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Genomics and Precision Health Database


Genomics & Health Impact Scan Database|Search|PHGKB
Last data update: Dec 12, 2019. (Total: 27572 Documents since 2012)
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Query Trace: All [orginal query]>>Non-Genomics Precision Health[Category]
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Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors
BR Greene, NPJ Digital Medicine, December 2019
Making Machine Learning Accessible and Actionable for Clinicians.
Schneider David F et al. JAMA network open 2019 Dec 2(12) e1917362
Artificial Intelligence and Surgical Decision-Making.
Loftus Tyler J et al. JAMA surgery 2019 Dec
Mass-producing wearable sensors: No sweat
LH Han, Sci Trans Med, December 11, 2019
Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients.
Dauvin Antonin et al. NPJ digital medicine 2019 2116
Artificial intelligence for precision medicine in neurodevelopmental disorders.
Uddin Mohammed et al. NPJ digital medicine 2019 2112
Integrating Machine Learning With Microsimulation to Classify Hypothetical, Novel Patients for Predicting Pregabalin Treatment Response Based on Observational and Randomized Data in Patients With Painful Diabetic Peripheral Neuropathy.
Alexander Joe et al. Pragmatic and observational research 2019 1067-76
Establishment of a new non-invasive imaging prediction model for liver metastasis in colon cancer.
Li Yu et al. American journal of cancer research 2019 9(11) 2482-2492
Big Data in Medicine, the Present and Hopefully the Future.
Riba Michela et al. Frontiers in medicine 2019 6263
Attitudes Of Chinese Cancer Patients Toward The Clinical Use Of Artificial Intelligence.
Yang Keyi et al. Patient preference and adherence 2019 131867-1875
Machine Learning For Tuning, Selection, And Ensemble Of Multiple Risk Scores For Predicting Type 2 Diabetes.
Liu Yujia et al. Risk management and healthcare policy 2019 12189-198
A two-dimensional feasibility study of deep learning-based feature detection and characterization directly from CT sinograms.
De Man Quinten et al. Medical physics 2019 Dec 46(12) e790-e800
Corneal thickness measurement by secondary speckle tracking and image processing using machine-learning algorithms.
Bennett Aviya et al. Journal of biomedical optics 2019 24(12) 1-10
Artificial intelligence and algorithmic bias: implications for health systems.
Panch Trishan et al. Journal of global health 2019 Dec 9(2) 010318
Evaluation of colorectal cancer subtypes and cell lines using deep learning.
Ronen Jonathan et al. Life science alliance 2019 Dec 2(6)
Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images.
Sena Paola et al. Oncology letters 2019 Dec 18(6) 6101-6107
Machine Learning Approach for Predicting Past Environmental Exposures From Molecular Profiling of Post-Exposure Human Serum Samples.
Khan Atif et al. Journal of occupational and environmental medicine 2019 Dec 61 Suppl 12S55-S64
Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches.
Ford Elizabeth et al. BMC medical informatics and decision making 2019 Dec 19(1) 248
Predicting Hepatitis B Virus Infection Based on Health Examination Data of Community Population.
Wang Ying et al. International journal of environmental research and public health 2019 Dec 16(23)
Deep Learning Enables Automatic Classification of Emphysema Pattern at CT.
Humphries Stephen M et al. Radiology 2019 Dec 191022
Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation.
Majkowska Anna et al. Radiology 2019 Dec 191293
[Artificial intelligence and radiomics in MRI-based prostate diagnostics].
Hamm Charlie Alexander et al. Der Radiologe 2019 Dec
Man or machine? Prospective comparison of the version 2018 EASL, LI-RADS criteria and a radiomics model to diagnose hepatocellular carcinoma.
Jiang Hanyu et al. Cancer imaging : the official publication of the International Cancer Imaging Society 2019 Dec 19(1) 84
Improving rare disease classification using imperfect knowledge graph.
Li Xuedong et al. BMC medical informatics and decision making 2019 Dec 19(Suppl 5) 238
Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study.
Park Sungkyu et al. JMIR mHealth and uHealth 2019 Dec 7(12) e14473
Psychotic disorders hospitalizations associated with cannabis abuse or dependence: A nationwide big data analysis.
Gonçalves-Pinho Manuel et al. International journal of methods in psychiatric research 2019 Dec e1813
Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches.
Wharton S B et al. Acta neuropathologica communications 2019 Dec 7(1) 198
Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning.
Wong Jordan et al. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2019 Dec 144152-158
Natural language processing for populating lung cancer clinical research data.
Wang Liwei et al. BMC medical informatics and decision making 2019 Dec 19(Suppl 5) 239
[Artificial intelligence in the diagnosis of breast cancer : Yesterday, today and tomorrow].
