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Following publication of the original article
The original article was published in BMC Medical Informatics and Decision Making 2019 19:136
Based on actual hospital medical records of infectious diseases from December 2012 to December 2020, a deep learning model for multi-classification research on infectious...
Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can hel...
Glaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), co...
Using NLP deep learning models, we could explore potential targets and...
This study aimed to develop a deep learning model to assist radiologists in detecting COVID-...
Accurate prediction of healthcare costs is important for optimally managing health costs. However, methods leveraging the medical richness from data such as health insurance claims or electronic health records...
To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence...
Our method uses a customized VGG16-based 15-layer 2-dimensional deep convolutional neural network (DNN) architecture with transfer learning. The DNN was trained and tested on...
For the mortality risk prediction, this research work proposes a COVID-19 mortality risk calculator based on a deep learning (DL) model and based on a...
Developmental dysplasia of the hip (DDH) is a relatively common disorder in newborns, with a reported prevalence of 1–5 per 1000 births. It can lead to developmental abnormalities in terms of mechanical difficult...
Deep learning (DL) models are highly vulnerable to...
This article is part of a Supplement: Volume 22 Supplement 2
The main objective of this study was to create an automated deep learning model capable of accurately classifying ECG signals ... were preprocessed and segmented before being utilized for deep learning model trai...
Computed tomography (CT) reports record a large volume of valuable information about patients’ conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-mak...
This article is part of a Supplement: Volume 21 Supplement 2
Interpretation of chest radiographs (CRs) by emergency department (ED) physicians is inferior to that by radiologists. Recent studies have investigated the effect of deep learning-based assistive technology on CR...
Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance...
We built a deep learning-based FVEP classification algorithm that promises to...
Various machine learning and artificial intelligence methods have been used ... -19 prediction. We developed a process mining/deep learning approach to predict mortality among COVID-19...
Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrativ...
Accurate segmentation of stroke lesions on MRI images is very important for neurologists in the planning of post-stroke care. Segmentation helps clinicians to better diagnose and evaluation of any treatment r...
We collected eye movement video and diagnostic data from 518 patients with BPPV who visited the hospital for examination from January to March 2021 and developed a BPPV dataset. Based on the characteristics of th...
The proposed approach leveraging weak supervision could significantly increase the sample size, which is required for training the deep learning models. By comparing with the traditional machine learning models, ...
This article is part of a Supplement: Volume 22 Supplement 1
The broad adoption of electronic health records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods based...
This study demonstrates the feasibility of leveraging unsupervised, deep-learning methods to identify potential procedure overutilization from...
Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can...
The epiretinal membrane (ERM) is a common retinal disorder characterized by abnormal fibrocellular tissue at the vitreomacular interface. Most patients with ERM are asymptomatic at early stages. Therefore, screen...
The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent ...
We developed ResoLSTM-Depth, a deep learning model to distinguish ESCC stages T1-T2...
A computer-aided deep-learning algorithm is considered a state-of-the-...
We examined the problem of using a large volume of heterogeneous EHR data to predict treatment effects and developed an adversarial deep treatment effect prediction model to address the ... . Our model employed t...
This article is part of a Supplement: Volume 20 Supplement 4
We developed a NYHA functional classification model for heart failure based on a deep learning method. We introduced an integrating attention mechanism ... signal segments could be used with the proposed deep learning
Saliency-based algorithms are able to explain the relationship between input image pixels and deep-learning model predictions. However, it may be ... proposes to enhance the interpretability of saliency-based deep
There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (che...
With the development of current medical technology, information management becomes perfect in the medical field. Medical big data analysis is based on a large amount of medical and health data stored in the el...
Extracting metastatic information from previous radiologic-text reports is important, however, laborious annotations have limited the usability of these texts. We developed a deep-learning model for extracting pr...
In the current study an innovative computer aided fetal distress diagnosing model is developed by using time frequency representation of FHR signal using generalized Morse wavelet and the concept of transfer learning
We proposed a temporal deep learning method, based on a time-aware long...
Low back pain (LBP) is a common condition made up of a variety of anatomic and clinical subtypes. Lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) are two subtypes highly associated with LBP. Patient...
Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitati...
Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all ... time, and compares it with the four deep learning mode...
Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefo...
Chronic kidney disease is a prevalent global health issue, particularly in advanced stages requiring dialysis. Vascular access (VA) quality is crucial for the well-being of hemodialysis (HD) patients, ensuring op...
Deep convolutional autoencoders were able to learn the image representation, encompassing the entire spectrum ... variables, and cluster analysis based on these learned representations for the movement behavior i...
Based on the labels of the clinical experts, the proposed deep learning algorithm exhibits high accuracy for automatic labeling....
Emotions after surviving cancer can be complicated. The survivors may have gained new strength to continue life, but some of them may begin to deal with complicated feelings and emotional stress due to trauma ...
This article is part of a Supplement: Volume 20 Supplement 4
The study data consist of two sets: (1) manual chart reviewed data—1039 clinical notes of 300 patients with asthma diagnosis, and (2) weakly labeled data (distant supervision)—27,363 clinical notes from 800 patie...
This article is part of a Supplement: Volume 21 Supplement 7
The main cause of fetal death, of infant morbidity or mortality during childhood years is attributed to congenital anomalies. They can be detected through a fetal morphology scan. An experienced sonographer (with...
In this paper, a highly accurate K-complex detection system is developed. Based on multiple convolutional neural network (CNN) feature extraction backbones and EEG waveform images, a regions with faster regions w...
Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival lengths, indicating a need to identify prognostic biomarkers for pers...
This study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (...
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