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Zhou H, Lin S, Watson M, Bernadt CT, Zhang O, Liao L, Govindan R, Cote RJ, Yang C. Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis. Sci Rep 2024; 14:22328. [PMID: 39333630 PMCID: PMC11436900 DOI: 10.1038/s41598-024-73428-2] [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: 07/01/2024] [Accepted: 09/17/2024] [Indexed: 09/29/2024] Open
Abstract
Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinical practice, even surpassing human pathologists in performance. However, as deep neural network (DNN) architectures grow in size and complexity, their explainability decreases, posing challenges in interpreting pathology features for broader clinical insights into physiological diseases. To better assess the interpretability of digital microscopic images and guide future microscopic system design, we developed a novel method to study the predictive feature length-scale that underpins a DNN's predictive power. We applied this method to analyze a DNN's capability in predicting brain metastasis from early-stage non-small-cell lung cancer biopsy slides. This study quantifies DNN's attention for brain metastasis prediction, targeting features at both the cellular scale and tissue scale in H&E-stained histological whole slide images. At the cellular scale, the predictive power of DNNs progressively increases with higher resolution and significantly decreases when the resolvable feature length exceeds 5 microns. Additionally, DNN uses more macro-scale features associated with tissue architecture and is optimized when assessing visual fields greater than 41 microns. Our study computes the length-scale requirements for optimal DNN learning on digital whole-slide microscopic images, holding the promise to guide future optical microscope designs in pathology applications and facilitating downstream deep learning analysis.
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Affiliation(s)
- Haowen Zhou
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Siyu Lin
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Mark Watson
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Cory T Bernadt
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Oumeng Zhang
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Ling Liao
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Ramaswamy Govindan
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Richard J Cote
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Changhuei Yang
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.
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2
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Chiu CC, Wu CM, Chien TN, Kao LJ, Li C. Predicting ICU Readmission from Electronic Health Records via BERTopic with Long Short Term Memory Network Approach. J Clin Med 2024; 13:5503. [PMID: 39336990 PMCID: PMC11432694 DOI: 10.3390/jcm13185503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 09/04/2024] [Accepted: 09/10/2024] [Indexed: 09/30/2024] Open
Abstract
Background: The increasing rate of intensive care unit (ICU) readmissions poses significant challenges in healthcare, impacting both costs and patient outcomes. Predicting patient readmission after discharge is crucial for improving medical quality and reducing expenses. Traditional analyses of electronic health record (EHR) data have primarily focused on numerical data, often neglecting valuable text data. Methods: This study employs a hybrid model combining BERTopic and Long Short-Term Memory (LSTM) networks to predict ICU readmissions. Leveraging the MIMIC-III database, we utilize both quantitative and text data to enhance predictive capabilities. Our approach integrates the strengths of unsupervised topic modeling with supervised deep learning, extracting potential topics from patient records and transforming discharge summaries into topic vectors for more interpretable and personalized predictions. Results: Utilizing a comprehensive dataset of 36,232 ICU patient records, our model achieved an AUROC score of 0.80, thereby surpassing the performance of traditional machine learning models. The implementation of BERTopic facilitated effective utilization of unstructured data, generating themes that effectively guide the selection of relevant predictive factors for patient readmission prognosis. This significantly enhanced the model's interpretative accuracy and predictive capability. Additionally, the integration of importance ranking methods into our machine learning framework allowed for an in-depth analysis of the significance of various variables. This approach provided crucial insights into how different input variables interact and impact predictions of patient readmission across various clinical contexts. Conclusions: The practical application of BERTopic technology in our hybrid model contributes to more efficient patient management and serves as a valuable tool for developing tailored treatment strategies and resource optimization. This study highlights the significance of integrating unstructured text data with traditional quantitative data to develop more accurate and interpretable predictive models in healthcare, emphasizing the importance of individualized care and cost-effective healthcare paradigms.
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Affiliation(s)
- Chih-Chou Chiu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan; (C.-C.C.); (C.-M.W.); (L.-J.K.)
| | - Chung-Min Wu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan; (C.-C.C.); (C.-M.W.); (L.-J.K.)
| | - Te-Nien Chien
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan;
| | - Ling-Jing Kao
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan; (C.-C.C.); (C.-M.W.); (L.-J.K.)
| | - Chengcheng Li
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan;
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Yang S, Huang Q, Yu M. Advancements in remote sensing for active fire detection: A review of datasets and methods. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 943:173273. [PMID: 38823698 DOI: 10.1016/j.scitotenv.2024.173273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/06/2024] [Accepted: 05/13/2024] [Indexed: 06/03/2024]
Abstract
This study comprehensively and critically reviews active fire detection advancements in remote sensing from 1975 to the present, focusing on two main perspectives: datasets and corresponding instruments, and detection algorithms. The study highlights the increasing role of machine learning, particularly deep learning techniques, in active fire detection. Looking forward, the review outlines current challenges and future research opportunities in remote sensing for active fire detection. These include exploring data quality management and multi-modal learning, developing spatiotemporally explicit models, investigating self-supervised learning models, improving explainable and interpretable models, integrating physical-process based models with machine learning, and building digital twins to replicate wildfire dynamics and perform what-if scenario analysis. The review aims to serve as a valuable resource for informing natural resource management and enhancing environmental protection efforts through the application of remote sensing technology.
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Affiliation(s)
- Songxi Yang
- Spatial Computing and Data Mining Lab, Department of Geography, University of Wisconsin-Madison, Madison 53705, WI, USA
| | - Qunying Huang
- Spatial Computing and Data Mining Lab, Department of Geography, University of Wisconsin-Madison, Madison 53705, WI, USA.
| | - Manzhu Yu
- Department of Geography, Pennsylvania State University, University Park, 16802, PA, USA
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4
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Askar M, Småbrekke L, Holsbø E, Bongo LA, Svendsen K. "Using network analysis modularity to group health code systems and decrease dimensionality in machine learning models". EXPLORATORY RESEARCH IN CLINICAL AND SOCIAL PHARMACY 2024; 14:100463. [PMID: 38974056 PMCID: PMC11227014 DOI: 10.1016/j.rcsop.2024.100463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/03/2024] [Accepted: 06/08/2024] [Indexed: 07/09/2024] Open
Abstract
Background Machine learning (ML) prediction models in healthcare and pharmacy-related research face challenges with encoding high-dimensional Healthcare Coding Systems (HCSs) such as ICD, ATC, and DRG codes, given the trade-off between reducing model dimensionality and minimizing information loss. Objectives To investigate using Network Analysis modularity as a method to group HCSs to improve encoding in ML models. Methods The MIMIC-III dataset was utilized to create a multimorbidity network in which ICD-9 codes are the nodes and the edges are the number of patients sharing the same ICD-9 code pairs. A modularity detection algorithm was applied using different resolution thresholds to generate 6 sets of modules. The impact of four grouping strategies on the performance of predicting 90-day Intensive Care Unit readmissions was assessed. The grouping strategies compared: 1) binary encoding of codes, 2) encoding codes grouped by network modules, 3) grouping codes to the highest level of ICD-9 hierarchy, and 4) grouping using the single-level Clinical Classification Software (CCS). The same methodology was also applied to encode DRG codes but limiting the comparison to a single modularity threshold to binary encoding.The performance was assessed using Logistic Regression, Support Vector Machine with a non-linear kernel, and Gradient Boosting Machines algorithms. Accuracy, Precision, Recall, AUC, and F1-score with 95% confidence intervals were reported. Results Models utilized modularity encoding outperformed ungrouped codes binary encoding models. The accuracy improved across all algorithms ranging from 0.736 to 0.78 for the modularity encoding, to 0.727 to 0.779 for binary encoding. AUC, recall, and precision also improved across almost all algorithms. In comparison with other grouping approaches, modularity encoding generally showed slightly higher performance in AUC, ranging from 0.813 to 0.837, and precision, ranging from 0.752 to 0.782. Conclusions Modularity encoding enhances the performance of ML models in pharmacy research by effectively reducing dimensionality and retaining necessary information. Across the three algorithms used, models utilizing modularity encoding showed superior or comparable performance to other encoding approaches. Modularity encoding introduces other advantages such as it can be used for both hierarchical and non-hierarchical HCSs, the approach is clinically relevant, and can enhance ML models' clinical interpretation. A Python package has been developed to facilitate the use of the approach for future research.
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Affiliation(s)
- Mohsen Askar
- Department of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, PO Box 6050, Stakkevollan, N-9037 Tromsø, Norway
| | - Lars Småbrekke
- Department of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, PO Box 6050, Stakkevollan, N-9037 Tromsø, Norway
| | - Einar Holsbø
- Department of Computer Science, Faculty of Science and Technology, UiT-The Arctic University of Norway, PO, Box 6050 Stakkevollan, N-9037 Tromsø, Norway
| | - Lars Ailo Bongo
- Department of Computer Science, Faculty of Science and Technology, UiT-The Arctic University of Norway, PO, Box 6050 Stakkevollan, N-9037 Tromsø, Norway
| | - Kristian Svendsen
- Department of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, PO Box 6050, Stakkevollan, N-9037 Tromsø, Norway
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5
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Watkins WS, Hernandez EJ, Miller TA, Blue NR, Zimmerman R, Griffiths ER, Frise E, Bernstein D, Boskovski MT, Brueckner M, Chung WK, Gaynor JW, Gelb BD, Goldmuntz E, Gruber PJ, Newburger JW, Roberts AE, Morton SU, Mayer JE, Seidman CE, Seidman JG, Shen Y, Wagner M, Yost HJ, Yandell M, Tristani-Firouzi M. Genome Sequencing is Critical for Forecasting Outcomes following Congenital Cardiac Surgery. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.03.24306784. [PMID: 38746151 PMCID: PMC11092705 DOI: 10.1101/2024.05.03.24306784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
While genome sequencing has transformed medicine by elucidating the genetic underpinnings of both rare and common complex disorders, its utility to predict clinical outcomes remains understudied. Here, we used artificial intelligence (AI) technologies to explore the predictive value of genome sequencing in forecasting clinical outcomes following surgery for congenital heart defects (CHD). We report results for a cohort of 2,253 CHD patients from the Pediatric Cardiac Genomics Consortium with a broad range of complex heart defects, pre- and post-operative clinical variables and exome sequencing. Damaging genotypes in chromatin-modifying and cilia-related genes were associated with an elevated risk of adverse post-operative outcomes, including mortality, cardiac arrest and prolonged mechanical ventilation. The impact of damaging genotypes was further amplified in the context of specific CHD phenotypes, surgical complexity and extra-cardiac anomalies. The absence of a damaging genotype in chromatin-modifying and cilia-related genes was also informative, reducing the risk for adverse postoperative outcomes. Thus, genome sequencing enriches the ability to forecast outcomes following congenital cardiac surgery.
