nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
1 |
A cascade deep learning model for diagnosing pharyngeal acid reflux episodes using hypopharyngeal multichannel intraluminal Impedance-pH signals
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Fu, Jachih |
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C |
p. |
artikel |
2 |
Acknowledgments to our reviewers in 2022
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C |
p. |
artikel |
3 |
A cortical thinning signature to identify World Trade Center responders with possible dementia
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Clouston, Sean A.P. |
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C |
p. |
artikel |
4 |
A critical analysis of COVID-19 research literature: Text mining approach
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Zengul, Ferhat D. |
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C |
p. |
artikel |
5 |
Agile clinical research: A data science approach to scrumban in clinical medicine
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Lei, Howard |
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C |
p. |
artikel |
6 |
A hybrid U-Net model with attention and advanced convolutional learning modules for simultaneous gland segmentation and cancer grade prediction in colorectal histopathological images
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Dabass, Manju |
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C |
p. |
artikel |
7 |
AI in healthcare startups and special challenges
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Young, Alan S. |
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C |
p. |
artikel |
8 |
A Machine Learning Algorithm Predicts Duration of hospitalization in COVID-19 patients
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Ebinger, Joseph |
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C |
p. |
artikel |
9 |
An automatic early screening system of eye diseases using UWF fundus images based on deep neural networks
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Wang, Shubin |
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C |
p. |
artikel |
10 |
An efficient image segmentation scheme for determination of cranial index in scaphocephalic patients
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Sabeti, M. |
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C |
p. |
artikel |
11 |
An ensemble approach for multi-stage transfer learning models for COVID-19 detection from chest CT scans
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Hernández Santa Cruz, Jose Francisco |
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C |
p. |
artikel |
12 |
A new convolutional neural network-construct for sepsis enhances pattern identification of microcirculatory dysfunction
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Toledo Ferraz, Carolina |
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C |
p. |
artikel |
13 |
A new vision of a simple 1D Convolutional Neural Networks (1D-CNN) with Leaky-ReLU function for ECG abnormalities classification
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Lakhdari, Kheira |
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C |
p. |
artikel |
14 |
A novel data augmentation approach for mask detection using deep transfer learning
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Prusty, Manas Ranjan |
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C |
p. |
artikel |
15 |
An unsupervised deep learning-based image translation method for retrospective motion correction of high resolution kidney MRI
|
Moinian, Shahrzad |
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C |
p. |
artikel |
16 |
Appropriate use of machine learning in healthcare
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Ozaydin, Bunyamin |
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C |
p. |
artikel |
17 |
Artificial Intelligence (AI) to improve chronic pain care: Evidence of AI learning
|
Piette, John D. |
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C |
p. |
artikel |
18 |
Artificial intelligence and COVID-19: Present state and future vision
|
Chang, Anthony C. |
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C |
p. |
artikel |
19 |
Artificial intelligence-based rapid on-site cytopathological evaluation for bronchoscopy examinations
|
Ai, Dilbar |
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C |
p. |
artikel |
20 |
Artificial intelligence-enabled predictive model of progression from moderate to severe aortic stenosis
|
Moualla, Soundos K. |
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C |
p. |
artikel |
21 |
Artificial intelligence for diagnosis of fractures on plain radiographs: A scoping review of current literature
|
Rainey, Clare |
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C |
p. |
artikel |
22 |
Artificial Intelligence Improves Readability of Digital Health Records
|
Vien, Peter |
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C |
p. |
artikel |
23 |
Artificial intelligence in echocardiography to diagnose congenital heart disease and fetal echocardiography
|
Gearhart, Addison |
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C |
p. |
artikel |
24 |
Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: A systematic review
|
Wang, Minhong |
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C |
p. |
artikel |
25 |
Artificial intelligence to assist physicians in identifying patients with severe aortic stenosis
|
Thomas, James D. |
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C |
p. |
artikel |
26 |
Artificial intelligence viewed through the lens of state regulation
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TerKonda, Sarvam P. |
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C |
p. |
artikel |
27 |
Artificial neural network to classify cognitive impairment using gait and clinical variables
|
Zhou, Yuhan |
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C |
p. |
artikel |
28 |
Assessment of patient perceptions of technology and the use of machine-based learning in a clinical encounter
|
Bett, Ean S. |
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C |
p. |
artikel |
29 |
A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions
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Ehwerhemuepha, Louis |
