Placebo effect estimation utilizing large language model

Neuropsychiatry is an interdisciplinary field of medicine that deals with the diagnosis, evaluation, and management of mental disorders resulting from neurological conditions. Neuropsychiatric disorders present a significant challenge in drug development, with a staggering 90% of clinical trials failing in Phase II. This high failure rate is attributed to various factors, with the placebo effect and the lack of predictive biomarkers of response are thought to be particularly prominent.

The placebo effect, when a person's physical or mental health appears to improve after taking a 'dummy' treatment, can mask genuine drug efficacy, especially in specific patient subpopulations. By accurately quantifying and predicting the placebo effect, we can better understand true drug responses and identify patients most likely to benefit from specific treatments. Allowing to match patients to drugs that benefit them most.
Neuropsychiatric disorders, such as depression, anxiety, and schizophrenia, affect millions of people worldwide. These conditions are complex and often difficult to treat, which makes finding new medications a significant challenge. Unfortunately, about 90% of clinical trials for new neuropsychiatric drugs fail during Phase II testing. This is a critical stage where researchers determine if a treatment is effective.

One major reason for these failures is the placebo effect. The placebo effect occurs when patients experience improvements in their condition simply because they believe they are receiving treatment, even when they are not. This effect can make it difficult to tell whether a drug is truly working. Additionally, there are currently no reliable tools (biomarkers) to predict how individual patients will respond to treatment. Without these tools, it’s harder to match patients with the right medications.

This research aims to address these problems by improving our understanding of the placebo effect and developing methods to predict it. We will use Artificial Intelligence (AI), specifically a Large Language Model (LLM), to analyze patient medical records and predict how likely a patient is to respond to a placebo. Large Language Model (LLM) (e.g. Large Language Models (LLMs) are a type of artificial intelligence that can understand, generate, and translate natural language. They are trained on massive amounts of text data, enabling them to recognize patterns and relationships within language and produce human-like text) The AI will identify key patterns in the records, and a simple prediction model will be used to understand which patient characteristics influence placebo responses.

By accurately measuring and predicting the placebo effect, researchers hope to uncover the real benefits of new treatments and help doctors identify patients who are more likely to respond to specific medications. This could lead to more successful drug trials and better treatment options for patients with neuropsychiatric disorders.