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6
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Khan MA, Koh RGL, Rashidiani S, Liu T, Tucci V, Kumbhare D, Doyle TE. Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research. Artif Intell Med 2024; 151:102849. [PMID: 38574636 DOI: 10.1016/j.artmed.2024.102849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 03/15/2024] [Accepted: 03/19/2024] [Indexed: 04/06/2024]
Abstract
OBJECTIVE The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management. METHODS A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used. RESULTS After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP. CONCLUSION The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures. SIGNIFICANCE This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.
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Affiliation(s)
- Md Asif Khan
- Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Ryan G L Koh
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON M5G 2A2, Canada
| | - Sajjad Rashidiani
- Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Theodore Liu
- Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Victoria Tucci
- Faculty of Health Sciences at McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Dinesh Kumbhare
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON M5G 2A2, Canada
| | - Thomas E Doyle
- Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON L8S 4K1, Canada.
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7
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Wu L, Wang H, Chen Y, Zhang X, Zhang T, Shen N, Tao G, Sun Z, Ding Y, Wang W, Bu J. Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning. iScience 2023; 26:108183. [PMID: 38026220 PMCID: PMC10654534 DOI: 10.1016/j.isci.2023.108183] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/22/2023] [Accepted: 10/09/2023] [Indexed: 12/01/2023] Open
Abstract
Accurate detection of liver lesions from multi-phase contrast-enhanced CT (CECT) scans is a fundamental step for precise liver diagnosis and treatment. However, the analysis of multi-phase contexts is heavily challenged by the misalignment caused by respiration coupled with the movement of organs. Here, we proposed an AI system for multi-phase liver lesion segmentation (named MULLET) for precise and fully automatic segmentation of real-patient CECT images. MULLET enables effectively embedding the important ROIs of CECT images and exploring multi-phase contexts by introducing a transformer-based attention mechanism. Evaluated on 1,229 CECT scans from 1,197 patients, MULLET demonstrated significant performance gains in terms of Dice, Recall, and F2 score, which are 5.80%, 6.57%, and 5.87% higher than state of the arts, respectively. MULLET has been successfully deployed in real-world settings. The deployed AI web server provides a powerful system to boost clinical workflows of liver lesion diagnosis and could be straightforwardly extended to general CECT analyses.
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Affiliation(s)
- Lei Wu
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
- Pujian Technology, Hangzhou, Zhejiang, China
| | - Haishuai Wang
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
| | - Yining Chen
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiang Zhang
- Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tianyun Zhang
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
| | - Ning Shen
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
| | - Guangyu Tao
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhongquan Sun
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuan Ding
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weilin Wang
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiajun Bu
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
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8
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Yan Y, Xu Y, Xue JH, Lu Y, Wang H, Zhu W. Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:7071-7084. [PMID: 35604981 DOI: 10.1109/tcyb.2022.3173356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Person attribute recognition (PAR) aims to simultaneously predict multiple attributes of a person. Existing deep learning-based PAR methods have achieved impressive performance. Unfortunately, these methods usually ignore the fact that different attributes have an imbalance in the number of noisy-labeled samples in the PAR training datasets, thus leading to suboptimal performance. To address the above problem of imbalanced noisy-labeled samples, we propose a novel and effective loss called drop loss for PAR. In the drop loss, the attributes are treated differently in an easy-to-hard way. In particular, the noisy-labeled candidates, which are identified according to their gradient norms, are dropped with a higher drop rate for the harder attribute. Such a manner adaptively alleviates the adverse effect of imbalanced noisy-labeled samples on model learning. To illustrate the effectiveness of the proposed loss, we train a simple ResNet-50 model based on the drop loss and term it DropNet. Experimental results on two representative PAR tasks (including facial attribute recognition and pedestrian attribute recognition) demonstrate that the proposed DropNet achieves comparable or better performance in terms of both balanced accuracy and classification accuracy over several state-of-the-art PAR methods.
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9
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Sheetrit E, Brief M, Elisha O. Predicting unplanned readmissions in the intensive care unit: a multimodality evaluation. Sci Rep 2023; 13:15426. [PMID: 37723231 PMCID: PMC10507073 DOI: 10.1038/s41598-023-42372-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 09/09/2023] [Indexed: 09/20/2023] Open
Abstract
A hospital readmission is when a patient who was discharged from the hospital is admitted again for the same or related care within a certain period. Hospital readmissions are a significant problem in the healthcare domain, as they lead to increased hospitalization costs, decreased patient satisfaction, and increased risk of adverse outcomes such as infections, medication errors, and even death. The problem of hospital readmissions is particularly acute in intensive care units (ICUs), due to the severity of the patients' conditions, and the substantial risk of complications. Predicting Unplanned Readmissions in ICUs is a challenging task, as it involves analyzing different data modalities, such as static data, unstructured free text, sequences of diagnoses and procedures, and multivariate time-series. Here, we investigate the effectiveness of each data modality separately, then alongside with others, using state-of-the-art machine learning approaches in time-series analysis and natural language processing. Using our evaluation process, we are able to determine the contribution of each data modality, and for the first time in the context of readmission, establish a hierarchy of their predictive value. Additionally, we demonstrate the impact of Temporal Abstractions in enhancing the performance of time-series approaches to readmission prediction. Due to conflicting definitions in the literature, we also provide a clear definition of the term Unplanned Readmission to enhance reproducibility and consistency of future research and to prevent any potential misunderstandings that could result from diverse interpretations of the term. Our experimental results on a large benchmark clinical data set show that Discharge Notes written by physicians, have better capabilities for readmission prediction than all other modalities.
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10
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Dong B, Wang Z, Li Z, Duan Z, Xu J, Pan T, Zhang R, Liu N, Li X, Wang J, Liu C, Dong L, Mao C, Gao J, Wang J. Toward a stable and low-resource PLM-based medical diagnostic system via prompt tuning and MoE structure. Sci Rep 2023; 13:12595. [PMID: 37537202 PMCID: PMC10400680 DOI: 10.1038/s41598-023-39543-2] [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: 11/25/2022] [Accepted: 07/26/2023] [Indexed: 08/05/2023] Open
Abstract
Machine learning (ML) has been extensively involved in assistant disease diagnosis and prediction systems to emancipate the serious dependence on medical resources and improve healthcare quality. Moreover, with the booming of pre-training language models (PLMs), the application prospect and promotion potential of machine learning methods in the relevant field have been further inspired. PLMs have recently achieved tremendous success in diverse text processing tasks, whereas limited by the significant semantic gap between the pre-training corpus and the structured electronic health records (EHRs), PLMs cannot converge to anticipated disease diagnosis and prediction results. Unfortunately, establishing connections between PLMs and EHRs typically requires the extraction of curated predictor variables from structured EHR resources, which is tedious and labor-intensive, and even discards vast implicit information.In this work, we propose an Input Prompting and Discriminative language model with the Mixture-of-experts framework (IPDM) by promoting the model's capabilities to learn knowledge from heterogeneous information and facilitating the feature-aware ability of the model. Furthermore, leveraging the prompt-tuning mechanism, IPDM can inherit the impacts of the pre-training in downstream tasks exclusively through minor modifications. IPDM remarkably outperforms existing models, proved by experiments on one disease diagnosis task and two disease prediction tasks. Finally, experiments with few-feature and few-sample demonstrate that IPDM achieves significant stability and impressive performance in predicting chronic diseases with unclear early-onset characteristics or sudden diseases with insufficient data, which verifies the superiority of IPDM over existing mainstream methods, and reveals the IPDM can powerfully address the aforementioned challenges via establishing a stable and low-resource medical diagnostic system for various clinical scenarios.
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Affiliation(s)
- Bowen Dong
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Zhuo Wang
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Zhenyu Li
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Zhichao Duan
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Jiacheng Xu
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Tengyu Pan
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Rui Zhang
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Ning Liu
- School of Software, Shandong University, Jinan, China
| | - Xiuxing Li
- Key Laboratory of Intelligent Information Processing Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS), Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jie Wang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China
| | - Caiyan Liu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China
| | - Liling Dong
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China
| | - Chenhui Mao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China
| | - Jing Gao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China
| | - Jianyong Wang
- Department of Computer Science and Technology, Tsinghua University, Beijing, China.
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11
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Gao X, Alam S, Shi P, Dexter F, Kong N. Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach. BMC Med Inform Decis Mak 2023; 23:104. [PMID: 37277767 DOI: 10.1186/s12911-023-02193-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 05/09/2023] [Indexed: 06/07/2023] Open
Abstract
BACKGROUND Advanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can achieve. However, their limited interpretability imposes barriers for practitioners to adopt them. Recent advancements in interpretable machine learning tools allow us to look inside the black box of advanced prediction methods to extract interpretable models while maintaining similar prediction accuracy, but few studies have investigated the specific hospital readmission prediction problem with this spirit. METHODS Our goal is to develop a machine-learning (ML) algorithm that can predict 30- and 90- day hospital readmissions as accurately as black box algorithms while providing medically interpretable insights into readmission risk factors. Leveraging a state-of-art interpretable ML model, we use a two-step Extracted Regression Tree approach to achieve this goal. In the first step, we train a black box prediction algorithm. In the second step, we extract a regression tree from the output of the black box algorithm that allows direct interpretation of medically relevant risk factors. We use data from a large teaching hospital in Asia to learn the ML model and verify our two-step approach. RESULTS The two-step method can obtain similar prediction performance as the best black box model, such as Neural Networks, measured by three metrics: accuracy, the Area Under the Curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC), while maintaining interpretability. Further, to examine whether the prediction results match the known medical insights (i.e., the model is truly interpretable and produces reasonable results), we show that key readmission risk factors extracted by the two-step approach are consistent with those found in the medical literature. CONCLUSIONS The proposed two-step approach yields meaningful prediction results that are both accurate and interpretable. This study suggests a viable means to improve the trust of machine learning based models in clinical practice for predicting readmissions through the two-step approach.
