Crowdsourcing adverse drug event prediction algorithms
There’s an interesting competition, Observational Medical Outcomes Partnership Cup (OMOP Cup), going on until March this year (so unfortunately a bit late for laggards like me to participate). The background is that a lot of data on adverse drug events has recently become available, but much of this data is in free text and unstandardized formats. The development of algorithms for identifying patterns in adverse drug events, an by extension for predicting new events, has therefore been lagging. A good way to find and predict adverse drug events could save a lot of lives worldwide. The OMOP has therefore constructed a ”simulated” data set which resembles the kind of information you get from insurance claims and medical records. There are two algorithmic tasks, the first of which resembles classical data mining problems where you get an entire data set which you try to characterize as well as possible, and the second of which is more in a stream mining style where your algorithm is continuously evaluated by running it against observations that become sequentially available over time. The prizes are USD10.000 for the first task and USD5.000 for the second.
The OMOP Cup appears to be hosted by Orwik, a company which I was very vaguely aware of but hadn’t really looked at. Its product appears to be a data management solution for scientists, either individual researchers or groups of collaborators who want to keep their data available (for example, after key people have left) and perhaps sharable (for multi-group collaborations), all (I assume) with the aim of supporting well-documented and reproducible research.