Published on in Vol 7, No 3 (2019): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13139, first published .
Cox Proportional Hazard Regression Versus a Deep Learning Algorithm in the Prediction of Dementia: An Analysis Based on Periodic Health Examination

Cox Proportional Hazard Regression Versus a Deep Learning Algorithm in the Prediction of Dementia: An Analysis Based on Periodic Health Examination

Cox Proportional Hazard Regression Versus a Deep Learning Algorithm in the Prediction of Dementia: An Analysis Based on Periodic Health Examination

Journals

  1. Nori V, Hane C, Sun Y, Crown W, Bleicher P, Chen K. Deep neural network models for identifying incident dementia using claims and EHR datasets. PLOS ONE 2020;15(9):e0236400 View
  2. Hatakeyama S, Narita S, Takahashi M, Sakurai T, Kawamura S, Hoshi S, Ishida M, Kawaguchi T, Ishidoya S, Shimoda J, Sato H, Hamano I, Okamoto T, Mitsuzuka K, Ito A, Tsuchiya N, Arai Y, Habuchi T, Ohyama C. Association of tumor burden with the eligibility of upfront intensification therapy in metastatic castration‐sensitive prostate cancer: A multicenter retrospective study. International Journal of Urology 2020;27(7):610 View
  3. Rios R, Miller R, Hu L, Otaki Y, Singh A, Diniz M, Sharir T, Einstein A, Fish M, Ruddy T, Kaufmann P, Sinusas A, Miller E, Bateman T, Dorbala S, DiCarli M, Van Kriekinge S, Kavanagh P, Parekh T, Liang J, Dey D, Berman D, Slomka P. Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry. Cardiovascular Research 2022;118(9):2152 View
  4. Andersen P. Fifty Years with the Cox Proportional Hazards Regression Model. Journal of the Indian Institute of Science 2022;102(4):1135 View
  5. Aguayo G, Zhang L, Vaillant M, Ngari M, Perquin M, Moran V, Huiart L, Krüger R, Azuaje F, Ferdynus C, Fagherazzi G. Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study. BMC Medical Research Methodology 2023;23(1) View
  6. Yu H, Huang T, Feng B, Lyu J. Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis. BMC Cancer 2022;22(1) View
  7. Wang S, Wang W, Li X, Liu Y, Wei J, Zheng J, Wang Y, Ye B, Zhao R, Huang Y, Peng S, Zheng Y, Zeng Y. Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people. Frontiers in Aging Neuroscience 2022;14 View
  8. Bahado‐Singh R, Turkoglu O, Aydas B, Vishweswaraiah S. Precision oncology: Artificial intelligence, circulating cell‐free DNA, and the minimally invasive detection of pancreatic cancer—A pilot study. Cancer Medicine 2023;12(19):19644 View
  9. So Y, Kim Z, Cheong T, Chung M, Baek C, Son Y, Seok J, Jung Y, Ahn M, Ahn Y, Oh D, Cho B, Chung M. Detection of Cancer Recurrence Using Systemic Inflammatory Markers and Machine Learning after Concurrent Chemoradiotherapy for Head and Neck Cancers. Cancers 2023;15(14):3540 View
  10. Kalam S, Numbers K, Lipnicki D, Lam B, Brodaty H, Reppermund S. The combination of olfactory dysfunction and depression increases the risk of incident dementia in older adults. International Psychogeriatrics 2024;36(2):130 View
  11. Mohanannair Geethadevi G, Quinn T, George J, Anstey K, Bell J, Sarwar M, Cross A. Multi-domain prognostic models used in middle-aged adults without known cognitive impairment for predicting subsequent dementia. Cochrane Database of Systematic Reviews 2023;2023(6) View
  12. Ávila-Jiménez J, Cantón-Habas V, Carrera-González M, Rich-Ruiz M, Ventura S. A deep learning model for Alzheimer’s disease diagnosis based on patient clinical records. Computers in Biology and Medicine 2024;169:107814 View
  13. Cabrera-León Y, Báez P, Fernández-López P, Suárez-Araujo C, Yamada Y. Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review. Journal of Alzheimer's Disease 2024;98(3):793 View