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Affiliation(s)
- Xiaoquan Gao
- School of Industrial Engineering, Purdue University, West Lafayette, USA
| | - Sabriya Alam
- Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, USA
| | - Pengyi Shi
- Krannert School of Management, Purdue University, West Lafayette, USA.
| | | | - Nan Kong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, USA
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12
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Burke HM, Carter J. Integration of patient experience factors improves readmission prediction. Medicine (Baltimore) 2023; 102:e32632. [PMID: 36701722 PMCID: PMC9857268 DOI: 10.1097/md.0000000000032632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Many readmission prediction models have marginal accuracy and are based on clinical and demographic data that exclude patient response data. The objective of this study was to evaluate the accuracy of a 30-day hospital readmission prediction model that incorporates patient response data capturing the patient experience. This was a prospective cohort study of 30-day hospital readmissions. A logistic regression model to predict readmission risk was created using patient responses obtained during interviewer-administered questionnaires as well as demographic and clinical data. Participants (N = 846) were admitted to 2 inpatient adult medicine units at Massachusetts General Hospital from 2012 to 2016. The primary outcome was the accuracy (measured by receiver operating characteristic) of a 30-day readmission risk prediction model. Secondary analyses included a readmission-focused factor analysis of individual versus collective patient experience questions. Of 1754 eligible participants, 846 (48%) were enrolled and 201 (23.8%) had a 30-day readmission. Demographic factors had an accuracy of 0.56 (confidence interval [CI], 0.50-0.62), clinical disease factors had an accuracy of 0.59 (CI, 0.54-0.65), and the patient experience factors had an accuracy of 0.60 (CI, 0.56-0.64). Taken together, their combined accuracy of receiver operating characteristic = 0.78 (CI, 0.74-0.82) was significantly more accurate than these factors were individually. The individual accuracy of patient experience, demographic, and clinical data was relatively poor and consistent with other risk prediction models. The combination of the 3 types of data significantly improved the ability to predict 30-day readmissions. This study suggests that more accurate 30-day readmission risk prediction models can be generated by including information about the patient experience.
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Affiliation(s)
| | - Jocelyn Carter
- Harvard Medical School, Boston, United States
- Massachusetts General Hospital, Boston, United States
- * Correspondence: Jocelyn Carter, Massachusetts General Hospital, 55 Fruit Street, Blake 15, Boston, MA 02114, United States (e-mail: )
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13
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Tang S, Tariq A, Dunnmon JA, Sharma U, Elugunti P, Rubin DL, Patel BN, Banerjee I. Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networks. IEEE J Biomed Health Inform 2023; PP:10.1109/JBHI.2023.3236888. [PMID: 37018684 PMCID: PMC11073780 DOI: 10.1109/jbhi.2023.3236888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Reduction in 30-day readmission rate is an important quality factor for hospitals as it can reduce the overall cost of care and improve patient post-discharge outcomes. While deep-learning-based studies have shown promising empirical results, several limitations exist in prior models for hospital readmission prediction, such as: (a) only patients with certain conditions are considered, (b) do not leverage data temporality, (c) individual admissions are assumed independent of each other, which ignores patient similarity, (d) limited to single modality or single center data. In this study, we propose a multimodal, spatiotemporal graph neural network (MM-STGNN) for prediction of 30-day all-cause hospital readmission, which fuses in-patient multimodal, longitudinal data and models patient similarity using a graph. Using longitudinal chest radiographs and electronic health records from two independent centers, we show that MM-STGNN achieved an area under the receiver operating characteristic curve (AUROC) of 0.79 on both datasets. Furthermore, MM-STGNN significantly outperformed the current clinical reference standard, LACE+ (AUROC=0.61), on the internal dataset. For subset populations of patients with heart disease, our model significantly outperformed baselines, such as gradient-boosting and Long Short-Term Memory models (e.g., AUROC improved by 3.7 points in patients with heart disease). Qualitative interpretability analysis indicated that while patients' primary diagnoses were not explicitly used to train the model, features crucial for model prediction may reflect patients' diagnoses. Our model could be utilized as an additional clinical decision aid during discharge disposition and triaging high-risk patients for closer post-discharge follow-up for potential preventive measures.
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14
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Kessler S, Schroeder D, Korlakov S, Hettlich V, Kalkhoff S, Moazemi S, Lichtenberg A, Schmid F, Aubin H. Predicting readmission to the cardiovascular intensive care unit using recurrent neural networks. Digit Health 2023; 9:20552076221149529. [PMID: 36644663 PMCID: PMC9834934 DOI: 10.1177/20552076221149529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 12/18/2022] [Indexed: 01/11/2023] Open
Abstract
If a patient can be discharged from an intensive care unit (ICU) is usually decided by the treating physicians based on their clinical experience. However, nowadays limited capacities and growing socioeconomic burden of our health systems increase the pressure to discharge patients as early as possible, which may lead to higher readmission rates and potentially fatal consequences for the patients. Therefore, here we present a long short-term memory-based deep learning model (LSTM) trained on time series data from Medical Information Mart for Intensive Care (MIMIC-III) dataset to assist physicians in making decisions if patients can be safely discharged from cardiovascular ICUs. To underline the strengths of our LSTM we compare its performance with a logistic regression model, a random forest, extra trees, a feedforward neural network and with an already known, more complex LSTM as well as an LSTM combined with a convolutional neural network. The results of our evaluation show that our LSTM outperforms most of the above models in terms of area under receiver operating characteristic curve. Moreover, our LSTM shows the best performance with respect to the area under precision-recall curve. The deep learning solution presented in this article can help physicians decide on patient discharge from the ICU. This may not only help to increase the quality of patient care, but may also help to reduce costs and to optimize ICU resources. Further, the presented LSTM-based approach may help to improve existing and develop new medical machine learning prediction models.
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Affiliation(s)
- Steven Kessler
- Digital Health Lab Düsseldorf, University Hospital Düsseldorf,
Düsseldorf, Germany,Department of Cardiac Surgery, University Hospital Düsseldorf,
Düsseldorf, Germany
| | - Dennis Schroeder
- Digital Health Lab Düsseldorf, University Hospital Düsseldorf,
Düsseldorf, Germany,Department of Cardiac Surgery, University Hospital Düsseldorf,
Düsseldorf, Germany
| | - Sergej Korlakov
- Digital Health Lab Düsseldorf, University Hospital Düsseldorf,
Düsseldorf, Germany,Department of Cardiac Surgery, University Hospital Düsseldorf,
Düsseldorf, Germany
| | - Vincent Hettlich
- Digital Health Lab Düsseldorf, University Hospital Düsseldorf,
Düsseldorf, Germany,Department of Cardiac Surgery, University Hospital Düsseldorf,
Düsseldorf, Germany
| | - Sebastian Kalkhoff
- Digital Health Lab Düsseldorf, University Hospital Düsseldorf,
Düsseldorf, Germany,Department of Cardiac Surgery, University Hospital Düsseldorf,
Düsseldorf, Germany
| | - Sobhan Moazemi
- Digital Health Lab Düsseldorf, University Hospital Düsseldorf,
Düsseldorf, Germany,Department of Cardiac Surgery, University Hospital Düsseldorf,
Düsseldorf, Germany
| | - Artur Lichtenberg
- Digital Health Lab Düsseldorf, University Hospital Düsseldorf,
Düsseldorf, Germany,Department of Cardiac Surgery, University Hospital Düsseldorf,
Düsseldorf, Germany
| | - Falko Schmid
- Digital Health Lab Düsseldorf, University Hospital Düsseldorf,
Düsseldorf, Germany,Department of Cardiac Surgery, University Hospital Düsseldorf,
Düsseldorf, Germany,Falko Schmid, Digital Health Lab
Düsseldorf, University Hospital Düsseldorf, Moorenstr. 5, Düsseldorf,
Düsseldorf, NRW 40225, Germany.
| | - Hug Aubin
- Digital Health Lab Düsseldorf, University Hospital Düsseldorf,
Düsseldorf, Germany,Department of Cardiac Surgery, University Hospital Düsseldorf,
Düsseldorf, Germany
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15
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The class imbalance problem in deep learning. Mach Learn 2022. [DOI: 10.1007/s10994-022-06268-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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16
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Zojaji Z, Tork Ladani B. Adaptive cost-sensitive stance classification model for rumor detection in social networks. SOCIAL NETWORK ANALYSIS AND MINING 2022; 12:134. [PMID: 36105920 PMCID: PMC9461462 DOI: 10.1007/s13278-022-00952-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 12/03/2022]
Abstract
As online social networks are experiencing extreme popularity growth, determining the veracity of online statements denoted by rumors automatically as earliest as possible is essential to prevent the harmful effects of propagating misinformation. Early detection of rumors is facilitated by considering the wisdom of the crowd through analyzing different attitudes expressed toward a rumor (i.e., users’ stances). Stance detection is an imbalanced problem as the querying and denying stances against a given rumor are significantly less than supportive and commenting stances. However, the success of stance-based rumor detection significantly depends on the efficient detection of “query” and “deny” classes. The imbalance problem has led the previous stance classifier models to bias toward the majority classes and ignore the minority ones. Consequently, the stance and subsequently rumor classifiers have been faced with the problem of low performance. This paper proposes a novel adaptive cost-sensitive loss function for learning imbalanced stance data using deep neural networks, which improves the performance of stance classifiers in rare classes. The proposed loss function is a cost-sensitive form of cross-entropy loss. In contrast to most of the existing cost-sensitive deep neural network models, the utilized cost matrix is not manually set but adaptively tuned during the learning process. Hence, the contributions of the proposed method are both in the formulation of the loss function and the algorithm for calculating adaptive costs. The experimental results of applying the proposed algorithm to stance classification of real Twitter and Reddit data demonstrate its capability in detecting rare classes while improving the overall performance. The proposed method improves the mean F-score of rare classes by about 13% in RumorEval 2017 dataset and about 20% in RumorEval 2019 dataset.
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17
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Luo M, Wang YT, Wang XK, Hou WH, Huang RL, Liu Y, Wang JQ. A multi-granularity convolutional neural network model with temporal information and attention mechanism for efficient diabetes medical cost prediction. Comput Biol Med 2022; 151:106246. [PMID: 36343403 DOI: 10.1016/j.compbiomed.2022.106246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/30/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
As the cost of diabetes treatment continues to grow, it is critical to accurately predict the medical costs of diabetes. Most medical cost studies based on convolutional neural networks (CNNs) ignore the importance of multi-granularity information of medical concepts and time interval characteristics of patients' multiple visit sequences, which reflect the frequency of patient visits and the severity of the disease. Therefore, this paper proposes a new end-to-end deep neural network structure, MST-CNN, for medical cost prediction. The MST-CNN model improves the representation quality of medical concepts by constructing a multi-granularity embedding model of medical concepts and incorporates a time interval vector to accurately measure the frequency of patient visits and form an accurate representation of medical events. Moreover, the MST-CNN model integrates a channel attention mechanism to adaptively adjust the channel weights to focus on significant medical features. The MST-CNN model systematically addresses the problem of deep learning models for temporal data representation. A case study and three comparative experiments are conducted using data collected from Pingjiang County. Through experiments, the methods used in the proposed model are analyzed, and the super contribution of the model performance is demonstrated.
