Article
Health Care Sciences & Services
Marianna Inglese, Matteo Ferrante, Tommaso Boccato, Allegra Conti, Chiara A. Pistolese, Oreste C. Buonomo, Rolando M. D'Angelillo, Nicola Toschi
Summary: Traditional imaging techniques such as X-rays and MRI have limitations in breast cancer diagnosis and prediction, leading to the emergence of PET as a more effective tool. This study used dynamic PET scans to extract radiomic features and trained a model for classification and prognosis prediction. The results showed superior performance of the dynamic radiomics approach, outperforming standard PET imaging in accuracy. This study demonstrates the enhanced clinical utility of dynomics in improving breast cancer diagnosis and prognosis.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Article
Oncology
Lichen Ji, Wei Zhang, Xugang Zhong, Tingxiao Zhao, Xixi Sun, Senbo Zhu, Yu Tong, Junchao Luo, Youjia Xu, Di Yang, Yao Kang, Jin Wang, Qing Bi
Summary: The risk of osteoporosis is higher in breast cancer patients compared to healthy populations. Machine learning models can be used to predict the risk of osteoporosis, fracture occurrence, and prognosis. These models show better performance than current models and can improve decision making.
FRONTIERS IN ONCOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Suyeon Lee, Heewon Jung, Jiwoo Park, Jaegyoon Ahn
Summary: This study demonstrated the accurate prediction of cancer prognoses by using patient-specific cancer driver genes. By generating patient-specific gene networks and using modified PageRank algorithm to generate feature vectors representing the impact of genes on the network, a deep feedforward network was trained for prediction. The proposed method showed significantly better prediction performance for some cancer types and indicated the association of heterogeneous cancer driver information with cancer prognosis.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Oncology
Ji-Yeon Kim, Yong Seok Lee, Jonghan Yu, Youngmin Park, Se Kyung Lee, Minyoung Lee, Jeong Eon Lee, Seok Won Kim, Seok Jin Nam, Yeon Hee Park, Jin Seok Ahn, Mira Kang, Young-Hyuck Im
Summary: A machine learning model using WTTE-RNN was developed for breast cancer prognosis prediction, based on 325 clinical data elements. The model, which outperformed other machine learning models, effectively predicted the risk of breast cancer recurrence in real time.
FRONTIERS IN ONCOLOGY
(2021)
Article
Immunology
Wenjie Shi, Zhilin Chen, Hui Liu, Chen Miao, Ruifa Feng, Guilin Wang, Guoping Chen, Zhitong Chen, Pingming Fan, Weiyi Pang, Chen Li
Summary: Machine learning algorithms were used to identify a novel biological target for breast cancer and explore its relationship with the tumor microenvironment and patient prognosis. COL11A1 was identified as a hub gene and found to be associated with a poor prognosis in breast cancer.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Oncology
Zoe Guan, Theodore Huang, Anne Marie McCarthy, Kevin Hughes, Alan Semine, Hajime Uno, Lorenzo Trippa, Giovanni Parmigiani, Danielle Braun
Summary: BRCAPRO is a breast cancer risk prediction model that does not consider non-genetic risk factors. We expand BRCAPRO by combining it with BCRAT, a model that uses mostly non-genetic risk factors, and show improved prediction accuracy. Accurate risk stratification is essential for targeted screening and prevention of cancer.
Article
Multidisciplinary Sciences
Nikhilanand Arya, Sriparna Saha, Archana Mathur, Snehanshu Saha
Summary: Early prognosis and diagnosis systems are crucial for breast cancer patients, providing oncologists with vital information for treatment plans and avoiding unnecessary therapies and their side effects.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
Ehtisham Khan Jadoon, Fiaz Gul Khan, Sajid Shah, Ahmad Khan, Muhammed ElAffendi
Summary: Ensemble models based on deep learning have made significant contributions to the medical field, particularly in the area of disease prediction. This article proposes a heterogeneous deep learning-based ensemble model for effective breast cancer prediction using multi-modal data.
Article
Oncology
Run Fan, Yufan Chen, Sarah Nechuta, Hui Cai, Kai Gu, Liang Shi, Pingping Bao, Yu Shyr, Xiao-Ou Shu, Fei Ye
Summary: Robust and reliable prognosis prediction models have been developed for Asian patients with breast cancer, incorporating age, tumor characteristics, treatment information, and lifestyle factors. The models showed high prediction accuracy and generalizability, particularly in Asian American women, after internal and external validation.
