AIMC Topic: User-Computer Interface

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The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes.

Artificial intelligence in medicine
Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. ...

Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships.

Journal of computer-aided molecular design
In this work, computational compound screening strategies on the basis of two- and three-dimensional (2D and 3D) molecular representations were investigated including similarity searching and support vector machine (SVM) ranking. Calculations based o...

A Natural-language-based Visual Query Approach of Uncertain Human Trajectories.

IEEE transactions on visualization and computer graphics
Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate ge...

The What-If Tool: Interactive Probing of Machine Learning Models.

IEEE transactions on visualization and computer graphics
A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs. To address this challenge, we created the What-If Tool, an open-source application that allows practitioners t...

Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening.

PloS one
Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes to distinguish binding from non-binding ligands for virtual screening. However, the dearth of reliab...

Visual Interaction with Deep Learning Models through Collaborative Semantic Inference.

IEEE transactions on visualization and computer graphics
Automation of tasks can have critical consequences when humans lose agency over decision processes. Deep learning models are particularly susceptible since current black-box approaches lack explainable reasoning. We argue that both the visual interfa...

Hyper-G: An Artificial Intelligence Tool for Optimal Decision-Making and Management of Blood Glucose Levels in Surgery Patients.

Methods of information in medicine
BACKGROUND: Hyperglycemia or high blood glucose during surgery is associated with poor postoperative outcome. Knowing in advance which patients may develop hyperglycemia allows optimal assignment of resources and earlier initiation of glucose managem...

Towards On-Demand Virtual Physical Therapist: Machine Learning-Based Patient Action Understanding, Assessment and Task Recommendation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In this paper, we propose a machine learning-based virtual physical therapist (PT) system to enable personalized remote training for patients with Parkinson's disease (PD). Three physical therapy tasks with multiple difficulty levels are selected to ...

Data-Driven Texture Modeling and Rendering on Electrovibration Display.

IEEE transactions on haptics
With the introduction of variable friction displays, either based on ultrasonic or electrovibration technology, new possibilities have emerged in haptic texture rendering on flat surfaces. In this work, we propose a data-driven method for realistic t...

Telepresence robots: Encouraging interactive communication between family carers and people with dementia.

Australasian journal on ageing
OBJECTIVE: The aim of the study was to explore the feasibility of using telepresence robots to encourage interactive communication in dementia care, from the perspective of family carers.