Name
Title | ||
|---|---|---|
Methods For Enhancing The Reproducibility Of Observational Research Using Electronic Health Records: Preliminary Findings From The Caliber Resource |
Abstract | ||
|---|---|---|
The ability of external investigators to reproduce published scientific findings is critical for the evaluation and validation of health research by the wider community. However, a substantial proportion of health research using electronic health records, data collected and generated during routine clinical care, potentially cannot reproduced. With the complexity, volume and variety of electronic health records made available for research steadily increasing, it is critical to ensure that findings from such data are reproducible and replicable by researchers. In this paper, we present some preliminary findings on how a series of methods and tools utilized in adjunct scientific disciplines can be used to enhance the reproducibility of research using electronic health records. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CBMS.2017.74 | 2017 IEEE 30TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS) |
Keywords | Field | DocType |
electronic health records, reproducibility | Data science,Reproducibility,Data mining,Observational study,Computer science | Conference |
ISSN | Citations | PageRank |
2372-9198 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Spiros Denaxas | 1 | 4 | 6.43 |
| Arturo Gonzalez-Izquierdo | 2 | 1 | 2.32 |
| Maria Pikoula | 3 | 0 | 1.01 |
| Kenan Direk | 4 | 0 | 1.01 |
| Natalie Fitzpatrick | 5 | 1 | 1.64 |
| Harry Hemingway | 6 | 19 | 5.72 |
| Liam Smeeth | 7 | 1 | 1.38 |