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Precision Health Database


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Family Health History

Last data update: May 28, 2023. (Total: 59975 Documents since 2012)
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Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes.
Xiaomin Fu et al. Frontiers in endocrinology 2023 131061507
Performance of cardiovascular disease risk prediction equations in more than 14 000 survivors of cancer in New Zealand primary care: a validation study.
Essa Tawfiq et al. Lancet (London, England) 2023
Stroke prevention in rural residents: development of a simplified risk assessment tool with artificial intelligence.
Ding Zhongao et al. Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 2023
Prediction model of obstructive sleep apnea-related hypertension: Machine learning-based development and interpretation study.
Shi Yewen et al. Frontiers in cardiovascular medicine 2022 91042996
Real-world data for precision public health of noncommunicable diseases: a scoping review.
Canfell Oliver J et al. BMC public health 2022 22(1) 2166
Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015.
Wong Kingsley et al. Scientific reports 2022 12(1) 19153
Predicting the Need for Biopsy to Detect Clinically Significant Prostate Cancer in Patients with a Magnetic Resonance Imaging-detected Prostate Imaging Reporting and Data System/Likert ≥3 Lesion: Development and Multinational External Validation of the Imperial Rapid Access to Prostate Imaging and Diagnosis Risk Score.
Peters Max et al. European urology 2022
Psychosocial markers of age at onset in bipolar disorder: a machine learning approach.
Bolton Sorcha et al. BJPsych open 2022 8(4) e133
The Mount Sinai Prebiopsy Risk Calculator for Predicting any Prostate Cancer and Clinically Significant Prostate Cancer: Development of a Risk Predictive Tool and Validation with Advanced Neural Networking, Prostate Magnetic Resonance Imaging Outcome Database, and European Randomized Study of Screening for Prostate Cancer Risk Calculator.
Parekh Sneha et al. European urology open science 2022 4145-54
Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study.
Lee Eunice Y et al. Environmental research 2022 113463
Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study.
Areia Miguel et al. The Lancet. Digital health 2022
Nonlaboratory-based risk assessment model for coronary heart disease screening: Model development and validation.
Zhang Liying et al. International journal of medical informatics 2022 162104746
An early model to predict the risk of gestational diabetes mellitus in the absence of blood examination indexes: application in primary health care centres.
Wang Jingyuan et al. BMC pregnancy and childbirth 2021 21(1) 814
Development and Validation of a Predictive Model to Identify Patients With an Ascending Thoracic Aortic Aneurysm.
Mori Makoto et al. Journal of the American Heart Association 2021 e022102
Application of a Neural Network Classifier to Radiofrequency-Based Osteopenia/Osteoporosis Screening.
Adams Johnathan W et al. IEEE journal of translational engineering in health and medicine 2021 94900907
Dietary Intake and Health Status of Elderly Patients With Type 2 Diabetes Mellitus: Cross-sectional Study Using a Mobile App in Primary Care.
Coleone Joane Diomara et al. JMIR formative research 2021 5(8) e27454
Prediction of Coronary Calcification and Stenosis: Role of Radiomics From Low-Dose CT.
Homayounieh Fatemeh et al. Academic radiology 2021 28(7) 972-979
Utilizing electronic health record data to understand comorbidity burden among people living with HIV: a machine learning approach.
Yang Xueying et al. AIDS (London, England) 2021 35(Suppl 1) S39-S51
The Way of the Future: Personalizing Treatment Plans Through Technology.
Liefaard Marte C et al. American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting 2021 411-12
Reviewing the use and quality of machine learning in developing clinical prediction models for cardiovascular disease.
Allan Simon et al. Postgraduate medical journal 2021
Detection of Asymptomatic Carotid Artery Stenosis in High-Risk Individuals of Stroke Using a Machine-Learning Algorithm.
Yin Jun Xiong et al. Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih 2020 Dec 35(4) 297-305
Derivation and validation of a predictive model for advanced colorectal neoplasia in asymptomatic adults.
Imperiale Thomas F et al. Gut 2020 Sep
The prevalence and factors associated with alcohol use disorder among people living with HIV/AIDS in Africa: a systematic review and meta-analysis.
Necho Mogesie et al. Substance abuse treatment, prevention, and policy 2020 Aug 15(1) 63
Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques.
Bennasar Mohamed et al. JMIR cardio 2020 May 4(1) e16975
Use of a Smartphone-Based Mobile App for Weight Management in Obese Minority Stroke Survivors: Pilot Randomized Controlled Trial With Open Blinded End Point.
Ifejika Nneka L et al. JMIR mHealth and uHealth 2020 Apr 8(4) e17816
Hybrid models for lung nodule malignancy prediction utilizing convolutional neural network ensembles and clinical data.
Paul Rahul et al. Journal of medical imaging (Bellingham, Wash.) 2020 Mar 7(2) 024502
Feasibility of Using a Wearable Biosensor Device in Patients at Risk for Alzheimer's Disease Dementia.
Saif N et al. The journal of prevention of Alzheimer's disease 2020 7(2) 104-111
Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data.
Zhang-James Yanli et al. Journal of child psychology and psychiatry, and allied disciplines 2020 Apr
Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015.
Malacova Eva et al. Scientific reports 2020 Mar 10(1) 5354
A practical model for the identification of congenital cataracts using machine learning.
Lin Duoru et al. EBioMedicine 2020 Jan 51102621
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