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C |
p. |
artikel |
30 |
Auto-contouring FDG-PET/MR images for cervical cancer radiation therapy: An intelligent sequential approach using focally trained, shallow U-Nets
|
Baydoun, Atallah |
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C |
p. |
artikel |
31 |
Automated analysis of ambulatory surgery patient experience comments using artificial intelligence for quality improvement: A patient centered approach
|
Mathur, Piyush |
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C |
p. |
artikel |
32 |
Automated diabetic retinopathy screening for primary care settings using deep learning
|
Bhuiyan, Alauddin |
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C |
p. |
artikel |
33 |
Automatic detection of decreased ejection fraction and left ventricular hypertrophy on 4D cardiac CTA: Use of artificial intelligence with transfer learning to facilitate multi-site operations
|
Rockenbach, Marcio A.B.C. |
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C |
p. |
artikel |
34 |
Automatic generation of operation notes in endoscopic pituitary surgery videos using workflow recognition
|
Das, Adrito |
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C |
p. |
artikel |
35 |
Automatic screening for diabetic retinopathy in interracial fundus images using artificial intelligence
|
Katada, Yusaku |
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C |
p. |
artikel |
36 |
Bridging the divide between data scientists and clinicians
|
Bastian, Grace |
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C |
p. |
artikel |
37 |
Case study - Feature engineering inspired by domain experts on real world medical data
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Björneld, Olof |
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C |
p. |
artikel |
38 |
Classification of Cervical Intraepithelial Neoplasia (CIN) using fine-tuned Convolutional Neural Networks
|
Aina, Oluwatomisin E. |
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C |
p. |
artikel |
39 |
Clinical applications of artificial intelligence and machine learning in the modern cardiac intensive care unit
|
Jentzer, Jacob C. |
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C |
p. |
artikel |
40 |
Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic
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Ghosheh, Ghadeer O. |
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C |
p. |
artikel |
41 |
Computer-vision based method for quantifying rising from chair in Parkinson's disease patients
|
Morinan, Gareth |
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C |
p. |
artikel |
42 |
Computer vision with smartphone microphotography for detection of carious lesions
|
Farook, Taseef Hasan |
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C |
p. |
artikel |
43 |
COVID-19 pneumonia accurately detected on chest radiographs with artificial intelligence
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Dorr, Francisco |
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C |
p. |
artikel |
44 |
Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections
|
Washington, Peter |
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C |
p. |
artikel |
45 |
Dealing with complexity: How to use a hybrid approach to incorporate complexity in health behavior interventions
|
Mkhitaryan, Samvel |
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C |
p. |
artikel |
46 |
Decoding ChatGPT: A primer on large language models for clinicians
|
Hunter, R. Brandon |
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C |
p. |
artikel |
47 |
Deep learning and its role in COVID-19 medical imaging
|
Desai, Sudhen B. |
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C |
p. |
artikel |
48 |
Deep learning assistance for the histopathologic diagnosis of Helicobacter pylori
|
Zhou, Sharon |
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C |
p. |
artikel |
49 |
Deep learning-based risk classification and auxiliary diagnosis of macular edema
|
Wu, Tianzhu |
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C |
p. |
artikel |
50 |
Development of an artificial intelligence model for triage in a military emergency department: Focusing on abdominal pain in soldiers
|
Kim, Yoon-Seop |
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C |
p. |
artikel |
51 |
DFU_MultiNet: A deep neural network approach for detecting diabetic foot ulcers through multi-scale feature fusion using the DFU dataset
|
Biswas, Shuvo |
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C |
p. |
artikel |
52 |
Digitizing paper based ECG files to foster deep learning based analysis of existing clinical datasets: An exploratory analysis
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Adedinsewo, Demilade A. |
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C |
p. |
artikel |
53 |
Discriminating Acute Respiratory Distress Syndrome from other forms of respiratory failure via iterative machine learning
|
Afshin-Pour, Babak |
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C |
p. |
artikel |
54 |
Dixon-based thorax synthetic CT generation using Generative Adversarial Network
|
Baydoun, Atallah |
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C |
p. |
artikel |
55 |
Doxorubicin Efficacy Prediction for Glioblastomas using Deep Learning and Differential Equations
|
Garg, Arnav |
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C |
p. |
artikel |
56 |
Dual autoencoders modeling of electronic health records for adverse drug event preventability prediction
|
Liao, Wenjun |
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C |
p. |
artikel |
57 |
Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study
|
Smit, J.M. |
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C |
p. |
artikel |
58 |
Early detection of neurological abnormalities using a combined phase space reconstruction and deep learning approach
|
Al Fahoum, Amjed |
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C |
p. |
artikel |
59 |
Equivalence of pathologists' and rule-based parser's annotations of Dutch pathology reports
|
Burger, Gerard TN. |
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C |
p. |
artikel |
60 |
Forecasting length of stay: Will it be clear or cloudy today?
