Combining structured and unstructured data for predictive models: a deep learning approach

Background: The broad adoption of Electronic Health Records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. However, while many research studies utilize temporal structured data on predictive modeling, they typically neglect potentially valuable information in unstructured clinical notes. Integrating heterogeneous data types across EHRs through deep learning techniques may help improve the performance of prediction models. Methods: In this research, we proposed 2 general-purpose multi-modal neural network architectures to enhance patient representation learning by combining sequential unstructured notes with structured data. The proposed fusion models leverage document embeddings for the representation of long clinical note documents and either convolutional neural network or long short-term memory networks to model the sequential clinical notes and temporal signals, and one-hot encoding for static information representation. The concatenated representation is the final patient representation which is used to make predictions. Results: We evaluate the performance of proposed models on 3 risk prediction tasks (i.e., in-hospital mortality, 30-day hospital readmission, and long length of stay prediction) using derived data from the publicly available Medical Information Mart for Intensive Care III dataset. Our results show that by combining unstructured clinical notes with structured data, the proposed models outperform other models that utilize either unstructured notes or structured data only. Conclusions: The proposed fusion models learn better patient representation by combining structured and unstructured data. Integrating heterogeneous data types across EHRs helps improve the performance of prediction models and reduce errors.

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Combining structured and unstructured data for predictive models: a deep learning approach

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01297-6 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Dongdong Zhang ◽

Changchang Yin ◽

Jucheng Zeng ◽

Xiaohui Yuan ◽

Ping Zhang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Medical Information ◽

Prediction Models ◽

Short Term Memory ◽

Heterogeneous Data ◽

Structured Data ◽

Unstructured Data ◽

Data Types ◽

Clinical Notes

Abstract Background The broad adoption of electronic health records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. However, while many research studies utilize temporal structured data on predictive modeling, they typically neglect potentially valuable information in unstructured clinical notes. Integrating heterogeneous data types across EHRs through deep learning techniques may help improve the performance of prediction models. Methods In this research, we proposed 2 general-purpose multi-modal neural network architectures to enhance patient representation learning by combining sequential unstructured notes with structured data. The proposed fusion models leverage document embeddings for the representation of long clinical note documents and either convolutional neural network or long short-term memory networks to model the sequential clinical notes and temporal signals, and one-hot encoding for static information representation. The concatenated representation is the final patient representation which is used to make predictions. Results We evaluate the performance of proposed models on 3 risk prediction tasks (i.e. in-hospital mortality, 30-day hospital readmission, and long length of stay prediction) using derived data from the publicly available Medical Information Mart for Intensive Care III dataset. Our results show that by combining unstructured clinical notes with structured data, the proposed models outperform other models that utilize either unstructured notes or structured data only. Conclusions The proposed fusion models learn better patient representation by combining structured and unstructured data. Integrating heterogeneous data types across EHRs helps improve the performance of prediction models and reduce errors.

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Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

Frontiers in Research Metrics and Analytics ◽

10.3389/frma.2021.673928 ◽

2021 ◽

Vol 6 ◽

Author(s):

Tahani Aljohani ◽

Alexandra I. Cristea

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Methodological Approach ◽

High Accuracy ◽

Directional Model ◽

Textual Representations ◽

The One

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

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A Comparative Study of Bitcoin Price Prediction Using Deep Learning

Mathematics ◽

10.3390/math7100898 ◽

2019 ◽

Vol 7 (10) ◽

pp. 898 ◽

Cited By ~ 12

Author(s):

Suhwan Ji ◽

Jongmin Kim ◽

Hyeonseung Im

Keyword(s):

Neural Network ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Learning Methods ◽

The Public ◽

Price Prediction ◽

Profitability Analysis ◽

The Media ◽

Long Short Term Memory

Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable.

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Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network

Computers ◽

10.3390/computers9040099 ◽

2020 ◽

Vol 9 (4) ◽

pp. 99

Author(s):

Sultan Daud Khan ◽

Louai Alarabi ◽

Saleh Basalamah

Keyword(s):

Neural Network ◽

Deep Learning ◽

Time Scale ◽

Prediction Models ◽

Time Series Data ◽

Short Term Memory ◽

Economic Recession ◽

Series Data ◽

Economic Activities ◽

Novel Approach

COVID-19 caused the largest economic recession in the history by placing more than one third of world’s population in lockdown. The prolonged restrictions on economic and business activities caused huge economic turmoil that significantly affected the financial markets. To ease the growing pressure on the economy, scientists proposed intermittent lockdowns commonly known as “smart lockdowns”. Under smart lockdown, areas that contain infected clusters of population, namely hotspots, are placed on lockdown, while economic activities are allowed to operate in un-infected areas. In this study, we proposed a novel deep learning prediction framework for the accurate prediction of hotpots. We exploit the benefits of two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) and propose a hybrid framework that has the ability to extract multi time-scale features from convolutional layers of CNN. The multi time-scale features are then concatenated and provide as input to 2-layers LSTM model. The LSTM model identifies short, medium and long-term dependencies by learning the representation of time-series data. We perform a series of experiments and compare the proposed framework with other state-of-the-art statistical and machine learning based prediction models. From the experimental results, we demonstrate that the proposed framework beats other existing methods with a clear margin.

