Query: electronic health records deep learning ensemble

title: Multi objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy.
abstract: Accurately predicting treatment outcome is crucial for creating personalized treatment plans and follow up schedules. Electronic health records (EHRs) contain valuable patient specific information that can be leveraged to improve outcome prediction. We propose a reliable multi objective ensemble deep learning (MoEDL) method that uses features extracted from EHRs to predict high risk of treatment failure after radiotherapy in patients with lung cancer. The dataset used in this study contains EHRs of 814 patients who had not achieved disease free status and 193 patients who were disease free with at least one year follow up time after lung cancer radiation therapy. The proposed MoEDL consists of three phases: 1) training with dynamic ensemble deep learning; 2) model selection with adaptive multi objective optimization; and 3) testing with evidential reasoning (ER) fusion. Specifically, in the training phase, we employ deep perceptron networks as base learners to handle various issues with EHR data. The architecture and key hyper parameters of each base learner are dynamically adjusted to increase the diversity of learners while reducing the time spent tuning hyper parameters. Furthermore, we integrate the Snapshot Ensembles (SE) restarting strategy, multi objective optimization, and ER fusion to improve the prediction robustness and accuracy of individual networks. The SE restarting strategy can yield multiple candidate models at no additional training cost in the training stage. The multi objective model simultaneously considers sensitivity, specificity, and AUC as objective functions, overcoming the limitations of single objective based model selection. For the testing stage, we utilized an analytic ER rule to fuse the output scores from each optimal model to obtain reliable and robust predictive results. Our experimental results demonstrate that MoEDL can perform better than other conventional methods.

title: Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods
abstract: Identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared task in extracting relations between medication related entities in electronic health records.  We proposed an ensemble approach for relation extraction and classification between drugs and medication related entities. We incorporated state of the art named entity recognition (NER) models based on bidirectional long short term memory (BiLSTM) networks and conditional random fields (CRF) for end to end extraction. We additionally developed separate models for intra and inter sentence relation extraction and combined them using an ensemble method. The intra sentence models rely on bidirectional long short term memory networks and attention mechanisms and are able to capture dependencies between multiple related pairs in the same sentence. For the inter sentence relations, we adopted a neural architecture that utilizes the Transformer network to improve performance in longer sequences.  Our team ranked third with a micro averaged F1 score of 94.72% and 87.65% for relation and end to end relation extraction, respectively (Tracks 2 and 3) Our ensemble effectively takes advantages from our proposed models. Analysis of the reported results indicated that our proposed approach is more generalizable than the top performing system, which employs additional training data and corpus driven processing techniques.  We proposed a relation extraction system to identify relations between drugs and medication related entities. The proposed approach is independent of external syntactic tools. Analysis showed that by using latent Drug Drug interactions we were able to significantly improve the performance of non Drug Drug pairs in EHRs.

title: Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records.
abstract: Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for sepsis 6 hours ahead on the basis of clinical electronic health records.  Challenge data are released by PhysioNet/Computing in Cardiology Challenge 2019 and obtained from ICU patients in three separate hospital systems. Part of the data from two datasets, including 40,336 subjects, are publicly available, and the remaining are used as hidden test set. A normalized utility score defined by the organizing committee is used for model performance evaluation.  The supervised machine learning is applied to tackle this challenge. Specifically, we establish the prediction model under the framework of ensemble learning by integrating the artificial features based on clinical prior knowledge of sepsis with deep features automatically extracted by long short term memory neural network.  Forty clinical variables, including eight vital signs, 26 laboratory values, and six demographics, were measured and recorded once an hour for each individual, and the binary label (0 or 1) was simultaneously provided for each item.  The proposed model was evaluated by 30 fold cross validation. The sensitivity, specificity, and normalized utility score were 0.641 0.022, 0.844 0.007, and 0.401 0.019 on publicly available datasets, respectively. The final normalized utility score our team (UCAS_DataMiner) has obtained was 0.313 on full hidden test set (0.406, 0.373, and 0.215 on test set A, B, and C, respectively) CONCLUSIONS We realized a 6 hour ahead early onset prediction of sepsis on the basis of clinical electronic health record by ensemble learning. The results indicated the proposed model functioned well in the early prediction of sepsis. In particular, ensemble learning had a significant (p 0.01) improvement than any single model in performance.

