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Precision Health Database


Precision Health Database|Search|PHGKB

Last data update: Jun 09, 2023. (Total: 60181 Documents since 2012)
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Records 1 - 26 (of 26 Records)

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Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis.
Weitong Huang et al. J Biomed Inform 2023 104365
OWL: an optimized and independently validated machine learning prediction model for lung cancer screening based on the UK Biobank, PLCO, and NLST populations.
Zoucheng Pan et al. EBioMedicine 2023 88104443
Application of machine learning techniques for predicting survival in ovarian cancer.
Sorayaie Azar Amir et al. BMC medical informatics and decision making 2022 22(1) 345
Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data.
Sidey-Gibbons Chris J et al. Scientific reports 2022 12(1) 21269
Use Case Evaluation and Digital Workflow of Breast Cancer Care by Artificial Intelligence and Blockchain Technology Application.
Griewing Sebastian et al. Healthcare (Basel, Switzerland) 2022 10(10)
A Machine Learning Approach Using XGBoost Predicts Lung Metastasis in Patients with Ovarian Cancer.
Yuan Yufei et al. BioMed research international 2022 20228501819
Data-driven analysis of a validated risk score for ovarian cancer identifies clinically distinct patterns during follow-up and treatment
S Enroth et al, Comm Medicine, October 3, 2022
Blood-based DNA methylation profiling for the detection of ovarian cancer.
Li Ning et al. Gynecologic oncology 2022
A deep-learning-enabled diagnosis of ovarian cancer.
Van Calster Ben et al. The Lancet. Digital health 2022 4(9) e630
Evaluation of Combined Cancer Markers With Lactate Dehydrogenase and Application of Machine Learning Algorithms for Differentiating Benign Disease From Malignant Ovarian Cancer.
Jeong Seri et al. Cancer control : journal of the Moffitt Cancer Center 2021 2810732748211033401
Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images.
Wang Guangxing et al. Biomedical optics express 2021 12(9) 5658-5669
Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study.
Ma Jun et al. OncoTargets and therapy 2021 143267-3280
Application of artificial intelligence in gynecologic malignancies: A review.
Sone Kenbun et al. The journal of obstetrics and gynaecology research 2021
Population-Based Screening for Endometrial Cancer: Human vs. Machine Intelligence.
Hart Gregory R et al. Frontiers in artificial intelligence 2021 3539879
Robust Machine Learning for Colorectal Cancer Risk Prediction and Stratification.
Nartowt Bradley J et al. Frontiers in big data 2021 36
Natural language processing with machine learning to predict outcomes after ovarian cancer surgery.
Barber Emma L et al. Gynecologic oncology 2020 Oct
Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models.
Laios Alexandros et al. Journal of ovarian research 2020 Sep 13(1) 117
Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model.
Lu Michael T et al. Annals of internal medicine 2020 Sep
PSA-based machine learning model improves prostate cancer risk stratification in a screening population.
Perera Marlon et al. World journal of urology 2020 Aug
Artificial Intelligence in Ovarian Cancer Diagnosis.
Akazawa Munetoshi et al. Anticancer research 2020 Aug 40(8) 4795-4800
The Adoption of Radiomics and machine learning improves the diagnostic processes of women with Ovarian MAsses (the AROMA pilot study).
Chiappa Valentina et al. Journal of ultrasound 2020 Jul
Predicting breast cancer risk using personal health data and machine learning models.
Stark Gigi F et al. PloS one 2019 14(12) e0226765
An Artificial Intelligence Tool to Improve Pancreatic Cancer Outcomes
S Rosen, Mato Clinic blog, August 13, 2019
Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer.
Bogani Giorgio et al. Journal of gynecologic oncology 2018 Sep 29(5) e66
Development of predictive models to identify advanced-stage cancer patients in a US healthcare claims database.
Esposito Daina B et al. Cancer epidemiology 2019 May 6130-37
Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.
Paik E Sun et al. Journal of gynecologic oncology 2019 Mar
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