Norén 2017 probabilistic topic models for unsupervised case identification

Probabilistic topic models for unsupervised case identification Niklas NorĂŠn

Disclosures • Employed by the Uppsala Monitoring Centre • No specific funding received for this research

The case series

The case

The case

14 years Male Risperidone

1 mg / day 2014-11-02

Nausea Dizziness Vision abnormal

2014-12

How to find the case series

When we know what we are looking for‌

When we do not know what we are looking for‌

Different different but same

Nausea Headache Vision abnormal

Dizziness Insomnia Migraine Photopsia

Same same but different

Nausea Headache Vision abnormal

Nausea Vomiting Abdominal pain Constipation

Probabilistic topic model

0.45

0.35 0.20

Topic model Marginal probability for each ’topic’ 0.45

0.35 0.20

Topic model Marginal probability for each ‘topic’ 0.45

0.35 0.20

Probabilities for each medical event, per ‘topic’

Supervised learning

Supervised learning

Unsupervised learning

Unsupervised learning

Unsupervised learning

Unsupervised learning

Unsupervised learning

Unsupervised learning

Making sense of it all

Risperidone in VigiBase 1883 reports ~3.2 AEs Agitation

21%

Aggressive reaction

18%

Condition aggravated

16%

Psychosis

16%

…

…

1407 reports

1799 reports

~2.5 AEs

~2.8 AEs Extrapyramidal disorder

33%

Hyperprolacti- 76% naemia

Dystonia

20%

46%

Hyperkinesia

18%

Lactation nonpuerperal

Hypertonia

18%

Amenorrhoea

38%

…

…

…

…

Rivaroxaban in THIN 1199 patients ~2.7 Medical Events Phlebitis and thrombophlebitis

74%

Deep vein phlebitis 74% and thrombophlebitis of the leg Suspected deep vein 21% thrombosis

1684 patients ~2.3 Medical Events

529 patients

Cardiac dysrhythmias

100%

~2.5 Medical Events

Atrial fibrillation and flutter

99%

…

…

Acute pulmonary heart disease

97%

Pulmonary embolism 97% Diagnostic imaging

6%

Phlebitis and thrombophlebitis

6%

…

…

Oedema

18%

Leg swelling

15%

…

…

References 1.

Hand DJ, Bolton R. Pattern discovery and detection: a unified statistical methodology.

Journal of Applied Statistics, 2004. 31(8):885-924.

2.

Norén GN, Fransson J, Juhlin K, Chandler R, Edwards IR. Adverse event cluster analysis for syndromic surveillance. Drug Safety 2015. 38:958 (Abstract).

3.

Orre R, Bate A, Norén GN, Swahn E, Arnborg S, Edwards IR. A Bayesian recurrent neural network for unsupervised pattern recognition in large incomplete data sets. International Journal of Neural Systems, 2005. 15(3):207-222.

4.

Chandler RE, Juhlin K, Fransson J, Caster O, Edwards iR, Norén GN. Current Safety Concerns with HPV vaccine: A Cluster Analysis of Reports in VigiBase. Drug Safety 2017; 40(1):81-90.