Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Clinical Application of Machine Learning Methods in Psychiatric Disorders
0
Zitationen
1
Autoren
2023
Jahr
Abstract
Abstract: Psychiatric disorders, especially bipolar affective disorder, present a significant burden on national and global healthcare systems, warranting advanced clinical management methods. Machine learning (ML) has emerged as a reliable tool to advance diagnosis, treatment, and monitoring. This paper compiles insights from literature to confirm the applicability of ML models in the clinical setting. The findings indicate that ML can predict psychiatric disorder symptoms from speech and imaging data with up to 89% accuracy. Furthermore, individual responses to treatment and remission cases can be forecasted with accuracies exceeding 80%. ML can also predict prevailing symptoms after treatment with up to 91.26% accuracy.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.794 Zit.
Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data
2005 · 10.558 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.990 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.602 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.127 Zit.