Abstract：
OBJECTIVE:Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction. METHODS:A systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1, 2017. RESULTS:Thirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts. CONCLUSIONS:In the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care.
journal_name
World Neurosurgjournal_title
World neurosurgeryauthors
Senders JT,Staples PC,Karhade AV,Zaki MM,Gormley WB,Broekman MLD,Smith TR,Arnaout Odoi
10.1016/j.wneu.2017.09.149subject
Has Abstractpub_date
2018-01-01 00:00:00pages
476-486.e1eissn
1878-8750issn
1878-8769pii
S1878-8750(17)31665-0journal_volume
109pub_type
杂志文章,评审abstract：OBJECTIVE:To describe a successful one-step intraoperative reduction of atlantoaxial rotatory subluxation (AARS) using neuromuscular blockade and intraoperative traction. METHODS:While the patient was undergoing continuous somatosensory evoked potential monitoring, crown halo traction was applied under general anesthe...
journal_title：World neurosurgery
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journal_title：World neurosurgery
pub_type： 杂志文章,评审
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journal_title：World neurosurgery
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journal_title：World neurosurgery
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更新日期：2017-11-01 00:00:00
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journal_title：World neurosurgery
pub_type： 杂志文章,meta分析,评审
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更新日期：2020-04-01 00:00:00
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journal_title：World neurosurgery
pub_type： 杂志文章
doi：10.1016/j.wneu.2016.05.049
更新日期：2016-08-01 00:00:00
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pub_type： 历史文章,杂志文章,评审
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更新日期：2020-08-01 00:00:00
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journal_title：World neurosurgery
pub_type： 杂志文章,评审
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更新日期：2017-02-01 00:00:00
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journal_title：World neurosurgery
pub_type：
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更新日期：2020-09-01 00:00:00
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journal_title：World neurosurgery
pub_type： 杂志文章
doi：10.1016/j.wneu.2017.09.206
更新日期：2018-01-01 00:00:00
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journal_title：World neurosurgery
pub_type： 杂志文章
doi：10.1016/j.wneu.2017.09.014
更新日期：2017-12-01 00:00:00
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pub_type： 杂志文章
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更新日期：2020-07-01 00:00:00
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pub_type： 杂志文章
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pub_type： 杂志文章,多中心研究
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更新日期：2016-07-01 00:00:00
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journal_title：World neurosurgery
pub_type： 杂志文章
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更新日期：2019-12-01 00:00:00
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journal_title：World neurosurgery
pub_type： 杂志文章
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更新日期：2015-11-01 00:00:00
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pub_type： 杂志文章,评审
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更新日期：2020-09-01 00:00:00
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pub_type： 杂志文章
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更新日期：2019-06-01 00:00:00
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pub_type： 杂志文章
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更新日期：2017-03-01 00:00:00
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pub_type： 杂志文章
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更新日期：2020-01-01 00:00:00
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journal_title：World neurosurgery
pub_type： 杂志文章
doi：10.1016/j.wneu.2015.04.030
更新日期：2015-09-01 00:00:00
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journal_title：World neurosurgery
pub_type： 杂志文章
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更新日期：2020-05-01 00:00:00
