Developing Artificial Neural Network Models to Predict Functioning One Year After Traumatic Spinal Cord Injury

详细信息    查看全文

• 作者：Timothy Belliveau ; PhD^a ; ^{tbelliveau@hfsc.org" class="auth_mail" title="E-mail the corresponding author} ; Alan M. Jette ; PT ; PhD^b ; Subramani Seetharama ; MD^c ; Jeffrey Axt ; MBA^d ; David Rosenblum ; MD^e ; Daniel Larose ; PhD^f ; Bethlyn Houlihan ; MSW ; MPH^b ; Mary Slavin ; PT ; PhD^b ; Chantal Larose ; PhD^g
• 关键词：Activities of daily living ; Decision support techniques ; Medical informatics ; Rehabilitation ; Spinal cord injuries
• 刊名：Archives of Physical Medicine and Rehabilitation
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：97
• 期：10
• 页码：1663-1668.e3
• 全文大小：222 K

文摘

To develop mathematical models for predicting level of independence with specific functional outcomes 1 year after discharge from inpatient rehabilitation for spinal cord injury.

Design

Statistical analyses using artificial neural networks and logistic regression.

Setting

Retrospective analysis of data from the national, multicenter Spinal Cord Injury Model Systems (SCIMS) Database.

Participants

Subjects (N=3142; mean age, 41.5y) with traumatic spinal cord injury who contributed data for the National SCIMS Database longitudinal outcomes studies.

Interventions

Not applicable.

Main Outcome Measures

Self-reported ambulation ability and FIM-derived indices of level of assistance required for self-care activities (ie, bed-chair transfers, bladder and bowel management, eating, toileting).

Results

Models for predicting ambulation status were highly accurate (>85% case classification accuracy; areas under the receiver operating characteristic curve between .86 and .90). Models for predicting nonambulation outcomes were moderately accurate (76%–86% case classification accuracy; areas under the receiver operating characteristic curve between .70 and .82). The performance of models generated by artificial neural networks closely paralleled the performance of models analyzed using logistic regression constrained by the same independent variables.

Conclusions

After further prospective validation, such predictive models may allow clinicians to use data available at the time of admission to inpatient spinal cord injury rehabilitation to accurately predict longer-term ambulation status, and whether individual patients are likely to perform various self-care activities with or without assistance from another person.