1
|
Bottrighi A, Pennisi M. Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community. INFORMATION 2023; 14:513. [DOI: 10.3390/info14090513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2025] Open
Abstract
Artificial intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade is remarkable. Specifically, machine learning (ML) and deep learning (DL) techniques in medicine have been increasingly adopted due to the growing abundance of health-related data, the improved suitability of such techniques for managing large datasets, and more computational power. ML and DL methodologies are fostering the development of new “intelligent” tools and expert systems to process data, to automatize human–machine interactions, and to deliver advanced predictive systems that are changing every aspect of the scientific research, industry, and society. The Italian scientific community was instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years. To this end, we selected all the papers published in the last five years with at least one of the authors affiliated to an Italian institution that in the title, in the abstract, or in the keywords present the terms “machine learning” or “deep learning” and reference a medical area. We focused our research on journal papers under the hypothesis that Italian researchers prefer to present novel but well-established research in scientific journals. We then analyzed the selected papers considering different dimensions, including the medical topic, the type of data, the pre-processing methods, the learning methods, and the evaluation methods. As a final outcome, a comprehensive overview of the Italian research landscape is given, highlighting how the community has increasingly worked on a very heterogeneous range of medical problems.
Collapse
Affiliation(s)
- Alessio Bottrighi
- Dipartimento di Scienze e Innovazione Tecnologica (DiSIT), Computer Science Institute, Università del Piemonte Orientale, 15121 Alessandria, Italy
- Laboratorio Integrato di Intelligenza Artificiale e Informatica in Medicina, Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo, Alessandria—e DiSIT—Università del Piemonte Orientale, 15121 Alessandria, Italy
| | - Marzio Pennisi
- Dipartimento di Scienze e Innovazione Tecnologica (DiSIT), Computer Science Institute, Università del Piemonte Orientale, 15121 Alessandria, Italy
- Laboratorio Integrato di Intelligenza Artificiale e Informatica in Medicina, Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo, Alessandria—e DiSIT—Università del Piemonte Orientale, 15121 Alessandria, Italy
| |
Collapse
|
2
|
A Fuzzy Knowledge Graph Pairs-Based Application for Classification in Decision Making: Case Study of Preeclampsia Signs. INFORMATION 2023. [DOI: 10.3390/info14020104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
Abstract
Problems of preeclampsia sign diagnosis are mostly based on symptom data with the characteristics of data collected periodically in uncertain, ambiguous, and obstetrician opinions. To reduce the effects of preeclampsia, many studies have investigated the disease, prevention, and complication. Conventional fuzzy inference techniques can solve several diagnosis problems in health such as fuzzy inference systems (FIS), and Mamdani complex fuzzy inference systems with rule reduction (M-CFIS-R), however, the computation time is quite high. Recently, the research direction of approximate inference based on fuzzy knowledge graph (FKG) has been proposed in the M-CFIS-FKG model with the combination of regimens in traditional medicine and subclinical data gathered from medical records. The paper has presented a proposed model of FKG-Pairs3 to support patients’ disease diagnosis, together with doctors’ preferences in decision-making. The proposed model has been implemented in real-world applications for disease diagnosis in traditional medicine based on input data sets with vague information, quantified by doctor’s preferences. To validate the proposed model, it has been tested in a real-world case study of preeclampsia signs in a hospital for disease diagnosis with the traditional medicine approach. Experimental results show that the proposed model has demonstrated the model’s effectiveness in the decision-making of preeclampsia signs.
Collapse
|
3
|
Van Pham H, Thai KP, Nguyen QH, Le DD, Le TT, Nguyen TXD, Phan TTK, Thao NX. Proposed distance and entropy measures of picture fuzzy sets in decision support systems. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-221556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The picture fuzzy set is an extension of the fuzzy and intuitionistic fuzzy set for solving real-world problems. Entropy and distance measures play significant roles in measures for solving problems involving fuzzy environments. This paper has presented some new distance and entropy measures using picture fuzzy sets to solve problems of medical diagnosis and multi-criteria decision making problems. In addition, the entropy measure is induced from the distances of picture fuzzy sets in order to determine entropy measure of picture fuzzy sets. The proposed methods combined entropy and distance measures to construct the Technique for Order of Preference by Similarity to Ideal Solution model to solve multi-criteria decision making problem. To validate the proposed methods, some numerical examples are given to demonstrate new measurements. The efficiency of the measure is proven by comparison to other measures when solving medical diagnosis in multi-criteria decision making for illustrations in numerical COVID-19 medicine selection.