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Affiliation(s)
- Min Luo
- School of Business, Central South University, Changsha, 410083, PR China
| | - Yi-Ting Wang
- School of Business, Central South University, Changsha, 410083, PR China
| | - Xiao-Kang Wang
- School of Business, Central South University, Changsha, 410083, PR China
| | - Wen-Hui Hou
- School of Business, Central South University, Changsha, 410083, PR China
| | - Rui-Lu Huang
- School of Business, Central South University, Changsha, 410083, PR China
| | - Ye Liu
- School of Business, Central South University, Changsha, 410083, PR China
| | - Jian-Qiang Wang
- School of Business, Central South University, Changsha, 410083, PR China.
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18
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Davis S, Zhang J, Lee I, Rezaei M, Greiner R, McAlister FA, Padwal R. Effective hospital readmission prediction models using machine-learned features. BMC Health Serv Res 2022; 22:1415. [PMID: 36434628 PMCID: PMC9700920 DOI: 10.1186/s12913-022-08748-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/05/2022] [Accepted: 10/14/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Hospital readmissions are one of the costliest challenges facing healthcare systems, but conventional models fail to predict readmissions well. Many existing models use exclusively manually-engineered features, which are labor intensive and dataset-specific. Our objective was to develop and evaluate models to predict hospital readmissions using derived features that are automatically generated from longitudinal data using machine learning techniques. METHODS We studied patients discharged from acute care facilities in 2015 and 2016 in Alberta, Canada, excluding those who were hospitalized to give birth or for a psychiatric condition. We used population-level linked administrative hospital data from 2011 to 2017 to train prediction models using both manually derived features and features generated automatically from observational data. The target value of interest was 30-day all-cause hospital readmissions, with the success of prediction measured using the area under the curve (AUC) statistic. RESULTS Data from 428,669 patients (62% female, 38% male, 27% 65 years or older) were used for training and evaluating models: 24,974 (5.83%) were readmitted within 30 days of discharge for any reason. Patients were more likely to be readmitted if they utilized hospital care more, had more physician office visits, had more prescriptions, had a chronic condition, or were 65 years old or older. The LACE readmission prediction model had an AUC of 0.66 ± 0.0064 while the machine learning model's test set AUC was 0.83 ± 0.0045, based on learning a gradient boosting machine on a combination of machine-learned and manually-derived features. CONCLUSION Applying a machine learning model to the computer-generated and manual features improved prediction accuracy over the LACE model and a model that used only manually-derived features. Our model can be used to identify high-risk patients, for whom targeted interventions may potentially prevent readmissions.
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Affiliation(s)
- Sacha Davis
- grid.17089.370000 0001 2190 316XDepartment of Computing Science, University of Alberta, Edmonton, AB Canada
| | - Jin Zhang
- grid.17089.370000 0001 2190 316XAlberta School of Business, University of Alberta, Edmonton, AB Canada
| | - Ilbin Lee
- grid.17089.370000 0001 2190 316XAlberta School of Business, University of Alberta, Edmonton, AB Canada
| | - Mostafa Rezaei
- grid.462233.20000 0001 1544 4083ESCP Business School, Paris, France
| | - Russell Greiner
- grid.17089.370000 0001 2190 316XDepartment of Computing Science, University of Alberta, Edmonton, AB Canada ,Alberta Machine Intelligence Institute, Edmonton, AB Canada
| | - Finlay A. McAlister
- grid.17089.370000 0001 2190 316XMedicine and Dentistry, University of Alberta, Edmonton, AB Canada
| | - Raj Padwal
- grid.17089.370000 0001 2190 316XMedicine and Dentistry, University of Alberta, Edmonton, AB Canada
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19
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Mandreoli F, Ferrari D, Guidetti V, Motta F, Missier P. Real-world data mining meets clinical practice: Research challenges and perspective. Front Big Data 2022; 5:1021621. [PMID: 36338334 PMCID: PMC9633944 DOI: 10.3389/fdata.2022.1021621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 09/28/2022] [Indexed: 11/06/2022] Open
Abstract
As Big Data Analysis meets healthcare applications, domain-specific challenges and opportunities materialize in all aspects of data science. Advanced statistical methods and Artificial Intelligence (AI) on Electronic Health Records (EHRs) are used both for knowledge discovery purposes and clinical decision support. Such techniques enable the emerging Predictive, Preventative, Personalized, and Participatory Medicine (P4M) paradigm. Working with the Infectious Disease Clinic of the University Hospital of Modena, Italy, we have developed a range of Data-Driven (DD) approaches to solve critical clinical applications using statistics, Machine Learning (ML) and Big Data Analytics on real-world EHR. Here, we describe our perspective on the challenges we encountered. Some are connected to medical data and their sparse, scarce, and unbalanced nature. Others are bound to the application environment, as medical AI tools can affect people's health and life. For each of these problems, we report some available techniques to tackle them, present examples drawn from our experience, and propose which approaches, in our opinion, could lead to successful real-world, end-to-end implementations. DESY report number DESY-22-153.
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Affiliation(s)
- Federica Mandreoli
- Department of Physics, Informatics and Mathematics, Università di Modena e Reggio Emilia, Modena, Italy
| | - Davide Ferrari
- Department of Population Health Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Fundation Trust, London, United Kingdom
| | | | - Federico Motta
- Department of Physics, Informatics and Mathematics, Università di Modena e Reggio Emilia, Modena, Italy
| | - Paolo Missier
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
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20
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Davazdahemami B, Zolbanin HM, Delen D. An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions. DECISION SUPPORT SYSTEMS 2022; 161:113730. [PMID: 35068629 PMCID: PMC8763415 DOI: 10.1016/j.dss.2022.113730] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 08/21/2021] [Accepted: 01/10/2022] [Indexed: 05/10/2023]
Abstract
One of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory-predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies.
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Affiliation(s)
- Behrooz Davazdahemami
- Department of IT & Supply Chain Management, University of Wisconsin-Whitewater, United States
| | - Hamed M Zolbanin
- Department of MIS, Operations & Supply Chain Management, Business Analytics, University of Dayton, United States
| | - Dursun Delen
- Center for Health Systems Innovation, Spears School of Business, Oklahoma State University, United States
- School of Business, Ibn Haldun University, Istanbul, Turkey
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21
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Wang S, Zhu X. Predictive Modeling of Hospital Readmission: Challenges and Solutions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2975-2995. [PMID: 34133285 DOI: 10.1109/tcbb.2021.3089682] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, e.g. 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital readmission rate, and eventually reduce the medical costs. Due to inherent complexity of diseases and healthcare ecosystems, modeling hospital readmission is facing many challenges. By now, a variety of methods have been developed, but existing literature fails to deliver a complete picture to answer some fundamental questions, such as what are the main challenges and solutions in modeling hospital readmission; what are typical features/models used for readmission prediction; how to achieve meaningful and transparent predictions for decision making; and what are possible conflicts when deploying predictive approaches for real-world usages. In this paper, we systematically review computational models for hospital readmission prediction, and propose a taxonomy of challenges featuring four main categories: (1) data variety and complexity; (2) data imbalance, locality and privacy; (3) model interpretability; and (4) model implementation. The review summarizes methods in each category, and highlights technical solutions proposed to address the challenges. In addition, a review of datasets and resources available for hospital readmission modeling also provides firsthand materials to support researchers and practitioners to design new approaches for effective and efficient hospital readmission prediction.
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22
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Goswami M, Daultani Y, Paul SK, Pratap S. A framework for the estimation of treatment costs of cardiovascular conditions in the presence of disease transition. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-40. [PMID: 36035451 PMCID: PMC9396609 DOI: 10.1007/s10479-022-04914-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
The current research aims to aid policymakers and healthcare service providers in estimating expected long-term costs of medical treatment, particularly for chronic conditions characterized by disease transition. The study comprised two phases (qualitative and quantitative), in which we developed linear optimization-based mathematical frameworks to ascertain the expected long-term treatment cost per patient considering the integration of various related dimensions such as the progression of the medical condition, the accuracy of medical treatment, treatment decisions at respective severity levels of the medical condition, and randomized/deterministic policies. At the qualitative research stage, we conducted the data collection and validation of various cogent hypotheses acting as inputs to the prescriptive modeling stage. We relied on data collected from 115 different cardio-vascular clinicians to understand the nuances of disease transition and related medical dimensions. The framework developed was implemented in the context of a multi-specialty hospital chain headquartered in the capital city of a state in Eastern India, the results of which have led to some interesting insights. For instance, at the prescriptive modeling stage, though one of our contributions related to the development of a novel medical decision-making framework, we illustrated that the randomized versus deterministic policy seemed more cost-competitive. We also identified that the expected treatment cost was most sensitive to variations in steady-state probability at the "major" as opposed to the "severe" stage of a medical condition, even though the steady-state probability of the "severe" state was less than that of the "major" state.
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Affiliation(s)
- Mohit Goswami
- Operations Management Group, Indian Institute of Management Raipur, Abhanpur, India
| | - Yash Daultani
- Operations Management Group, Indian Institute of Management Lucknow, Lucknow, India
| | - Sanjoy Kumar Paul
- UTS Business School, University of Technology Sydney, Sydney, Australia
| | - Saurabh Pratap
- Department of Mechanical Engineering, Indian Institute of Technology (BHU), Varanasi, India
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23
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Wang L, Zhang L, Qi X, Yi Z. Deep Attention-Based Imbalanced Image Classification. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:3320-3330. [PMID: 33507873 DOI: 10.1109/tnnls.2021.3051721] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Class imbalance is a common problem in real-world image classification problems, some classes are with abundant data, and the other classes are not. In this case, the representations of classifiers are likely to be biased toward the majority classes and it is challenging to learn proper features, leading to unpromising performance. To eliminate this biased feature representation, many algorithm-level methods learn to pay more attention to the minority classes explicitly according to the prior knowledge of the data distribution. In this article, an attention-based approach called deep attention-based imbalanced image classification (DAIIC) is proposed to automatically pay more attention to the minority classes in a data-driven manner. In the proposed method, an attention network and a novel attention augmented logistic regression function are employed to encapsulate as many features, which belongs to the minority classes, as possible into the discriminative feature learning process by assigning the attention for different classes jointly in both the prediction and feature spaces. With the proposed object function, DAIIC can automatically learn the misclassification costs for different classes. Then, the learned misclassification costs can be used to guide the training process to learn more discriminative features using the designed attention networks. Furthermore, the proposed method is applicable to various types of networks and data sets. Experimental results on both single-label and multilabel imbalanced image classification data sets show that the proposed method has good generalizability and outperforms several state-of-the-art methods for imbalanced image classification.
