Vital signs prediction and early warning score calculation based on continuous monitoring of hospitalised patients using wearable technology

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration

Clinical deterioration detection for continuous vital signs monitoring using wearable sensors

Surgical patients are at risk of experiencing clinical deterioration events, especially when transferred to general wards during the postoperative period of their hospital stay. Cur rently, such events are detected by combining Early Warning Scores (EWS) with manual and periodical vital signs measurements, performed by nurses every 4 to 6 hours. Hence, deterioration may remain unnoticed for hours, delaying patient treatment, which might lead to increased morbidity and mortality. Also, EWS are inadequate to predict events so physiologically complex. So that early warning of deterioration could be provided, it was investigated the potential of warning systems that combine machine learning-based prediction models with continuous vital signs monitoring, provided by wearable sensors. This dissertation presents the development of such a warning system, fully indepen dent of manual measurements and based on a logistic regression prediction model with 85% sensitivity, 79% precision and 98% specificity. Additionally, a new personalized ap proach to handle missing data periods in vital signs and a novel variation of a RR-interval preprocessing technique were developed. The results obtained revealed a relevant im provement in the detection of deterioration events and a significant reduction in false alarms, when comparing the warning system with a commonly employed EWS (42% sensitivity, 14% precision and 90% specificity). It was also found that the developed sys tem can assess patient’s condition much more frequently and with timely deterioration detection, without even requiring nurses to interrupt their workflow. These findings sup port the idea that these warning systems are reliable, more practical, more appropriate and produce smarter alarms than current methods, making early deterioration detection possible, thus contributing for better patients outcomes. Nonetheless, the performance achieved may yet reveal insufficient for application in real clinical contexts. Therefore, further work is necessary to improve prediction performance to a greater extent and to confirm these systems reliability

Toward More Predictive Models by Leveraging Multimodal Data

Data is often composed of structured and unstructured data. Both forms of data have information that can be exploited by machine learning models to increase their prediction performance on a task. However, integrating the features from both these data forms is a hard, complicated task. This is all the more true for models which operate on time-constraints. Time-constrained models are machine learning models that work on input where time causality has to be maintained such as predicting something in the future based on past data. Most previous work does not have a dedicated pipeline that is generalizable to different tasks and domains, especially under time-constraints. In this work, we present a systematic, domain-agnostic pipeline for integrating features from structured and unstructured data while maintaining time causality for building models. We focus on the healthcare and consumer market domain and perform experiments, preprocess data, and build models to demonstrate the generalizability of the pipeline. More specifically, we focus on the task of identifying patients who are at risk of an imminent ICU admission. We use our pipeline to solve this task and show how augmenting unstructured data with structured data improves model performance. We found that by combining structured and unstructured data we can get a performance improvement of up to 8.5

Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection

In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failure. The goal of anomaly detection is to identify in the test data anomalous behaviors that are either rare or unseen in the training data. This is a common goal in predictive maintenance, which aims to forecast the imminent faults of an appliance given abundant samples of normal behaviors. Local outlier factor (LOF) is one of the state-of-the-art models used for anomaly detection, but the predictive performance of LOF depends greatly on the selection of hyperparameters. In this paper, we propose a novel, heuristic methodology to tune the hyperparameters in LOF. A tuned LOF model that uses the proposed method shows good predictive performance in both simulations and real data sets.Comment: 15 pages, 5 figure

Explainable artificial intelligence model to predict acute critical illness from electronic health records

We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness. While maintaining a high predictive performance, our system explains to the clinician on which relevant electronic health records (EHRs) data the prediction is grounded. Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as Early Warning Scores (EWS). The predictive performance of EWSs yields a tradeoff between sensitivity and specificity that can lead to negative outcomes for the patient. Previous work on EHR-trained AI systems offers promising results with high levels of predictive performance in relation to the early, real-time prediction of acute critical illness. However, without insight into the complex decisions by such system, clinical translation is hindered. In this letter, we present our xAI-EWS system, which potentiates clinical translation by accompanying a prediction with information on the EHR data explaining it