Article
Biology
Xiuquan Du, Yuefan Zhao
Summary: With the increasing incidence of breast cancer, accurate prognosis prediction plays a significant role in cancer research, psychological rehabilitation, and clinical decision-making for patients. Integrating data from different modalities has shown greater success in prognostic prediction compared to using only one modality. However, existing approaches often fail to reduce the modality gap, highlighting the need for a method that effectively integrates multimodal data. This study proposes a multimodal data adversarial representation framework (MDAR) to reduce modality heterogeneity and improve prognostic performance by aligning distributions. Experimental results on the METABRIC dataset demonstrate enhanced prognostic prediction of breast cancer patients using the proposed method.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Yang Yang, Li Xu, Liangdong Sun, Peng Zhang, Suzanne S. Farid
Summary: Machine learning is widely used in cancer diagnosis and prognosis prediction. This study integrates genomic, clinical, and demographic data of lung adenocarcinoma and squamous cell carcinoma patients to develop predictive models for recurrence and survivability using machine learning algorithms. The decision tree models reveal the importance of genomic information, clinical status, and demographics in predicting outcomes.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Genetics & Heredity
Guoqi Li, Diwei Huo, Naifu Guo, Yi Li, Hongzhe Ma, Lei Liu, Hongbo Xie, Denan Zhang, Bo Qu, Xiujie Chen
Summary: This study identified immune-related lncRNAs and developed a prognostic model for predicting the prognosis of gastric cancer patients. By dividing patients into high- and low-risk groups based on the model, precision medicine can be effectively carried out.
FRONTIERS IN GENETICS
(2023)
Article
Engineering, Biomedical
S. Nanglia, Muneer Ahmad, Fawad Ali Khan, N. Z. Jhanjhi
Summary: Breast cancer, common in both men and women, is difficult to detect in early stages and can be costly and complex to treat, leading to high fatality rates. This paper introduces a heterogeneous ensemble machine learning approach for early detection of breast cancer.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Clinical Neurology
Mohamed Sobhi Jabal, Olivier Joly, David Kallmes, George Harston, Alejandro Rabinstein, Thien Huynh, Waleed Brinjikji
Summary: This study developed machine learning models using clinical and imaging features to predict the functional outcome at 3 months after thrombectomy in acute ischemic stroke patients. Combining clinical and imaging features resulted in the best prediction. Age, NIHSS score, degree of brain atrophy, early ischemic core, and collateral circulation deficit volume on CTA were the most important classifying features.
FRONTIERS IN NEUROLOGY
(2022)
Article
Genetics & Heredity
Shanshan Hu, Shengying Gu, Shuowen Wang, Chendong Qi, Chenyang Shi, Fengdan Qian, Guorong Fan
Summary: This study comprehensively analyzed ferroptosis-related genes in bladder cancer (BCa) and constructed a prognosis model using machine learning algorithm. The results showed that ferroptosis score was significantly associated with survival outcome in BCa patients and correlated with tumor immunity-related pathways. These findings are important for understanding the pathogenesis of BCa and guiding treatment strategies.
Article
Infectious Diseases
Jin-Hua Li, Chin-Chieh Wu, Yi-Ju Tseng, Shih-Tsung Han, Andrew Pekosz, Richard Rothman, Kuan-Fu Chen
Summary: This study analyzed the relationship between symptoms and influenza virus infection and found that different symptoms have different predictive values during different stages of the illness. The findings of this study provide important guidance for understanding and preventing influenza virus infection.
INFLUENZA AND OTHER RESPIRATORY VIRUSES
(2023)
Article
Surgery
Jo-Chun Hsiao, Nicole A. Zelenski, Yi-Ju Tseng, Chung-Chen Hsu, Shih-Heng Chen, Chih-Hung Lin, Cheng-Hung Lin
Summary: The study evaluated the cost equivalence of free versus pedicled perforator flap for distal lower leg defects. It found that pedicled perforator flap is a viable and cost-effective option for small to medium-sized defects, with similar clinical outcomes and shorter operative duration and ICU stay.
JOURNAL OF RECONSTRUCTIVE MICROSURGERY
(2023)
Article
Biochemistry & Molecular Biology
Hsin-Yao Wang, Chi-Heng Kuo, Chia-Ru Chung, Wan-Ying Lin, Yu-Chiang Wang, Ting-Wei Lin, Jia-Ruei Yu, Jang-Jih Lu, Ting-Shu Wu
Summary: A novel approach using MALDI-TOF MS and machine learning algorithms was developed for rapid and accurate identification of MABC subspecies. The model based on random forest algorithm outperformed other algorithms in discriminating different subspecies. This diagnostic tool provides guidance for more precise and timely subspecies-specific treatment, considering the significant correlation between subspecies and antibiotics resistance.
