TY - JOUR
T1 - Artificial-intelligence-based prediction of clinical events among hemodialysis patients using non-contact sensor data
AU - Thakur, Saurabh Singh
AU - Abdul, Shabbir Syed
AU - Shannon Chiu, Hsiao Yean
AU - Roy, Ram Babu
AU - Huang, Po Yu
AU - Malwade, Shwetambara
AU - Nursetyo, Aldilas Achmad
AU - Jack Li, Yu Chuan
N1 - Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Heart rate
KW - Heart rate variability
KW - Hemodialysis
KW - Non-contact sensor
KW - Predictive analytics
KW - Respiration rate
KW - Supervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=85052572165&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052572165&partnerID=8YFLogxK
U2 - 10.3390/s18092833
DO - 10.3390/s18092833
M3 - Article
C2 - 30150592
AN - SCOPUS:85052572165
SN - 1424-8220
VL - 18
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 9
M1 - 2833
ER -