Table 1 shows five different kinds of drug-AEs detected relationship. 181 among the 360 concepts expressing a potential signal are not restricted to a given population (their intent do not include any patient properties, neither age nor sex) We evaluated a sample of 95 of them: 61 signals where already known, for example {Abciximab, Thrombopenia}; 34 were not described in the literature, for example {Lidocaine, Tachycardia} and need further investigations. Among the 56 potential syndromes, 53 consisted of two AEs and three consisted of three AEs.

| Table 2. Classification of potential drug interactions   |       |                               |
|----------------------------------------------------------|-------|-------------------------------|
| Drug association                                         | Count | Example                       |
| Therapeutic associations                                 | 57    | Fluorouracil + oxaliplatine   |
| Successive prescriptions                                 | 8     | Heparin + enoxaparin          |
| Medicinal product                                        | 22    | Amoxicillin + clavulanic acid |
| Concomitant drugs                                        | 23    | Heparin + sucralfate          |
| Total                                                    | 110   |                               |

The lattice helps to identify 110 pairs of drugs that might be classified as drug interactions. Among 57 pairs related to drugs commonly prescribed in therapeutic associations, 34 were dedicated to anti-HIV prescriptions. In 8 cases the associated drugs were likely to be prescribed successively rather than at the same time for example heparin was prescribed first then replaced by enoxaparin.

## 4. Discussion And Conclusion

We evaluated in this article the properties of the concept lattice for generating potential relationships between one or several drugs and AEs. Sample size was sufficient for generating several signals. Syndromes mainly consisted of two AEs. No database on syndromes is available to verify their plausibility but we were able to detect known patterns such as hypersensitivity to abacavir. FCA also highlighted more complex relationships that are more difficult to interpret because they are associations of more than two drugs and AEs. This could help the generation of signals related to a whole protocol instead of single drugs. Due to sample size few drug interactions were detected and the interest of the lattice was more focused on the identification of false positives and concomitant drugs.

In a previous experience [7], disproportinality measures extracted 523 statistically significant {drug, AE} couples with many false positives due to concomitant prescription. The new approach generates only 360 signals. Indeed many signals generated with the previous approach are now clustered in concept nodes of the lattice that present higher order relations, i.e. several drugs and/or AEs. FCA helps to reduce:
1. The number of signals to review. For example the medicinal product consisting of tiemonium + colchicine + opium generated a single signal for the whole association whereas the previous method generated three different signals related to each single active ingredient.

2. The number of false positives related to several active ingredients in the same medicinal products. In previous method a signal of diarrhoea was generated 