Key result
A supervised machine-learning model using non-contact sensor data and demographics predicted clinical events in hemodialysis patients with a mean ROC AUC of 90.16%.
Why the study?
Can a machine-learning model using non-contact sensor data predict clinical events in hemodialysis patients?
Observational (n=109)
Can a machine-learning model using non-contact sensor data predict clinical events in hemodialysis patients?
An AI-based prediction model using non-contact sensor data can accurately predict clinical events in hemodialysis patients, potentially enabling early interventions.
Should not yet change hemodialysis monitoring; leaves open clinical utility of sensor-based ML models pending prospective validation.
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.
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Thakur et al. (2018) conducted an observational in Hemodialysis (n=109). Non-contact sensor monitoring and AI-based prediction model vs. No-event group was evaluated on Prediction of clinical events (muscle spasms, inpatient stays, emergency visits, or death). A supervised machine-learning model using non-contact sensor data and demographics predicted clinical events in hemodialysis patients with a mean ROC AUC of 90.16%.
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