1
|
Shaik T, Tao X, Higgins N, Gururajan R, Li Y, Zhou X, Acharya UR. FedStack: Personalized activity monitoring using stacked federated learning. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
2
|
Cai Y, Yu F, Kumar M, Gladney R, Mostafa J. Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15115. [PMID: 36429832 PMCID: PMC9690602 DOI: 10.3390/ijerph192215115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/10/2022] [Accepted: 11/13/2022] [Indexed: 06/16/2023]
Abstract
A health recommender system (HRS) provides a user with personalized medical information based on the user's health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management.
Collapse
Affiliation(s)
- Yao Cai
- School of Information and Library Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Fei Yu
- School of Information and Library Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Carolina Health Informatics Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Manish Kumar
- Public Health Leadership Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Roderick Gladney
- Carolina Health Informatics Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Javed Mostafa
- School of Information and Library Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Carolina Health Informatics Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| |
Collapse
|
3
|
Chen B, Yu L, Luo W, Wu C, Li M, Tan H, Huang J, Wan Z. Hybrid tree model for root cause analysis of wireless network fault localization. WEB INTELLIGENCE 2022. [DOI: 10.3233/web-220016] [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
Localizing the root cause of network faults is crucial to network operation and maintenance. Operational expenses will be saved if the root cause can be identified accurately. However, due to the complicated wireless environments and network architectures, accurate root cause localization of network falut meets the difficulties including missing data, hybrid fault behaviors, and short of well-labeled data. In this study, global and local features are constructed to make new feature representation for data sample, which can highlight the temporal characteristics and contextual information of the root cause analysis data. A hybrid tree model (HTM) ensembled by CatBoost, XGBoost and LightGBM is proposed to interpret the hybrid fault behaviors from several perspectives and discriminate different root causes. Based on the combination of global and local features, a semi-supervised training strategy is utilized to train the HTM for dealing with short of well-labeled data. The experiments are conducted on the real-world dataset from ICASSP 2022 AIOps Challenge, and the results show that the global and local feature based HTM achieves the best model performance comparing with other models. Meanwhile, our solution achieves third place in the competition leaderboard which shows the model effectiveness.
Collapse
Affiliation(s)
- Bin Chen
- Information Engineering College, Nanchang University, Jiangxi, China
| | - Li Yu
- Department of Computer Engineering, Honam University, Gwangju, South Korea
| | - Weiyi Luo
- Information Engineering College, Nanchang University, Jiangxi, China
| | - Chizhong Wu
- Beijing Institute of Technology, Beijing, China
| | - Manyu Li
- Information Engineering College, Nanchang University, Jiangxi, China
| | - Hai Tan
- Nanjing Audit University, Jiangsu, China
| | - Jiajin Huang
- Beijing University of Technology, Beijing, China
| | - Zhijiang Wan
- Information Engineering College, Nanchang University, Jiangxi, China
- Industrial Institute of Artificial Intelligence, Nanchang University, Jiangxi, China
| |
Collapse
|
4
|
Tseng VS, Chen CL, Liang CM, Tai MC, Liu JT, Wu PY, Deng MS, Lee YW, Huang TY, Chen YH. Leveraging Multimodal Deep Learning Architecture with Retina Lesion Information to Detect Diabetic Retinopathy. Transl Vis Sci Technol 2020; 9:41. [PMID: 32855845 PMCID: PMC7424907 DOI: 10.1167/tvst.9.2.41] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 05/28/2020] [Indexed: 01/27/2023] Open
Abstract
Purpose To improve disease severity classification from fundus images using a hybrid architecture with symptom awareness for diabetic retinopathy (DR). Methods We used 26,699 fundus images of 17,834 diabetic patients from three Taiwanese hospitals collected in 2007 to 2018 for DR severity classification. Thirty-seven ophthalmologists verified the images using lesion annotation and severity classification as the ground truth. Two deep learning fusion architectures were proposed: late fusion, which combines lesion and severity classification models in parallel using a postprocessing procedure, and two-stage early fusion, which combines lesion detection and classification models sequentially and mimics the decision-making process of ophthalmologists. Messidor-2 was used with 1748 images to evaluate and benchmark the performance of the architecture. The primary evaluation metrics were classification accuracy, weighted κ statistic, and area under the receiver operating characteristic curve (AUC). Results For hospital data, a hybrid architecture achieved a good detection rate, with accuracy and weighted κ of 84.29% and 84.01%, respectively, for five-class DR grading. It also classified the images of early stage DR more accurately than conventional algorithms. The Messidor-2 model achieved an AUC of 97.09% in referral DR detection compared to AUC of 85% to 99% for state-of-the-art algorithms that learned from a larger database. Conclusions Our hybrid architectures strengthened and extracted characteristics from DR images, while improving the performance of DR grading, thereby increasing the robustness and confidence of the architectures for general use. Translational Relevance The proposed fusion architectures can enable faster and more accurate diagnosis of various DR pathologies than that obtained in current manual clinical practice.
Collapse
Affiliation(s)
- Vincent S Tseng
- Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan.,Institute of Data Science and Engineering, National Chiao Tung University, Hsinchu, Taiwan
| | - Ching-Long Chen
- Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chang-Min Liang
- Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ming-Cheng Tai
- Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Jung-Tzu Liu
- Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Po-Yi Wu
- Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Ming-Shan Deng
- Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Ya-Wen Lee
- Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Teng-Yi Huang
- Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Yi-Hao Chen
- Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| |
Collapse
|
5
|
Goh WP, Tao X, Zhang J, Yong J. Feature-Based Learning in Drug Prescription System for Medical Clinics. Neural Process Lett 2020; 52:1703-1721. [PMID: 32837244 PMCID: PMC7331919 DOI: 10.1007/s11063-020-10296-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Rapid increases in data volume and variety pose a challenge to safe drug prescription for health professionals like doctors and dentists. This is addressed by our study, which presents innovative approaches in mining data from drug corpus and extracting feature vectors to combine this knowledge with individual patient medical profiles. Within our three-tiered framework-the prediction layer, the knowledge layer and the presentation layer-we describe multiple approaches in computing similarity ratios from the feature vectors, illustrated with an example of applying the framework in a typical medical clinic. Experimental evaluation shows that the word embedding model performs better than the adverse network model, with a F score of 0.75. The F score is a common metrics used for evaluating the performance of classification algorithms. Similarity to a drug the patient is allergic to or is taking are important considerations for the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors thereby increasing patient health outcomes.
Collapse
Affiliation(s)
- Wee Pheng Goh
- University of Southern Queensland, Toowoomba, Australia
| | - Xiaohui Tao
- University of Southern Queensland, Toowoomba, Australia
| | - Ji Zhang
- University of Southern Queensland, Toowoomba, Australia
| | - Jianming Yong
- University of Southern Queensland, Toowoomba, Australia
| |
Collapse
|
6
|
DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering. COMPUTATION 2019. [DOI: 10.3390/computation7020025] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient’s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient’s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient’s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches.
Collapse
|
7
|
Narayan S, Sathiyamoorthy E. A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3662-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|