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Takefuji Y. Artificial intelligence universal biomarker prediction tool. J Thromb Thrombolysis 2024; 57:341-343. [PMID: 38117437 DOI: 10.1007/s11239-023-02930-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/25/2023] [Indexed: 12/21/2023]
Abstract
Through experiencing cardiopulmonary arrest, an artificial intelligence universal biomarker prediction tool was developed to help patients understand improvement in the trends of their disease. PyPI tool handles two biomarkers, hbA1c for diabetes and NP-proBNP for heart failure, to predict the next hospital visit. Predicting improvement in disease is a great hope for patients.
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Affiliation(s)
- Yoshiyasu Takefuji
- Faculty of Data Science, Musashino University, 3-3-3 Ariake Koto-ku, Tokyo, 135-8181, Japan.
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Di Bidino R, Piaggio D, Andellini M, Merino-Barbancho B, Lopez-Perez L, Zhu T, Raza Z, Ni M, Morrison A, Borsci S, Fico G, Pecchia L, Iadanza E. Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure. Bioengineering (Basel) 2023; 10:1109. [PMID: 37892839 PMCID: PMC10604154 DOI: 10.3390/bioengineering10101109] [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: 07/25/2023] [Revised: 09/13/2023] [Accepted: 09/17/2023] [Indexed: 10/29/2023] Open
Abstract
Artificial intelligence and machine learning (AI/ML) are playing increasingly important roles, permeating the field of medical devices (MDs). This rapid progress has not yet been matched by the Health Technology Assessment (HTA) process, which still needs to define a common methodology for assessing AI/ML-based MDs. To collect existing evidence from the literature about the methods used to assess AI-based MDs, with a specific focus on those used for the management of heart failure (HF), the International Federation of Medical and Biological Engineering (IFMBE) conducted a scoping meta-review. This manuscript presents the results of this search, which covered the period from January 1974 to October 2022. After careful independent screening, 21 reviews, mainly conducted in North America and Europe, were retained and included. Among the findings were that deep learning is the most commonly utilised method and that electronic health records and registries are among the most prevalent sources of data for AI/ML algorithms. Out of the 21 included reviews, 19 focused on risk prediction and/or the early diagnosis of HF. Furthermore, 10 reviews provided evidence of the impact on the incidence/progression of HF, and 13 on the length of stay. From an HTA perspective, the main areas requiring improvement are the quality assessment of studies on AI/ML (included in 11 out of 21 reviews) and their data sources, as well as the definition of the criteria used to assess the selection of the most appropriate AI/ML algorithm.
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Affiliation(s)
- Rossella Di Bidino
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS—The Graduate School of Health Economics and Management (ALTEMS), 00168 Rome, Italy
| | - Davide Piaggio
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
| | - Martina Andellini
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
| | - Beatriz Merino-Barbancho
- Life Supporting Technologies, Photonics Technology and Bioengineering Department, School of Telecommunication Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain (L.L.-P.); (G.F.)
| | - Laura Lopez-Perez
- Life Supporting Technologies, Photonics Technology and Bioengineering Department, School of Telecommunication Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain (L.L.-P.); (G.F.)
| | - Tianhui Zhu
- NIHR London In-Vitro Diagnostics Cooperative, Imperial College of London, London W2 1NY, UK
| | - Zeeshan Raza
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
| | - Melody Ni
- NIHR London In-Vitro Diagnostics Cooperative, Imperial College of London, London W2 1NY, UK
| | - Andra Morrison
- Canadian Agency for Drugs and Technologies in Health, Ottawa, ON K1S 5S8, Canada;
| | - Simone Borsci
- NIHR London In-Vitro Diagnostics Cooperative, Imperial College of London, London W2 1NY, UK
- Department of Learning, Data Analysis, and Technology, Cognition, Data and Education (CODE) Group, Faculty of Behavioural Management and Social Sciences, University of Twente, 7522 Enschede, The Netherlands
| | - Giuseppe Fico
- Life Supporting Technologies, Photonics Technology and Bioengineering Department, School of Telecommunication Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain (L.L.-P.); (G.F.)
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
- School of Engineering, University Campus Bio-Medico, 00128 Rome, Italy
- International Federation of Medical and Biological Engineering, B-1090 Brussels, Belgium
| | - Ernesto Iadanza
- International Federation of Medical and Biological Engineering, B-1090 Brussels, Belgium
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
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Ma M, Hao X, Zhao J, Luo S, Liu Y, Li D. Predicting heart failure in-hospital mortality by integrating longitudinal and category data in electronic health records. Med Biol Eng Comput 2023:10.1007/s11517-023-02816-z. [PMID: 36959414 DOI: 10.1007/s11517-023-02816-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 03/02/2023] [Indexed: 03/25/2023]
Abstract
Heart failure is a life-threatening syndrome that is diagnosed in 3.6 million people worldwide each year. We propose a deep fusion learning model (DFL-IMP) that uses time series and category data from electronic health records to predict in-hospital mortality in patients with heart failure. We considered 41 time series features (platelets, white blood cells, urea nitrogen, etc.) and 17 category features (gender, insurance, marital status, etc.) as predictors, all of which were available within the time of the patient's last hospitalization, and a total of 7696 patients participated in the observational study. Our model was evaluated against different time windows. The best performance was achieved with an AUC of 0.914 when the observation window was 5 days and the prediction window was 30 days. Outperformed other baseline models including LR (0.708), RF (0.717), SVM (0.675), LSTM (0.757), GRU (0.759), GRU-U (0.766) and MTSSP (0.770). This tool allows us to predict the expected pathway of heart failure patients and intervene early in the treatment process, which has significant implications for improving the life expectancy of heart failure patients.
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Affiliation(s)
- Meikun Ma
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China
- Technology Research Center of Spatial Information Network Engineering of Shanxi, Taiyuan, 030024, China
| | - Xiaoyan Hao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jumin Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China
- Intelligent Perception Engineering Technology Center of Shanxi, Taiyuan, 030024, China
| | - Shijie Luo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Yi Liu
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China
- Technology Research Center of Spatial Information Network Engineering of Shanxi, Taiyuan, 030024, China
- College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Dengao Li
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China.
- Technology Research Center of Spatial Information Network Engineering of Shanxi, Taiyuan, 030024, China.
- College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China.
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Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure. Biomolecules 2022; 12:biom12111616. [DOI: 10.3390/biom12111616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/26/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022] Open
Abstract
Acute heart failure (AHF) is a common and severe condition with a poor prognosis. Its course is often complicated by worsening renal function (WRF), exacerbating the outcome. The population of AHF patients experiencing WRF is heterogenous, and some novel possibilities for its analysis have recently emerged. Clustering is a machine learning (ML) technique that divides the population into distinct subgroups based on the similarity of cases (patients). Given that, we decided to use clustering to find subgroups inside the AHF population that differ in terms of WRF occurrence. We evaluated data from the three hundred and twelve AHF patients hospitalized in our institution who had creatinine assessed four times during hospitalization. Eighty-six variables evaluated at admission were included in the analysis. The k-medoids algorithm was used for clustering, and the quality of the procedure was judged by the Davies–Bouldin index. Three clinically and prognostically different clusters were distinguished. The groups had significantly (p = 0.004) different incidences of WRF. Inside the AHF population, we successfully discovered that three groups varied in renal prognosis. Our results provide novel insight into the AHF and WRF interplay and can be valuable for future trial construction and more tailored treatment.
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