Machine learning identifies clusters of multimorbidity among decedents with inflammatory bowel disease
Notice bibliographique
Résumé
Multimorbidity is the co-occurrence of two or more chronic conditions in one person. Providing quality, patient-centered care requires understanding multimorbidity. Our objective was to identify patterns of multimorbidity that occur prior to death among people with inflammatory bowel disease (IBD). Using a retrospective population-based matched cohort derived from linked health administrative data of individuals with and without IBD who died between 2010 and 2020 in Ontario, Canada, we compared multimorbidity accumulation and leveraged unsupervised machine learning to identify multimorbidity clusters. Here we show decedents with IBD have a greater prevalence of complex multimorbidity (42% vs 34% with 8+ conditions, standardized difference: 22%). Among those with IBD at death, IBD is commonly developed as their first condition. At death, people with IBD have high prevalences of osteo- and other arthritis (77%), hypertension (73%), mood disorders (69%), renal failure (50%) and cancer (46%). Among those with IBD, we identify 3 clusters: ( $$\alpha $$ ) mood disorder and/or osteo- and other arthritis; ( $$\beta $$ ) cancer and low multimorbidity; ( $$\gamma $$ ) cardiovascular comorbidities. The clusters that we identify are stable across numerous validation techniques, including re-derivation and sex-specific clustering. These findings can inform future research and potential multimorbidity care in populations with IBD. Consideration of these clusters also lends to the need for further research, guidelines, and care programs to manage distinct subgroups of comorbidities among those with IBD, highlighting avenues for greater personalized care. People with inflammatory bowel disease (IBD) have inflammation in their digestive system that can cause stomach pain and diarrhoea. They often also live with other long-term health conditions. This study aimed to understand how different health conditions occur together before death in people with IBD. We used health records to compare people with and without IBD who died between 2010 and 2020. We assessed the conditions that occur with IBD and looked for common patterns of conditions co-occurring using computational methods. We found that people with IBD commonly had other health conditions, specifically arthritis, high blood pressure and mood disorders. Three main patterns of health condition co-occurrence in IBD emerged. These findings highlight the importance of developing tailored care programs to better support people with IBD facing multiple health challenges. Postill et al. apply unsupervised machine learning methods to cluster longitudinal administrative health data of deceased people with IBD. Their model identifies three clusters of multimorbidity present at death among those with IBD: mood disorder and/or osteo- and other arthritis; cancer and low multimorbidity; and cardiovascular comorbidities.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».