1
|
Gu T, Wang Z, Fang Z, Zhu Z, Yang H, Li D, Du W. Multilabel Convolutional Network With Feature Denoising and Details Supplement. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:8349-8361. [PMID: 35213316 DOI: 10.1109/tnnls.2022.3149760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In multilabel images, the changeable size, posture, and position of objects in the image will increase the difficulty of classification. Moreover, a large amount of irrelevant information interferes with the recognition of objects. Therefore, how to remove irrelevant information from the image to improve the performance of label recognition is an important problem. In this article, we propose a convolutional network based on feature denoising and details supplement (FDDS) to address this issue. In FDDS, we first design a cascade convolution module (CCM) to collect spatial details of upper features, in order to enhance the information expression of features. Second, the feature denoising module (FDM) is further put forward to reallocate the weight of the feature semantic area, in order to enrich the effective semantic information of the current feature and perform denoising operations on object-irrelevant information. Experimental results show that the proposed FDDS outperforms the existing state-of-the-art models on several benchmark datasets, especially for complex scenes.
Collapse
|
2
|
Zhan F, He L, Yu Y, Chen Q, Guo Y, Wang L. A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer. Sci Rep 2023; 13:16397. [PMID: 37773310 PMCID: PMC10541909 DOI: 10.1038/s41598-023-43543-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023] Open
Abstract
We developed and validated a multimodal radiomic machine learning approach to noninvasively predict the expression of lymphocyte cell-specific protein-tyrosine kinase (LCK) expression and clinical prognosis of patients with high-grade serous ovarian cancer (HGSOC). We analyzed gene enrichment using 343 HGSOC cases extracted from The Cancer Genome Atlas. The corresponding biomedical computed tomography images accessed from The Cancer Imaging Archive were used to construct the radiomic signature (Radscore). A radiomic nomogram was built by combining the Radscore and clinical and genetic information based on multimodal analysis. We compared the model performances and clinical practicability via area under the curve (AUC), Kaplan-Meier survival, and decision curve analyses. LCK mRNA expression was associated with the prognosis of HGSOC patients, serving as a significant prognostic marker of the immune response and immune cells infiltration. Six radiomic characteristics were chosen to predict the expression of LCK and overall survival (OS) in HGSOC patients. The logistic regression (LR) radiomic model exhibited slightly better predictive abilities than the support vector machine model, as assessed by comparing combined results. The performance of the LR radiomic model for predicting the level of LCK expression with five-fold cross-validation achieved AUCs of 0.879 and 0.834, respectively, in the training and validation sets. Decision curve analysis at 60 months demonstrated the high clinical utility of our model within thresholds of 0.25 and 0.7. The radiomic nomograms were robust and displayed effective calibration. Abnormally high expression of LCK in HGSOC patients is significantly correlated with the tumor immune microenvironment and can be used as an essential indicator for predicting the prognosis of HGSOC. The multimodal radiomic machine learning approach can capture the heterogeneity of HGSOC, noninvasively predict the expression of LCK, and replace LCK for predictive analysis, providing a new idea for predicting the clinical prognosis of HGSOC and formulating a personalized treatment plan.
Collapse
Affiliation(s)
- Feng Zhan
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China
- College of Engineering, Fujian Jiangxia University, Fuzhou, Fujian, People's Republic of China
| | - Lidan He
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China
| | - Yuanlin Yu
- Department of Medical Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China
| | - Qian Chen
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China
| | - Yina Guo
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China.
| | - Lili Wang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China
| |
Collapse
|
3
|
Multi-view multi-manifold learning with local and global structure preservation. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04101-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
4
|
Chi Z, Wang Z, Du W. Explicit Metric-Based Multiconcept Multi-Instance Learning With Triplet and Superbag. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5888-5897. [PMID: 33882006 DOI: 10.1109/tnnls.2021.3071814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Multi-instance learning (MIL) has garnered considerable attention in recent years due to its favorable performance in various scenarios. Nonetheless, most previous studies have implicitly expressed the correlation between instances and bags. Moreover, the importance of negative instances has been largely overlooked. Hence, we seek to present an explicit and intuitively understandable method that can compensate for these deficiencies. In this article, we creatively introduce a metric-based multiconcept MIL approach based on two aspects. First, the triplet-based bag embedding method identifies instance categories and builds attention weights for every instance explicitly. Accordingly, bag embedding is accomplished under the limitation of weak supervision. Second, the developed instance correlation metric approach in the superbag considers the multiconcept issue to boost the model generalization performance. We have designed a rich variety of experiments to demonstrate the performance of our algorithm. The artificial data experiment reveals the interpretability of the proposed network. The results of the comparison experiment confirm that our method shows favorable performance in multiple tasks. Finally, we illustrate the motivation of the presented method by the ablation experiments.
