Summary of Voice-driven Mortality Prediction in Hospitalized Heart Failure Patients: a Machine Learning Approach Enhanced with Diagnostic Biomarkers, by Nihat Ahmadli et al.
Voice-Driven Mortality Prediction in Hospitalized Heart Failure Patients: A Machine Learning Approach Enhanced with Diagnostic Biomarkers
by Nihat Ahmadli, Mehmet Ali Sarsil, Berk Mizrak, Kurtulus Karauzum, Ata Shaker, Erol Tulumen, Didar Mirzamidinov, Dilek Ural, Onur Ergen
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning has been used to develop a powerful and effective model for predicting mortality rates in hospitalized heart failure patients using voice biomarkers. The study demonstrates that by seamlessly integrating voice biomarkers into routine patient monitoring, this strategy can improve patient outcomes, optimize resource allocation, and advance patient-centered heart failure management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Heart failure is a big problem worldwide. Doctors need to find better ways to take care of people with it. One way they do this is by predicting how likely someone is to die from the condition. This helps them give patients the right treatment and make good decisions about their care. Recently, scientists found that using special computer programs called machine learning can help predict heart failure. They also discovered that listening to people’s voices can be helpful in making these predictions. |
Keywords
* Artificial intelligence * Machine learning