title: Deep learning predicts extreme preterm birth from electronic health records
abstract: Models for predicting preterm birth generally have focused on very preterm (28 to 32 weeks) and moderate to late preterm (32 to 37 weeks) settings. However, extreme preterm birth (EPB) before the 28th week of gestational age, accounts for the majority of newborn deaths. We investigated the extent to which deep learning models that consider temporal relations documented in electronic health records (EHRs) can predict EPB.  EHR data were subject to word embedding and a temporal deep learning model, in the form of recurrent neural networks (RNNs) to predict EPB. Due to the low prevalence of EPB, the models were trained on datasets where controls were undersampled to balance the case control ratio. We then applied an ensemble approach to group the trained models to predict EPB in an evaluation setting with a nature EPB ratio. We evaluated the RNN ensemble models with 10 years of EHR data from 25,689 deliveries at Vanderbilt University Medical Center. We compared their performance with traditional machine learning models (logistical regression, support vector machine, gradient boosting) trained on the datasets with balanced and natural EPB ratio. Risk factors associated with EPB were identified using an adjusted odds ratio.  The RNN ensemble models trained on artificially balanced data achieved a higher AUC (0.827 vs. 0.744) and sensitivity (0.965 vs. 0.682) than those RNN models trained on the datasets with naturally imbalanced EPB ratio. In addition, the AUC (0.827) and sensitivity (0.965) of the RNN ensemble models were better than the AUC (0.777) and sensitivity (0.819) of the best baseline models trained on balanced data. Also, risk factors, including twin pregnancy, short cervical length, hypertensive disorder, systemic lupus erythematosus, and hydroxychloroquine sulfate, were found to be associated with EPB at a significant level.  Temporal deep learning can predict EPB up to 8 weeks earlier than its occurrence. Accurate prediction of EPB may allow healthcare organizations to allocate resources effectively and ensure patients receive appropriate care.

title: BIG DATA ENSEMBLE CLINICAL PREDICTION FOR HEALTHCARE DATA BY USING DEEP LEARNING MODEL
abstract: Big data has revolutionised the healthcare industry. Electronic health records (EHRs) is growing at an exponential rate. Healthcare data being unstructured in nature requires a complete new technology to process the data. Clinical applications also need machine learning techniques and data mining methods which include decision trees and artificial neural networks. Classification algorithms have to be considered for developing predictive models. Ensemble model is gaining popularity among various other individual contributors. Ensemble systems can provide better accuracy. In this paper, we combine four algorithms support vector machines, naive Bayes, random forest and deep learning models are used to design the ensemble framework. Deep learning model is used to find the predicted labels. The data sets are collected from MIMIC III clinical database repository. Results shows that the proposed ensemble model provides the better accuracy results when deep learning model is included as deep learning is an efficient method for complex problems and large data sets.

title: 2018 N2c2 Shared Task on Adverse Drug Events and Medication Extraction in Electronic Health Records
abstract: This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on extraction of adverse drug events (ADEs) from clinical records and evaluated 3 tasks: concept extraction, relation classification, and end to end systems. We perform an analysis of the results to identify the state of the art in these tasks, learn from it, and build on it.  For all tasks, teams were given raw text of narrative discharge summaries, and in all the tasks, participants proposed deep learning based methods with hand designed features. In the concept extraction task, participants used sequence labelling models (bidirectional long short term memory being the most popular) whereas in the relation classification task, they also experimented with instance based classifiers (namely support vector machines and rules) Ensemble methods were also popular.  A total of 28 teams participated in task 1, with 21 teams in tasks 2 and 3. The best performing systems set a high performance bar with F1 scores of 0.9418 for concept extraction, 0.9630 for relation classification, and 0.8905 for end to end. However, the results were much lower for concepts and relations of Reasons and ADEs. These were often missed because local context is insufficient to identify them.  This challenge shows that clinical concept extraction and relation classification systems have a high performance for many concept types, but significant improvement is still required for ADEs and Reasons. Incorporating the larger context or outside knowledge will likely improve the performance of future systems.

