Random Forest Classifiers to predict psychotic symptoms in Alzheimer’s disease
Sara Scarfo, Yashar Moshfeghi, William J McGeown · 2025
Read Random Forest Classifiers to predict psychotic symptoms in Alzheimer’s disease by Sara Scarfo, Yashar Moshfeghi, William J McGeown (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11710845.txt.
Description
Sara Scarfo, Yashar Moshfeghi, and William McGeown ask whether machine learning can spot which Alzheimer's patients will develop delusions or hallucinations, in a study published in 2025. Such psychotic symptoms strike about half of patients and bring distress, faster decline, and higher risks of hospitalization and death. The team drew matched patients from the ADNI database, balancing the psychosis and comparison groups on stage, age, sex, education, genetics, and overall cognition.
A random forest classifier weighed three families of predictors: other psychiatric symptoms, detailed cognitive test scores, and brain measures such as regional volumes and cortical thickness. The study's detailed findings follow in the full report. A reliable risk signal could let clinicians watch high-risk patients closely and intervene before a crisis.
Catalog record
| CREATOR | Sara Scarfo, Yashar Moshfeghi, William J McGeown |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 3 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |