Choosing the right treatment - combining clinicians’ expert knowledge with data-driven predictions
Bibliographic Data
| ID | 15529734 |
|---|---|
| Authors | Eduardo Maekawa (0009-0006-6657-9678, University of Limerick, corresponding author), Esben Kjems Jensen (0000-0002-9689-3776, University of Southern Denmark), Esben Jensen, Pepijn Van de Ven (0000-0003-3321-450X, University of Limerick), Kim Mathiasen (0000-0001-6067-8866, University of Southern Denmark) |
| Year | 2024 |
| Volume | 15 |
| Pages | 1422587-1422587 |
| Publication date | 2024-09-03 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2024.1422587 |
| PMID | 39290309 |
| OpenAlex | W4402199314 |
| Language | EN |
| References cited | 40 |
In 90.1% of cases, the hybrid model ranked the actual disorder treated as either the highest (67.3%) or second-highest (22.8%) on the test data. This emphasizes that instead of suggesting a single disorder to be treated, the model can offer the probabilities for multiple disorders. This allows individuals seeking treatment or their therapists to incorporate this information as an additional data-driven factor when collectively deciding on which treatment to prioritize
Data science · Advanced Causal Inference Techniques · Computer Science · Machine Learning in Healthcare · Medicine · Psychology · Statistical Methods in Clinical Trials
Learning Bayesian Networks with the bnlearn R Package
The validity of the Mini International Neuropsychiatric Interview (Mini) according to the SCID-P and its reliability
A Brief Measure for Assessing Generalized Anxiety Disorder
The PHQ-9
Panic Detection Using Machine Learning and Real-Time Biometric and Spatiotemporal Data
Toward explainable AI (XAI) for mental health detection based on language behavior
Man versus model of man
| Highly cited | No |
|---|