1
|
Caruana A, Bandara M, Musial K, Catchpoole D, Kennedy PJ. Machine learning for administrative health records: A systematic review of techniques and applications. Artif Intell Med 2023; 144:102642. [PMID: 37783537 DOI: 10.1016/j.artmed.2023.102642] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 10/04/2023]
Abstract
Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of EHR analytics emphasise that the data-modality of the EHR limits the breadth of suitable machine learning techniques, and pursuable healthcare applications. Despite emphasising the importance of data modality, the literature fails to analyse which techniques and applications are relevant to AHRs. AHRs contain uniquely well-structured, categorically encoded records which are distinct from other data-modalities captured by EHRs, and they can provide valuable information pertaining to how patients interact with the healthcare system. This paper systematically reviews AHR-based research, analysing 70 relevant studies and spanning multiple databases. We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research. We also analyse how these techniques are applied in pursuit of each application, and identify the limitations of these approaches. We find that while AHR-based studies are disconnected from each other, the use of AHRs in health informatics research is substantial and accelerating. Our synthesis of these studies highlights the utility of AHRs for pursuing increasingly complex and diverse research objectives despite a number of pervading data- and technique-based limitations. Finally, through our findings, we propose a set of future research directions that can enhance the utility of AHR data and machine learning techniques for health informatics research.
Collapse
Affiliation(s)
- Adrian Caruana
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia.
| | - Madhushi Bandara
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia
| | - Katarzyna Musial
- Complex Adaptive Systems Lab, Data Science Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia
| | - Daniel Catchpoole
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia; Biospecimen Research Services, The Children's Cancer Research Unit, The Children's Hospital at Westmead, Australia
| | - Paul J Kennedy
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia; Joint Research Centre in AI for Health and Wellness, University of Technology Sydney, Australia, and Ontario Tech University, Canada
| |
Collapse
|
2
|
Larraga-García B, Castañeda López L, Monforte-Escobar F, Quintero Mínguez R, Quintana-Díaz M, Gutiérrez Á. Design and Development of an Objective Evaluation System for a Web-Based Simulator for Trauma Management. Appl Clin Inform 2023; 14:714-724. [PMID: 37673097 PMCID: PMC10482499 DOI: 10.1055/s-0043-1771396] [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: 04/25/2023] [Accepted: 06/15/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Trauma injuries are one of the main leading causes of death in the world. Training with guidelines and protocols is adequate to provide a fast and efficient treatment to patients that suffer a trauma injury. OBJECTIVES This study aimed to evaluate deviations from a set protocol, a new set of metrics has been proposed and tested in a pilot study. METHODS The participants were final-year students from the Universidad Autónoma de Madrid and first-year medical residents from the Hospital Universitario La Paz. They were asked to train four trauma scenarios with a web-based simulator for 2 weeks. A test was performed pre-training and another one post-training to evaluate the evolution of the treatment to those four trauma scenarios considering a predefined trauma protocol and based on the new set of metrics. The scenarios were pelvic and lower limb traumas in a hospital and in a prehospital setting, which allow them to learn and assess different trauma protocols. RESULTS The results show that, in general, there is an improvement of the new metrics after training with the simulator. CONCLUSION These new metrics provide comprehensive information for both trainers and trainees. For trainers, the evaluation of the simulation is automated and contains all relevant information to assess the performance of the trainee. And for trainees, it provides valuable real-time information that could support the trauma management learning process.
Collapse
Affiliation(s)
- Blanca Larraga-García
- Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | - Luis Castañeda López
- Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | - Manuel Quintana-Díaz
- Servicio de Medicina Intensiva, Hospital La Paz Institute for Health Research, IdiPAZ, Madrid, Spain
| | - Álvaro Gutiérrez
- Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| |
Collapse
|
3
|
Hybrid CNN-LSTM and modified wild horse herd Model-based prediction of genome sequences for genetic disorders. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103840] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
4
|
Kumar R, Sharma SC. Hybrid optimization and ontology-based semantic model for efficient text-based information retrieval. THE JOURNAL OF SUPERCOMPUTING 2022; 79:2251-2280. [PMID: 35967462 PMCID: PMC9364863 DOI: 10.1007/s11227-022-04708-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
Query expansion is an important approach utilized to improve the efficiency of data retrieval tasks. Numerous works are carried out by the researchers to generate fair constructive results; however, they do not provide acceptable results for all kinds of queries particularly phrase and individual queries. The utilization of identical data sources and weighting strategies for expanding such terms are the major cause of this issue which leads the model unable to capture the comprehensive relationship between the query terms. In order to tackle this issue, we developed a novel approach for query expansion technique to analyze the different data sources namely WordNet, Wikipedia, and Text REtrieval Conference. This paper presents an Improved Aquila Optimization-based COOT(IAOCOOT) algorithm for query expansion which retrieves the semantic aspects that match the query term. The semantic heterogeneity associated with document retrieval mainly impacts the relevance matching between the query and the document. The main cause of this issue is that the similarity among the words is not evaluated correctly. To overcome this problem, we are using a Modified Needleman Wunsch algorithm algorithm to deal with the problems of uncertainty, imprecision in the information retrieval process, and semantic ambiguity of indexed terms in both the local and global perspectives. The k most similar word is determined and returned from a candidate set through the top-k words selection technique and it is widely utilized in different tasks. The proposed IAOCOOT model is evaluated using different standard Information Retrieval performance metrics to compute the validity of the proposed work by comparing it with other state-of-art techniques.
