Review
Medicine, General & Internal
Khandaker Reajul Islam, Johayra Prithula, Jaya Kumar, Toh Leong Tan, Mamun Bin Ibne Reaz, Md. Shaheenur Islam Sumon, Muhammad E. H. Chowdhury
Summary: This systematic review examines the application of machine learning and deep learning in predicting sepsis using electronic health records. The study highlights the importance of these methods in early sepsis detection and improving patient outcomes.
JOURNAL OF CLINICAL MEDICINE
(2023)
Article
Medicine, General & Internal
Abin Abraham, Brian Le, Idit Kosti, Peter Straub, Digna R. Velez-Edwards, Lea K. Davis, J. M. Newton, Louis J. Muglia, Antonis Rokas, Cosmin A. Bejan, Marina Sirota, John A. Capra
Summary: Machine learning models based on billing codes from electronic health records can accurately predict singleton preterm birth risk and outperform models trained on known risk factors. These models also stratify deliveries into interpretable groups and predict preterm birth subtypes, mode of delivery, and recurrent preterm birth. This study suggests that machine learning has great potential to improve medical care during pregnancy.
Review
Computer Science, Information Systems
Melissa Y. Yan, Lise Tuset Gustad, Oystein Nytro
Summary: Studies have shown that utilizing both unstructured text and structured data in machine learning can improve the identification and early detection of sepsis, with fewer studies focusing on predicting patient histories beyond the current episode of care.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2022)
Article
Psychiatry
Jessica Irving, Rashmi Patel, Dominic Oliver, Craig Colling, Megan Pritchard, Matthew Broadbent, Helen Baldwin, Daniel Stahl, Robert Stewart, Paolo Fusar-Poli
Summary: The study utilized novel data mining methods such as natural language processing (NLP) to screen and detect individuals at risk for psychosis by refining and externally validating a risk calculator, which included additional NLP predictors. The results showed improved prognostic accuracy, indicating the potential of NLP on EHRs in enhancing psychosis risk calculators and facilitating early detection for better patient outcomes.
SCHIZOPHRENIA BULLETIN
(2021)
Article
Medicine, General & Internal
Sujit Bebortta, Subhranshu Sekhar Tripathy, Shakila Basheer, Chiranji Lal Chowdhary
Summary: In contemporary healthcare, leveraging IoT devices and EHRs for accurate prediction of heart disease while protecting data privacy is crucial. This study integrates federated learning with a soft-margin L1-regularised Support Vector Machine classifier to solve the large-scale sSVM problem, improving computational complexity and scalability.
Review
Computer Science, Information Systems
Siyue Yang, Paul Varghese, Ellen Stephenson, Karen Tu, Jessica Gronsbell
Summary: This study evaluates the application of machine learning-based phenotyping in terms of data sources, phenotypes considered, methods applied, and reporting and evaluation methods. The results show that most studies used data from a single institution, including information from clinical notes. While most studies focused on binary phenotypes such as chronic conditions, machine learning also enabled the characterization of nuanced phenotypes. The study discusses the application of supervised deep learning, semi-supervised learning, and unsupervised learning methods. Machine learning approaches did not uniformly outperform rule-based algorithms, but deep learning offered a marginal improvement over traditional machine learning methods for many conditions.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2023)
Review
Health Care Sciences & Services
Christophe A. T. Stevens, Alexander R. M. Lyons, Kanika Dharmayat, Alireza Mahani, Kausik K. Ray, Antonio J. Vallejo-Vaz, Mansour T. A. Sharabiani
Summary: Electronic health records provide opportunities for machine learning techniques to identify undiagnosed individuals with diseases and improve medical screening and case finding. Ensemble machine learning models, especially those with complex combination strategies and heterogeneous classifiers, often outperform other models in this context. However, there is a lack of comprehensive reporting on the methodologies used in clinical research involving machine learning.
Article
Medical Informatics
Akhil Vaid, Suraj K. Jaladanki, Jie Xu, Shelly Teng, Arvind Kumar, Samuel Lee, Sulaiman Somani, Ishan Paranjpe, Jessica K. De Freitas, Tingyi Wanyan, Kipp W. Johnson, Mesude Bicak, Eyal Klang, Young Joon Kwon, Anthony Costa, Shan Zhao, Riccardo Miotto, Alexander W. Charney, Erwin Boettinger, Zahi A. Fayad, Girish N. Nadkarni, Fei Wang, Benjamin S. Glicksberg
Summary: This study aimed to predict mortality in hospitalized COVID-19 patients within 7 days using federated learning technique. The results showed that the models trained with federated learning outperformed those trained with local data at multiple hospitals.
