Estimating One-Year Risk of Incident Chronic Kidney Disease: Retrospective Development and Validation Study Using Electronic Medical Record Data From the State of Maine

Journals

1. Guo W, Ge W, Cui L, Li H, Kong L. An Interpretable Disease Onset Predictive Model Using Crossover Attention Mechanism From Electronic Health Records. IEEE Access 2019;7:134236 View
2. Ye C, Fu T, Hao S, Zhang Y, Wang O, Jin B, Xia M, Liu M, Zhou X, Wu Q, Guo Y, Zhu C, Li Y, Culver D, Alfreds S, Stearns F, Sylvester K, Widen E, McElhinney D, Ling X. Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning. Journal of Medical Internet Research 2018;20(1):e22 View
3. Zheng L, Wang O, Hao S, Ye C, Liu M, Xia M, Sabo A, Markovic L, Stearns F, Kanov L, Sylvester K, Widen E, McElhinney D, Zhang W, Liao J, Ling X. Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records. Translational Psychiatry 2020;10(1) View
4. Wang X, Zhang Y, Hao S, Zheng L, Liao J, Ye C, Xia M, Wang O, Liu M, Weng C, Duong S, Jin B, Alfreds S, Stearns F, Kanov L, Sylvester K, Widen E, McElhinney D, Ling X. Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine. Journal of Medical Internet Research 2019;21(5):e13260 View
5. Wang H, Wang Y, Liang C, Li Y. Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer. JAMA Dermatology 2019;155(11):1277 View
6. Chandir S, Siddiqi D, Hussain O, Niazi T, Shah M, Dharma V, Habib A, Khan A. Using Predictive Analytics to Identify Children at High Risk of Defaulting From a Routine Immunization Program: Feasibility Study. JMIR Public Health and Surveillance 2018;4(3):e63 View
7. Guo Y, Zheng G, Fu T, Hao S, Ye C, Zheng L, Liu M, Xia M, Jin B, Zhu C, Wang O, Wu Q, Culver D, Alfreds S, Stearns F, Kanov L, Bhatia A, Sylvester K, Widen E, McElhinney D, Ling X. Assessing Statewide All-Cause Future One-Year Mortality: Prospective Study With Implications for Quality of Life, Resource Utilization, and Medical Futility. Journal of Medical Internet Research 2018;20(6):e10311 View
8. Lee S, Doktorchik C, Martin E, D'Souza A, Eastwood C, Shaheen A, Naugler C, Lee J, Quan H. Electronic Medical Record–Based Case Phenotyping for the Charlson Conditions: Scoping Review. JMIR Medical Informatics 2021;9(2):e23934 View
9. Kuo H, Hao S, Jin B, Chou C, Han Z, Chang L, Huang Y, Hwa K, Whitin J, Sylvester K, Reddy C, Chubb H, Ceresnak S, Kanegaye J, Tremoulet A, Burns J, McElhinney D, Cohen H, Ling X. Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan. Frontiers in Immunology 2022;13 View
10. Abdullah Alfayez A, Kunz H, Grace Lai A. Predicting the risk of cancer in adults using supervised machine learning: a scoping review. BMJ Open 2021;11(9):e047755 View
11. Kotsyfakis S, Iliaki-Giannakoudaki E, Anagnostopoulos A, Papadokostaki E, Giannakoudakis K, Goumenakis M, Kotsyfakis M. The application of machine learning to imaging in hematological oncology: A scoping review. Frontiers in Oncology 2022;12 View
12. Manlhiot C, van den Eynde J, Kutty S, Ross H. A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology. Canadian Journal of Cardiology 2022;38(2):169 View
13. González-Rocha A, Colli V, Denova-Gutiérrez E. Risk Prediction Score for Chronic Kidney Disease in Healthy Adults and Adults With Type 2 Diabetes: Systematic Review. Preventing Chronic Disease 2023;20 View
14. Hirsch J, Danna S, Desai N, Gluckman T, Jhamb M, Newlin K, Pellechio B, Elbedewe A, Norfolk E. Optimizing Care Delivery in Patients with Chronic Kidney Disease in the United States: Proceedings of a Multidisciplinary Roundtable Discussion and Literature Review. Journal of Clinical Medicine 2024;13(5):1206 View