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24
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Ebiaredoh-Mienye SA, Swart TG, Esenogho E, Mienye ID. A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease. Bioengineering (Basel) 2022; 9:350. [PMID: 36004875 PMCID: PMC9405039 DOI: 10.3390/bioengineering9080350] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/06/2022] [Accepted: 07/21/2022] [Indexed: 11/25/2022] Open
Abstract
The high prevalence of chronic kidney disease (CKD) is a significant public health concern globally. The condition has a high mortality rate, especially in developing countries. CKD often go undetected since there are no obvious early-stage symptoms. Meanwhile, early detection and on-time clinical intervention are necessary to reduce the disease progression. Machine learning (ML) models can provide an efficient and cost-effective computer-aided diagnosis to assist clinicians in achieving early CKD detection. This research proposed an approach to effectively detect CKD by combining the information-gain-based feature selection technique and a cost-sensitive adaptive boosting (AdaBoost) classifier. An approach like this could save CKD screening time and cost since only a few clinical test attributes would be needed for the diagnosis. The proposed approach was benchmarked against recently proposed CKD prediction methods and well-known classifiers. Among these classifiers, the proposed cost-sensitive AdaBoost trained with the reduced feature set achieved the best classification performance with an accuracy, sensitivity, and specificity of 99.8%, 100%, and 99.8%, respectively. Additionally, the experimental results show that the feature selection positively impacted the performance of the various classifiers. The proposed approach has produced an effective predictive model for CKD diagnosis and could be applied to more imbalanced medical datasets for effective disease detection.
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Affiliation(s)
- Sarah A. Ebiaredoh-Mienye
- Center for Telecommunications, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (S.A.E.-M.); (E.E.)
| | - Theo G. Swart
- Center for Telecommunications, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (S.A.E.-M.); (E.E.)
| | - Ebenezer Esenogho
- Center for Telecommunications, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (S.A.E.-M.); (E.E.)
| | - Ibomoiye Domor Mienye
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa;
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25
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Gopukumar D, Ghoshal A, Zhao H. A Machine Learning Approach for Predicting Readmission Charges Billed by Hospitals. JMIR Med Inform 2022; 10:e37578. [PMID: 35896038 PMCID: PMC9472041 DOI: 10.2196/37578] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/02/2022] [Accepted: 07/26/2022] [Indexed: 11/29/2022] Open
Abstract
Background The Centers for Medicare and Medicaid Services projects that health care costs will continue to grow over the next few years. Rising readmission costs contribute significantly to increasing health care costs. Multiple areas of health care, including readmissions, have benefited from the application of various machine learning algorithms in several ways. Objective We aimed to identify suitable models for predicting readmission charges billed by hospitals. Our literature review revealed that this application of machine learning is underexplored. We used various predictive methods, ranging from glass-box models (such as regularization techniques) to black-box models (such as deep learning–based models). Methods We defined readmissions as readmission with the same major diagnostic category (RSDC) and all-cause readmission category (RADC). For these readmission categories, 576,701 and 1,091,580 individuals, respectively, were identified from the Nationwide Readmission Database of the Healthcare Cost and Utilization Project by the Agency for Healthcare Research and Quality for 2013. Linear regression, lasso regression, elastic net, ridge regression, eXtreme gradient boosting (XGBoost), and a deep learning model based on multilayer perceptron (MLP) were the 6 machine learning algorithms we tested for RSDC and RADC through 10-fold cross-validation. Results Our preliminary analysis using a data-driven approach revealed that within RADC, the subsequent readmission charge billed per patient was higher than the previous charge for 541,090 individuals, and this number was 319,233 for RSDC. The top 3 major diagnostic categories (MDCs) for such instances were the same for RADC and RSDC. The average readmission charge billed was higher than the previous charge for 21 of the MDCs in the case of RSDC, whereas it was only for 13 of the MDCs in RADC. We recommend XGBoost and the deep learning model based on MLP for predicting readmission charges. The following performance metrics were obtained for XGBoost: (1) RADC (mean absolute percentage error [MAPE]=3.121%; root mean squared error [RMSE]=0.414; mean absolute error [MAE]=0.317; root relative squared error [RRSE]=0.410; relative absolute error [RAE]=0.399; normalized RMSE [NRMSE]=0.040; mean absolute deviation [MAD]=0.031) and (2) RSDC (MAPE=3.171%; RMSE=0.421; MAE=0.321; RRSE=0.407; RAE=0.393; NRMSE=0.041; MAD=0.031). The performance obtained for MLP-based deep neural networks are as follows: (1) RADC (MAPE=3.103%; RMSE=0.413; MAE=0.316; RRSE=0.410; RAE=0.397; NRMSE=0.040; MAD=0.031) and (2) RSDC (MAPE=3.202%; RMSE=0.427; MAE=0.326; RRSE=0.413; RAE=0.399; NRMSE=0.041; MAD=0.032). Repeated measures ANOVA revealed that the mean RMSE differed significantly across models with P<.001. Post hoc tests using the Bonferroni correction method indicated that the mean RMSE of the deep learning/XGBoost models was statistically significantly (P<.001) lower than that of all other models, namely linear regression/elastic net/lasso/ridge regression. Conclusions Models built using XGBoost and MLP are suitable for predicting readmission charges billed by hospitals. The MDCs allow models to accurately predict hospital readmission charges.
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Affiliation(s)
- Deepika Gopukumar
- Department of Health and Clinical Outcomes Research, School of Medicine, Saint Louis University, SALUS Center, 3545 Lafayette Ave., 4rth floor, Room 409 B, St.Louis, US
| | - Abhijeet Ghoshal
- Department of Business Administration, Gies College of Business, University of Illinois Urbana-Champaign, Champaign, US
| | - Huimin Zhao
- Sheldon B. Lubar College of Business, University of Wisconsin-Milwaukee, Milwaukee, US
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Xu Y, Yu Z, Chen CLP. Classifier Ensemble Based on Multiview Optimization for High-Dimensional Imbalanced Data Classification. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; PP:870-883. [PMID: 35657843 DOI: 10.1109/tnnls.2022.3177695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
High-dimensional class imbalanced data have plagued the performance of classification algorithms seriously. Because of a large number of redundant/invalid features and the class imbalanced issue, it is difficult to construct an optimal classifier for high-dimensional imbalanced data. Classifier ensemble has attracted intensive attention since it can achieve better performance than an individual classifier. In this work, we propose a multiview optimization (MVO) to learn more effective and robust features from high-dimensional imbalanced data, based on which an accurate and robust ensemble system is designed. Specifically, an optimized subview generation (OSG) in MVO is first proposed to generate multiple optimized subviews from different scenarios, which can strengthen the classification ability of features and increase the diversity of ensemble members simultaneously. Second, a new evaluation criterion that considers the distribution of data in each optimized subview is developed based on which a selective ensemble of optimized subviews (SEOS) is designed to perform the subview selective ensemble. Finally, an oversampling approach is executed on the optimized view to obtain a new class rebalanced subset for the classifier. Experimental results on 25 high-dimensional class imbalanced datasets indicate that the proposed method outperforms other mainstream classifier ensemble methods.
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Forecasting Hospital Readmissions with Machine Learning. Healthcare (Basel) 2022; 10:healthcare10060981. [PMID: 35742033 PMCID: PMC9222500 DOI: 10.3390/healthcare10060981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/21/2022] [Accepted: 05/21/2022] [Indexed: 11/17/2022] Open
Abstract
Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast patients’ readmissions allows for timely intervention and better post-discharge strategies, preventing future life-threatening events, and reducing medical costs to either the patient or the healthcare system. In this paper, four machine learning models are used to forecast readmissions: support vector machines with a linear kernel, support vector machines with an RBF kernel, balanced random forests, and weighted random forests. The dataset consists of 11,172 actual records of hospitalizations obtained from the General Hospital of Komotini “Sismanogleio” with a total of 24 independent variables. Each record is composed of administrative, medical-clinical, and operational variables. The experimental results indicate that the balanced random forest model outperforms the competition, reaching a sensitivity of 0.70 and an AUC value of 0.78.
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Wang YY, Hamad AS, Palaniappan K, Lever TE, Bunyak F. LARNet-STC: Spatio-temporal orthogonal region selection network for laryngeal closure detection in endoscopy videos. Comput Biol Med 2022; 144:105339. [PMID: 35263687 PMCID: PMC8995389 DOI: 10.1016/j.compbiomed.2022.105339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 02/18/2022] [Accepted: 02/19/2022] [Indexed: 11/28/2022]
Abstract
The vocal folds (VFs) are a pair of muscles in the larynx that play a critical role in breathing, swallowing, and speaking. VF function can be adversely affected by various medical conditions including head or neck injuries, stroke, tumor, and neurological disorders. In this paper, we propose a deep learning system for automated detection of laryngeal adductor reflex (LAR) events in laryngeal endoscopy videos to enable objective, quantitative analysis of VF function. The proposed deep learning system incorporates our novel orthogonal region selection network and temporal context. This network learns to directly map its input to a VF open/close state without first segmenting or tracking the VF region. This one-step approach drastically reduces manual annotation needs from labor-intensive segmentation masks or VF motion tracks to frame-level class labels. The proposed spatio-temporal network with an orthogonal region selection subnetwork allows integration of local image features, global image features, and VF state information in time for robust LAR event detection. The proposed network is evaluated against several network variations that incorporate temporal context and is shown to lead to better performance. The experimental results show promising performance for automated, objective, and quantitative analysis of LAR events from laryngeal endoscopy videos with over 90% and 99% F1 scores for LAR and non-LAR frames respectively.
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Affiliation(s)
- Yang Yang Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA
| | - Ali S Hamad
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA
| | - Kannappan Palaniappan
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA
| | - Teresa E Lever
- Department of Otolaryngology - Head and Neck Surgery, University of Missouri, Columbia, 65211, Missouri, USA
| | - Filiz Bunyak
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA.
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Wang G, Zhou T, Choi KS, Lu J. A Deep-Ensemble-Level-Based Interpretable Takagi-Sugeno-Kang Fuzzy Classifier for Imbalanced Data. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:3805-3818. [PMID: 32946410 DOI: 10.1109/tcyb.2020.3016972] [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
Existing research reveals that the misclassification rate for imbalanced data depends heavily on the problematic areas due to the existence of small disjoints, class overlap, borderline, and rare data samples. In this study, by stacking zero-order Takagi-Sugeno-Kang (TSK) fuzzy subclassifiers on the minority class and its problematic areas in the deep ensemble, a novel deep-ensemble-level-based TSK fuzzy classifier (IDE-TSK-FC) for imbalanced data classification tasks is presented to achieve both promising classification performance and high interpretability of zero-order TSK fuzzy classifiers. Simultaneously, according to the stacked generalization principle, the proposed classifier lifts up oversampling from the data level to the deep ensemble level with a guarantee of enhanced generalization capability for class imbalance learning. In the structure of IDE-TSK-FC, the first interpretable zero-order TSK fuzzy subclassifier is built on the original training dataset. After that, several successive zero-order TSK fuzzy subclassifiers are stacked layer by layer on the newly identified problematic areas from the original training dataset plus the corresponding interpretable predictions obtained by the averaging strategy on all previous layers. IDE-TSK-FC simply takes the classical K -nearest neighboring algorithm at each layer to identify its problematic area that consists of the minority samples and its surrounding K majority neighbors. After randomly neglecting certain input features and randomly selecting the five Gaussian membership functions for all the chosen input features and the augmented feature in the premise of each fuzzy rule, each subclassifier can be quickly obtained by using the least learning machine to determine the consequent part of each fuzzy rule. The experimental results on both the public datasets and a real-world healthcare dataset demonstrate IDE-TSK-FC's superiority in class imbalanced learning.
