infrared spectroscopy, and a posture recognition sensor using a 3-axis accelerometer. These signals are collected and stored in a data logger. The logger has an ultra–low power Wi-Fi module and supports long-term data transmission by relying upon hospital networks. The central monitoring system receives information, processes data, and delivers and displays information. The algorithms deployed on the system included signal quality evaluation, signal processing, real-time abnormal event monitoring and early prediction, and patients’ health assessment, which were packaged as a toolkit (Midas). The accuracy, stability, and effectiveness of our system have been validated in previous studies [44-46].

Patients admitted to the hospital were assessed by a doctor using the system. Continuous monitoring physiological signals were transmitted to the hospital server and the data in numeric format were acquired based on the waveform processing function in Midas. The clinical information was stored separately in the hospital information system. Data from the different sources were linked (Figure 5) using patient_id, a unique identification of patients similar to subject_id in MIMIC III.

Diagnostic tachycardia onset criteria were determined by 3 clinical experts from the Emergency Department, the general ward, and surgical ICU. A tachycardia event was defined as any of the following: (1) heart rate above 100 bpm sustained over 30 minutes; (2) heart rate above 130 bpm sustained over 20 minutes; (3) heart rate above 150 bpm sustained over 5 minutes. The initial timepoint meeting of any of these conditions was recognized as tachycardia onset.

To further investigate the performance of TOP-Net, we designed subexperiments 1, 2, and 3 to obtain a comprehensive assessment. In subexperiment 1, the model was acquired without considering personal information and bidirection memory functions. That is, LSTM and convolutional neural network models were obtained in a total cohort without considering the personal information of patients. The structure of the LSTM was consistent with that of a BiLSTM, and the convolutional neural network model had 2 convolutional layers. In subexperiment 2, conventional machine learning methods, including extreme gradient boosting [49], multilayer perceptron, and random forest, were compared with TOP-Net with default model parameters. In subexperiment 3, different feature combinations were examined: (1) all vital signs, (2) heart rate, (3) heart rate and respiratory rate, and (4) heart rate and SpO₂.

Prediction performance was measured with 6 metrics: sensitivity, specificity, accuracy, F1 score, precision, and area under the receiver operating characteristic curve (AUROC).

The performance of TOP-Net was validated using the data collected in the general ward (small data set obtained within 1 year) by the SensEcho system. A transferrable model suitable for non-ICU patients was acquired by finetuning the ICU scenario model. The model performance was also assessed with the 6 metrics using 5-fold cross-validation due to the small sample size.

We utilized PostgreSQL (version 9.6; PostgreSQL Global Development Group) to extract the clinical data. All data processing and analyses, model development, and result visualization was performed with Python (version 3.7.1) and CUDA (version 10.0).

Table 2 shows admission information summary statistics for the study cohorts. The patients’ ages were slightly higher in the ICU cohort and most of them were admitted to the hospital for emergencies. A large proportion of patients were admitted for elective reasons in the cardiovascular disease department of our hospital. Furthermore, a higher proportion of patients had a history of cardiovascular diseases in the general ward.

In this study, we developed a model using a publicly accessible data set and transferred it to a real clinical scenario. The performance of TOP-Net for predicting tachycardia onset 0 to 6 hours in advance was better than that of the baseline models (timeseries prognosis methods and conventional machine learning methods without timing characteristics); TOP-Net outperformed benchmarks of 2 deep learning models, 2 ensemble, and 1 neural network models for predictions 6 hour in advance.

Many continuous monitoring physiological status studies have indicated the deterioration of vital signs occurred more than 6 to 12 hours before serious adverse events [50]. Continuous monitoring, early prediction, and intervention tachycardia can reduce the occurrence of heart failure, cardiac arrest, and death. This paper proposed TOP-Net, a tachycardia onset early prediction model leveraging the BiLSTM algorithm with 8 easily accessible vital signs and personal information. TOP-Net was trained using a large ICU data set and transferred to the general ward scenario with patients monitored by wearable sensors. TOP-Net has been validated to be consistently superior to the baseline models when predicting tachycardia onset from 0 to 6 hours in advance. Including patient characteristics allowed more accurate tachycardia onset prediction than those by other models without this information. Moreover, TOP-Net achieved forecasting tachycardia onset 6 hours beforehand, and the transferred model also performed well in our clinical scenario.

