Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Characteristics</td><td class="align_center">Description</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">SBP</td><td class="align_center">Systolic blood pressure (mmHg)</td></tr><tr><td class="align_left">DBP</td><td class="align_center">Diastolic blood pressure (mmHg)</td></tr><tr><td class="align_left">O2Sat</td><td class="align_center">Oxygen saturation (%)</td></tr><tr><td class="align_left">HR</td><td class="align_center">Heart rate (bpm)</td></tr><tr><td class="align_left">Weight</td><td class="align_center">Body weight (kg)</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Ambulatory patients monitored characteristics of the study population.</div>

Mobile Information Systems

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Table 3

Table 3: Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model 