Collapse
|
5
|
Zhu Z, Wang Z, Li D, Du W. Globalized Multiple Balanced Subsets With Collaborative Learning for Imbalanced Data. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:2407-2417. [PMID: 32609619 DOI: 10.1109/tcyb.2020.3001158] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The skewed distribution of data brings difficulties to classify minority and majority samples in the imbalanced problem. The balanced bagging randomly undersampes majority samples several times and combines the selected majority samples with minority samples to form several balanced subsets, in which the numbers of minority and majority samples are roughly equal. However, the balanced bagging is the lack of a unified learning framework. Moreover, it fails to concern the connection of all subsets and the global information of the entire data distribution. To this end, this article puts several balanced subsets into an effective learning framework with a criterion function. In the learning framework, one regularization term called RS establishes the connection and realizes the collaborative learning of all subsets by requiring the consistent outputs of the minority samples in different subsets. Besides, another regularization term called RW provides the global information to each basic classifier by reducing the difference between the direction of the solution vector in each subset and that in the entire dataset. The proposed learning framework is called globalized multiple balanced subsets with collaborative learning (GMBSCL). The experimental results validate the effectiveness of the proposed GMBSCL.
Collapse
|
6
|
Jiang M, Yang Y, Qiu H. Fuzzy entropy and fuzzy support-based boosting random forests for imbalanced data. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02620-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
7
|
Guo W, Wang Z, Ma M, Chen L, Yang H, Li D, Du W. Semi‐supervised multiple empirical kernel learning with pseudo empirical loss and similarity regularization. INT J INTELL SYST 2022. [DOI: 10.1002/int.22690] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Wei Guo
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education East China University of Science and Technology Shanghai People's Republic of China
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Zhe Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education East China University of Science and Technology Shanghai People's Republic of China
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Menghao Ma
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education East China University of Science and Technology Shanghai People's Republic of China
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Lilong Chen
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Hai Yang
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Dongdong Li
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Wenli Du
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education East China University of Science and Technology Shanghai People's Republic of China
| |
Collapse
|
8
|
Wang Z, Jia P, Xu X, Wang B, Zhu Y, Li D. Sample and feature selecting based ensemble learning for imbalanced problems. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
9
|
|
10
|
Wang Z, Gu T, Zhu Y, Li D, Yang H, Du W. FLDNet: Frame-Level Distilling Neural Network for EEG Emotion Recognition. IEEE J Biomed Health Inform 2021; 25:2533-2544. [PMID: 33400657 DOI: 10.1109/jbhi.2021.3049119] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Based on the current research on EEG emotion recognition, there are some limitations, such as hand-engineered features, redundant and meaningless signal frames and the loss of frame-to-frame correlation. In this paper, a novel deep learning framework is proposed, named the frame-level distilling neural network (FLDNet), for learning distilled features from the correlations of different frames. A layer named the frame gate is designed to integrate weighted semantic information on multiple frames to remove redundant and meaningless signal frames. A triple-net structure is introduced to distill the learned features net by net to replace the hand-engineered features with professional knowledge. Specifically, one neural network is normally trained for several epochs. Then, a second network of the same structure will be initialized again to learn the extracted features from the frame gate of the first neural network based on the output of the first net. Similarly, the third net improves the features based on the frame gate of the second network. To utilize the representation ability of the triple neural network, an ensemble layer is conducted to integrate the discriminative ability of the proposed framework for final decisions. Consequently, the proposed FLDNet provides an effective method for capturing the correlation between different frames and automatically learn distilled high-level features for emotion recognition. The experiments are carried out in a subject-independent emotion recognition task on public emotion datasets of DEAP and DREAMER benchmarks, which have demonstrated the effectiveness and robustness of the proposed FLDNet.