JMIR MEDICAL INFORMATICS
(2021)
Article
Obstetrics & Gynecology
Guy Amit, Irena Girshovitz, Karni Marcus, Yiye Zhang, Jyotishman Pathak, Vered Bar, Pinchas Akiva
Summary: Utilizing machine learning to predict risk of postpartum depression (PPD) with primary care electronic health records (EHR) data can enhance the accuracy of PPD screening and enable early identification of at-risk women. Combining EHR-based prediction with Edinburgh postnatal depression scale (EPDS) score increases sensitivity and can lead to timely interventions, potentially improving outcomes for mothers and children.
BMC PREGNANCY AND CHILDBIRTH
(2021)
Article
Cardiac & Cardiovascular Systems
Victor M. Ruiz, Michael P. Goldsmith, Lingyun Shi, Allan F. Simpao, Jorge A. Galvez, Maryam Y. Naim, Vinay Nadkarni, J. William Gaynor, Fuchiang (Rich) Tsui
Summary: This study developed and evaluated a high-dimensional, data-driven model to identify patients at high risk of clinical deterioration using routinely collected electronic health record (EHR) data. The model accurately predicted deterioration events up to 8 hours in advance.
JOURNAL OF THORACIC AND CARDIOVASCULAR SURGERY
(2022)
Article
Medical Informatics
Jiaxin Fan, Mengying Chen, Jian Luo, Shusen Yang, Jinming Shi, Qingling Yao, Xiaodong Zhang, Shuang Du, Huiyang Qu, Yuxuan Cheng, Shuyin Ma, Meijuan Zhang, Xi Xu, Qian Wang, Shuqin Zhan
Summary: A study used machine learning models to predict asymptomatic CAS, finding that the LR model showed the best predictive performance, laying the foundation for establishing an early warning system to allocate CAS prevention measures more accurately.
BMC MEDICAL INFORMATICS AND DECISION MAKING
(2021)
Article
Health Care Sciences & Services
Yiqing Zhao, Sunyang Fu, Suzette J. Bielinski, Paul A. Decker, Alanna M. Chamberlain, Veronique L. Roger, Hongfang Liu, Nicholas B. Larson
Summary: The study developed a machine learning-based algorithm for identifying incident stroke and determining stroke type, which performed well in an AF cohort and a general population sample. It demonstrated good generalizability and potential for adoption by other institutions.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2021)
Article
Mathematical & Computational Biology
Duo Yu, Hulin Wu
Summary: This study investigates the interpretability and variable importance of machine learning models. A novel and computationally efficient evaluation framework called VIPOR is proposed. VIPOR is a model-agnostic method that can evaluate variable importance locally and globally using the concept of personalized odds ratio. The method groups predictors into different categories and ranks their importance based on different statistics. The proposed method is demonstrated using real-world electronic health records data and compared with other interpretation methods.
STATISTICS IN MEDICINE
(2023)
Article
Medicine, Research & Experimental
Siim Kurvits, Ainika Harro, Anu Reigo, Anne Ott, Sven Laur, Dage Sarg, Ardi Tampuu, Kaur Alasoo, Jaak Vilo, Lili Milani, Toomas Haller
Summary: This study utilized a nationwide Electronic Health Record (EHR) database in Estonia to extract and evaluate structured and unstructured data from participants. By applying different analytical and machine learning methods, several early trends and risk factors associated with ischemic stroke (IS) were identified. The results highlight the value of EHR databases in screening for IS risk, constructing disease risk scores, and improving IS prediction models through machine learning techniques.
EUROPEAN JOURNAL OF MEDICAL RESEARCH
(2023)
Article
Public, Environmental & Occupational Health
Liling Huang, Bo Xie, Kai Zhang, Yuanlong Xu, Lingsong Su, Yu Lv, Yangjie Lu, Jianqiu Qin, Xianwu Pang, Hong Qiu, Lanxiang Li, Xihua Wei, Kui Huang, Zhihao Meng, Yanling Hu, Jiannan Lv
Summary: A predictive model was developed using machine learning and electronic medical records to evaluate the risk of cytopenia in HIV patients during hospitalization. This model is crucial for designing a more individualized and evidence-based treatment strategy for HIV patients.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Chemistry, Medicinal
Stephanie L. Slania, Deepankar Das, Ala Lisok, Yong Du, Zirui Jiang, Ronnie C. Mease, Steven P. Rowe, Sridhar Nimmagadda, Xing Yang, Martin G. Pomper
Summary: Two new small molecules, QCP01 and [In-111]QCP02, based on (4-quinolinoyl)-glycyl-2-cyanopyrrolidine were synthesized and characterized for imaging of fibroblast activation protein (FAP). Both molecules demonstrated nanomolar inhibition of FAP and selective binding to FAP-expressing tumors in vivo. [In-111]QCP02 showed high uptake in FAP-positive tumors, indicating its potential as an imaging agent for FAP-targeted therapy.
JOURNAL OF MEDICINAL CHEMISTRY
(2021)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)