DeRisk improves credit risk prediction using deep learning.
problem Challenges in training deep neural networks with real-world financial data.
method DeRisk, an effective deep learning framework for credit risk prediction.
result DeRisk outperforms statistical learning methods in credit risk prediction.
A novel model combines deep learning and extreme value theory for multivariate cyber risk prediction.
problem High dimensionality and heavy tails in multivariate cyber risk patterns.
method Combines deep learning for point predictions and extreme value theory for quantile predictions.
result The model provides satisfactory high quantile predictions and accurate point predictions.
Study combines quantum and classical deep learning for better credit risk assessment.
problem Enhancing accuracy and efficiency in credit risk evaluation.
method Hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis.
result Proposed framework enhances predictive models for different loan categories.
Deep Evidence Regression improves credit risk prediction uncertainty.
problem Quantifying uncertainty in credit risk predictions.
method Applying Deep Evidence Regression to credit risk settings.
result Demonstrated improved prediction of Loss Given Default.
Proposes a method to quantify uncertainty in DNN models for discrete inputs.
problem Uncertainty quantification for DNN models with categorical and discrete feature variables.
method Develops a mathematical framework to quantify prediction uncertainty from discrete input noise and model parameters.
result Identifies risk-sensitive cases prone to misclassification due to discrete predictor errors.
Deep learning improves asset pricing and risk premium measurement.
problem Improving asset pricing and risk premium measurement using deep learning.
method Investigates various deep learning methods for asset pricing, especially for risk premia measurement.
result RNNs with memory mechanism and attention have the best performance in terms of predictivity.
SSH-Net: A Deep Neural Network for Predicting Failure Time Distribution Functions under Competing Risks with GPU Data
problem Predicting failure time distribution functions under competing risks
method Structured Segmented Hazard Deep Neural Network (SSH-Net)
result Prediction accuracy validated through simulation studies and GPU data
Deep neural networks reduce loan portfolio risk.
problem Minimizing risk in peer-to-peer lending portfolios.
method Proposed DeNN and DSNN models to predict default probability and time.
result DeNN model significantly reduces portfolio VaRs at various confidence levels.
Deep learning improves CVD risk prediction from health records.
problem Predicting cardiovascular disease risk from administrative health data.
method Combined survival analysis and deep learning models.
result Deep learning models outperform traditional Cox models in accuracy and explained time-to-event occurrence.
PredACGAN optimizes portfolios by balancing returns and risk.
problem Difficulty in considering portfolio risk with deterministic deep learning models.
method PredACGAN uses ACGAN structure for probabilistic predictions and risk measurement.
result PredACGAN portfolios outperform non-PredACGAN portfolios in terms of returns and risk metrics.
Deep learning predicts opioid use disorder risk in patients.
problem Identifying patients at high risk of opioid use disorder.
method Applied LSTM models to analyze electronic health records of opioid users.
result LSTM model outperformed other methods with F1 score of 0.8023 and AUCROC of 0.9369.
The paper examines the potential of deep learning to support decisions in financial risk management. We develop a deep learning model for predicting whether individual spread traders secure profits from future trades. This task embodies typical modeling challenges faced in risk and behavior forecasting. Conventional ma…
Deep neural networks improve portfolio construction by jointly modeling returns and risks.
problem Traditional portfolio construction methods fail under time-varying market conditions.
method Jointly modeling dynamic expected returns and risk structures using deep neural networks.
result Deep forecasting model achieves competitive predictive accuracy and economically meaningful directional accuracy.
We propose to represent a return model and risk model in a unified manner with deep learning, which is a representative model that can express a nonlinear relationship. Although deep learning performs quite well, it has significant disadvantages such as a lack of transparency and limitations to the interpretability of …
In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clinical risk factors to improve survival prediction in heart failure. Different methods are evaluated to find the optimal way to insert convent…
SCRIB assigns multiple labels to each example to control class-specific prediction risks.
problem Lack of a sound mechanism to decide when to refrain from predicting in DL classifiers.
method Set-classifier with Class-specific Risk Bounds (SCRIB) that assigns multiple labels to each example and controls class-specific prediction risks.
result SCRIB obtained class-specific risks 35%-88% closer to the target risks than baseline methods.
