Optimizing Parkinson’s Disease Prediction: A Comparative Analysis of Data Aggregation Methods Using Multiple Voice Recordings via an Automated Artificial Intelligence Pipeline

Patient-level grouped data are prevalent in public health and medical fields, and multiple instance learning (MIL) offers a framework to address the challenges associated with this type of data structure. This study compares four data aggregation methods designed to tackle the grouped structure in c...

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Veröffentlicht in:	Data (Basel) Jg. 10; H. 1; S. 4
Hauptverfasser:	Yang, Zhengxiao, Zhou, Hao, Srivastav, Sudesh, Shaffer, Jeffrey G., Abraham, Kuukua E., Naandam, Samuel M., Kakraba, Samuel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.01.2025
Schlagworte:	Accuracy Algorithms Artificial intelligence artificial intelligence (AI) Biomarkers Brain research Classification Clinical outcomes data aggregation Data management Data mining Data structures Datasets Health care Intervention Machine learning machine learning (ML) Medical research multiple instance learning (MIL) Parkinson's disease Parkinson’s disease (PD) Patients Performance measurement Public health Quality of life Speech Voice Wavelet transforms United States
ISSN:	2306-5729, 2306-5729
Online-Zugang:	Volltext
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