Bennani-Baiti B et al. Der Radiologe 2019 Dec
The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases.
Son Hye Joo et al. European journal of nuclear medicine and molecular imaging 2019 Dec
A computer-aided diagnosing system in the evaluation of thyroid nodules-experience in a specialized thyroid center.
Xia Shujun et al. World journal of surgical oncology 2019 Dec 17(1) 210
Technical Note: A Feasibility Study on Deep Learning-Based Radiotherapy Dose Calculation.
Xing Yixun et al. Medical physics 2019 Dec
Deep learning: definition and perspectives for thoracic imaging.
Chassagnon Guillaume et al. European radiology 2019 Dec
An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI.
Shapey Jonathan et al. Journal of neurosurgery 2019 Dec 1-9
Support system of cystoscopic diagnosis for bladder cancer based on artificial intelligence.
Ikeda Atsushi et al. Journal of endourology 2019 Dec
Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.
Kann Benjamin H et al. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2019 Dec JCO1902031
Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques.
Kanegae Hiroshi et al. Journal of clinical hypertension (Greenwich, Conn.) 2019 Dec
Automatic Multi-organ Segmentation in Dual Energy CT (DECT) with Dedicated 3D Fully Convolutional DECT Networks.
Chen Shuqing et al. Medical physics 2019 Dec
Methods for algorithmic diagnosis of metabolic syndrome.
Vrbaški Dunja et al. Artificial intelligence in medicine 2019 Nov 101101708
A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset.
Liu Tianyu et al. Artificial intelligence in medicine 2019 Nov 101101723
Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques.
Gurchiek Reed D et al. Sensors (Basel, Switzerland) 2019 Nov 19(23)
Developing an Artificial Intelligence-Enabled Health Care Practice: Rewiring Health Care Professions for Better Care.
Wiljer David et al. Journal of medical imaging and radiation sciences 2019 Nov
Artificial intelligence enabled healthcare: A hype, hope or harm.
Bhattacharya Sudip et al. Journal of family medicine and primary care 2019 Nov 8(11) 3461-3464
Classifying cancer pathology reports with hierarchical self-attention networks.
Gao Shang et al. Artificial intelligence in medicine 2019 Nov 101101726
Automated detection of focal cortical dysplasia using a deep convolutional neural network.
Wang Huiquan et al. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 2019 Nov 79101662
Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning.
Meinlschmidt Gunther et al. Journal of affective disorders 2019 Nov
Artificial intelligence for detection of optic disc abnormalities.
Milea Dan et al. Current opinion in neurology 2019 Nov
A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta.
Liang Liang et al. Journal of biomechanics 2019 Nov 109544
Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images.
Moreno-Armendáriz Marco A et al. Sensors (Basel, Switzerland) 2019 Nov 19(23)
Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images.
Moreno-Armendáriz Marco A et al. Sensors (Basel, Switzerland) 2019 Nov 19(23)
Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning.
Zhao Wei et al. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 2020 25139-148
Monitoring ICU Mortality Risk with A Long Short-Term Memory Recurrent Neural Network.
Yu Ke et al. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 2020 25103-114
The role of artificial intelligence in learning and professional development for healthcare professionals.
Randhawa Gurprit K et al. Healthcare management forum 2020 Jan 33(1) 19-24
Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor
R Ueno et al, NPJ Digital Medicine, December 2019
New AI-powered imaging technique IDs colorectal cancer with 100% accuracy
M Walter, AI in Health Care, December 10, 2019
Machines match humans in image-based diagnoses
H Stower, Nature Medicine, December 2019
How Twitter is changing medical research
N Wetsman, Nature Medicine, December 9, 2019
Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs.
Dunnmon Jared A et al. Radiology 2019 290(2) 537-544
Machine learning for segmenting cells in corneal endothelium images.
Kolluru Chaitanya et al. Proceedings of SPIE--the International Society for Optical Engineering 2019 Feb 10950
Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation.
van Rooij Ward et al. International journal of radiation oncology, biology, physics 2019 104(3) 677-684
Photonic Crystal-Enhanced Fluorescence Imaging Immunoassay for Cardiovascular Disease Biomarker Screening with Machine Learning Analysis.
Squire Kenneth J et al. Sensors and actuators. B, Chemical 2019 Jul 290118-124
Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs.
Hwang Eui Jin et al. JAMA network open 2019 2(3) e191095
Deep learning only by normal brain PET identify unheralded brain anomalies.
Choi Hongyoon et al. EBioMedicine 2019 May 43447-453
Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope.
Dascalu A et al. EBioMedicine 2019 May 43107-113
Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network.