Article
Medicine, General & Internal
Shao-Ju Chien, Yi-Ju Tseng, Ying-Hua Huang, Hsi-Yun Liu, Yi-Hua Wu, Ling-Sai Chang, Yao-Hsu Yang, Ying-Jui Lin
Summary: This study compares clinical outcomes between pediatric patients with and without heart disease diagnosed with infective endocarditis (IE), and determines the risk of in-hospital death. The results show that patients with heart disease are more prone to streptococcal infections and cardiac complications, but have a lower mortality rate. Platelet count can serve as a risk factor for in-hospital mortality in pediatric patients with IE.
JOURNAL OF CLINICAL MEDICINE
(2023)
Article
Microbiology
Chia-Ru Chung, Hsin-Yao Wang, Chun-Han Yao, Li-Ching Wu, Jang-Jih Lu, Jorng-Tzong Horng, Tzong-Yi Lee
Summary: In this study, a two-stage framework based on MALDI-TOF MS data was developed for predicting antimicrobial resistance in E. coli. The XGBoost model outperformed other machine learning models and showed potential for improving accuracy by approximately 2.8%. This research provides a promising method for aiding physicians in decision-making and may reveal new insights for further studies.
MICROBIOLOGY SPECTRUM
(2023)
Review
Medicine, General & Internal
Sriram Kalpana, Wan-Ying Lin, Yu-Chiang Wang, Yiwen Fu, Amrutha Lakshmi, Hsin-Yao Wang
Summary: Antibiotic resistance is a pressing global pandemic. Rapid diagnostic assays can differentiate bacterial infections from other diseases, aiding antimicrobial stewardship, therapy optimization, and surveillance. Traditional methods often have longer turnaround times for definitive results. On the other hand, proteomic studies have advanced in qualitative and quantitative analysis, with reduced error rates due to the availability of diverse datasets. This review provides insights into state-of-the-art proteomic techniques for diagnosing antibiotic resistance in ESKAPE pathogens, with a future outlook for tackling the imminent pandemic.
Article
Medicine, General & Internal
Tzong-Shi Chiueh, Hsin-Yao Wang, Min-Hsien Wu, Yu-Shan Hsueh, Hui-Chu Chen
Summary: Most current methods for detecting antiplatelet antibodies are time-consuming and require manual labor. In this study, we developed a rapid and convenient method called filtration enzyme-linked immunosorbent assay (fELISA) to effectively detect alloimmunization during platelet transfusion. By comparing the reactivity ratios of fELISA, which were obtained by dividing the final chromogen intensity of each test serum with the background chromogen intensity of whole platelets, positive and negative sera of antiplatelet antibodies could be differentiated. The sensitivity and specificity of fELISA were 93.9% and 93.3% respectively, and the area under the ROC curve reached 0.96 when compared with the routine solid-phase red cell adherence test (SPRCA).
Article
Biochemical Research Methods
Zhuo Wang, Yuxuan Pang, Chia-Ru Chung, Hsin-Yao Wang, Haiyan Cui, Ying-Chih Chiang, Jorng-Tzong Horng, Jang-Jih Lu, Tzong-Yi Lee
Summary: The emergence of multidrug-resistant bacteria, especially multidrug-resistant Staphylococcus aureus, is a critical global crisis that threatens public health. This study developed a novel risk assessment framework for S. aureus using mass spectrometry and machine learning, accurately predicting the resistance to multiple antibiotics. Additionally, the framework evaluated the level of multidrug resistance and analyzed the performance contribution of different sample groups.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Virology
Yueh Lin, Wan-Ying Lin, Ting-Wei Lin, Yi-Ju Tseng, Yu-Chiang Wang, Jia-Ruei Yu, Chia-Ru Chung, Hsin-Yao Wang
Summary: This study explores the evolving landscape of HPV molecular epidemiology in Taiwan over a decade and reveals shifting trends in HPV genotype distribution and infection patterns. The 12 high-risk genotypes were identified, with HPV 52, 58, and 16 being the predominant ones. The study emphasizes the need for continued surveillance and research to guide effective public health interventions targeting HPV-associated diseases.
