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Xie J, Wang Y, Sheng Q, Liu X, Li J, Sun F, Wang Y, Li S, Li Y, Yu Y, Yu G. Identification of mycoplasma pneumonia in children based on fusion of multi-modal clinical free-text description and structured test data. Health Informatics J 2024; 30:14604582241255818. [PMID: 38779978 DOI: 10.1177/14604582241255818] [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] [Indexed: 05/25/2024]
Abstract
Mycoplasma pneumonia may lead to hospitalizations and pose life-threatening risks in children. The automated identification of mycoplasma pneumonia from electronic medical records holds significant potential for improving the efficiency of hospital resource allocation. In this study, we proposed a novel method for identifying mycoplasma pneumonia by integrating multi-modal features derived from both free-text descriptions and structured test data in electronic medical records. Our approach begins with the extraction of free-text and structured data from clinical records through a systematic preprocessing pipeline. Subsequently, we employ a pre-trained transformer language model to extract features from the free-text, while multiple additive regression trees are used to transform features from the structured data. An attention-based fusion mechanism is then applied to integrate these multi-modal features for effective classification. We validated our method using clinic records of 7157 patients, retrospectively collected for training and testing purposes. The experimental results demonstrate that our proposed multi-modal fusion approach achieves significant improvements over other methods across four key performance metrics.
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Affiliation(s)
- Jingna Xie
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yingshuo Wang
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiuyang Sheng
- Deepwise Healthcare Artificial Intelligence Laboratory, Beijing, China
| | - Xiaoqing Liu
- Deepwise Healthcare Artificial Intelligence Laboratory, Beijing, China
| | - Jing Li
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China; Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China
| | - Fenglei Sun
- Deepwise Healthcare Artificial Intelligence Laboratory, Beijing, China
| | - Yuqi Wang
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shuxian Li
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yiming Li
- Deepwise Healthcare Artificial Intelligence Laboratory, Beijing, China
| | - Yizhou Yu
- Deepwise Healthcare Artificial Intelligence Laboratory, Beijing, China; Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Gang Yu
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China; Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China; Polytechnic Institute, Zhejiang University, Hangzhou, China
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Cinteza E, Vasile CM, Busnatu S, Armat I, Spinu AD, Vatasescu R, Duica G, Nicolescu A. Can Artificial Intelligence Revolutionize the Diagnosis and Management of the Atrial Septal Defect in Children? Diagnostics (Basel) 2024; 14:132. [PMID: 38248009 PMCID: PMC10814919 DOI: 10.3390/diagnostics14020132] [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: 12/04/2023] [Revised: 12/26/2023] [Accepted: 01/04/2024] [Indexed: 01/23/2024] Open
Abstract
Atrial septal defects (ASDs) present a significant healthcare challenge, demanding accurate and timely diagnosis and precise management to ensure optimal patient outcomes. Artificial intelligence (AI) applications in healthcare are rapidly evolving, offering promise for enhanced medical decision-making and patient care. In the context of cardiology, the integration of AI promises to provide more efficient and accurate diagnosis and personalized treatment strategies for ASD patients. In interventional cardiology, sometimes the lack of precise measurement of the cardiac rims evaluated by transthoracic echocardiography combined with the floppy aspect of the rims can mislead and result in complications. AI software can be created to generate responses for difficult tasks, like which device is the most suitable for different shapes and dimensions to prevent embolization or erosion. This paper reviews the current state of AI in healthcare and its applications in cardiology, emphasizing the specific opportunities and challenges in applying AI to ASD diagnosis and management. By exploring the capabilities and limitations of AI in ASD diagnosis and management. This paper highlights the evolution of medical practice towards a more AI-augmented future, demonstrating the capacity of AI to unlock new possibilities for healthcare professionals and patients alike.
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Affiliation(s)
- Eliza Cinteza
- Department of Pediatrics, Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (E.C.)