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Morid MA, Sheng ORL, Dunbar J. Time Series Prediction Using Deep Learning Methods in Healthcare. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2022. [DOI: 10.1145/3531326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Traditional Machine Learning (ML) methods face unique challenges when applied to healthcare predictive analytics. The high-dimensional nature of healthcare data necessitates labor-intensive and time-consuming processes when selecting an appropriate set of features for each new task. Furthermore, ML methods depend heavily on feature engineering to capture the sequential nature of patient data, oftentimes failing to adequately leverage the temporal patterns of medical events and their dependencies. In contrast, recent Deep Learning (DL) methods have shown promising performance for various healthcare prediction tasks by specifically addressing the high-dimensional and temporal challenges of medical data. DL techniques excel at learning useful representations of medical concepts and patient clinical data as well as their nonlinear interactions from high-dimensional raw or minimally-processed healthcare data.
In this paper we systematically reviewed research works that focused on advancing deep neural networks to leverage patient structured time series data for healthcare prediction tasks. To identify relevant studies, we searched MEDLINE, IEEE, Scopus, and ACM digital library for relevant publications through November 4
th
, 2021. Overall, we found that researchers have contributed to deep time series prediction literature in ten identifiable research streams: DL models, missing value handling, addressing temporal irregularity, patient representation, static data inclusion, attention mechanisms, interpretation, incorporation of medical ontologies, learning strategies, and scalability. This study summarizes research insights from these literature streams, identifies several critical research gaps, and suggests future research opportunities for DL applications using patient time series data.
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Affiliation(s)
- Mohammad Amin Morid
- Department of Information Systems and Analytics, Leavey School of Business, Santa Clara University, Santa Clara, CA, USA
| | - Olivia R. Liu Sheng
- Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, UT, USA
| | - Joseph Dunbar
- Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, UT, USA
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Huynh T, Nibali A, He Z. Semi-supervised learning for medical image classification using imbalanced training data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106628. [PMID: 35101700 DOI: 10.1016/j.cmpb.2022.106628] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/20/2021] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Medical image classification is often challenging for two reasons: a lack of labelled examples due to expensive and time-consuming annotation protocols, and imbalanced class labels due to the relative scarcity of disease-positive individuals in the wider population. Semi-supervised learning methods exist for dealing with a lack of labels, but they generally do not address the problem of class imbalance. Hence, the purpose of this study is to explore a new approach to perturbation-based semi-supervised learning which tackles the problem of applying semi-supervised learning to medical image classification with imbalanced training data. METHODS In this study we propose Adaptive Blended Consistency Loss (ABCL), a simple yet effective drop-in replacement for consistency loss in perturbation-based semi-supervised learning methods. ABCL counteracts data skew by adaptively mixing the target class distribution of the consistency loss in accordance with class frequency. Our proposed method is evaluated and compared with existing methods on two different imbalanced medical image classification datasets. An ablation study is also provided to analyse the properties and effectiveness of our proposed method. RESULTS Our experiments with ABCL reveal improvements to unweighted average recall (UAR) when compared with existing consistency losses that are not designed to counteract class imbalance and other existing methods. Our proposed ABCL method is able to improve the performance of the baseline consistency loss approach from 0.59 to 0.67 UAR and outperforms methods that address the class imbalance problem for labelled data (between 0.51 and 0.59 UAR) and for unlabelled data (0.61 UAR) on the imbalanced skin cancer dataset. On the imbalanced retinal fundus glaucoma dataset, ABCL (combined with Weighted Cross Entropy loss) achieves 0.67 UAR, which is an improvement over the best existing approach (0.57 UAR). CONCLUSIONS Overall the results show the effectiveness of ABCL to alleviate the class imbalance problem for semi-supervised classification for medical images.
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Affiliation(s)
- Tri Huynh
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia.
| | - Aiden Nibali
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
| | - Zhen He
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
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Dangut MD, Jennions IK, King S, Skaf Z. A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07167-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
AbstractThe use of aircraft operation logs to develop a data-driven model to predict probable failures that could cause interruption poses many challenges and has yet to be fully explored. Given that aircraft is high-integrity assets, failures are exceedingly rare. Hence, the distribution of relevant log data containing prior signs will be heavily skewed towards the typical (healthy) scenario. Thus, this study presents a novel deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks to handle extremely rare failure predictions in aircraft predictive maintenance modelling. The auto-encoder is modified and trained to detect rare failures, and the result from the auto-encoder is fed into the convolutional bidirectional gated recurrent unit network to predict the next occurrence of failure. The proposed network architecture with the rescaled focal loss addresses the imbalance problem during model training. The effectiveness of the proposed method is evaluated using real-world test cases of log-based warning and failure messages obtained from the fleet database of aircraft central maintenance system records. The proposed model is compared to other similar deep learning approaches. The results indicated an 18% increase in precision, a 5% increase in recall, and a 10% increase in G-mean values. It also demonstrates reliability in anticipating rare failures within a predetermined, meaningful time frame.
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Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data. ENTROPY 2022; 24:e24040442. [PMID: 35455105 PMCID: PMC9029105 DOI: 10.3390/e24040442] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/17/2022] [Accepted: 03/19/2022] [Indexed: 11/30/2022]
Abstract
As a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide detection, but related research still faces some difficulties, such as data imbalance and expression implicitness. In this paper, we propose a Deep Hierarchical Ensemble model for Suicide Detection (DHE-SD) based on a hierarchical ensemble strategy, and construct a dataset based on Sina Weibo, which contains more than 550 thousand posts from 4521 users. To verify the effectiveness of the model, we also conduct experiments on a public Weibo dataset containing 7329 users’ posts. The proposed model achieves the best performance on both the constructed dataset and the public dataset. In addition, in order to make the model applicable to a wider population, we use the proposed sentence-level mask mechanism to delete user posts with strong suicidal ideation. Experiments show that the proposed model can still effectively identify social media users with suicidal ideation even when the performance of the baseline models decrease significantly.
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Yang TY, Chien TW, Lai FJ. Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study. JMIR Med Inform 2022; 10:e33006. [PMID: 35262505 PMCID: PMC9282670 DOI: 10.2196/33006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 11/08/2021] [Accepted: 01/10/2022] [Indexed: 12/03/2022] Open
Abstract
Background Web-based computerized adaptive testing (CAT) implementation of the skin cancer (SC) risk scale could substantially reduce participant burden without compromising measurement precision. However, the CAT of SC classification has not been reported in academics thus far. Objective We aim to build a CAT-based model using machine learning to develop an app for automatic classification of SC to help patients assess the risk at an early stage. Methods We extracted data from a population-based Australian cohort study of SC risk (N=43,794) using the Rasch simulation scheme. All 30 feature items were calibrated using the Rasch partial credit model. A total of 1000 cases following a normal distribution (mean 0, SD 1) based on the item and threshold difficulties were simulated using three techniques of machine learning—naïve Bayes, k-nearest neighbors, and logistic regression—to compare the model accuracy in training and testing data sets with a proportion of 70:30, where the former was used to predict the latter. We calculated the sensitivity, specificity, receiver operating characteristic curve (area under the curve [AUC]), and CIs along with the accuracy and precision across the proposed models for comparison. An app that classifies the SC risk of the respondent was developed. Results We observed that the 30-item k-nearest neighbors model yielded higher AUC values of 99% and 91% for the 700 training and 300 testing cases, respectively, than its 2 counterparts using the hold-out validation but had lower AUC values of 85% (95% CI 83%-87%) in the k-fold cross-validation and that an app that predicts SC classification for patients was successfully developed and demonstrated in this study. Conclusions The 30-item SC prediction model, combined with the Rasch web-based CAT, is recommended for classifying SC in patients. An app we developed to help patients self-assess SC risk at an early stage is required for application in the future.
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Affiliation(s)
- Ting-Ya Yang
- Department of Family Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Feng-Jie Lai
- Department of Dermatology, Chi-Mei Medical Center, Tainan, Taiwan
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Telikani A, Gandomi AH, Choo KKR, Shen J. A Cost-Sensitive Deep Learning-Based Approach for Network Traffic Classification. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022. [DOI: 10.1109/tnsm.2021.3112283] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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36
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Wang H, Bowe B, Cui Z, Yang H, Swamidass SJ, Xie Y, Al-Aly Z. A Deep Learning Approach for the Estimation of Glomerular Filtration Rate. IEEE Trans Nanobioscience 2022; 21:560-569. [PMID: 35100119 DOI: 10.1109/tnb.2022.3147957] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
An accurate estimation of glomerular filtration rate (GFR) is clinically crucial for kidney disease diagnosis and predicting the prognosis of chronic kidney disease (CKD). Machine learning methodologies such as deep neural networks provide a potential avenue for increasing accuracy in GFR estimation. We developed a novel deep learning architecture, a deep and shallow neural network, to estimate GFR (dlGFR for short) and examined its comparative performance with estimated GFR from Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations. The dlGFR model jointly trains a shallow learning model and a deep neural network to enable both linear transformation from input features to a log GFR target, and non-linear feature embedding for stage of kidney function classification. We validate the proposed methods on the data from multiple studies obtained from the NIDDK Central Database Repository. The deep learning model predicted values of GFR within 30% of measured GFR with 88.3% accuracy, compared to the 87.1% and 84.7% of the accuracy achieved by CKD-EPI and MDRD equations (p=0.051 and p<0.001, respectively). Our results suggest that deep learning methods are superior to equations resulting from traditional statistical methods in estimating glomerular filtration rate. Based on these results, an end-to-end predication system has been deployed to facilitate use of the proposed dlGFR algorithm.
