References¶
pyPheWAS Main Package¶
Kerley, C.I., Chaganti, S., Nguyen, T.Q. et al. pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis. Neuroinformatics (2022). https://doi.org/10.1007/s12021-021-09553-4
The following articles have all contributed to the development of the pyPheWAS package.
ICD-PheCode mapping¶
| [Denny2010] | Denny, J. C., Ritchie, M. D., Basford, M. A., et al. PheWAS: Demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 2010 March; 26(9), 1205–1210. |
| [Denny2013] | Denny, J. C., Bastarache, L., Ritchie, M. D., et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nature Biotechnology. 2013 Dec; 31(12): 1102–1110. |
| [Wei2017] | Wei, W. Q., Bastarache, L. A., Carroll, R. J., et al. Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLoS ONE. 2017 Jul; 12(7), 1–16. |
| [Wu2019] | Wu, P., Gifford, A., Meng, X., et al. Mapping ICD-10 and ICD-10-CM codes to phecodes: Workflow development and initial evaluation. Journal of Medical Internet Research. 2019; 21(11), 1–13. |
PheDAS¶
| [Chaganti2019a] | Chaganti, S., Mawn, L. A., Kang, H., et al. Electronic Medical Record Context Signatures Improve Diagnostic Classification Using Medical Image Computing. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2019 Sept; 23(5), 2052–2062 |
| [Chaganti2019b] | Chaganti, S., Welty, V. F., Taylor, W., et al. Discovering novel disease comorbidities using electronic medical records. PLoS ONE. 2019 Nov; 14(11), 1-14. |
Statistical Modeling¶
| [Statsmodels] | Seabold, S., & Perktold, J. Statsmodels: Econometric and Statistical Modeling with Python. PROC. OF THE 9th PYTHON IN SCIENCE CONF. 2010 Jan; 92-96 |
Visualization¶
| [Matplotlib] | Hunter, J. D. Matplotlib : a 2D Graphics Environment. Computing in Science and Engineering. 2007 May; 9, 90–95. |