title: Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients Using Primary Care Electronic Health Records
abstract: A growing elderly population suffering from incurable, chronic conditions such as dementia present a continual strain on medical services due to mental impairment paired with high comorbidity resulting in increased hospitalization risk. The identification of at risk individuals allows for preventative measures to alleviate said strain. Electronic health records provide opportunity for big data analysis to address such applications. Such data however, provides a challenging problem space for traditional statistics and machine learning due to high dimensionality and sparse data elements. This article proposes a novel machine learning methodology: entropy regularization with ensemble deep neural networks (ECNN) which simultaneously provides high predictive performance of hospitalization of patients with dementia whilst enabling an interpretable heuristic analysis of the model architecture, able to identify individual features of importance within a large feature domain space. Experimental results on health records containing 54,647 features were able to identify 10 event indicators within a patient timeline: a collection of diagnostic events, medication prescriptions and procedural events, the highest ranked being essential hypertension. The resulting subset was still able to provide a highly competitive hospitalization prediction (Accuracy: 0.759) as compared to the full feature domain (Accuracy: 0.755) or traditional feature selection techniques (Accuracy: 0.737) a significant reduction in feature size. The discovery and heuristic evidence of correlation provide evidence for further clinical study of said medical events as potential novel indicators. There also remains great potential for adaption of ECNN within other medical big data domains as a data mining tool for novel risk factor identification.

title: Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico
abstract: Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble learning framework of different neural network models, and a method of aggregating random under sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1 score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

title: Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission
abstract: Electronic Health Records (EHRs) have been heavily used to predict various downstream clinical tasks such as readmission or mortality. One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature. Although recent advances in deep learning (DL) enables models to extract interpretable features from unstructured data, they often require a large amount of training data. However, many tasks in medical domains inherently consist of small sample data with lengthy documents; for a kidney transplant as an example, data from only a few thousand of patients are available and each patient's document consists of a couple of millions of words in major hospitals. Thus, complex DL methods cannot be applied to these kind of domains. In this paper, we present a comprehensive ensemble model using vector space modeling and topic modeling. Our proposed model is evaluated on the readmission task of kidney transplant patients, and improves 0.0211 in terms of c statistics from the previous state of the art approach using structured data, while typical DL methods fails to beat this approach. The proposed architecture provides the interpretable score for each feature from both modalities, structured and unstructured data, which is shown to be meaningful through a physician's evaluation.

title: Ensembles of randomized trees using diverse distributed representations of clinical events
abstract: BackgroundLearning deep representations of clinical events based on their distributions in electronic health records has been shown to allow for subsequent training of higher performing predictive models compared to the use of shallow, count based representations. The predictive performance may be further improved by utilizing multiple representations of the same events, which can be obtained by, for instance, manipulating the representation learning procedure. The question, however, remains how to make best use of a set of diverse representations of clinical events modeled in an ensemble of semantic spaces for the purpose of predictive modeling.MethodsThree different ways of exploiting a set of (ten) distributed representations of four types of clinical events diagnosis codes, drug codes, measurements, and words in clinical notes are investigated in a series of experiments using ensembles of randomized trees. Here, the semantic space ensembles are obtained by varying the context window size in the representation learning procedure. The proposed method trains a forest wherein each tree is built from a bootstrap replicate of the training set whose entire original feature set is represented in a randomly selected set of semantic spaces corresponding to the considered data types of a given context window size.ResultsThe proposed method significantly outperforms concatenating the multiple representations of the bagged dataset; it also significantly outperforms representing, for each decision tree, only a subset of the features in a randomly selected set of semantic spaces. A follow up analysis indicates that the proposed method exhibits less diversity while significantly improving average tree performance. It is also shown that the size of the semantic space ensemble has a significant impact on predictive performance and that performance tends to improve as the size increases.ConclusionsThe strategy for utilizing a set of diverse distributed representations of clinical events when constructing ensembles of randomized trees has a significant impact on predictive performance. The most successful strategy significantly outperforming the considered alternatives involves randomly sampling distributed representations of the clinical events when building each decision tree in the forest.