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Found 15 papers, 8 papers with code
Counterfactual Explanations for Time Series Forecasting
In this paper, we formulate the novel problem of counterfactual generation for time series forecasting, and propose an algorithm, called ForecastCF, that solves the problem by applying gradient-based perturbations to the original time series.
An End-to-End Workflow using Topic Segmentation and Text Summarisation Methods for Improved Podcast Comprehension
The consumption of podcast media has been increasing rapidly.
Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients
In this paper, we propose a counterfactual solution MedSeqCF for preventing the mortality of three cohorts of ICU patients, by representing their electronic health records as medical event sequences, and generating counterfactuals by adopting and employing a text style-transfer technique.
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction
Thus, the federated approach can be seen as a valid and privacy-preserving alternative to centralized machine learning for classifying ICU mortality when sharing sensitive patient data between hospitals is not possible.
Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks
There is a lack of research on the analysis of per-user traffic in cellular networks, for deriving and following traffic-aware network management.
Learning Time Series Counterfactuals via Latent Space Representations
Counterfactual explanations can provide sample-based explanations of features required to modify from the original sample to change the classification result from an undesired state to a desired state; hence it provides interpretability of the model.
Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients
In recent years, machine learning methods have been rapidly implemented in the medical domain.
Robust Explanations for Private Support Vector Machines
For such classifiers, counterfactual explanations need to be robust against the uncertainties in the SVM weights in order to ensure, with high confidence, that the classification of the data instance to be explained is different than its explanation.
Clinical Predictive Keyboard using Statistical and Neural Language Modeling
We show that a neural language model can achieve as high as 51. 3% accuracy in radiology reports (one out of two words predicted correctly).
RTEX: A novel methodology for Ranking, Tagging, and Explanatory diagnostic captioning of radiography exams
This paper introduces RTEx, a novel methodology for a) ranking radiography exams based on their probability to contain an abnormality, b) generating abnormality tags for abnormal exams, and c) providing a diagnostic explanation in natural language for each abnormal exam.
Aggregate-Eliminate-Predict: Detecting Adverse Drug Events from Heterogeneous Electronic Health Records
The challenge in this work is to aggregate heterogeneous data types involving diagnosis codes, drug codes, as well as lab measurements.
Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA
In this paper, we study the problem of network traffic traffic prediction and classification by employing standard machine learning and statistical learning time series prediction methods, including long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), respectively.
User Traffic Prediction for Proactive Resource Management: Learning-Powered Approaches
Traffic prediction plays a vital role in efficient planning and usage of network resources in wireless networks.
Explainable time series tweaking via irreversible and reversible temporal transformations
We show that the problem is NP-hard, and focus on two instantiations of the problem, which we refer to as reversible and irreversible time series tweaking.
Clustering with Confidence: Finding Clusters with Statistical Guarantees
In this study, we propose a technique for quantifying the instability of a clustering solution and for finding robust clusters, termed core clusters, which correspond to clusters where the co-occurrence probability of each data item within a cluster is at least $1 - \alpha$.