Collapse
Affiliation(s)
- Ram Kumar
- Electronics and Computer Discipline, DPT, Indian Institute of Technology, Roorkee, India
| | - S. C. Sharma
- Electronics and Computer Discipline, DPT, Indian Institute of Technology, Roorkee, India
| |
Collapse
|
5
|
Tian T, Deng D. Performance Evaluation of Hospital Economic Management with the Clustering Algorithm Oriented towards Electronic Health Management. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3603353. [PMID: 35432826 PMCID: PMC9007649 DOI: 10.1155/2022/3603353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/11/2022] [Accepted: 01/15/2022] [Indexed: 11/17/2022]
Abstract
In order to study the clustering algorithm based on density grid, the performance evaluation index system of hospital economic management under the application of electronic health management system is constructed. Firstly, this work designs the basic architecture of electronic health management system, classifies and screens the process of index system of electronic health management system, compares the clustering algorithm based on density grid with the simple clustering algorithm based on density or grid, and then applies it to the performance evaluation index system of hospital economic management. According to the principle of Mitchell scoring method, the expert questionnaire of hospital economic management performance evaluation index system was designed, and Delphi method was used to evaluate the candidate indexes from the three dimensions of right, legitimacy, and urgency. The results show that, compared with simple network clustering algorithm and density clustering algorithm, the clustering algorithm based on density network produces higher purity (94% VS 73% VS 67%) and lower entropy (0.9 VS 1.4 VS 1.54), which effectively saves memory consumption, and the difference is statistically significant (P < 0.05). The core indicators with scores above 4.5 in both dimensions include budget revenue implementation rate, budget expenditure implementation rate, implementation rate of special financial appropriation, asset-liability ratio, hospitalization income cost rate, medical insurance settlement rate, average cost of discharged patients, and drug proportion. The coefficient of variation of the first grade index is between 0.05 and 0.14 and that of the second grade index is between 0.05 and 0.15. Clustering algorithm based on density network has higher purity and lower entropy, which can effectively save memory consumption. The performance evaluation index system of hospital economic management finally determines 6 first-level indexes: budget management, financial fund management, cost management, medical expense management, medical efficiency, medical quality, and 25 second-level indexes.
Collapse
Affiliation(s)
- Tian Tian
- Youth League Committee, The First Affiliated Hospital, University of South China, Hengyang 421001, Hunan, China
| | - Dixin Deng
- Finance Department, The First Affiliated Hospital of University of South China, Hengyang 421001, Hunan, China
| |
Collapse
|
6
|
Zuo Z, Tang C, Xu Y, Wang Y, Wu Y, Qi J, Shi X. Gene Position Index Mutation Detection Algorithm Based on Feedback Fast Learning Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:1716182. [PMID: 34306047 PMCID: PMC8279879 DOI: 10.1155/2021/1716182] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 06/17/2021] [Indexed: 11/18/2022]
Abstract
In the detection of genome variation, the research on the internal correlation of reference genome is deepening; the detection of variation in genome sequence has become the focus of research, and it has also become an effective path to find new genes and new functional proteins. The targeted sequencing sequence is used to sequence the exon region of a specific gene in cancer gene detection, and the sequencing depth is relatively large. Traditional alignment algorithms will lose some sequences, which will lead to inaccurate mutation detection. This paper proposes a mutation detection algorithm based on feedback fast learning neural network position index. By establishing a position index relationship for ACGT in the DNA sequence, the subsequence is decomposed into the position relationship of different subsequences corresponding to the main sequence. The positional relationship of the subsequence in the main sequence is determined by the positional relationship. Analyzing SNP and InDel mutations, even structural mutations, through the position correlation of sequences has the advantages of high precision and easy implementation by personal computers. The feedback fast learning neural network is used to verify whether there is a linear relationship between two or more positions. Experimental results show that the mutation points detected by position index are more than those detected by Bcftools, Freebye, Vanscan2, and Gatk.
Collapse
Affiliation(s)
- Zhike Zuo
- Chongqing Key Laboratory of Spatial Data Mining and Big Data Integration for Ecology and Environment, Chongqing Finance and Economics College, Chongqing 401320, China
| | - Chao Tang
- Radiation & Cancer Biology Laboratory, Radiation Oncology Center, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030, China
| | - Yu Xu
- Radiation & Cancer Biology Laboratory, Radiation Oncology Center, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030, China
- College of Bioengineering, Chongqing University, Chongqing, China
| | - Ying Wang
- Radiation & Cancer Biology Laboratory, Radiation Oncology Center, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030, China
| | - Yongzhong Wu
- Radiation & Cancer Biology Laboratory, Radiation Oncology Center, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030, China
| | - Jun Qi
- Radiation & Cancer Biology Laboratory, Radiation Oncology Center, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030, China
| | - Xiaolong Shi
- Radiation & Cancer Biology Laboratory, Radiation Oncology Center, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030, China
| |
Collapse
|