Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder
Abstract In real-world psychiatric practice, patients may experience complex treatment journeys, including various diagnoses and lines of therapy. Insurance claims databases could potentially provide insight into outcomes of psychiatric treatment processes, but the diversity of event sequences restr...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-05-01
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| Series: | Brain Informatics |
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| Online Access: | https://doi.org/10.1186/s40708-025-00258-x |
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| author | Matthew Littman Huy-Binh Nguyen Joanna Campbell Katelyn R. Keyloun |
| author_facet | Matthew Littman Huy-Binh Nguyen Joanna Campbell Katelyn R. Keyloun |
| author_sort | Matthew Littman |
| collection | DOAJ |
| description | Abstract In real-world psychiatric practice, patients may experience complex treatment journeys, including various diagnoses and lines of therapy. Insurance claims databases could potentially provide insight into outcomes of psychiatric treatment processes, but the diversity of event sequences restricts analyses with currently available methods. Here, we developed a novel kernel k-means clustering algorithm for event sequences that can accommodate highly diverse event types and sequence lengths. The approach, Divisive Optimized Clustering using Kernel K-means for Event Sequences (DOCKKES), also leverages a novel performance metric, the transition score, which measures sequence coherence in individual clusters. The performance of DOCKKES was evaluated in the context of bipolar I disorder, which is characterized by heterogeneous treatment journeys. We conducted a retrospective, observational analysis of a large sample (n = 31,578) of patients with bipolar I disorder from the MarketScan® Commercial Database. Using insurance claims, bipolar episode diagnoses and mental health–related lines of therapy were identified as events of interest for patient clustering. The dataset included 202,122 events; 75% of the cohort experienced unique treatment journeys. Based on an optimal run, DOCKKES identified 16 treatment journey clusters, which were evenly split for initial manic/mixed or depressive episodes (8 clusters each) and varied in sequence length and early lines of therapy. Variability across clusters was also observed for demographics, comorbidities, and mental health–related healthcare resource utilization and cost. This proof-of-concept study demonstrated the use of DOCKKES for integrating information from large datasets, enabling comparisons between patient clusters and evaluation of real-world treatment journeys in the context of evidence-based guidelines. |
| format | Article |
| id | doaj-art-5c179130c82e49949e67b7eca8f8c1c8 |
| institution | OA Journals |
| issn | 2198-4018 2198-4026 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj-art-5c179130c82e49949e67b7eca8f8c1c82025-08-20T01:53:15ZengSpringerOpenBrain Informatics2198-40182198-40262025-05-0112111810.1186/s40708-025-00258-xTreatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorderMatthew Littman0Huy-Binh Nguyen1Joanna Campbell2Katelyn R. Keyloun3AbbVieAbbVieAbbVieAbbVieAbstract In real-world psychiatric practice, patients may experience complex treatment journeys, including various diagnoses and lines of therapy. Insurance claims databases could potentially provide insight into outcomes of psychiatric treatment processes, but the diversity of event sequences restricts analyses with currently available methods. Here, we developed a novel kernel k-means clustering algorithm for event sequences that can accommodate highly diverse event types and sequence lengths. The approach, Divisive Optimized Clustering using Kernel K-means for Event Sequences (DOCKKES), also leverages a novel performance metric, the transition score, which measures sequence coherence in individual clusters. The performance of DOCKKES was evaluated in the context of bipolar I disorder, which is characterized by heterogeneous treatment journeys. We conducted a retrospective, observational analysis of a large sample (n = 31,578) of patients with bipolar I disorder from the MarketScan® Commercial Database. Using insurance claims, bipolar episode diagnoses and mental health–related lines of therapy were identified as events of interest for patient clustering. The dataset included 202,122 events; 75% of the cohort experienced unique treatment journeys. Based on an optimal run, DOCKKES identified 16 treatment journey clusters, which were evenly split for initial manic/mixed or depressive episodes (8 clusters each) and varied in sequence length and early lines of therapy. Variability across clusters was also observed for demographics, comorbidities, and mental health–related healthcare resource utilization and cost. This proof-of-concept study demonstrated the use of DOCKKES for integrating information from large datasets, enabling comparisons between patient clusters and evaluation of real-world treatment journeys in the context of evidence-based guidelines.https://doi.org/10.1186/s40708-025-00258-xBipolar 1 disorderClustering algorithmMachine learningReal-world evidenceSequence clusteringTreatment journey |
| spellingShingle | Matthew Littman Huy-Binh Nguyen Joanna Campbell Katelyn R. Keyloun Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder Brain Informatics Bipolar 1 disorder Clustering algorithm Machine learning Real-world evidence Sequence clustering Treatment journey |
| title | Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder |
| title_full | Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder |
| title_fullStr | Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder |
| title_full_unstemmed | Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder |
| title_short | Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder |
| title_sort | treatment journey clustering with a novel kernel k means machine learning algorithm a retrospective analysis of insurance claims in bipolar i disorder |
| topic | Bipolar 1 disorder Clustering algorithm Machine learning Real-world evidence Sequence clustering Treatment journey |
| url | https://doi.org/10.1186/s40708-025-00258-x |
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