Article
Medicine, General & Internal
Yu-Chiang Wang, Wan-Ying Lin, Yi-Ju Tseng, Yiwen Fu, Weijia Li, Yu-Chen Huang, Hsin-Yao Wang
Summary: This study developed and validated a risk stratification model for identifying high-risk patients with HSV bronchopneumonia, which could benefit from aggressive treatment.
JOURNAL OF CLINICAL MEDICINE
(2023)
Review
Medicine, General & Internal
Sriram Kalpana, Wan-Ying Lin, Yu-Chiang Wang, Yiwen Fu, Hsin-Yao Wang
Summary: New antimicrobial approaches are necessary due to antimicrobial resistance. Existing drug development has been hindered by resistance, leading to unsuccessful trials. Unconventional antimicrobial molecules, such as monoclonal antibodies, peptides, aptamers, and phages, are being considered as potential alternatives. Diagnostic-based treatments for infectious diseases using alternate therapeutics, considering the detection, monitoring of response, and resistance mechanism identification, disrupt traditional therapeutic development. This review highlights the correlation between alternate antimicrobial therapeutics and infectious diseases, analyzing pharmacodynamic parameters and the potential use of companion diagnostic applications.
Article
Biochemistry & Molecular Biology
Chia-Ru Chung, Hsin-Yao Wang, Po-Han Chou, Li-Ching Wu, Jang-Jih Lu, Jorng-Tzong Horng, Tzong-Yi Lee
Summary: This study used an ensemble of multiple preprocessing methods to extract critical information from complicated mass spectrometry spectral data for identifying microorganisms and predicting antibiotic resistance. The ensemble method outperformed individual methods, achieving the highest accuracy on independent testing datasets. Important peaks related to antibiotic resistance could be detected, providing valuable information for further investigation of the resistance mechanism.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Kuan-Hui Liu, Cheng-Yu Chiang, Hsin-Yao Wang, Yi-Ju Tseng
Summary: This study proposes a temporal phenotype matrix engineering approach with auxiliary data layers to extract important hidden information from electronic health records. The approach shows significant improvement in early prediction of coronary artery disease.
2023 IEEE EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS, BHI
(2023)
Review
Computer Science, Information Systems
Xinyi Chen, Xiang Liu, Yuke Wu, Zhenglei Wang, Shuo Hong Wang
Summary: The current research on the diagnosis and staging of prostate cancer using machine learning and deep learning is in its infancy, with low accuracy in diagnosis and classification. There is a lack of studies on CT images and ultrasound images.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Information Systems
Jahanpour Alipour, Roxana Sharifian, Javid Dehghan Haghighi, Mehrnaz Hashemzehi, Afsaneh Karimi
Summary: This study investigated patients' perceptions of the e-prescribing system. The majority of patients were aware of e-prescribing, and preferred it over traditional prescriptions. Patients reported overall positive satisfaction and relatively positive perceptions and experiences with the e-prescribing system.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Review
Computer Science, Information Systems
Sarang Hashemi, Lu Bai, Shijia Gao, Frada Burstein, Kate Renzenbrink
Summary: This study demonstrates the value of the MINDSPACE framework in designing clinical decision support alerts and reminders. The framework addresses the challenges faced by designers in identifying behavioral effects relevant to alert and reminder designs.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Review
Computer Science, Information Systems
Ivan Otero-Gonzalez, Moises R. Pacheco-Lorenzo, Manuel J. Fernandez-Iglesias, Luis E. Anido-Rifon
Summary: This study explores the applications of conversational agents in detecting mental health disorders, specifically depression screening. The findings indicate that conversational agents are effective in detecting depression, and voice interaction is the future direction of development.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Information Systems
Pedro A. Moreno-Sanchez, Ruben Arroyo-Fernandez, Elisabeth Bravo-Esteban, Asuncion Ferri-Morales, Mark van Gils
Summary: This study analyzed data from fibromyalgia patients to assess the impact of mental health factors on fibromyalgia severity compared to pain factors. The findings suggest that mental health factors are more relevant for fibromyalgia severity.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Information Systems
Muhammad Asaduzzaman, Zeleke Mekonnen, Ernst Kristian Rodland, Sundeep Sahay, Andrea Sylvia Winkler, Christoph Gradmann
Summary: Our study explored the perspectives of stakeholders in Jimma, Ethiopia on the use of DHIS2 as a One Health Antimicrobial Resistance (AMR) surveillance platform. The findings suggest that DHIS2 has the potential to be a user-friendly and acceptable platform for OH-AMR surveillance. Despite some challenges, most participants perceived DHIS2 as suitable for OH-AMR surveillance and expressed their willingness to contribute in their current professional roles.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Information Systems