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Petsani D, Ahmed S, Petronikolou V, Kehayia E, Alastalo M, Santonen T, Merino-Barbancho B, Cea G, Segkouli S, Stavropoulos TG, Billis A, Doumas M, Almeida R, Nagy E, Broeckx L, Bamidis P, Konstantinidis E. Digital Biomarkers for Supporting Transitional Care Decisions: Protocol for a Transnational Feasibility Study. JMIR Res Protoc 2022; 11:e34573. [PMID: 35044303 PMCID: PMC8811685 DOI: 10.2196/34573] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
Background Virtual Health and Wellbeing Living Lab Infrastructure is a Horizon 2020 project that aims to harmonize Living Lab procedures and facilitate access to European health and well-being research infrastructures. In this context, this study presents a joint research activity that will be conducted within Virtual Health and Wellbeing Living Lab Infrastructure in the transitional care domain to test and validate the harmonized Living Lab procedures and infrastructures. The collection of data from various sources (information and communications technology and clinical and patient-reported outcome measures) demonstrated the capacity to assess risk and support decisions during care transitions, but there is no harmonized way of combining this information. Objective This study primarily aims to evaluate the feasibility and benefit of collecting multichannel data across Living Labs on the topic of transitional care and to harmonize data processes and collection. In addition, the authors aim to investigate the collection and use of digital biomarkers and explore initial patterns in the data that demonstrate the potential to predict transition outcomes, such as readmissions and adverse events. Methods The current research protocol presents a multicenter, prospective, observational cohort study that will consist of three phases, running consecutively in multiple sites: a cocreation phase, a testing and simulation phase, and a transnational pilot phase. The cocreation phase aims to build a common understanding among different sites, investigate the differences in hospitalization discharge management among countries, and the willingness of different stakeholders to use technological solutions in the transitional care process. The testing and simulation phase aims to explore ways of integrating observation of a patient’s clinical condition, patient involvement, and discharge education in transitional care. The objective of the simulation phase is to evaluate the feasibility and the barriers faced by health care professionals in assessing transition readiness. Results The cocreation phase will be completed by April 2022. The testing and simulation phase will begin in September 2022 and will partially overlap with the deployment of the transnational pilot phase that will start in the same month. The data collection of the transnational pilots will be finalized by the end of June 2023. Data processing is expected to be completed by March 2024. The results will consist of guidelines and implementation pathways for large-scale studies and an analysis for identifying initial patterns in the acquired data. Conclusions The knowledge acquired through this research will lead to harmonized procedures and data collection for Living Labs that support transitions in care. International Registered Report Identifier (IRRID) PRR1-10.2196/34573
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Affiliation(s)
- Despoina Petsani
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Sara Ahmed
- Faculty of Medicine, School of Physical & Occupational Therapy, McGill University, Montreal, QC, Canada.,Centre de Recherche Interdisciplinaire en Réadaptation, Constance-Lethbridge Rehabilitation Center du CIUSSS du Centre-Ouest-de-l'Île-de-Montréal, Montreal, QC, Canada.,Clinical Epidemiology, Centre for Outcomes Research and Evaluation (CORE), McGill University Health Center Research Institute, Montreal, QC, Canada
| | - Vasileia Petronikolou
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Eva Kehayia
- Faculty of Medicine, School of Physical & Occupational Therapy, McGill University, Montreal, QC, Canada.,Centre de Recherche Interdisciplinaire en Réadaptation, Constance-Lethbridge Rehabilitation Center du CIUSSS du Centre-Ouest-de-l'Île-de-Montréal, Montreal, QC, Canada
| | - Mika Alastalo
- Laurea University of Applied Sciences, Vantaa, Finland
| | | | | | - Gloria Cea
- Life Supporting Technologies, Universidad Politécnica de Madrid, Madrid, Spain
| | - Sofia Segkouli
- Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece
| | - Thanos G Stavropoulos
- Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece
| | - Antonis Billis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Michael Doumas
- Second Propedeutic Department of Internal Medicine, General Hospital "Hippokration", Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Rosa Almeida
- Fundación INTRAS, RDi Projects Department, Valladolid, Spain
| | - Enikő Nagy
- Nagykovácsi Wellbeing Living Lab, Nagykovácsi, Hungary
| | - Leen Broeckx
- Thomas More University of Applied Sciences - LiCalab, Antwerp, Belgium
| | - Panagiotis Bamidis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Evdokimos Konstantinidis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,European Network of Living Labs, Brussels, Belgium
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Wesołowski S, Lemmon G, Hernandez EJ, Henrie A, Miller TA, Weyhrauch D, Puchalski MD, Bray BE, Shah RU, Deshmukh VG, Delaney R, Yost HJ, Eilbeck K, Tristani-Firouzi M, Yandell M. An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records. PLOS DIGITAL HEALTH 2022; 1:e0000004. [PMID: 35373216 PMCID: PMC8975108 DOI: 10.1371/journal.pdig.0000004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/17/2021] [Indexed: 11/19/2022]
Abstract
Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.
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Affiliation(s)
- Sergiusz Wesołowski
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Gordon Lemmon
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Edgar J. Hernandez
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Alex Henrie
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Thomas A. Miller
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Derek Weyhrauch
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Michael D. Puchalski
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Bruce E. Bray
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- University of Utah, Biomedical Informatics, Salt Lake City, UT 84108, United States of America
| | - Rashmee U. Shah
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Vikrant G. Deshmukh
- University of Utah Health Care CMIO Office, Salt Lake City, UT, United States of America
| | - Rebecca Delaney
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States of America
| | - H. Joseph Yost
- Molecular Medicine Program, University of Utah, Salt Lake City, UT, United States of America
| | - Karen Eilbeck
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States of America
| | - Martin Tristani-Firouzi
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- Nora Eccles Harrison CVRTI, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Mark Yandell
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
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Zhang Y, Lam S, Yu T, Teng X, Zhang J, Lee FKH, Au KH, Yip CWY, Wang S, Cai J. Integration of an imbalance framework with novel high-generalizable classifiers for radiomics-based distant metastases prediction of advanced nasopharyngeal carcinoma. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107649] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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40
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Hsu CF, Chien TW, Yan YH. An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing: A development and usability study. Medicine (Baltimore) 2021; 100:e28457. [PMID: 34967385 PMCID: PMC8718177 DOI: 10.1097/md.0000000000028457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 12/02/2021] [Accepted: 12/09/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND The classification of a respondent's opinions online into positive and negative classes using a minimal number of questions is gradually changing and helps turn techniques into practices. A survey incorporating convolutional neural networks (CNNs) into web-based computerized adaptive testing (CAT) was used to collect perceptions on My Health Bank (MHB) from users in Taiwan. This study designed an online module to accurately and efficiently turn a respondent's perceptions into positive and negative classes using CNNs and web-based CAT. METHODS In all, 640 patients, family members, and caregivers with ages ranging from 20 to 70 years who were registered MHB users were invited to complete a 3-domain, 26-item, 5-category questionnaire asking about their perceptions on MHB (PMHB26) in 2019. The CNN algorithm and k-means clustering were used for dividing respondents into 2 classes of unsatisfied and satisfied classes and building a PMHB26 predictive model to estimate parameters. Exploratory factor analysis, the Rasch model, and descriptive statistics were used to examine the demographic characteristics and PMHB26 factors that were suitable for use in CNNs and Rasch multidimensional CAT (MCAT). An application was then designed to classify MHB perceptions. RESULTS We found that 3 construct factors were extracted from PMHB26. The reliability of PMHB26 for each subscale beyond 0.94 was evident based on internal consistency and stability in the data. We further found the following: the accuracy of PMHB26 with CNN yields a higher accuracy rate (0.98) with an area under the curve of 0.98 (95% confidence interval, 0.97-0.99) based on the 391 returned questionnaires; and for the efficiency, approximately one-third of the items were not necessary to answer in reducing the respondents' burdens using Rasch MCAT. CONCLUSIONS The PMHB26 CNN model, combined with the Rasch online MCAT, is recommended for improving the accuracy and efficiency of classifying patients' perceptions of MHB utility. An application developed for helping respondents self-assess the MHB cocreation of value can be applied to other surveys in the future.
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Affiliation(s)
- Chen-Fang Hsu
- Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan
- School of Medicine, College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research Department, Chi-Mei Medical Center, Tainan, Taiwan
| | - Yu-Hua Yan
- Superintendent Office, Tainan Municipal Hospital (Managed by Show Chwan Medical Care Corporation), Tainan, Taiwan
- Department of Hospital and Health Care Administration, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
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The Promise for Reducing Healthcare Cost with Predictive Model: An Analysis with Quantized Evaluation Metric on Readmission. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9208138. [PMID: 34765104 PMCID: PMC8577942 DOI: 10.1155/2021/9208138] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/15/2021] [Indexed: 11/23/2022]
Abstract
Quality of care data has gained transparency captured through various measurements and reporting. Readmission measure is especially related to unfavorable patient outcomes that directly bends the curve of healthcare cost. Under the Hospital Readmission Reduction Program, payments to hospitals were reduced for those with excessive 30-day rehospitalization rates. These penalties have intensified efforts from hospital stakeholders to implement strategies to reduce readmission rates. One of the key strategies is the deployment of predictive analytics stratified by patient population. The recent research in readmission model is focused on making its prediction more accurate. As cost-saving improvements through artificial intelligent-based health solutions are expected, the broad economic impact of such digital tool remains unknown. Meanwhile, reducing readmission rate is associated with increased operating expenses due to targeted interventions. The increase in operating margin can surpass native readmission cost. In this paper, we propose a quantized evaluation metric to provide a methodological mean in assessing whether a predictive model represents cost-effective way of delivering healthcare. Herein, we evaluate the impact machine learning has had on transitional care and readmission with proposed metric. The final model was estimated to produce net healthcare savings at over $1 million given a 50% rate of successfully preventing a readmission.
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42
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Compensating class imbalance for acoustic chimpanzee detection with convolutional recurrent neural networks. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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43
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Tong LL, Gu JB, Li JJ, Liu GX, Jin SW, Yan AY. Application of Bayesian network and regression method in treatment cost prediction. BMC Med Inform Decis Mak 2021; 21:284. [PMID: 34656109 PMCID: PMC8520647 DOI: 10.1186/s12911-021-01647-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 10/04/2021] [Indexed: 11/24/2022] Open
Abstract
Charging according to disease is an important way to effectively promote the reform of medical insurance mechanism, reasonably allocate medical resources and reduce the burden of patients, and it is also an important direction of medical development at home and abroad. The cost forecast of single disease can not only find the potential influence and driving factors, but also estimate the active cost, and tell the management and reasonable allocation of medical resources. In this paper, a method of Bayesian network combined with regression analysis is proposed to predict the cost of treatment based on the patient's electronic medical record when the amount of data is small. Firstly, a set of text-based medical record data conversion method is established, and in the clustering method, the missing value interpolation is carried out by weighted method according to the distance, which completes the data preparation and processing for the realization of data prediction. Then, aiming at the problem of low prediction accuracy of traditional regression model, this paper establishes a prediction model combined with local weight regression method after Bayesian network interpretation and classification of patients' treatment process. Finally, the model is verified with the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy.
