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Found 8 papers, 1 papers with code
HoloPOCUS: Portable Mixed-Reality 3D Ultrasound Tracking, Reconstruction and Overlay
The effective usage of conventional 2D US for interventional guidance requires extensive experience to project the image plane onto the patient, and the interpretation of images in diagnostics suffers from high intra- and inter-user variability.
MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines
In this paper, we identify two main challenges that arise during the deployment of machine learning pipelines, and address them with the design of versioning for an end-to-end analytics system MLCask.
TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications
In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions.
Learning from the Experience of Doctors: Automated Diagnosis of Appendicitis Based on Clinical Notes
The objective of this work is to develop an automated diagnosis system that is able to predict the probability of appendicitis given a free-text emergency department (ED) note and additional structured information (e. g., lab test results).
Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey
Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in delivering personalized care.
A Self-Correcting Deep Learning Approach to Predict Acute Conditions in Critical Care
Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data.
Medical Concept Embedding with Time-Aware Attention
Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics.
Automated Anonymization as Spelling Variant Detection
Personal health information (PHI) (such as name and identification number) needs to be removed so that patients cannot be identified.
