A cost-aware framework for the development of AI models for healthcare applications
G Erion, JD Janizek, C Hudelson… - Nature Biomedical …, 2022 - nature.com
Accurate artificial intelligence (AI) for disease diagnosis could lower healthcare workloads.
However, when time or financial resources for gathering input data are limited, as in …
However, when time or financial resources for gathering input data are limited, as in …
DxFormer: a decoupled automatic diagnostic system based on decoder–encoder transformer with dense symptom representations
Motivation Symptom-based automatic diagnostic system queries the patient's potential
symptoms through continuous interaction with the patient and makes predictions about …
symptoms through continuous interaction with the patient and makes predictions about …
CAFS: cost-aware features selection method for multimodal stress monitoring on wearable devices
Objective: Today, stress monitoring on wearable devices is challenged by the tension
between high-detection accuracy and battery lifetime driven by multimodal data acquisition …
between high-detection accuracy and battery lifetime driven by multimodal data acquisition …
[HTML][HTML] CoAI: Cost-aware artificial intelligence for health care
G Erion, JD Janizek, C Hudelson… - Nature biomedical …, 2022 - ncbi.nlm.nih.gov
The recent emergence of accurate artificial intelligence (AI) models for disease diagnosis
raises the possibility that AI-based clinical decision support could substantially lower the …
raises the possibility that AI-based clinical decision support could substantially lower the …
Dynamic instance-wise joint feature selection and classification
YW Liyanage, DS Zois… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In this article, a dynamic instance-wise joint feature selection and classification framework
during testing is presented. Specifically, the proposed framework sequentially selects …
during testing is presented. Specifically, the proposed framework sequentially selects …
Active acquisition for multimodal temporal data: A challenging decision-making task
We introduce a challenging decision-making task that we call active acquisition for
multimodal temporal data (A2MT). In many real-world scenarios, input features are not …
multimodal temporal data (A2MT). In many real-world scenarios, input features are not …
BSODA: a bipartite scalable framework for online disease diagnosis
A growing number of people are seeking healthcare advice online. Usually, they diagnose
their medical conditions based on the symptoms they are experiencing, which is also known …
their medical conditions based on the symptoms they are experiencing, which is also known …
Why people skip music? On predicting music skips using deep reinforcement learning
Music recommender systems are an integral part of our daily life. Recent research has seen
a significant effort around black-box recommender based approaches such as Deep …
a significant effort around black-box recommender based approaches such as Deep …
Neuralsympcheck: A symptom checking and disease diagnostic neural model with logic regularization
The symptom checking systems inquire users for their symptoms and perform a rapid and
affordable medical assessment of their condition. The basic symptom checking systems …
affordable medical assessment of their condition. The basic symptom checking systems …
Dynamic instance-wise classification in correlated feature spaces
YW Liyanage, DS Zois… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In a typical supervised machine learning setting, the predictions on all test instances are
based on a common subset of features discovered during model training. However, using a …
based on a common subset of features discovered during model training. However, using a …