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A Practical Tutorial on Graph Neural Networks

ACM Computing Surveys ◽

10.1145/3503043 ◽

2022 ◽

Author(s):

Isaac Ronald Ward ◽

Jack Joyner ◽

Casey Lickfold ◽

Yulan Guo ◽

Mohammed Bennamoun

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Deep Learning ◽

Input Data ◽

Unstructured Data ◽

Data Types ◽

Learning Techniques ◽

Graph Neural Networks ◽

Unique Ability

Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data. Although some elements of the GNN architecture are conceptually similar in operation to traditional neural networks (and neural network variants), other elements represent a departure from traditional deep learning techniques. This tutorial exposes the power and novelty of GNNs to AI practitioners by collating and presenting details regarding the motivations, concepts, mathematics, and applications of the most common and performant variants of GNNs. Importantly, we present this tutorial concisely, alongside practical examples, thus providing a practical and accessible tutorial on the topic of GNNs.

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MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset

10.21203/rs.3.rs-402058/v1 ◽

2021 ◽

Author(s):

Chuizheng Meng ◽

Loc Trinh ◽

Nan Xu ◽

Yan Liu

Keyword(s):

Deep Learning ◽

Large Scale ◽

Medical Information ◽

Prediction Models ◽

Short Term Memory ◽

Mortality Prediction ◽

Learning Models ◽

Healthcare Applications ◽

Disparate Treatment ◽

Mortality Predictors

Abstract The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV (Medical Information Mart for Intensive Care, version IV), the largest publicly available healthcare dataset, and conduct comprehensive analyses of dataset representation bias as well as interpretability and prediction fairness of deep learning models for in-hospital mortality prediction. In terms of interpretability, we observe that (1) the best-performing interpretability method successfully identifies critical features for mortality prediction on various prediction models; (2) demographic features are important for prediction. In terms of fairness, we observe that (1) there exists disparate treatment in prescribing mechanical ventilation among patient groups across ethnicity, gender and age; (2) all of the studied mortality predictors are generally fair while the IMV-LSTM (Interpretable Multi-Variable Long Short-Term Memory) model provides the most accurate and unbiased predictions across all protected groups. We further draw concrete connections between interpretability methods and fairness metrics by showing how feature importance from interpretability methods can be beneficial in quantifying potential disparities in mortality predictors.

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A comprehensive comparative analysis of deep learning tools for modeling failures in smart water taps

Water Science & Technology Water Supply ◽

10.2166/ws.2021.261 ◽

2021 ◽

Author(s):

N. M. Offiong ◽

Y. Wu ◽

D. Muniandy ◽

F. A. Memon

Keyword(s):

Neural Network ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Failure Prediction ◽

Water Withdrawal ◽

Short Term ◽

Term Memory ◽

Smart Water ◽

Long Short Term Memory

Abstract Predicting early-stage failure in smart water taps (SWT) and selecting the most efficient tools to build failure prediction models are many challenges that water institutions face. In this study, three Deep Learning (DL) algorithms, i.e., the Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM), were selected to analyse and determine the most appropriate among them for failure prediction in SWTs. This study uses a historical dataset acquired from smart water withdrawal taps to determine the most efficient DL neural network architecture for failure prediction in the SWT, leading to a hybrid model's development. After a comprehensive evaluation of the three ML models, findings show that a hybrid combination of the CNN and Bi-LSTM (CNN-BiLSTM) models is a better solution for investigating failures in the SWT.

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Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation

PLoS ONE ◽

10.1371/journal.pone.0253443 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0253443

Author(s):

Naomi George ◽

Edward Moseley ◽

Rene Eber ◽

Jennifer Siu ◽

Mathew Samuel ◽

...

Keyword(s):

Neural Network ◽

Intensive Care Unit ◽

Mechanical Ventilation ◽

Intensive Care ◽

Deep Learning ◽

Medical Information ◽

Prediction Models ◽

Prolonged Mechanical Ventilation ◽

External Validation ◽

Saps Ii Score

Background Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models. Methods Retrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model. Results There were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality. Discussion We developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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Applying Deep Learning to Audit Procedures: An Illustrative Framework

Accounting Horizons ◽

10.2308/acch-52455 ◽

2019 ◽

Vol 33 (3) ◽

pp. 89-109 ◽

Cited By ~ 9

Author(s):

Ting (Sophia) Sun

Keyword(s):

Decision Making ◽

Deep Learning ◽

Data Warehouse ◽

Visual Recognition ◽

Prediction Models ◽

Historical Data ◽

Structured Data ◽

Text Understanding ◽

Audit Data ◽

Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

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