DICE: Deep Significance Clustering for Outcome-Driven Stratification
Abstract We present deep significance clustering (DICE), a framework for jointly performing representation learning and clustering for “outcome-driven” stratification. Motivated by practical needs in medicine to risk-stratify patients into subgroups, DICE brings self-supervision to unsupervised tasks to generate cluster membership that may be used to categorize unseen patients by risk levels. DICE is driven by a combined objective function and constraint which require a statistically significant association between the outcome and cluster membership of learned representations. DICE also performs a neural architecture search to optimize cluster membership and hyper-parameters for model likelihood and classification accuracy. The performance of DICE was evaluated using two datasets with different outcome ratios extracted from real-world electronic health records of patients who were treated for coronavirus disease 2019 and heart failure. Outcomes are defined as in-hospital mortality (15.9%) and discharge home (36.8%), respectively. Results show that DICE has superior performance as measured by the difference in outcome distribution across clusters, Silhouette score, Calinski-Harabasz index, and Davies-Bouldin index for clustering, and Area under the ROC Curve for outcome classification compared to baseline approaches..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
bioRxiv.org - (2022) vom: 22. Nov. Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Huang, Yufang [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
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| doi: |
10.1101/2020.10.04.20204321 |
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| PPN (Katalog-ID): |
XBI019077394 |
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| 520 | |a Abstract We present deep significance clustering (DICE), a framework for jointly performing representation learning and clustering for “outcome-driven” stratification. Motivated by practical needs in medicine to risk-stratify patients into subgroups, DICE brings self-supervision to unsupervised tasks to generate cluster membership that may be used to categorize unseen patients by risk levels. DICE is driven by a combined objective function and constraint which require a statistically significant association between the outcome and cluster membership of learned representations. DICE also performs a neural architecture search to optimize cluster membership and hyper-parameters for model likelihood and classification accuracy. The performance of DICE was evaluated using two datasets with different outcome ratios extracted from real-world electronic health records of patients who were treated for coronavirus disease 2019 and heart failure. Outcomes are defined as in-hospital mortality (15.9%) and discharge home (36.8%), respectively. Results show that DICE has superior performance as measured by the difference in outcome distribution across clusters, Silhouette score, Calinski-Harabasz index, and Davies-Bouldin index for clustering, and Area under the ROC Curve for outcome classification compared to baseline approaches. | ||
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| 700 | 1 | |a Subramanian, Lakshminarayanan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yiye |e verfasserin |4 aut | |
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