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Found 20 papers, 9 papers with code
Improving Latent User Models in Online Social Media
Modern social platforms are characterized by the presence of rich user-behavior data associated with the publication, sharing and consumption of textual content.
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research.
Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images
The 74 images of test data set are tested with the proposed SVM based method and according to the ground truth, the accuracy of 82. 43% was achieved for the SVM based model, which was higher than the 79. 73% achieved in past work using the multidimensional scaling analysis (MDS) approach
DeepAISE -- An End-to-End Development and Deployment of a Recurrent Neural Survival Model for Early Prediction of Sepsis
Sepsis, a dysregulated immune system response to infection, is among the leading causes of morbidity, mortality, and cost overruns in the Intensive Care Unit (ICU).
A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images
Executing machine learning (ML) pipelines in real-time on radiology images is hard due to the limited computing resources in clinical environments and the lack of efficient data transfer capabilities to run them on research clusters.
Embedding-based Retrieval in Facebook Search
In this paper, we discuss the techniques for applying EBR to a Facebook Search system.
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support
We develop a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based conversations.
Classification of 12-lead ECGs: the PhysioNet/ Computing in Cardiology Challenge 2020
Main results: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry.
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
Learning such transformations is challenging and requires a deep understanding of empathy while maintaining conversation quality through text fluency and specificity to the conversational context.
Applications and Techniques for Fast Machine Learning in Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support
Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks like scheduling meetings and grammar-checking text.
Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022
Objective Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs for follow-up diagnostic screening and treatment, especially in resource-constrained environments.
Gendered Mental Health Stigma in Masked Language Models
Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men.
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction
In this paper, we conduct a human-centered study of how language models may assist people in reframing negative thoughts.
Investigating Agency of LLMs in Human-AI Collaboration Tasks
While LLMs are being developed to simulate human behavior and serve as human-like agents, little attention has been given to the Agency that these models should possess in order to proactively manage the direction of interaction and collaboration.
Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring
In an IRB-approved randomized field study on a large mental health website with 15, 531 participants, we design and evaluate a system that uses language models to support people through various steps of cognitive restructuring.
A Computational Framework for Behavioral Assessment of LLM Therapists
Subsequently, we compare the behavior of LLM therapists against that of high- and low-quality human therapy, and study how their behavior can be modulated to better reflect behaviors observed in high-quality therapy.
IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction
With IMBUE's additional just-in-time feedback, participants demonstrate 17% improvement in skill mastery, along with greater enhancements in self-efficacy (27% more) and reduction of negative emotions (16% more) compared to simulation-only.
Correcting misinformation on social media with a large language model
By retrieving evidence as refutations or contexts, MUSE identifies and explains (in)accuracies in a piece of content--not presupposed to be misinformation--with references.
A Linguistic Annotation Framework to Study Interactions in Multilingual Healthcare Conversational Forums
In recent years, remote digital healthcare using online chats has gained momentum, especially in the Global South.