|
Deng, Charles |
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C |
p. |
artikel |
61 |
FuzzyDeepNets based feature extraction for classification of mammograms
|
Dabass, Jyoti |
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C |
p. |
artikel |
62 |
Glaucoma detection in retinal fundus images using U-Net and supervised machine learning algorithms
|
Shinde, Rutuja |
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C |
p. |
artikel |
63 |
Harnessing machine learning to support evidence-based medicine: A pragmatic reconciliation framework
|
Abujaber, Ahmad A. |
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C |
p. |
artikel |
64 |
HeartPredict algorithm: Machine intelligence for the early detection of heart failure
|
Amadou Boubacar, Habiboulaye |
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C |
p. |
artikel |
65 |
Hospital-acquired infections surveillance and prevention: using Natural Language Processing to analyze unstructured text of hospital discharge letters for surgical site infections identification and risk-stratification.
|
De Angelis, Luigi |
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C |
p. |
artikel |
66 |
Improved and robust deep learning agent for preliminary detection of diabetic retinopathy using public datasets
|
Saxena, Gaurav |
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C |
p. |
artikel |
67 |
Integrating unsupervised and supervised learning techniques to predict traumatic brain injury: A population-based study
|
Zulbayar, Suvd |
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C |
p. |
artikel |
68 |
Integration of a deep learning system for automated chest x-ray interpretation in the emergency department: A proof-of-concept
|
Mosquera, Candelaria |
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C |
p. |
artikel |
69 |
Intelligence-based medicine: Medici Effect of the modern medical era
|
Chang, Anthony |
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C |
p. |
artikel |
70 |
Local integration of deep learning for advanced visualization in congenital heart disease surgical planning
|
Nainamalai, Varatharajan |
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C |
p. |
artikel |
71 |
Machine learning algorithms for classifying corneas by Zernike descriptors
|
del Río, María S. |
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C |
p. |
artikel |
72 |
Machine learning-based approach to the diagnosis of cardiovascular vascular disease using a combined dataset
|
Mohi Uddin, Khandaker Mohammad |
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C |
p. |
artikel |
73 |
Machine learning-based prediction of low-value care for hospitalized patients
|
King, Andrew J. |
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C |
p. |
artikel |
74 |
Machine learning for ambulatory applications of neuropsychological testing
|
Chandler, Chelsea |
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C |
p. |
artikel |
75 |
Machine learning for metabolomics research in drug discovery
|
Martinelli, Dominic D. |
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C |
p. |
artikel |
76 |
Machine learning models using mobile game play accurately classify children with autism
|
Deveau, Nicholas |
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C |
p. |
artikel |
77 |
Machine learning's performance in classifying postmenopausal osteoporosis Thai patients
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Thawnashom, Kittisak |
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C |
p. |
artikel |
78 |
Medical image editing in the latent space of Generative Adversarial Networks
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Fernández Blanco, Rubén |
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C |
p. |
artikel |
79 |
Model drift: When it can be a sign of success and when it can be an occult problem
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Carter, Rickey E. |
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C |
p. |
artikel |
80 |
Model utility of a deep learning-based segmentation is not Dice coefficient dependent: A case study in volumetric brain blood vessel segmentation
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Alidoost, Mohammadali |
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C |
p. |
artikel |
81 |
Neural networks for cognitive testing: Cognitive test drawing classification
|
Howard, Calvin W. |
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C |
p. |
artikel |
82 |
Novel integration of governmental data sources using machine learning to identify super-utilization among U.S. counties
|
Ricket, Iben M. |
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C |
p. |
artikel |
83 |
Optimal hyperparameter selection of deep learning models for COVID-19 chest X-ray classification
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Adedigba, Adeyinka P. |
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C |
p. |
artikel |
84 |
Optimizing a de novo artificial intelligence-based medical device under a predetermined change control plan: Improved ability to detect or rule out pediatric autism
|
Wall, Dennis P. |
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C |
p. |
artikel |
85 |
Outlier-SMOTE: A refined oversampling technique for improved detection of COVID-19
|
Turlapati, Venkata Pavan Kumar |
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C |
p. |
artikel |
86 |
Patent ductus arteriosus (PDA) detection in echocardiograms using deep learning
|
Lei, Howard |
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C |
p. |
artikel |
87 |
Physician documentation matters. Using natural language processing to predict mortality in sepsis
|
Cooley-Rieders, Keaton |