Collapse
|
11
|
|
12
|
Dongdong L, Ziqiu C, Bolu W, Zhe W, Hai Y, Wenli D. Entropy‐based hybrid sampling ensemble learning for imbalanced data. INT J INTELL SYST 2021. [DOI: 10.1002/int.22388] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Li Dongdong
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education East China University of Science and Technology Shanghai China
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai China
- Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China
| | - Chi Ziqiu
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai China
| | - Wang Bolu
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai China
| | - Wang Zhe
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education East China University of Science and Technology Shanghai China
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai China
| | - Yang Hai
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education East China University of Science and Technology Shanghai China
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai China
| | - Du Wenli
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education East China University of Science and Technology Shanghai China
| |
Collapse
|
13
|
Wang Z, Cao C, Zhu Y. Entropy and Confidence-Based Undersampling Boosting Random Forests for Imbalanced Problems. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:5178-5191. [PMID: 31995503 DOI: 10.1109/tnnls.2020.2964585] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this article, we propose a novel entropy and confidence-based undersampling boosting (ECUBoost) framework to solve imbalanced problems. The boosting-based ensemble is combined with a new undersampling method to improve the generalization performance. To avoid losing informative samples during the data preprocessing of the boosting-based ensemble, both confidence and entropy are used in ECUBoost as benchmarks to ensure the validity and structural distribution of the majority samples during the undersampling. Furthermore, different from other iterative dynamic resampling methods, ECUBoost based on confidence can be applied to algorithms without iterations such as decision trees. Meanwhile, random forests are used as base classifiers in ECUBoost. Furthermore, experimental results on both artificial data sets and KEEL data sets prove the effectiveness of the proposed method.
Collapse
|
14
|
Wang Z, Zhu Y, Li D, Yin Y, Zhang J. Feature rearrangement based deep learning system for predicting heart failure mortality. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 191:105383. [PMID: 32062185 DOI: 10.1016/j.cmpb.2020.105383] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/22/2020] [Accepted: 02/03/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Heart Failure is a clinical syndrome commonly caused by any structural or functional impairment. Fast and accurate mortality prediction for Heart Failure is essential to improve the health care of patients and prevent them from death. However, due to the imbalance problem and poor feature representation in Heart Failure data, mortality prediction of Heart Failure is difficult with some simple models. To handle these problems, this study is focused on proposing a fast and accurate Heart Failure mortality prediction framework. METHODS This paper proposes a feature rearrangement based deep learning system for heart failure mortality prediction. The proposed framework improves the performance of predicting heart failure mortality by handling imbalance problem and achieving better feature representation. This paper also proposes a method named Feature rearrangement based convolutional layer, which demonstrates that the order of the input features is essential for the convolutional network. RESULTS The proposed system is experimentally evaluated on real-world Heart Failure data collected from the EHR system of Shanghai Shuguang Hospital, where 10,198 in-patients records are extracted between March 2009 and April 2016. Internal comparison results illustrate that the proposed framework achieves the best performance for Heart Failure mortality prediction. Extensive experimental results compared with other machine learning methods demonstrate that the proposed method has the highest average accuracy and area under the curve while predicting the three goals of in-hospital mortality, 30-day mortality, and 1-year mortality. Finally, top 12 essential clinical features are mined with their chi-square scores, which can help to assist clinicians in the treatment and research of heart failure. CONCLUSIONS The proposed method successfully predict different target in three observation windows. Feature rearrangement based convolutional layer and Focal loss are employed into the proposed framework, which helps promote the prediction accuracy of Heart Failure death. The proposed method is fast and accurate for predicting heart failure mortality, especially for imbalance situation. This paper also provide a reasonable pipeline to model EHRs data and handle imbalance problem in medical data.
Collapse
Affiliation(s)
- Zhe Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China; Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China.
| | - Yiwen Zhu
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China
| | - Dongdong Li
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China.
| | - Yichao Yin
- Shanghai Shuguang Hospital, Shanghai 200021, PR China
| | - Jing Zhang
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China
| |
Collapse
|
15
|
Efficient matrixized classification learning with separated solution process. Neural Comput Appl 2020. [DOI: 10.1007/s00521-019-04595-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
16
|
Wang Z, Li Y, Li D, Zhu Z, Du W. Entropy and gravitation based dynamic radius nearest neighbor classification for imbalanced problem. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105474] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
17
|
Zhu Z, Wang Z, Li D, Du W. NearCount: Selecting critical instances based on the cited counts of nearest neighbors. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105196] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
18
|
Wang Z, Chen L, Zhang J, Yin Y, Li D. Multi-view ensemble learning with empirical kernel for heart failure mortality prediction. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2020; 36:e3273. [PMID: 31680466 DOI: 10.1002/cnm.3273] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/30/2019] [Accepted: 09/30/2019] [Indexed: 06/10/2023]
Abstract
Heart failure (HF) refers to the heart's inability to pump sufficient blood to maintain the body's needs, which has a very serious impact on human health. In recent years, the prevalence of HF has remained high. This paper proposes a multi-view ensemble learning algorithm based on empirical kernel mapping called MVE-EK, which predicts the mortality of patient through hospital records. Multi-view ensemble learning can take advantage of the consistency and complementarity of different views. The MVE-EK first divides the patient's features into multiple views and then divides the samples of each view to multiple subsets through under sampling, which can reduce the imbalance rate of the original dataset and obtain some relatively balanced subsets. Each subset is mapped into kernel space by empirical kernel mapping, which can map samples from linearly inseparable spaces to linearly separable spaces. Finally, the multi-view ensemble learning is performed by the designed loss of acquaintance between views. The effectiveness of the algorithm is verified on the three datasets of HF patient in the real world. The performance of the algorithm is better than other comparison algorithms. The datasets are collected from Shanghai Shuguang Hospital and involve 10 203 hospitalization records for 4682 HF patients between March 2009 and April 2016. The prediction information provided by the algorithm can assist the clinician in providing a more personalized treatment plan for patients with HF.