In recent years, some novel models for early risk prediction of adverse events have been developed based on electronic health records or physiological signals. Pan et al [51] utilized a self-correcting deep learning approach to predict whether acute kidney injury would occur in a subsequent 6 hours. Futoma et al [52] developed a multitask Gaussian process recurrent neural network classifier to early detect sepsis achieving 4 hours in advance. Tonekaboni et al [53] trained a convolutional neural network and LSTM fusion model to predict cardiac arrest from physiological signals 24 hours in advance. For tachycardia onset prediction, Lee et al [17] used an artificial neural network–based model and 104 samples to predict ventricular tachycardia 1-hour before occurrence. Yoon et al [54] adopted a random forest–based model and 1494 samples achieving detection 75 minutes in advance. Our real-time prediction model, using the deep neural architecture on 4878 sample sets, demonstrated better and more robust performance than those of multiple baseline models, which included artificial neural network and random forest models, when predicting tachycardia onset 0 to more than 6 hours beforehand.

It is necessary for clinicians to combine a patient’s current symptoms, basic information, and past medical history to diagnose disease severity [55]. For example, the proportion who might have cardiovascular disease and the risk of sustained high heart rate is not the same for patients of different ages with different histories of disease. This useful information is usually recorded in electronic health records. Recently, several researchers have tried to combine the analysis of 2 kinds of materials to represent comprehensive information and improve the performance of the models: Xu et al [56] proposed a model to predict physiological decompensation and length of ICU stay by analyzing ECG and medical records data, and Nemati et al [57] employed high-resolution vital signs and electronic health records to achieve early sepsis prediction. However, little attention has been paid to tachycardia prognosis. In this paper, we integrated electronic health record and biosensor data to accomplish early prediction. The results of subexperiment 1 show that fusing electronic health record information can improve the accuracy of early prediction compared with the LSTM and convolutional neural network models.

Risk prediction is a core task in the artificial intelligence–assisted medical domain. Cardiovascular disease prediction models based on electronic health record analysis have been studied [58-60]. Doctor AI [58] requires diagnosis codes, medication codes, or procedure codes to achieve multilabel predictions including heart failure. Jin et al [60] utilized 1864 diagnostic events to train a sequential model to predict the risk of heart failure but because they were limited by the need to obtain more information, the model cannot be used in hospitals with low information integration or in homes. Deep learning models using ECG signals have also been used for predictive health care tasks [61]. While ECG signals are susceptible to interference from physical artifacts, sensors can obtain heart rate using photoplethysmography instead of ECG signals. Therefore, models based on core vital signs can easily be used and to improve prediction performance. We selected 3 vital signs and 5 types of personal information that can easily be acquired from wearable sensors and hospital information systems, respectively. TOP-Net was developed using a large data set and transferred to our actual demand scenario. The results show that it has the potential to be used in ICU and the general ward, which also can be extended to home use. Table 6 presents a comparison between TOP-Net and other state-of-the-art approaches based on input information, model types, scenario for evaluating the model, sample sizes, and performance.

This study had some limitations. Because SensEcho was deployed in the clinic for only 1 year after our research project began, the limited data collected prevented us from directly developing a general ward model. Moreover, interventions such as beta-blocker medication may affect the occurrence of tachycardia onset and cause it to not be captured by the input features. Electronic health records contain rich information such as laboratory tests, clinical orders, and nursing notes that can characterize a patient’s health status and depict the trajectory of diseases. Further studies involving the integration of multivariate timeseries from electronic health records are expected to improve the prediction performance of tachycardia onset, and more data from the general ward for TOP-Net performance evaluation are required.

TOP-Net for real-time evaluation and early prediction of the risk of tachycardia onset, which made it possible to achieve an early forecast of tachycardia onset 6 hours in advance with clinically acceptable performance. TOP-Net was assessed using 6 metrics, 3 subexperiments, different prediction times from 0 to 6 hours. The comparison between the TOP-Net and the other 5 approaches (2 deep learning models, 2 ensemble models, and 1 artificial neural network model) showed that TOP-Net was superior to the other models. The model with personal information from electronic health records had better performance than those without. The easily accessible input data of the model (3 vital signs and 5 types of personal information) and the good performance of the transferred model in the general ward indicated the early prediction of tachycardia onset using wearable sensors is possible in hospitals or houses.