no code implementations • 15 Feb 2024 • Uttam Dhakal, Aniket Kumar Singh, Suman Devkota, Yogesh Sapkota, Bishal Lamichhane, Suprinsa Paudyal, Chandra Dhakal
This study investigates GPT-4's assessment of its performance in healthcare applications.
1 code implementation • 28 Sep 2023 • Aniket Kumar Singh, Suman Devkota, Bishal Lamichhane, Uttam Dhakal, Chandra Dhakal
We exploit these models with diverse sets of questionnaires and real-world scenarios and extract how LLMs exhibit confidence in their responses.
no code implementations • 28 Mar 2023 • Bishal Lamichhane
In this work, we report the performance of LLM-based ChatGPT (with gpt-3. 5-turbo backend) in three text-based mental health classification tasks: stress detection (2-class classification), depression detection (2-class classification), and suicidality detection (5-class classification).
no code implementations • 8 Sep 2022 • Bishal Lamichhane, Nidal Moukaddam, Ankit B. Patel, Ashutosh Sabharwal
Psychomotor retardation in depression has been associated with speech timing changes from dyadic clinical interviews.
no code implementations • 24 May 2022 • Bishal Lamichhane, Joanne Zhou, Akane Sano
The CrossCheck dataset consisting of continuous mobile sensing data obtained from 63 schizophrenia patients, each monitored for up to a year, was used for our evaluations.
no code implementations • 25 Jun 2021 • Bishal Lamichhane, Dror Ben-Zeev, Andrew Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Mikio Obuchi, Emily Scherer, Megan Walsh, Rui Wang, Weichen Wang, Akane Sano
In this work, we investigated a machine learning based schizophrenia relapse prediction model using mobile sensing data to characterize behavioral features.
no code implementations • 22 Jun 2021 • Joanne Zhou, Bishal Lamichhane, Dror Ben-Zeev, Andrew Campbell, Akane Sano
The clustering model based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0. 24 for the relapse prediction task in a leave-one-patient-out evaluation setting.