Saathi – AI Mental Health Companion
IEEE-published AI chatbot for mental health support — 4th at IIT Bombay Research Conclave
Led technical development as primary engineer. Presented at IEEE National Students' Conference on Innovation in Rural Development (June 2023) and IIT Bombay Research Conclave 2023.
Overview
Saathi is an AI-powered mental health support platform designed to make therapy more accessible, particularly in underserved areas. It combines a CNN-based disorder classifier (trained on 100,000+ Reddit posts) with a seq2seq conversational model to detect depression, anxiety, and bipolar disorder from user messages and generate empathetic responses. The system also surfaces AI-generated summaries for clinicians, enabling better-informed care. Placed 4th at IIT Bombay Research Conclave (National Level, 2023). Published in IEEE National Students' Conference on Innovation in Rural Development.
Disorder Classification (CNN Model)
- Trained a multi-class CNN on 100,000+ posts sourced from Reddit's mental health communities (r/depression, r/anxiety, r/bipolar). Posts were cleaned, deduplicated, and label-verified against community context.
- Architecture: text embedding layer → three parallel convolutional blocks (1D CNN) with varying kernel sizes (2, 3, 4) to capture different n-gram patterns → max-pooling → dense layers → softmax output over {depression, anxiety, bipolar, none}.
- Achieved 85% classification accuracy on held-out test set. Precision/recall balanced across classes using weighted loss.
- Preprocessing pipeline: tokenization, stopword removal, lemmatization, and sequence padding to fixed length. Experimented with GloVe and FastText embeddings; GloVe gave better results for clinical vocabulary.
Conversational Response Generation (Seq2Seq)
- Built a sequence-to-sequence encoder-decoder with attention mechanism using Keras/TensorFlow. Trained on the CounselChat dataset — real therapist-client conversations from licensed counsellors.
- Encoder: bidirectional LSTM processes the user message into a context vector. Attention mechanism allows the decoder to focus on relevant encoder states. Decoder LSTM generates the response token by token.
- Post-processing layer conditions the response on the detected disorder class from the CNN — responses for depression vs anxiety vs bipolar are tonally calibrated.
- Evaluation: BLEU score used for automated evaluation; qualitative review by psychology graduates confirmed appropriateness of generated responses.
Clinician Dashboard
- Built a separate clinician-facing interface that displays AI-generated summaries of user sessions — key themes, detected disorder signals, and risk indicators.
- Enables psychiatrists to review AI assessments before sessions, improving preparation and allowing more focused appointments.
- Designed with privacy in mind: session data isolated per user, clinician access gated by role-based authentication.
Research & Recognition
- Published in IEEE National Students' Conference on Innovation in Rural Development (NSIRD) — June 2023. Paper covers the CNN architecture, dataset curation methodology, and evaluation results.
- Placed 4th at IIT Bombay Research Conclave (RESCON 2023) — a national-level research competition. Competed against postgraduate and PhD research teams.
- Research framing: the paper positions AI mental health tools as a scalable solution for rural India where psychiatrist density is <1 per 100,000 population vs WHO recommendation of 3 per 100,000.