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Found 8 papers, 2 papers with code
Benchmarking changepoint detection algorithms on cardiac time series
This work represents the first time change point detection algorithms have been compared in a meaningful way and utilized in a classification task, which demonstrates the effect of changepoint algorithm choice on application performance.
An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms
Modern deep learning algorithms have the highest potential of such leakage due to complexity of the models.
Reinforcement Learning in Healthcare: A Survey
As a subfield of machine learning, reinforcement learning (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback.
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).
Does the "Artificial Intelligence Clinician" learn optimal treatment strategies for sepsis in intensive care?
The results of studies involving machine learning, artificial intelligence (AI), and big data have captured the attention of healthcare practitioners, healthcare managers, and the public at a time when Western medicine grapples with unmitigated cost increases and public demands for accountability.
Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates
To quantify how well the distributions of the surrogates and the original data match, we evaluated a trained classifier on surrogates of correctly classified examples, and summarized these conditional predictions in a confusion matrix.
Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks
We also demonstrate the cross-domain generalizablity of the approach by adapting the learned model parameters from one recording modality (ECG) to another (photoplethysmogram) with improved AF detection performance.
Patient Flow Prediction via Discriminative Learning of Mutually-Correcting Processes
By treating a sequence of transition events as a point process, we develop a novel framework for modeling patient flow through various CUs and jointly predicting patients' destination CUs and duration days.
