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

1
Rajendra Mishra School of Engineering Entrepreneurship, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
2
Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 110, Taiwan
3
International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
4
School of Nursing, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
5
School of Medicine, Research Center of Sleep Medicine, Taipei Medical University, Taipei 110, Taiwan
6
School of Medicine, Taipei Medical University, Taipei 110, Taiwan
7
TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(9), 2833; https://doi.org/10.3390/s18092833
Received: 3 July 2018 / Revised: 8 August 2018 / Accepted: 23 August 2018 / Published: 27 August 2018
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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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 clinical settings to monitor the vital parameters of patients and the further development of early warning solutions using artificial intelligence (AI) for the prediction of clinical events. These models could assist healthcare professionals in taking decisions and designing better care plans for patients by early detecting changes to vital parameters. View Full-Text
Keywords: artificial intelligence; supervised machine learning; predictive analytics; hemodialysis; non-contact sensor; heart rate; respiration rate; heart rate variability artificial intelligence; supervised machine learning; predictive analytics; hemodialysis; non-contact sensor; heart rate; respiration rate; heart rate variability
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Thakur, S.S.; Abdul, S.S.; Chiu, H.-Y.S.; Roy, R.B.; Huang, P.-Y.; Malwade, S.; Nursetyo, A.A.; Li, Y.-C.J. Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data. Sensors 2018, 18, 2833.

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