Cui Shaoguo et al. Journal of healthcare engineering 2018 20184940593
Evaluation of Commonly Used Algorithms for Thyroid Ultrasound Images Segmentation and Improvement Using Machine Learning Approaches.
Poudel Prabal et al. Journal of healthcare engineering 2018 20188087624
Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data.
Reps Jenna M et al. Journal of the American Medical Informatics Association : JAMIA 2018 25(8) 969-975
Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer.
Bibault Jean-Emmanuel et al. Scientific reports 2018 8(1) 12611
Algorithms on regulatory lockdown in medicine
B Babic et al, Science December 6, 2019
Unpacking the Black Box in Artificial Intelligence for Medicine
E Bender, Undark, December 4, 2019
Translational Informatics for Parkinson's Disease: from Big Biomedical Data to Small Actionable Alterations.
Shen Bairong et al. Genomics, proteomics & bioinformatics 2019 Nov
Internet-Based Management for Depressive Disorder.
Wang Zuowei et al. Advances in experimental medicine and biology 2019 1180267-276
Human factors challenges for the safe use of artificial intelligence in patient care.
Sujan Mark et al. BMJ health & care informatics 2019 Nov 26(1)
Towards spike-based machine intelligence with neuromorphic computing.
Roy Kaushik et al. Nature 2019 Nov 575(7784) 607-617
Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning.
Miché Marcel et al. Journal of affective disorders 2019 Nov
Cost-effective survival prediction for patients with advanced prostate cancer using clinical trial and real-world hospital registry datasets.
Murtojärvi Mika et al. International journal of medical informatics 2019 Nov 133104014
Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma.
Wang Hao et al. European journal of radiology 2019 Nov 122108755
Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer.
Gao Yuan et al. Chinese medical journal 2019 Nov
Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.
Lo Gullo Roberto et al. Breast (Edinburgh, Scotland) 2019 Nov 49115-122
Machine Learning Mortality-Classification in Clinical Documentation with Increased Accuracy in Visual-Based Analyses.
Slattery Susan M et al. Acta paediatrica (Oslo, Norway : 1992) 2019 Nov
Deep learning for lesion detection, progression, and prediction of musculoskeletal disease.
Kijowski Richard et al. Journal of magnetic resonance imaging : JMRI 2019 Nov
Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer's disease to Parkinson's disease.
Choi Hongyoon et al. European journal of nuclear medicine and molecular imaging 2019 Nov
Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset.
Filice Ross W et al. Journal of digital imaging 2019 Nov
A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods.
Mattie Heather et al. Population health management 2019 Nov
A novel artificial intelligence system for the assessment of bowel preparation (with video).
Zhou Jie et al. Gastrointestinal endoscopy 2019 Nov
Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration.
González-Gonzalo Cristina et al. Acta ophthalmologica 2019 Nov
Ethical Considerations in the Advent of Artificial Intelligence in Otolaryngology.
Arambula Alexandra M et al. Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery 2019 Nov 194599819889686
Using a Machine Learning System to Identify and Prevent Medication Prescribing Errors: A Clinical and Cost Analysis Evaluation.
Rozenblum Ronen et al. Joint Commission journal on quality and patient safety 2019 Nov
Artificial intelligence and robotics: a combination that is changing the operating room.
Andras Iulia et al. World journal of urology 2019 Nov
Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.
Ahn Sang Hee et al. Radiation oncology (London, England) 2019 Nov 14(1) 213
Clinical Practice Settings vs Clinical Trials: Is Artificial Intelligence the Answer?
Jampol Lee Merrill et al. JAMA ophthalmology 2019 Nov
Clinical Practice Settings vs Clinical Trials: Is Artificial Intelligence the Answer?
Jampol Lee Merrill et al. JAMA ophthalmology 2019 Nov
PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China.
Li Haomin et al. Scientific reports 2019 Nov 9(1) 17867
Utilizing wavelet deep learning network to classify different states of task-fMRI for verifying activation regions.
Gui Shanquan et al. The International journal of neuroscience 2019 Nov 1-16
Machine learning with sparse nutrition data to improve cardiovascular mortality risk prediction in the USA using nationally randomly sampled data.
Rigdon Joseph et al. BMJ open 2019 Nov 9(11) e032703
Decision tree algorithm in locally advanced rectal cancer: an example of over-interpretation and misuse of a machine learning approach.
De Felice Francesca et al. Journal of cancer research and clinical oncology 2019 Nov
A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration.
Jiang Zhuoran et al. Physics in medicine and biology 2019 Nov
Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.
Wissel Benjamin D et al. Epilepsia 2019 Nov
Empirical Research on Acceptance of Digital Technologies in Medicine Among Patients and Healthy Users: Questionnaire Study.
Safi Sabur et al. JMIR human factors 2019 Nov 6(4) e13472
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