Multivariate Time-Series Cluster Analysis for Multiple Functional Domains to Identify Recovery Patterns of Patients With Fragility Hip Fracture After Surgery
Patients who have undergone hip fracture surgery have the primary goal of recovering their premorbid level of function across diverse functional domains, including walking ability, balance, cognitive function, emotional well-being, frailty, and activities of daily living. As the speed and level of r...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10486891/ |
| _version_ | 1797217391401238528 |
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| author | Chanyoung Park Hongbum Kim Jungwon Suh Jinhee Ko Jun Hwan Choi Sang Yoon Lee Jaewon Beom Jae-Young Lim Bo Ryun Kim Hyo Kyung Lee |
| author_facet | Chanyoung Park Hongbum Kim Jungwon Suh Jinhee Ko Jun Hwan Choi Sang Yoon Lee Jaewon Beom Jae-Young Lim Bo Ryun Kim Hyo Kyung Lee |
| author_sort | Chanyoung Park |
| collection | DOAJ |
| description | Patients who have undergone hip fracture surgery have the primary goal of recovering their premorbid level of function across diverse functional domains, including walking ability, balance, cognitive function, emotional well-being, frailty, and activities of daily living. As the speed and level of recovery can vary substantially across functional domains and individuals, the varying recovery patterns of different outcome measures should be considered when designing rehabilitation plans for patients. However, the lack of knowledge of recovery trajectories and their variations in hip fracture patients impedes such efforts. In this study, we develop a multivariate time-series clustering algorithm to analyze the recovery patterns and identify patient groups with similar recovery patterns across multiple functional outcomes. Five distinct recovery patterns were observed that exhibit varying maximum recovery levels and speeds. These findings demonstrated the significance of utilizing multiple outcome measures concurrently to assess the patient’s recovery level. Recovery patterns are identified to exhibit variations across different domains, revealing contrasting trends between walking ability and cognitive outcomes. Furthermore, we present predictions on the trajectory of recovery during the post-acute phase solely based on the acute-phase information. This approach facilitates the early identification of patient groups with an unfavorable prognosis for recovery. |
| first_indexed | 2024-04-24T12:01:07Z |
| format | Article |
| id | doaj.art-aa4e536749844058b404708dcebcb422 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T12:01:07Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-aa4e536749844058b404708dcebcb4222024-04-08T23:00:53ZengIEEEIEEE Access2169-35362024-01-0112486994871210.1109/ACCESS.2024.338378610486891Multivariate Time-Series Cluster Analysis for Multiple Functional Domains to Identify Recovery Patterns of Patients With Fragility Hip Fracture After SurgeryChanyoung Park0https://orcid.org/0009-0000-9530-9024Hongbum Kim1Jungwon Suh2Jinhee Ko3Jun Hwan Choi4https://orcid.org/0000-0003-3296-3349Sang Yoon Lee5https://orcid.org/0000-0002-2906-3094Jaewon Beom6https://orcid.org/0000-0001-7984-9661Jae-Young Lim7Bo Ryun Kim8https://orcid.org/0000-0001-7788-7904Hyo Kyung Lee9https://orcid.org/0000-0001-7788-7904School of Industrial and Management Engineering, Korea University, Seoul, Republic of KoreaSchool of Industrial and Management Engineering, Korea University, Seoul, Republic of KoreaDepartment of Physical Medicine and Rehabilitation, Korea University College of Medicine, Seoul, Republic of KoreaDepartment of Physical Medicine and Rehabilitation, Korea University College of Medicine, Seoul, Republic of KoreaDepartment of Rehabilitation Medicine, Regional Rheumatoid and Degenerative Arthritis Center, Jeju National University Hospital, Jeju National University College of Medicine, Jeju, Republic of KoreaDepartment of Rehabilitation Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of KoreaDepartment of Rehabilitation Medicine, Seoul National University Bundang Hospital, Seoul National University, College of Medicine, Seongnam-si, Republic of KoreaDepartment of Rehabilitation Medicine, Seoul National University Bundang Hospital, Seoul National University, College of Medicine, Seongnam-si, Republic of KoreaDepartment of Physical Medicine and Rehabilitation, Korea University College of Medicine, Seoul, Republic of KoreaSchool of Industrial and Management Engineering, Korea University, Seoul, Republic of KoreaPatients who have undergone hip fracture surgery have the primary goal of recovering their premorbid level of function across diverse functional domains, including walking ability, balance, cognitive function, emotional well-being, frailty, and activities of daily living. As the speed and level of recovery can vary substantially across functional domains and individuals, the varying recovery patterns of different outcome measures should be considered when designing rehabilitation plans for patients. However, the lack of knowledge of recovery trajectories and their variations in hip fracture patients impedes such efforts. In this study, we develop a multivariate time-series clustering algorithm to analyze the recovery patterns and identify patient groups with similar recovery patterns across multiple functional outcomes. Five distinct recovery patterns were observed that exhibit varying maximum recovery levels and speeds. These findings demonstrated the significance of utilizing multiple outcome measures concurrently to assess the patient’s recovery level. Recovery patterns are identified to exhibit variations across different domains, revealing contrasting trends between walking ability and cognitive outcomes. Furthermore, we present predictions on the trajectory of recovery during the post-acute phase solely based on the acute-phase information. This approach facilitates the early identification of patient groups with an unfavorable prognosis for recovery.https://ieeexplore.ieee.org/document/10486891/Machine learningpatient rehabilitationpattern clusteringtime series analysis |
| spellingShingle | Chanyoung Park Hongbum Kim Jungwon Suh Jinhee Ko Jun Hwan Choi Sang Yoon Lee Jaewon Beom Jae-Young Lim Bo Ryun Kim Hyo Kyung Lee Multivariate Time-Series Cluster Analysis for Multiple Functional Domains to Identify Recovery Patterns of Patients With Fragility Hip Fracture After Surgery IEEE Access Machine learning patient rehabilitation pattern clustering time series analysis |
| title | Multivariate Time-Series Cluster Analysis for Multiple Functional Domains to Identify Recovery Patterns of Patients With Fragility Hip Fracture After Surgery |
| title_full | Multivariate Time-Series Cluster Analysis for Multiple Functional Domains to Identify Recovery Patterns of Patients With Fragility Hip Fracture After Surgery |
| title_fullStr | Multivariate Time-Series Cluster Analysis for Multiple Functional Domains to Identify Recovery Patterns of Patients With Fragility Hip Fracture After Surgery |
| title_full_unstemmed | Multivariate Time-Series Cluster Analysis for Multiple Functional Domains to Identify Recovery Patterns of Patients With Fragility Hip Fracture After Surgery |
| title_short | Multivariate Time-Series Cluster Analysis for Multiple Functional Domains to Identify Recovery Patterns of Patients With Fragility Hip Fracture After Surgery |
| title_sort | multivariate time series cluster analysis for multiple functional domains to identify recovery patterns of patients with fragility hip fracture after surgery |
| topic | Machine learning patient rehabilitation pattern clustering time series analysis |
| url | https://ieeexplore.ieee.org/document/10486891/ |
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