Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data

Sensors
Saurabh Singh ThakurYu-Chuan Jack Li

Abstract

Non-contact sensors are gaining popularity in clinical settings to monitor the vital parameters of patients. In this study, we used a non-contact sensor device to monitor vital parameters like the heart rate, respiration rate, and heart rate variability of hemodialysis (HD) patients for a period of 23 weeks during their HD sessions. During these 23 weeks, a total number of 3237 HD sessions were observed. Out of 109 patients enrolled in the study, 78 patients reported clinical events such as muscle spasms, inpatient stays, emergency visits or even death during the study period. We analyzed the sensor data of these two groups of patients, namely an event and no-event group. We found a statistically significant difference in the heart rates, respiration rates, and some heart rate variability parameters among the two groups of patients when their means were compared using an independent sample t-test. We further developed a supervised machine-learning-based prediction model to predict event or no-event based on the sensor data and demographic information. A mean area under curve (ROC AUC) of 90.16% with 96.21% mean precision, and 88.47% mean recall was achieved. Our findings point towards the novel use of non-contact sensors in cli...Continue Reading

Citations

Apr 30, 2020·Sensors·Ju WangThomas M Deserno
Jul 3, 2019·Sensors·Nguyen Thi Phuoc VanSubhas Mukhopadhyay
Feb 13, 2021·Clinical Journal of the American Society of Nephrology : CJASN·Hojun LeeSeung Seok Han

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scikit
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Anaconda
Python
pandas
R Programming Language
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