US20240428913
2024-12-26
Physics
G16H20/10
The patent application outlines a method for predicting patient responses to psilocybin therapy for treatment-resistant depression (TRD). This approach involves analyzing audio recordings from therapy sessions using natural language processing (NLP) techniques. By transcribing these sessions into text and parsing them into utterances, the system evaluates the sentiment of each utterance to forecast the therapy's effectiveness.
Psilocybin, a psychedelic compound, has shown promise in treating mood disorders like major depressive disorder (MDD) and TRD. However, its success varies among patients, making early prediction of treatment outcomes crucial. The application aims to enhance therapeutic efficacy by predicting patient responses through sentiment analysis of therapy session transcripts.
The process involves recording therapy sessions post-psilocybin dosing and transcribing them into text. These transcripts are parsed into utterances, which are then analyzed for sentiment using a machine learning model. The model calculates valence and arousal scores for each utterance, providing insights into the patient's potential response to the therapy.
Sentiment analysis distinguishes between positive and negative sentiments while also measuring intensity through valence and arousal scores. This dual-dimensional scoring captures the emotional tone of utterances more accurately than traditional models. The system uses these scores to predict whether a patient will respond positively to the treatment.
Audio recordings are processed using NLP techniques to generate transcripts, which are then anonymized and parsed into utterances. A trained large language model evaluates these utterances, assigning sentiment scores that inform predictions about treatment outcomes. This innovative approach aims to optimize psilocybin therapy by identifying patients likely to benefit from it early in their treatment journey.