Collapse
Affiliation(s)
- Zhe Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Lilong Chen
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Jing Zhang
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Yichao Yin
- Information Center, Shanghai Shuguang Hospital, Shanghai, People's Republic of China
| | - Dongdong Li
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| |
Collapse
|
19
|
Zhu Z, Wang Z, Li D, Du W, Zhou Y. Multiple Partial Empirical Kernel Learning with Instance Weighting and Boundary Fitting. Neural Netw 2019; 123:26-37. [PMID: 31821948 DOI: 10.1016/j.neunet.2019.11.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 09/26/2019] [Accepted: 11/19/2019] [Indexed: 10/25/2022]
Abstract
By dividing the original data set into several sub-sets, Multiple Partial Empirical Kernel Learning (MPEKL) constructs multiple kernel matrixes corresponding to the sub-sets, and these kernel matrixes are decomposed to provide the explicit kernel functions. Then, the instances in the original data set are mapped into multiple kernel spaces, which provide better performance than single kernel space. It is known that the instances in different locations and distributions behave differently. Therefore, this paper defines the weight of instance in accordance with the location and distribution of the instances. According to the location, the instances can be categorized into intrinsic instances, boundary instances and noise instances. Generally, the boundary instances, as well as the minority instances in the imbalanced data set, are assigned high weight. Meanwhile, a regularization term, which regulates the classification hyperplane to fit the distribution trend of the class boundary, is constructed by the boundary instances. Then, the weight of instance and the regularization term are introduced into MPEKL to form an algorithm named Multiple Partial Empirical Kernel Learning with Instance Weighting and Boundary Fitting (IBMPEKL). Experiments demonstrate the good performance of IBMPEKL and validate the effectiveness of the instance weighting and boundary fitting.
Collapse
Affiliation(s)
- Zonghai Zhu
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China; Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Zhe Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China; Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, PR China.
| | - Dongdong Li
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Wenli Du
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Yangming Zhou
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, PR China
| |
Collapse
|
20
|
Wang Z, Wang B, Zhou Y, Li D, Yin Y. Weight-based multiple empirical kernel learning with neighbor discriminant constraint for heart failure mortality prediction. J Biomed Inform 2019; 101:103340. [PMID: 31756495 DOI: 10.1016/j.jbi.2019.103340] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 06/14/2019] [Accepted: 11/10/2019] [Indexed: 11/16/2022]
Abstract
Heart Failure (HF) is one of the most common causes of hospitalization and is burdened by short-term (in-hospital) and long-term (6-12 month) mortality. Accurate prediction of HF mortality plays a critical role in evaluating early treatment effects. However, due to the lack of a simple and effective prediction model, mortality prediction of HF is difficult, resulting in a low rate of control. To handle this issue, we propose a Weight-based Multiple Empirical Kernel Learning with Neighbor Discriminant Constraint (WMEKL-NDC) method for HF mortality prediction. In our method, feature selection by calculating the F-value of each feature is first performed to identify the crucial clinical features. Then, different weights are assigned to each empirical kernel space according to the centered kernel alignment criterion. To make use of the discriminant information of samples, neighbor discriminant constraint is finally integrated into multiple empirical kernel learning framework. Extensive experiments were performed on a real clinical dataset containing 10, 198 in-patients records collected from Shanghai Shuguang Hospital in March 2009 and April 2016. Experimental results demonstrate that our proposed WMEKL-NDC method achieves a highly competitive performance for HF mortality prediction of in-hospital, 30-day and 1-year. Compared with the state-of-the-art multiple kernel learning and baseline algorithms, our proposed WMEKL-NDC is more accurate on mortality prediction Moreover, top 10 crucial clinical features are identified together with their meanings, which are very useful to assist clinicians in the treatment of HF disease.
Collapse
Affiliation(s)
- Zhe Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
| | - Bolu Wang
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Yangming Zhou
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
| | - Dongdong Li
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Yichao Yin
- Shanghai Shuguang Hospital, Shanghai 200021, China
| |
Collapse
|