scholarly journals Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches)

2021 ◽  
Vol 22 ◽  
Author(s):  
Seo Young Park ◽  
Ji Eun Park ◽  
Hyungjin Kim ◽  
Seong Ho Park
Keyword(s):  
Deep Learning ◽  
Statistical Methods ◽  
Prediction Models ◽  
Learning Approaches ◽  
Time To Event ◽  
Event Prediction

scholarly journals Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

2021 ◽  
Author(s):  
Rowland W. Pettit ◽  
Robert Fullem ◽  
Chao Cheng ◽  
Christopher I. Amos
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Outcome ◽  
Clinical Outcomes ◽  
Prediction Models ◽  
Learning Approaches ◽  
Growth Machine ◽  
Broad Concept ◽  
Tree Methods ◽  
Health Care Records

AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

scholarly journals A Deep Learning Method to Detect Opioid Prescription and Opioid Use Disorder from Electronic Health Records

2021 ◽  
Author(s):  
Aditya Kashyap ◽  
Chris Callison-Burch ◽  
Mary Regina Boland
Keyword(s):  
Deep Learning ◽  
Prediction Models ◽  
The United States ◽  
Opioid Use Disorder ◽  
Unstructured Data ◽  
Learning Approaches ◽  
Learning Models ◽  
Opioid Use ◽  
Opioid Prescription ◽  
Patients At Risk

Objective: As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD. Materials and Methods: We developed an informatics algorithm that trains two deep learning models over patient EHRs using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both of these challenging outcomes. Results: Our deep learning models incorporate both structured and unstructured data elements from the EHRs to predict opioid prescription with an F1-score of 0.88 +/- 0.003 and an AUC-ROC of 0.93 +/- 0.002. We also constructed a model to predict OUD diagnosis achieving an F1-score of 0.82 +/- 0.05 and AUC-ROC of 0.94 +/- 0.008. Discussion: Our model for OUD prediction outperformed prior algorithms for specificity, F1 score and AUC-ROC while achieving equivalent sensitivity. This demonstrates the importance of a.) deep learning approaches in predicting OUD and b.) incorporating both structured and unstructured data for this prediction task. No prediction models for opioid prescription as an outcome were found in the literature and therefore this represents an important contribution of our work as opioid prescriptions are more common than OUDs. Conclusion: Algorithms such as those described in this paper will become increasingly important to understand the drivers underlying this national epidemic.

scholarly journals Comparing Deep Learning and Statistical Methods in Forecasting Crowd Distribution from Aggregated Mobile Phone Data

2020 ◽  
Vol 10 (18) ◽  
pp. 6580 ◽  
Author(s):  
Alket Cecaj ◽  
Marco Lippi ◽  
Marco Mamei ◽  
Franco Zambonelli
Keyword(s):  
Deep Learning ◽  
Mobile Phone ◽  
Statistical Methods ◽  
Urban Areas ◽  
Domain Knowledge ◽  
Mobile Phone Data ◽  
Learning Approaches ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Crowd Density

Accurately forecasting how crowds of people are distributed in urban areas during daily activities is of key importance for the smart city vision and related applications. In this work we forecast the crowd density and distribution in an urban area by analyzing an aggregated mobile phone dataset. By comparing the forecasting performance of statistical and deep learning methods on the aggregated mobile data we show that each class of methods has its advantages and disadvantages depending on the forecasting scenario. However, for our time-series forecasting problem, deep learning methods are preferable when it comes to simplicity and immediacy of use, since they do not require a time-consuming model selection for each different cell. Deep learning approaches are also appropriate when aiming to reduce the maximum forecasting error. Statistical methods instead show their superiority in providing more precise forecasting results, but they require data domain knowledge and computationally expensive techniques in order to select the best parameters.

scholarly journals Validation of discrete time‐to‐event prediction models in the presence of competing risks

2019 ◽  
Vol 62 (3) ◽  
pp. 643-657 ◽  
Author(s):  
Rachel Heyard ◽  
Jean‐François Timsit ◽  
Leonhard Held ◽  
Keyword(s):  
Competing Risks ◽  
Discrete Time ◽  
Prediction Models ◽  
Time To Event ◽  
Event Prediction

scholarly journals Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data

2019 ◽  
Vol 33 ◽  
pp. 5199-5206 ◽  
Author(s):  
Amin Vahedian ◽  
Xun Zhou ◽  
Ling Tong ◽  
W. Nick Street ◽  
Yanhua Li
Keyword(s):  
Survival Analysis ◽  
Deep Learning ◽  
Learning Approaches ◽  
Two Stage ◽  
Demand Prediction ◽  
Mobility Data ◽  
Event Prediction ◽  
Safety Risks ◽  
Short Period ◽  
Dispersal Events

Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data. However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time. Instead, in this paper we argue that dispersal events follow a complex pattern of trips and other related features in the past, which can be used to predict such events. Therefore, we formulate the dispersal event prediction problem as a survival analysis problem. We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 20142016. Results show that DILSA can predict events in the next 5 hours with F1-score of 0:7 and with average time error of 18 minutes. It is orders of magnitude better than the state-of-the-art deep learning approaches for taxi demand prediction.

OC-0526 Deep learning based time-to-event prediction for a large multicentric cohort of H&N cancer patients

2021 ◽  
Vol 161 ◽  
pp. S412-S413
Author(s):  
E. Lombardo ◽  
C. Kurz ◽  
S. Marschner ◽  
M. Avanzo ◽  
V. Gagliardi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Patients ◽  
Time To Event ◽  
Event Prediction

scholarly journals Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maor Lewis ◽  
Guy Elad ◽  
Moran Beladev ◽  
Gal Maor ◽  
Kira Radinsky ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Prediction Models ◽  
Evaluation Metrics ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Learning Models ◽  
Preventable Hospitalizations ◽  
Targeted Interventions ◽  
Ed Visits

AbstractRecent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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