It is useful, sometimes crucial in medicine domain, to discover a temporal association or causal relationship among events. Such a mining problem is often challenging because ‘consequence events’ may not reliably occur after each trigger event of int

The work is motivated by real-world applications of detecting Adverse Drug Reactions (ADRs) from administrative health databases. ADRs are a leading cause of hospitalization and death worldwide. Almost all current postmarket ADR signaling techniques are based on spontaneous ADR case reports, which suffer from serious underreporting and latency. However, administrative health data are widely and routinely collected. They, especially linked together, would contain evidence of all ADRs. To signal unexpected and infrequent patterns characteristic of ADRs, we propose a domain-driven knowledge representation Unexpected Temporal Association Rule (UTAR), its interestingness measure, unexlev, and a mining algorithm MUTARA (Mining UTARs given the Antecedent). We then establish an improved algorithm, HUNT, for highlighting infrequent and unexpected patterns by comparing their ranks based on unexlev with those based on traditional leverage. Various experimental results on real-world data substantiate that both MUTARA and HUNT can signal suspected ADRs while traditional association mining techniques cannot. HUNT can reliably shortlist statistically significantly more ADRs than MUTARA (p=0.00078). HUNT, e.g., not only shortlists the drug alendronate associated with esophagitis as MUTARA does, but also shortlists alendronate with diarrhoea and vomiting for older ({\rm age} \ge 60) females. We also discuss signaling ADRs systematically by using HUNT.

In various real-world applications, it is very useful mining unanticipated episodes where certain event patterns unexpectedly lead to outcomes, e.g., taking two medicines together sometimes causing an adverse reaction. These unanticipated episodes are usually unexpected and infrequent, which makes existing data mining techniques, mainly designed to find frequent patterns, ineffective. In this paper, we propose unexpected temporal association rules (UTARs) to describe them. To handle the unexpectedness, we introduce a new interestingness measure, residual-leverage, and develop a novel case-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle the infrequency, we develop a new algorithm MUTARC to find pairwise UTARs. The MUTARC is applied to generate adverse drug reaction (ADR) signals from real-world healthcare administrative databases. It reliably shortlists not only six known ADRs, but also another ADR, flucloxacillin possibly causing hepatitis, which our algorithm designers and experiment runners have not known before the experiments. The MUTARC performs much more effectively than existing techniques. This paper clearly illustrates the great potential along the new direction of ADR signal generation from healthcare administrative databases.

In this paper, we discuss a problem of finding risk patterns in medical data. We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.