New algorithm predicts lung cancer progression and mortality.
problem Predicting semi-competing risk outcomes in lung cancer.
method Neural Expectation-Maximization algorithm for multi-state outcomes.
result Estimates non-parametric baseline hazards and risk functions.
Work proposes a new framework to improve uncertainty estimation in deep Bayesian models.
problem Traditional training procedures underestimate uncertainty in NLMs, leading to unreliable predictions.
method Introduces a novel training framework that captures useful predictive uncertainties for out-of-distribution inputs.
result Demonstrates that traditional methods for NLMs significantly underestimate uncertainty and propose a new framework to address this issue.
Deep learning improves credit risk assessment without new data.
problem Improving credit risk assessment in banking without new data.
method Sequential deep learning using temporal convolutional networks.
result Sequential deep learning outperformed tree-based models in credit risk assessment.
Objective: To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Methods: Several deep learning architectures making use of attentio…
DSVM model predicts financial market volatility with better accuracy.
problem Predicting financial market volatility accurately.
method Deep latent variable models with variational inference.
result DSVM outperforms GARCH models in predicting volatility.
Deep learning models predict option prices from 3D tensor data.
problem Predicting option prices for risk management and trading.
method 3D tensor representation of financial data, deep learning models (2D tensors in 3 channels).
result Proposed models outperform traditional methods like B-S model and vector-based LSTM.
Deep learning has demonstrated success in health risk prediction especially for patients with chronic and progressing conditions. Most existing works focus on learning disease Network (StageNet) model to extract disease stage information from patient data and integrate it into risk prediction. StageNet is enabled by (1…
This paper proposes a deep learning model combining CNN and Transformer for improved credit default prediction.
problem Traditional machine learning models struggle with complex financial data and risk patterns.
method Combines CNN for local feature extraction and Transformer for global dependency modeling.
result The CNN+Transformer model outperforms traditional models in accuracy, AUC, and KS value.
The paper proves deep learning can be robust with certain loss functions.
problem The robustness of deep learning models under flawed data.
method Empirical-risk minimization with unbounded, Lipschitz-continuous loss functions.
result These loss functions provide efficient prediction under minimal data assumptions.
Deep learning models predict postoperative complications more accurately than random forests.
problem Predicting postoperative complications to inform patient care decisions.
method Multi-task deep neural networks integrating intraoperative physiological data.
result Deep learning models improved prediction accuracy and provided interpretable risk factors.
This research predicts cryptocurrency price volatility using deep learning models.
problem Predicting the volatility of cryptocurrency prices to mitigate investment risk.
method Used CNN, LSTM, BiLSTM, and GRU models to predict the risk factor of 20 cryptocurrency parameters.
result Developed a new model with RMSE of 0.0089, significantly outperforming existing models.
Deep learning model predicts severe COVID-19 outcomes.
problem Predicting severe COVID-19 outcomes in ED patients.
method Deep feature fusion model using EHR data and CXR images.
result CO-RISK score achieved AUC of 0.95 and 0.92 for 24 and 72 hours predictions, superior to human performance.
Deep learning predicts back-pain risk during manual lifting.
problem Detecting incorrect lifting to prevent back injuries.
method 2D Convolutional Neural Network (CNN) without manual feature extraction.
result Deep CNN achieved 90.6% accuracy in classifying lifting risk.
The paper bounds the excess risk of deep neural networks for weakly dependent processes.
problem Learning with weakly dependent data using deep neural networks.
method Approximation of smooth functions by deep neural networks and a bound on excess risk.
result The excess risk bound for deep learning under weak dependence is close to O(n−1/2) for sufficiently smooth functions. Fast risk assessment for autonomous vehicles using learned agent futures.
problem Risk assessment for autonomous vehicles given probabilistic predictions of other agents' futures.
method Non-sampling based methods using deep neural networks for probabilistic predictions, with Gaussian and non-Gaussian mixture models for agent positions and controls.
result Effective risk assessment for low probability events using learned models of agent futures.
