Learning from adverse events

Adverse events in healthcare: learning from mistakes.

Large national reviews of patient charts estimate that approximately 10% of hospital admissions are associated with an adverse event (defined as an injury resulting in prolonged hospitalization, disability or death, caused by healthcare management). Apart from having a significant impact on patient morbidity and mortality, adverse events also result in increased healthcare costs due to longer h...

Learning to Detect Adverse Traffic Events from Noisily Labeled Data

Many deployed traffic incident detection systems use algorithms that require significant manual tuning. We seek machine learning incident detection solutions that reduce the need for manual adjustments by taking advantage of massive databases of traffic sensor network measurements. First, we show that a rather straightforward supervised learner based on the SVM model outperforms a fixed detecti...

Identifying Adverse Drug Events by Relational Learning

The pharmaceutical industry, consumer protection groups, users of medications and government oversight agencies are all strongly interested in identifying adverse reactions to drugs. While a clinical trial of a drug may use only a thousand patients, once a drug is released on the market it may be taken by millions of patients. As a result, in many cases adverse drug events (ADEs) are observed i...

Learning from adverse events in the nuclear power industry: Organizational learning, policy making and normalization

Nuclear power accidents repeatedly reveal that the industry has an incomplete understanding of the complex risks involved in its operation. Through analyzing the investigation of a nuclear power incident in Sweden in 2006, I show how the industry’s learning practices shape recurrent normalization of risk regulation after such surprises. Learning is shaped through institutionalized measures of s...

Classifying Adverse Events from Clinical Trials