Sari Kujala, Saija Simola, Bo Wang, Hedvig Soone, Josefin Hagstrom, Annika Barkas, Iiris Horhammer, Asa Cajander, Asbjorn Johansen Fagerlund, Bridget Kane, Anna Kharko, Eli Kristiansen, Jonas Moll, Hanife Rexphepi, Maria Hagglund, Monika A. Johansen
Summary: This study benchmarks the usability of national patient portals in Estonia, Finland, Norway, and Sweden using a mixed-methods survey approach. The results indicate variations in usability across countries and highlight the influence of very positive and very negative experiences on usability ratings. The survey approach proves effective in evaluating user experiences and identifying areas for improvement and desirable features.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Information Systems
R. F. Willemsen, J. J. Aardoom, O. P. van der Galien, S. van de Vijver, N. H. Chavannes, A. Versluis
Summary: This study evaluated healthcare usage and costs of patients using a digital platform, showing an increase in GP consultations and costs after implementation.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Information Systems
Danya Arif Siddiqi, Fatima Miraj, Humdiya Raza, Owais Ahmed Hussain, Mehr Munir, Vijay Kumar Dharma, Mubarak Taighoon Shah, Ali Habib, Subhash Chandir
Summary: This study developed and evaluated an AI chatbot in local language for providing immunization information in low-resource, low-literacy settings in Pakistan. The results showed that the chatbot was feasible and acceptable, meeting the needs of caregivers and reducing the workload of helpline operators.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Information Systems
Liss Hernandez, Estefania Estevez-Priego, Laura Lopez-Perez, Maria Fernanda Cabrera-Umpierrez, Maria Teresa Arredondo, Giuseppe Fico
Summary: HeNeCOn is a reusable, extendible and standardized ontology that provides a clinically reliable data model for Head and Neck Cancer. It consists of 502 classes and 283 medical terms with detailed relations between them, allowing for information extraction and knowledge management.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Information Systems
Savina Mannarino, Valeria Calcaterra, Giulia Fini, Andrea Foppiani, Antonio Sanzo, Martina Pisarra, Gabriele Infante, Marta Marsilio, Irene Raso, Sara Santacesaria, Gianvincenzo Zuccotti
Summary: This study introduces an innovative pediatric telecardiology system, seamlessly integrated with a hospital telemedicine platform, which enhances patient management in the community. The results demonstrate the system's value as a diagnostic tool to facilitate the execution, transmission, and reporting of ECG data between primary care pediatrician clinics and the hospital.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Information Systems
Matthew Ho, Todd J. Levy, Ioannis Koulas, Kyriaki Founta, Kevin Coppa, Jamie S. Hirsch, Karina W. Davidson, Alex C. Spyropoulos, Theodoros P. Zanos
Summary: This study identified clinical phenotypes of hospitalized COVID-19 patients and investigated their longitudinal dynamics throughout the pandemic. Four distinct clinical phenotypes were associated with different mortality rates and showed variability across different viral variants. Factors such as sex, race/ethnicity, and treatment modalities revealed significant differences between the observed phenotypes. This methodology has the potential to guide evidence-based treatment strategies in a dynamic fashion.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Review
Computer Science, Information Systems
Louis Agha-Mir-Salim, Lucas McCullum, Enrico Dahnert, Yanick-Daniel Scheel, Ainsley Wilson, Marianne Carpio, Carmen Chan, Claudia Lo, Lindsay Maher, Corinna Dressler, Felix Balzer, Leo Anthony Celi, Akira-Sebastian Poncette, Michele M. Pelter
Summary: Alarm fatigue is a significant issue in the intensive care unit, and collaboration between nurses and engineers is crucial for finding solutions. However, the current research lacks sufficient involvement of nurses, leading to a lack of successful real-world solutions.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Review
Computer Science, Information Systems
Ana Raquel Costa-Brito, Antonio Bovolini, Maria Rua-Alonso, Claudia Vaz, Juan Francisco Ortega-Moran, J. Blas Pagador, Carolina Vila-Cha
Summary: This scoping review investigates the use of home-based technological tools to improve physical function in older adults. The majority of studies suggest high levels of technology usage and positive health outcomes. However, the lack of international consensus on technology usage measures and the exclusion of older adults without technology ownership or experience may limit the findings.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)