Abstract
Aims
Traditional pharmacovigilance detects single drug–event associations but cannot readily characterize multi‐system adverse‐event patterns such as fluoroquinolone‐associated disability. We evaluated unsupervised machine learning for this purpose, using fluoroquinolones as an exemplar.
Methods
We analysed 40 127 adult fluoroquinolone primary‐suspect reports from the FDA Adverse Event Reporting System (FAERS, 2004–2025). Events were encoded as binary MedDRA Preferred Term (PT) and System Organ Class matrices; seriousness and outcome were excluded from clustering and used only post hoc. Multiple correspondence analysis with k‐means was primary, validated by k‐modes and hierarchical clustering. Cluster number (k = 2–15) was assessed by internal validity indices with bootstrap stability. Comparators were macrolides (
n
= 39 948) and amoxicillin (
n
= 16 310).
Results
At PT level, silhouette and Calinski–Harabasz indices were optimal at two clusters (Davies–Bouldin at three; silhouette 0.560), separating a multi‐system cluster (4072 reports; 10.1%) enriched for neuropsychiatric, musculoskeletal and sensory terms (lifts: memory impairment 8.6, muscle atrophy 8.3, dry eye 7.8). Excluding seriousness and outcome left the cluster essentially unchanged (91.4% retained; κ 0.95), indicating definition by event topology, not case severity; independent algorithms recovered the same subgroup across granularities. Post hoc, the cluster was disproportionately disabling (43.0%
vs
. 11.7%). The fixed signature classified 14.9% of fluoroquinolone reports
vs
. 5.3% (macrolides) and 3.9% (amoxicillin).
Conclusions
Unsupervised clustering of FAERS data identified, without prior clinical input, a reproducible multi‐system adverse‐event pattern among fluoroquinolone reports, potentially supporting the feasibility of unsupervised exploratory pattern detection in pharmacovigilance. Given the limitations of spontaneous reporting databases, the findings are strictly hypothesis‐generating.