Takeaway
- 4 machine-learning models using measures from medical records in the 5 years prediagnosis showed good performance in discriminating between matched primary care patients with and without dementia.
Why this matters
- Diagnosis of dementia is often missed in primary care.
- Timely diagnosis allows patients to access specialist assessment, treatment, and support.
Key results
- Similarly good discrimination (area under receiver-operating characteristic curve) seen with:
- Logistic regression model (0.736),
- Support vector machine model (0.737),
- Neural network model (0.737), and
- Random forest model (0.734).
- Poorest discrimination seen with naive Bayes model (0.682).
- Top features retained in logistic regression model:
- Disorientation and wandering,
- Behavior change,
- Schizophrenia,
- Self-neglect,
- Difficulty managing,
- Personality change, and
- Family history of dementia.
- Highest positive predictive value of 0.312 seen with neural network model.
Study design
- UK case-control study of 93,120 patients ages >65 (median, 82.6) years.
- Patients with dementia diagnosis (cases) matched 1:1 by sex, age to patients with no evidence of dementia (controls).
- 5 machine-learning models developed using 70 clinical measures in 5 years leading up to diagnosis.
- Main outcome: discrimination between cases and controls.
- Funding: Wellcome Trust.
Limitations
- Case-control design, as opposed to cohort design.
- Need for replication.
- Reliance on binary clinical measures.
References
References
