Vocal changes as an effective predictor of worsening congestive heart failure
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Congestive heart failure presents numerous clinical symptoms. Increased edema, fluid accumulation in body cavities, intensified pulmonary wheezing, elevated heart rate, and others indicate worsening congestion. In remote patient monitoring, physicians often rely on body weight, instructing patients to measure it each morning upon waking. However, this test lacks specificity as body weight fluctuates due to various factors, such as food and fluid intake, and gastrointestinal activity. Conversely, a more effective symptom to monitor is changes in vocal characteristics. In congestive heart failure patients, a weakening voice correlates directly with increased congestion levels, offering a straightforward method for clinical assessment. Leveraging this approach, scientists at The Ohio State University utilized artificial intelligence (AI) to analyze patients' vocal patterns during remote conversations with their healthcare providers to detect worsening congestive heart failure. The outcomes surpassed expectations: while the sensitivity of predicting heart failure events via daily weight change was approximately 35%, the speech algorithm achieved an 80% accuracy rate. While awaiting the development of a simple AI algorithm from these scientists to aid congestive heart failure patients, healthcare providers can proactively observe and consider changes in their patients' vocal characteristics long before such events occur.
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