Study proposes a multimodal model for cardiovascular risk prediction using EHRs.
problem Lack of comprehensive risk prediction from EHRs due to unstructured text.
method Proposes a multimodal BiLSTM model integrating structured and unstructured EHR data.
result Proposed BiLSTM model outperforms other DNN architectures in cardiovascular risk prediction.
Develops a framework to control risk in online learning models.
problem Rigorous uncertainty quantification for online learning models.
method A framework for constructing uncertainty sets that provably control risk.
result Guarantees risk control at any user-specified level even with distribution shifts.
FinBERT-XRC model assesses financial report risk, offering transparent explanations.
problem Assessing post-event return volatility risk in financial reports.
method Deep-learning model FinBERT-XRC with explainability at word, sentence, and corpus levels.
result FinBERT-XRC outperforms state-of-the-art models in predictive accuracy.
Novel model improves clinical risk prediction by transferring knowledge between tasks over time.
problem Negative transfer in multi-task learning for clinical risk prediction.
method Temporal Probabilistic Asymmetric Multi-Task Learning (TPAMTL).
result Significantly outperforms various deep learning models for time-series prediction.
Advanced ML/DL models predict stock prices using technical analysis.
problem Accurately predicting stock prices in a complex market.
method Use of deep learning models for stock price prediction.
result Deep learning models can predict stock prices with high accuracy.
New method estimates survival risks without strong proportional hazard assumptions.
problem Time-to-event prediction with censored data and competing risks.
method Jointly learns deep nonlinear representations for fully parametric survival regression.
result Demonstrates benefits in real-world datasets with different censoring levels.
Accurate prediction of suicide risk in mental health patients remains an open problem. Existing methods including clinician judgments have acceptable sensitivity, but yield many false positives. Exploiting administrative data has a great potential, but the data has high dimensionality and redundancies in the recording …
The paper uses conformal prediction to detect railway signals with confidence.
problem Deploying deep learning models in certified systems requires accurate uncertainty estimates.
method The paper uses conformal prediction and risk control to detect railway signals.
result The conformal prediction framework provides reliable and trustworthy uncertainty estimates for model performance.
Method predicts NAFLD risk with high accuracy and distribution-free coverage guarantees.
problem Insufficient population-level screening tools for NAFLD.
method Gradient-boosted decision trees with conformal prediction.
result Method achieves AUROC of 0.912 internally and 0.891 externally, superior to other models.
Proposes a novel model for healthcare and SME credit risk prediction.
problem Lack of guidance from global view in sequence representation learning for time series modeling.
method Hierarchical Global View-guided (HGV) sequence representation learning framework with GGE and β-Attn modules. result Competitive prediction performance compared with other known baselines.
We develop a model using deep learning techniques and natural language processing on unstructured text from medical records to predict hospital-wide 30-day unplanned readmission, with c-statistic .70. Our model is constructed to allow physicians to interpret the significant features for prediction.
Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that …
New method stabilizes deep learning models for clinical risk prediction.
problem Stability issues in deep learning models for clinical risk prediction.
method Bootstrapping-based regularisation framework embedded in deep neural networks.
result Improved prediction stability across multiple datasets.
Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification…
Deep learning predicts ICU mortality with enhanced interpretability.
problem Improving mortality prediction accuracy and clinician trust in AI.
method Trained a deep learning model on MIMIC-III to interpret nursing notes.
result Model reaches ROC of 0.8629, outperforming SAPS-II.
Logistic Regression and Support Vector Machine algorithms, together with Linear and Non-Linear Deep Neural Networks, are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two phase model is proposed; the first phase predicts loan rejection,…
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network t…