ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2013
Lessons learned in replicating data-driven experiments in multiple medical systems and patient populations.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- How causal information affects decisions.Cognitive research: principles and implications · 2020Article
- Replicability, Reproducibility, and Agent-based Simulation of Interventions.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data.Journal of clinical monitoring and computing · 2016Article
- Using uncertain data from body-worn sensors to gain insight into type 1 diabetes.Journal of biomedical informatics · 2016Article
- Causal Structure of Brain Physiology after Brain Injury from Subarachnoid Hemorrhage.PloS one · 2016Article
- A prediction model to estimate completeness of electronic physician claims databases.BMJ open · 2015Article
- HARVEST, a longitudinal patient record summarizer.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Survival Analysis with Electronic Health Record Data: Experiments with Chronic Kidney Disease.Statistical analysis and data mining · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Electronic health records are an increasingly important source of data for research, allowing for large-scale longitudinal studies on the same population that is being treated. Unlike in controlled studies, though, these data vary widely in quality, quantity, and structure. In order to know whether algorithms can accurately uncover new knowledge from these records, or whether findings can be extrapolated to new populations, they must be validated. One approach is to conduct the same study in multiple sites and compare results, but it is a challenge to determine whether differences are due to artifacts of the medical process, population differences, or failures of the methods used. In this paper we describe the results of replicating a data-driven experiment to infer possible causes of congestive heart failure and their timing using data from two medical systems and two patient populations. We focus on the difficulties faced in this type of work, lessons learned, and recommendations for future research.
Indexed as
Identifiers
24551375PMC3900216What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.