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Affiliation(s)
- Li-Li Tong
- Cancer Hospital of China Medical University, Shenyang, China. .,Liaoning Cancer Hospital & Institute, Shenyang, China.
| | - Jin-Bo Gu
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Jing-Jiao Li
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Guang-Xuan Liu
- Cancer Hospital of China Medical University, Shenyang, China.,Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Shuo-Wei Jin
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Ai-Yun Yan
- College of Information Science and Engineering, Northeastern University, Shenyang, China
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Du J, Zeng D, Li Z, Liu J, Lv M, Chen L, Zhang D, Ji S. An interpretable outcome prediction model based on electronic health records and hierarchical attention. INT J INTELL SYST 2021. [DOI: 10.1002/int.22697] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Juan Du
- Department of Gastroenterology First Affiliated Hospital of Zhejiang University School of Medicine Hangzhou China
| | - Dajian Zeng
- College of Computer Science and Technology Zhejiang University of Technology Hangzhou China
| | - Zhao Li
- Department of Gastroenterology and Hepatobiliary Ri Zhao Hospital of Traditional Chinese Medicine Rizhao China
| | - Jingxuan Liu
- College of Computer Science and Technology Zhejiang University of Technology Hangzhou China
| | - Mingqi Lv
- College of Computer Science and Technology Zhejiang University of Technology Hangzhou China
| | - Ling Chen
- College of Computer Science and Technology Zhejiang University Hangzhou China
| | - Dan Zhang
- Key Laboratory of Reproductive Genetics (Ministry of Education), Women's Hospital, School of Medicine Zhejiang University Hangzhou China
| | - Shouling Ji
- College of Computer Science and Technology Zhejiang University Hangzhou China
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Lemmon G, Wesolowski S, Henrie A, Tristani-Firouzi M, Yandell M. A Poisson binomial-based statistical testing framework for comorbidity discovery across electronic health record datasets. NATURE COMPUTATIONAL SCIENCE 2021; 1:694-702. [PMID: 35252879 PMCID: PMC8896515 DOI: 10.1038/s43588-021-00141-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 09/16/2021] [Indexed: 01/28/2023]
Abstract
Discovering the concomitant occurrence of distinct medical conditions in a patient, also known as comorbidities, is a prerequisite for creating patient outcome prediction tools. Current comorbidity discovery applications are designed for small datasets and use stratification to control for confounding variables such as age, sex or ancestry. Stratification lowers false positive rates, but reduces power, as the size of the study cohort is decreased. Here we describe a Poisson binomial-based approach to comorbidity discovery (PBC) designed for big-data applications that circumvents the need for stratification. PBC adjusts for confounding demographic variables on a per-patient basis and models temporal relationships. We benchmark PBC using two datasets to compute comorbidity statistics on 4,623,841 pairs of potentially comorbid medical terms. The results of this computation are provided as a searchable web resource. Compared with current methods, the PBC approach reduces false positive associations while retaining statistical power to discover true comorbidities.
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Affiliation(s)
- Gordon Lemmon
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
- Utah Center for Genetic Discovery and Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Sergiusz Wesolowski
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
- Utah Center for Genetic Discovery and Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Alex Henrie
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
- Utah Center for Genetic Discovery and Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Martin Tristani-Firouzi
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Nora Eccles Harrison CVRTI, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Mark Yandell
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
- Utah Center for Genetic Discovery and Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
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Lin C, Hsu S, Lu HF, Pan LF, Yan YH. Comparison of Back-Propagation Neural Network, LACE Index and HOSPITAL Score in Predicting All-Cause Risk of 30-Day Readmission. Risk Manag Healthc Policy 2021; 14:3853-3864. [PMID: 34548831 PMCID: PMC8449689 DOI: 10.2147/rmhp.s318806] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 08/27/2021] [Indexed: 11/30/2022] Open
Abstract
Background The main purpose of this study is to predict the all-cause risk of 30-day readmission by employing the back-propagation neural network (BPNN) in comparison with traditional risk assessment tools of LACE index and HOSPITAL scores. Methods This was a retrospective cohort study from January 1st, 2018 to December 31st, 2019. A total of 55,688 hospitalizations from a medical center in Taiwan were examined. The LACE index (length of stay, acute admission, Charlson comorbidity index score, emergency department visits in previous 6 months) and HOSPITAL score (hemoglobin level at discharge, discharge from an Oncology service, sodium level at discharge, procedure during hospital stay, Index admission type, number of hospital admissions during the previous year, length of stay) are calculated. We employed variables from LACE index and HOSPITAL score as the input vector of BPNN for comparison purposes. Results The BPNN constructed in the current study has a considerably better ability with a C statistics achieved 0.74 (95% CI 0.73 to 0.75), which is statistically significant larger than that of the other two models using DeLong’s test. Also, it was possible to achieve higher sensitivity (70.32%) without penalizing the specificity (71.76%) and accuracy (71.68%) at its optimal threshold, which is at the 20% of patients with the highest predicted risk. Moreover, it is much more informative than the other two methods because of a considerably higher LR+ and a lower LR-. Conclusion Our findings suggest that more attention should be paid to methods based on non-linear classification systems, as they lead to substantial differences in risk-scores.
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Affiliation(s)
- Chaohsin Lin
- Department of Risk Management and Insurance, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Shuofen Hsu
- Department of Risk Management and Insurance, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Hsiao-Feng Lu
- Department of Anesthesiology, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.,College of Medicine, Chang Gung University, Kaohsiung, Taiwan
| | - Li-Fei Pan
- Department of Medical Affair Administration, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Yu-Hua Yan
- Department of Medical Research, Tainan Municipal Hospital (Managed by Show Chwan Medical Care Corporation), Tainan, Taiwan
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Miswan NH, Chan CS, Ng CG. Predictive modelling of hospital readmission: Evaluation of different preprocessing techniques on machine learning classifiers. INTELL DATA ANAL 2021. [DOI: 10.3233/ida-205468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Hospital readmission is a major cost for healthcare systems worldwide. If patients with a higher potential of readmission could be identified at the start, existing resources could be used more efficiently, and appropriate plans could be implemented to reduce the risk of readmission. Therefore, it is important to predict the right target patients. Medical data is usually noisy, incomplete, and inconsistent. Hence, before developing a prediction model, it is crucial to efficiently set up the predictive model so that improved predictive performance is achieved. The current study aims to analyse the impact of different preprocessing methods on the performance of different machine learning classifiers. The preprocessing applied by previous hospital readmission studies were compared, and the most common approaches highlighted such as missing value imputation, feature selection, data balancing, and feature scaling. The hyperparameters were selected using Bayesian optimisation. The different preprocessing pipelines were assessed using various performance metrics and computational costs. The results indicated that the preprocessing approaches helped improve the model’s prediction of hospital readmission.
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Affiliation(s)
- Nor Hamizah Miswan
- Centre of Image and Signal Processing, Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
- Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia
| | - Chee Seng Chan
- Centre of Image and Signal Processing, Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
| | - Chong Guan Ng
- Department of Psychological Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
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Sayed GI, Soliman MM, Hassanien AE. A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization. Comput Biol Med 2021; 136:104712. [PMID: 34388470 DOI: 10.1016/j.compbiomed.2021.104712] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 07/28/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
Skin lesion classification plays a crucial role in diagnosing various gene and related local medical cases in the field of dermoscopy. In this paper, a new model for the classification of skin lesions as either normal or melanoma is presented. The proposed melanoma prediction model was evaluated on a large publicly available dataset called ISIC 2020. The main challenge of this dataset is severe class imbalance. This paper proposes an approach to overcome this problem using a random over-sampling method followed by data augmentation. Moreover, a new hybrid version of a convolutional neural network architecture and bald eagle search (BES) optimization is proposed. The BES algorithm is used to find the optimal values of the hyperparameters of a SqueezeNet architecture. The proposed melanoma skin cancer prediction model obtained an overall accuracy of 98.37%, specificity of 96.47%, sensitivity of 100%, f-score of 98.40%, and area under the curve of 99%. The experimental results showed the robustness and efficiency of the proposed model compared with VGG19, GoogleNet, and ResNet50. Additionally, the results showed that the proposed model was very competitive compared with the state of the art.
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Affiliation(s)
| | - Mona M Soliman
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt.
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49
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Yu G, Zeng J, Wang J, Zhang H, Zhang X, Guo M. Imbalance deep multi‐instance learning for predicting isoform–isoform interactions. INT J INTELL SYST 2021. [DOI: 10.1002/int.22402] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Guoxian Yu
- School of Software Shandong University Jinan China
- College of Computer and Information Science Southwest University Chongqing China
- Joint SDU‐NTU Centre for Artificial Intelligence Research Shandong University Jinan China
| | - Jie Zeng
- College of Computer and Information Science Southwest University Chongqing China
| | - Jun Wang
- College of Computer and Information Science Southwest University Chongqing China
- Joint SDU‐NTU Centre for Artificial Intelligence Research Shandong University Jinan China
| | - Hong Zhang
- College of Computer and Information Science Southwest University Chongqing China
| | - Xiangliang Zhang
- CEMSE King Abdullah University of Science and Technology Thuwal Saudi Arabia
| | - Maozu Guo
- School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China
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50
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Dashtban M, Li W. Predicting non-attendance in hospital outpatient appointments using deep learning approach. Health Syst (Basingstoke) 2021; 11:189-210. [PMID: 36147556 PMCID: PMC9487947 DOI: 10.1080/20476965.2021.1924085] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
The hospital outpatient non-attendance imposes a substantial financial burden on hospitals and roots in multiple diverse reasons. This research aims to build an advanced predictive model for predicting non-attendance regarding the whole spectrum of probable contributing factors to non-attendance that could be collated from heterogeneous sources including electronic patients records and external non-hospital data. We proposed a new non-attendance prediction model based on deep neural networks and machine learning models. The proposed approach works upon sparse stacked denoising autoencoders (SDAEs) to learn the underlying manifold of data and thereby compacting information and providing a better representation that can be utilised afterwards by other learning models as well. The proposed approach is evaluated over real hospital data and compared with several well-known and scalable machine learning models. The evaluation results reveal the proposed approach with softmax layer and logistic regression outperforms other methods in practice.
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Affiliation(s)
- M. Dashtban
- Informatics Research Centre, Henley Business School, University of Reading, Reading, UK
| | - Weizi Li
- Informatics Research Centre, Henley Business School, University of Reading, Reading, UK
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