Lin S - Search Results

1

Interpretable Machine Learning for Fall Prediction Among Older Adults in China.

Chen X, He L, Shi K, Wu Y, Lin S, Fang Y. Chen X, et al. Among authors: lin s. Am J Prev Med. 2023 Oct;65(4):579-586. doi: 10.1016/j.amepre.2023.04.006. Epub 2023 Apr 20. Am J Prev Med. 2023. PMID: 37087076

2

Comparison of Regression and Machine Learning Methods in Depression Forecasting Among Home-Based Elderly Chinese: A Community Based Study.

Lin S, Wu Y, Fang Y. Lin S, et al. Front Psychiatry. 2022 Jan 17;12:764806. doi: 10.3389/fpsyt.2021.764806. eCollection 2021. Front Psychiatry. 2022. PMID: 35111085 Free PMC article.

3

Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques.

Lin S, Wu Y, He L, Fang Y. Lin S, et al. Aging Ment Health. 2023 Jan;27(1):8-17. doi: 10.1080/13607863.2022.2031868. Epub 2022 Feb 4. Aging Ment Health. 2023. PMID: 35118924

4

Seasonal prediction of daily PM_2.5 concentrations with interpretable machine learning: a case study of Beijing, China.

Wu Y, Lin S, Shi K, Ye Z, Fang Y. Wu Y, et al. Among authors: lin s. Environ Sci Pollut Res Int. 2022 Jun;29(30):45821-45836. doi: 10.1007/s11356-022-18913-9. Epub 2022 Feb 12. Environ Sci Pollut Res Int. 2022. PMID: 35150424

A 10-fold cross validation was used to tune hyperparameters, and root mean square error (RMSE), mean absolute error (MAE), ratio of performance to deviation (RPD), and Lin's concordance correlation coefficient (LCCC) were used to evaluate models' performance. ...

A 10-fold cross validation was used to tune hyperparameters, and root mean square error (RMSE), mean absolute error (MAE), ratio of performa …

5

Predicting the long-term cognitive trajectories using machine learning approaches: A Chinese nationwide longitudinal database.

Wu Y, Jia M, Xiang C, Lin S, Jiang Z, Fang Y. Wu Y, et al. Among authors: lin s. Psychiatry Res. 2022 Apr;310:114434. doi: 10.1016/j.psychres.2022.114434. Epub 2022 Feb 7. Psychiatry Res. 2022. PMID: 35172247

6

A hybrid machine learning model of depression estimation in home-based older adults: a 7-year follow-up study.

Lin S, Wu Y, Fang Y. Lin S, et al. BMC Psychiatry. 2022 Dec 21;22(1):816. doi: 10.1186/s12888-022-04439-4. BMC Psychiatry. 2022. PMID: 36544119 Free PMC article.

7

Long-term trajectories of depressive symptoms and machine learning techniques for fall prediction in older adults: Evidence from the China Health and Retirement Longitudinal Study (CHARLS).

Chen X, Lin S, Zheng Y, He L, Fang Y. Chen X, et al. Among authors: lin s. Arch Gerontol Geriatr. 2023 Aug;111:105012. doi: 10.1016/j.archger.2023.105012. Epub 2023 Mar 30. Arch Gerontol Geriatr. 2023. PMID: 37030148

8

Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis.

Zhu J, Wu Y, Lin S, Duan S, Wang X, Fang Y. Zhu J, et al. Among authors: lin s. J Affect Disord. 2024 Apr 1;350:590-599. doi: 10.1016/j.jad.2024.01.095. Epub 2024 Jan 11. J Affect Disord. 2024. PMID: 38218258

9

Surgical treatment of esophageal carcinoma.

Wu YK, Chen PT, Fang JP, Lin SS. Wu YK, et al. Among authors: lin ss. Am J Surg. 1980 Jun;139(6):805-9. doi: 10.1016/0002-9610(80)90386-4. Am J Surg. 1980. PMID: 6155790

10

Cost-effectiveness analysis of mosunetuzumab for treatment of relapsed or refractory follicular lymphoma after two or more lines of systemic therapy in the United States.

Matasar M, Sanchez Alvarez J, Parisé H, Zuk E, Di Maio D, Shapouri S, Kim E, Lin SW. Matasar M, et al. Among authors: lin sw. J Med Econ. 2024 May 7:1-36. doi: 10.1080/13696998.2024.2352820. Online ahead of print. J Med Econ. 2024. PMID: 38712895

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