Collapse
Affiliation(s)
- Hai Van Pham
- School of Information and Communication Technology, Hanoi University of Science and Technology
| | - Kim Phung Thai
- School of Business Information Technology, University of Economics Ho Chi Minh City (UEH)
| | - Quoc Hung Nguyen
- School of Business Information Technology, University of Economics Ho Chi Minh City (UEH)
| | - Duy Dong Le
- School of Business Information Technology, University of Economics Ho Chi Minh City (UEH)
| | - Thanh Trung Le
- School of Business Information Technology, University of Economics Ho Chi Minh City (UEH)
| | - Thi Xuan Dao Nguyen
- School of Business Information Technology, University of Economics Ho Chi Minh City (UEH)
| | - Thi Thuy Kieu Phan
- School of Business Information Technology, University of Economics Ho Chi Minh City (UEH)
| | - Nguyen Xuan Thao
- Faculty of Information Technology-Vietnam National University of Agriculture
| |
Collapse
|
4
|
Humphreys P, Spratt B, Tariverdi M, Burdett RL, Cook D, Yarlagadda PKDV, Corry P. An Overview of Hospital Capacity Planning and Optimisation. Healthcare (Basel) 2022; 10:826. [PMID: 35627963 PMCID: PMC9140785 DOI: 10.3390/healthcare10050826] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/18/2022] [Accepted: 04/27/2022] [Indexed: 02/04/2023] Open
Abstract
Health care is uncertain, dynamic, and fast growing. With digital technologies set to revolutionise the industry, hospital capacity optimisation and planning have never been more relevant. The purposes of this article are threefold. The first is to identify the current state of the art, to summarise/analyse the key achievements, and to identify gaps in the body of research. The second is to synthesise and evaluate that literature to create a holistic framework for understanding hospital capacity planning and optimisation, in terms of physical elements, process, and governance. Third, avenues for future research are sought to inform researchers and practitioners where they should best concentrate their efforts. In conclusion, we find that prior research has typically focussed on individual parts, but the hospital is one body that is made up of many interdependent parts. It is also evident that past attempts considering entire hospitals fail to incorporate all the detail that is necessary to provide solutions that can be implemented in the real world, across strategic, tactical and operational planning horizons. A holistic approach is needed that includes ancillary services, equipment medicines, utilities, instrument trays, supply chain and inventory considerations.
Collapse
Affiliation(s)
- Peter Humphreys
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia; (B.S.); (R.L.B.); (P.K.D.V.Y.); (P.C.)
| | - Belinda Spratt
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia; (B.S.); (R.L.B.); (P.K.D.V.Y.); (P.C.)
| | | | - Robert L. Burdett
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia; (B.S.); (R.L.B.); (P.K.D.V.Y.); (P.C.)
| | - David Cook
- Princess Alexandra Hospital, Brisbane, QLD 4000, Australia;
| | - Prasad K. D. V. Yarlagadda
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia; (B.S.); (R.L.B.); (P.K.D.V.Y.); (P.C.)
| | - Paul Corry
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia; (B.S.); (R.L.B.); (P.K.D.V.Y.); (P.C.)
| |
Collapse
|
5
|
Huang H, Shih PC, Zhu Y, Gao W. An integrated model for medical expense system optimization during diagnosis process based on artificial intelligence algorithm. JOURNAL OF COMBINATORIAL OPTIMIZATION 2022; 44:2515-2532. [PMID: 34220290 PMCID: PMC8235905 DOI: 10.1007/s10878-021-00761-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/18/2021] [Indexed: 05/11/2023]
Abstract
In the era of artificial intelligence, the healthcare industry is undergoing tremendous innovation and development based on sophisticated AI algorithms. Focusing on diagnosis process and target disease, this study theoretically proposed an integrated model to optimize traditional medical expense system, and ultimately helps medical staff and patients make more reliable decisions. From the new perspective of total expense estimation and detailed expense analysis, the proposed model innovatively consists of two intelligent modules, with theoretical contribution. The two modules are SVM-based module and SOM-based module. According to the rigorous comparative analysis with two classic AI techniques, back propagation neural networks and random forests, it is demonstrated that the SVM-based module achieved better capability of total expense estimation. Meanwhile, by designing a two-stage clustering process, SOM-based module effectively generated decision clusters and corresponding cluster centers were obtained, that clarified the complex relationship between detailed expense and patient information. To achieve practical contribution, the proposed model was applied to the diagnosis process of coronary heart disease. The real data from a hospital in Shanghai was collected, and the validity and accuracy of the proposed model were verified with rigorous experiments. The proposed model innovatively optimized traditional medical expense system, and intelligently generated reliable decision-making information for both total expense and detailed expense. The successful application on the target disease further indicates that this model is a user-friendly tool for medical expense control and therapeutic regimen strategy.
Collapse
Affiliation(s)
- He Huang
- Business School, University of Shanghai for Science and Technology, Shanghai, China
| | - Po-Chou Shih
- College of Science and Engineering, Chaoyang University of Technology, Taichung, Taiwan China
| | - Yuelan Zhu
- Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Gao
- Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
6
|
Forecasting mortality rates using hybrid Lee–Carter model, artificial neural network and random forest. COMPLEX INTELL SYST 2020. [DOI: 10.1007/s40747-020-00185-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
AbstractInaccurate prediction would cause the insurance company encounter catastrophic losses and may lead to overpriced premiums where low-earning consumers cannot afford to insure themselves. The ability to forecast mortality rates accurately can allow the insurance company to take preventive measures to introduce new policies with reasonable prices. In this paper, several Lee–Carter (LC) based models are used to forecast the mortality rates in a case study of the Malaysian population. The LC-ARIMA model and also a combination of the LC model with two machine learning (ML) methods, namely the random forest (RF) and artificial neural network (ANN) methods are utilized on the prediction of mortality rates for males and females in Malaysia, whereby the LC-Random Forest (LC-RF) hybrid model is a new model that is introduced in this paper. Seventeen years of mortality data in Malaysia are selected as the dataset for this research. To analyze how the forecasting models perform for other countries, we have determined the model that has the best fit and produced the best forecasted mortality rates for all the other countries that are studied. This research has showed that LC-ANN and LC-ARIMA are the best model in predicting the mortality rates of males and females in Malaysia, respectively. This study has also found that the LC-ARIMA model is the best performing model in forecasting the mortality rates in countries that have longer life expectancy and a good healthcare system such as Sweden, Ireland, Japan, Hong Kong, Norway, Switzerland and Czechia. In contrast, the LC-ANN model is the best performing model in forecasting the mortality rates in countries that have a less efficiency, less accessibility healthcare system, and bad personal behavior such as Malaysia, Canada and Latvia.
Collapse
|