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C |
p. |
artikel |
88 |
Physician leadership in the new era of AI and digital health tools
|
Ehrenfeld, Jesse |
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C |
p. |
artikel |
89 |
Predicting health-related quality of life change using natural language processing in thyroid cancer
|
Lian, Ruixue |
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C |
p. |
artikel |
90 |
Predicting Hospital Readmission Risk in Patients with Severe Bronchopulmonary Dysplasia: Exploring the Impact of Neighborhood-Level Social Determinants of Health
|
Gorham, Tyler |
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C |
p. |
artikel |
91 |
Predicting prolonged length of stay in patients with traumatic brain injury: A machine learning approach
|
Abujaber, Ahmad |
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C |
p. |
artikel |
92 |
Predicting shunt infection in children with hydrocephalus
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Sabeti, M. |
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C |
p. |
artikel |
93 |
Prediction of Alzheimer's disease from magnetic resonance imaging using a convolutional neural network
|
de Silva, Kevin |
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C |
p. |
artikel |
94 |
Privacy aspects of direct-to-consumer artificial intelligence/machine learning health apps
|
Gerke, Sara |
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C |
p. |
artikel |
95 |
Prospective clinical evaluation of a machine-learning trained algorithm for detection of arterial pressure transducer drop
|
Rinehart, Joseph |
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C |
p. |
artikel |
96 |
Publishing Artificial Intelligence research papers: A tale of three journals
|
Shortliffe, Edward H. |
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C |
p. |
artikel |
97 |
Rhi3DGen: Analyzing Rhinophyma using 3D face models and synthetic data
|
Mohanty, Anwesha |
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C |
p. |
artikel |
98 |
Sex, ethnicity, and race data are often unreported in artificial intelligence and machine learning studies in medicine
|
Elmahdy, Mahmoud |
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C |
p. |
artikel |
99 |
Stratification of keratoconus progression using unsupervised machine learning analysis of tomographical parameters
|
Cao, Ke |
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C |
p. |
artikel |
100 |
Synthea™ Novel coronavirus (COVID-19) model and synthetic data set
|
Walonoski, Jason |
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C |
p. |
artikel |
101 |
The challenges of health inequities and AI
|
Moore, Candace Makeda |
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C |
p. |
artikel |
102 |
The ethical considerations including inclusion and biases, data protection, and proper implementation among AI in radiology and potential implications
|
Martin, Clarissa |
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C |
p. |
artikel |
103 |
Toward enhanced free-living fall risk assessment: Data mining and deep learning for environment and terrain classification
|
Moore, Jason |
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C |
p. |
artikel |
104 |
Towards a national capability framework for Artificial Intelligence and Digital Medicine tools – A learning needs approach
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Abdulhussein, Hatim |
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C |
p. |
artikel |
105 |
Towards automation of transcranial Doppler ultrasound data in pediatric cerebral malaria
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Zhang, Bo |
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C |
p. |
artikel |
106 |
Towards early sepsis detection from measurements at the general ward through deep learning
|
Oei, Sebastiaan P. |
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C |
p. |
artikel |
107 |
Twelve key challenges in medical machine learning and solutions
|
Ellis, Randall J. |
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C |
p. |
artikel |
108 |
Unsupervised learning for county-level typological classification for COVID-19 research
|
Lai, Yuan |
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C |
p. |
artikel |
109 |
Using artificial intelligence to risk stratify COVID-19 patients based on chest X-ray findings
|
Hipolito Canario, Diego A. |
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C |
p. |
artikel |
110 |
Using machine learning and clinical registry data to uncover variation in clinical decision making
|
James, Charlotte |
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C |
p. |
artikel |
111 |
VacSIM: Learning effective strategies for COVID-19 vaccine distribution using reinforcement learning
|
Awasthi, Raghav |
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C |
p. |
artikel |
112 |
Validation in the age of machine learning: A framework for describing validation with examples in transcranial magnetic stimulation and deep brain stimulation
|
Baxter, John S.H. |
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C |
p. |
artikel |
113 |
What we talk about when we talk about trust: Theory of trust for AI in healthcare
|
Gille, Felix |
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C |
p. |
artikel |
114 |
Who will stay a little longer? Predicting length of stay in hip and knee arthroplasty patients using machine learning
|
Langenberger, Benedikt |
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C |
p. |
artikel |
115 |
Zero-shot learning and its applications from autonomous vehicles to COVID-19 diagnosis: A review
|
Rezaei, Mahdi |
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C |
p. |
artikel |