Acta neurochirurgica. Supplement · 2012
Automated extraction of decision rules for predicting lumbar drain outcome by analyzing overnight intracranial pressure.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
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Hu Xiao, Hamilton Robert, Baldwin Kevin, Vespa Paul M, Bergsneider Marvin
Аннотация
BACKGROUND: Extended lumbar drain (ELD) has become a popular pre-shunt workup test to help diagnose normal pressure hydrocephalus (NPH). Unfortunately, this procedure requires a substantial time investment for both the family and hospital. In this study, we investigate how accurate the prediction of ELD outcome can be achieved by using simple decision rules automatically derived from pulse morphological metrics of overnight ICP recordings. Our ultimate goal is to test the hypothesis that overnight ICP monitoring, empowered by subsequent signal analysis, could be an alternative to ELD.
METHODS: The present study involved 54 patients with both ELD and overnight ICP recordings; the ICP morphological analysis was performed using the MOCAIP algorithm. Furthermore, the distribution of individual metric from the overnight recording was characterized using five aggregation functions (features). Then an algorithm was developed to automatically discover the most accurate "if-then" decision rule for each of the five feature functions. In addition, the best combination of two decision rules, either using "AND" or "OR" operator, was obtained.
FINDINGS: Rules based on five individual feature functions achieved an accuracy of 70.4%, 72.2%, 74.1%, 72.2%, and 79.6% respectively. However, "OR" combination of two features improved accuracy to 88.9%.
CONCLUSION: We showed an algorithm to discover decision rules that can potentially predict ELD outcome.
METHODS: The present study involved 54 patients with both ELD and overnight ICP recordings; the ICP morphological analysis was performed using the MOCAIP algorithm. Furthermore, the distribution of individual metric from the overnight recording was characterized using five aggregation functions (features). Then an algorithm was developed to automatically discover the most accurate "if-then" decision rule for each of the five feature functions. In addition, the best combination of two decision rules, either using "AND" or "OR" operator, was obtained.
FINDINGS: Rules based on five individual feature functions achieved an accuracy of 70.4%, 72.2%, 74.1%, 72.2%, and 79.6% respectively. However, "OR" combination of two features improved accuracy to 88.9%.
CONCLUSION: We showed an algorithm to discover decision rules that can potentially predict ELD outcome.