- Pediatric Cardiology Department, “Marie Skolodowska Curie” Emergency Children’s Hospital, 041451 Bucharest, Romania; (I.A.); (A.N.)
| | - Corina Maria Vasile
- Department of Pediatric and Adult Congenital Cardiology, University Hospital of Bordeaux, F-33600 Bordeaux, France;
| | - Stefan Busnatu
- Cardio-Thoracic Department, Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Cardiology Department, “Prof. Dr. Bagdasar Arseni” Clinical Hospital, 041915 Bucharest, Romania
| | - Ionel Armat
- Pediatric Cardiology Department, “Marie Skolodowska Curie” Emergency Children’s Hospital, 041451 Bucharest, Romania; (I.A.); (A.N.)
| | - Arsenie Dan Spinu
- “Dr. Carol Davila” Central Emergency University Military Hospital, 010825 Bucharest, Romania;
- Department 3, Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Radu Vatasescu
- Cardio-Thoracic Department, Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Emergency Clinical Hospital, 014461 Bucharest, Romania
| | - Gabriela Duica
- Department of Pediatrics, Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (E.C.)
- Pediatric Cardiology Department, “Marie Skolodowska Curie” Emergency Children’s Hospital, 041451 Bucharest, Romania; (I.A.); (A.N.)
| | - Alin Nicolescu
- Pediatric Cardiology Department, “Marie Skolodowska Curie” Emergency Children’s Hospital, 041451 Bucharest, Romania; (I.A.); (A.N.)
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Jone PN, Gearhart A, Lei H, Xing F, Nahar J, Lopez-Jimenez F, Diller GP, Marelli A, Wilson L, Saidi A, Cho D, Chang AC. Artificial Intelligence in Congenital Heart Disease: Current State and Prospects. JACC. ADVANCES 2022; 1:100153. [PMID: 38939457 PMCID: PMC11198540 DOI: 10.1016/j.jacadv.2022.100153] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/04/2022] [Accepted: 10/07/2022] [Indexed: 06/29/2024]
Abstract
The current era of big data offers a wealth of new opportunities for clinicians to leverage artificial intelligence to optimize care for pediatric and adult patients with a congenital heart disease. At present, there is a significant underutilization of artificial intelligence in the clinical setting for the diagnosis, prognosis, and management of congenital heart disease patients. This document is a call to action and will describe the current state of artificial intelligence in congenital heart disease, review challenges, discuss opportunities, and focus on the top priorities of artificial intelligence-based deployment in congenital heart disease.
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Affiliation(s)
- Pei-Ni Jone
- Section of Pediatric Cardiology, Department of Pediatrics, Lurie Children’s Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Addison Gearhart
- Department of Cardiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Howard Lei
- Division of Pediatric Cardiology, Children’s Hospital of Orange County, Orange, California, USA
| | - Fuyong Xing
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Jai Nahar
- Department of Cardiology, Children's National Hospital, Washington, DC, USA
| | | | - Gerhard-Paul Diller
- Department of Cardiology III-Adult Congenital and Valvular Heart Disease, University Hospital Muenster, Muenster, Germany
- Adult Congenital Heart Centre and National Centre for Pulmonary Hypertension, Royal Brompton and Harefield National Health Service Foundation Trust, Imperial College London, London, UK
- National Register for Congenital Heart Defects, Berlin, Germany
| | - Ariane Marelli
- McGill Adult Unit for Congenital Heart Disease Excellence, Department of Medicine, McGill University, Montréal, Québec, Canada
| | - Laura Wilson
- Department of Pediatrics, University of Florida-Congenital Heart Center, Gainesville, Florida, USA
| | - Arwa Saidi
- Department of Pediatrics, University of Florida-Congenital Heart Center, Gainesville, Florida, USA
| | - David Cho
- Department of Cardiology, University of California at Los Angeles, Los Angeles, California, USA
| | - Anthony C. Chang
- Division of Pediatric Cardiology, Children’s Hospital of Orange County, Orange, California, USA
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