11 citations · 11 across the 7 of their papers we have counts for
7 papers
An Agentic LLM-Based Framework for Population-Scale Mental Health Screening
Giuliano Lorenzoni, Paulo Alencar, Donald Cowan
Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical data generated from electronic records, telemedicin…
LLM-X: A Scalable Negotiation-Oriented Exchange for Communication Among Personal LLM Agents
Giuliano Lorenzoni, Paulo Alencar, Donald Cowan
We propose a personal-LLM exchange (LLM-X), a scalable negotiation-oriented environment that enables direct, structured communication across populations of personal agents (LLMs),…
CPEMH: An Agentic Framework for Prompt-Driven Behavior Evaluation and Assurance in Foundation-Model Systems for Mental Health Screening
Giuliano Lorenzoni, Ivens Portugal, Paulo Alencar +1
This paper presents CPEMH, an agentic framework designed to evaluate prompt-driven behavior in foundation-model systems operating on transcript-based datasets for mental-health scr…
Exploring Variability in Fine-Tuned Models for Text Classification with DistilBERT
Giuliano Lorenzoni, Ivens Portugal, Paulo Alencar +1
This study evaluates fine-tuning strategies for text classification using the DistilBERT model, specifically the distilbert-base-uncased-finetuned-sst-2-english variant. Through st…
GPT-4 on Clinic Depression Assessment: An LLM-Based Pilot Study
Giuliano Lorenzoni, Pedro Elkind Velmovitsky, Paulo Alencar +1
Depression has impacted millions of people worldwide and has become one of the most prevalent mental disorders. Early mental disorder detection can lead to cost savings for public…
Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study
Giuliano Lorenzoni, Cristina Tavares, Nathalia Nascimento +2
Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental disorder detection can reduce costs for public health agen…
Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review
Giuliano Lorenzoni, Paulo Alencar, Nathalia Nascimento +1
Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Develop…