{
  "abstract": "Objective To develop and validate a large language model (LLM) prompt capable of ascertaining a patient’s depression from their multiple sclerosis (MS) neurologist’s note and to explore its potential for earlier detection of depression in MS care.Materials and methods This single-center retrospective study analysed prospectively collected electronic health record notes. In phase I, an institutionally secure ChatGPT-4 prompt was iteratively refined to infer the presence of depression using the neurologist’s note and compared with manual annotation of the neurologist’s impression (depression: present, absent, no mention) and patient-reported outcomes (PROs): Hospital Anxiety and Depression Scale or Patient Health Questionnaire-9. In phase II, longitudinal analysis compared timing of depression detection by the prompt and the neurologist across 5 years of notes for 250 patients.Results In phase I (n=278 adults with MS), the LLM prompt detected depression in 60.4% of notes (168/278). When compared with neurologist impression in the clinical notes, the prompt achieved 97.3% sensitivity and 84.4% accuracy. Specificity was more modest (68.3%): when neurologists did not mention depression, the prompt inferred depression based on symptoms, history and medications. When PRO and neurologist impression disagreed, the prompt aligned with PROs 61.9% of the time. In phase II, the LLM inferred depression earlier than the neurologist in 18.8% of patients, at an average of 2.45 (SD 1.54) years earlier.Conclusion The prompt was highly sensitive to neurologist documentation of depression in clinical notes; it inferred both present/treated depression from other note components. Potential applications include quality improvement initiatives aiming to improve depression care on a cohort level.",
  "authors": [
    {
      "affiliations": [
        "Neurology, University of California San Francisco Weill Institute for Neurosciences, San Francisco, California, USA"
      ],
      "name": "Maya Julian-Kwong"
    },
    {
      "affiliations": [
        "Neurology, University of California San Francisco Weill Institute for Neurosciences, San Francisco, California, USA"
      ],
      "name": "Shane Poole"
    },
    {
      "affiliations": [
        "Neurology, University of California San Francisco Weill Institute for Neurosciences, San Francisco, California, USA"
      ],
      "name": "Kyra Henderson"
    },
    {
      "affiliations": [
        "Neurology, University of California San Francisco Weill Institute for Neurosciences, San Francisco, California, USA"
      ],
      "name": "Jaeleene Wijangco"
    },
    {
      "affiliations": [
        "Neurology, University of California San Francisco Weill Institute for Neurosciences, San Francisco, California, USA"
      ],
      "name": "Nikki Sisodia"
    },
    {
      "affiliations": [
        "Neurology, University of California San Francisco Weill Institute for Neurosciences, San Francisco, California, USA"
      ],
      "name": "Jeffrey Marc Gelfand"
    },
    {
      "affiliations": [
        "Neurology, University of California San Francisco Weill Institute for Neurosciences, San Francisco, California, USA"
      ],
      "name": "Chu-Yueh Guo"
    },
    {
      "affiliations": [
        "Neurology, University of California San Francisco Weill Institute for Neurosciences, San Francisco, California, USA"
      ],
      "name": "Riley Bove"
    }
  ],
  "title": "Large language models to infer depression in patients with neurological conditions",
  "uid": "70527e17-6c08-50f1-9704-7d94f948cfa1"
}