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Machine Learning and Risk Prediction Tools in Neurosurgery: A Rapid Review
Machine Learning and Risk Prediction Tools in Neurosurgery: A Rapid Review
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151048
- ISBN
- 9798382321387
- DDC
- 617
- 저자명
- Sherman, Josiah.
- 서명/저자
- Machine Learning and Risk Prediction Tools in Neurosurgery: A Rapid Review
- 발행사항
- [Sl] : Yale University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 117 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 학위논문주기
- Thesis (M.D.)--Yale University, 2024.
- 초록/해제
- 요약INTRODUCTION: Artificial intelligence (AI) and machine learning (ML) techniques have become highly visible in society and medicine, with some AI/ML-enabled devices receiving approval by the Federal Drug Administration for use in direct patient care. In neurosurgery, ML algorithms have been developed for clinical outcome prediction, many of which have achieved higher predictive ability than standard statistical analysis techniques. However, few have synthesized the ML literature in neurosurgery and its subspecialties. Given the rapid rate at which ML algorithms for outcome prediction and prognostication have developed in neurosurgery, additional synthesis of the current ML landscape in this area is necessary. The aim of this study was to perform a rapid review of PubMed-indexed neurosurgery literature concerning the development, validation, and/or use of ML algorithms for clinical outcome prediction.METHODS: Studies describing the development, validation, and/or use of ML algorithms for clinical outcome prediction published through October 15, 2023 in 14 prominent neurosurgery journals were identified via PubMed. Articles were screened by title and abstract. Manuscripts passing title-abstract screening were manually reviewed for inclusion. Studies were placed into groups based on subspeciality. Studies concerning the use of ML for radiomics were excluded.RESULTS: A total of 741 articles were identified from the initial PubMed query. Of these articles, 247 articles (33.3%) passed title-abstract screening. Of the articles that passed initial screening, 202 were included (27.3% of original 741 articles; 81.8% of articles that passed title-abstract screening). Of the 202 articles that passed both initial title-abstract screening and manuscript review, 114 (56.4%) were in the Spine cohort, 29 (14.4%) were in the Neuro-Oncology cohort, 7 (3.5%) were in the Pediatric Neurosurgery cohort, 31 (15.3%) were in the Cerebrovascular Neurosurgery cohort, 4 (2.0%) were in the Epilepsy/Functional Neurosurgery cohort, 12 (5.9%) were in the Trauma cohort, and 5 (2.5%) were in the Other cohort. External validation was performed in 22 studies (10.9%). Many reported algorithms achieved high model performance for clinical outcome prediction in neurosurgery and its subspecialties.CONCLUSION: The ML literature for clinical outcome precision in neurosurgery has grown rapidly in recent years. Few articles report external validation of developed ML algorithms, limiting their generalizability and clinical practicality. Future ML algorithms developed for clinical outcome prediction in neurosurgery should perform external validation of developed ML models.
- 일반주제명
- Surgery
- 일반주제명
- Neurosciences
- 키워드
- ML algorithms
- 키워드
- Machine learning
- 키워드
- Neurosurgery
- 키워드
- Risk prediction
- 기타저자
- DiLuna, Michael
- 기타저자
- Yale University Yale School of Medicine
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017160609
■00520250211151048
■006m o d
■007cr#unu||||||||
■020 ▼a9798382321387
■035 ▼a(MiAaPQ)AAI31141061
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a617
■1001 ▼aSherman, Josiah.
■24510▼aMachine Learning and Risk Prediction Tools in Neurosurgery: A Rapid Review
■260 ▼a[Sl]▼bYale University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a117 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■5021 ▼aThesis (M.D.)--Yale University, 2024.
■520 ▼aINTRODUCTION: Artificial intelligence (AI) and machine learning (ML) techniques have become highly visible in society and medicine, with some AI/ML-enabled devices receiving approval by the Federal Drug Administration for use in direct patient care. In neurosurgery, ML algorithms have been developed for clinical outcome prediction, many of which have achieved higher predictive ability than standard statistical analysis techniques. However, few have synthesized the ML literature in neurosurgery and its subspecialties. Given the rapid rate at which ML algorithms for outcome prediction and prognostication have developed in neurosurgery, additional synthesis of the current ML landscape in this area is necessary. The aim of this study was to perform a rapid review of PubMed-indexed neurosurgery literature concerning the development, validation, and/or use of ML algorithms for clinical outcome prediction.METHODS: Studies describing the development, validation, and/or use of ML algorithms for clinical outcome prediction published through October 15, 2023 in 14 prominent neurosurgery journals were identified via PubMed. Articles were screened by title and abstract. Manuscripts passing title-abstract screening were manually reviewed for inclusion. Studies were placed into groups based on subspeciality. Studies concerning the use of ML for radiomics were excluded.RESULTS: A total of 741 articles were identified from the initial PubMed query. Of these articles, 247 articles (33.3%) passed title-abstract screening. Of the articles that passed initial screening, 202 were included (27.3% of original 741 articles; 81.8% of articles that passed title-abstract screening). Of the 202 articles that passed both initial title-abstract screening and manuscript review, 114 (56.4%) were in the Spine cohort, 29 (14.4%) were in the Neuro-Oncology cohort, 7 (3.5%) were in the Pediatric Neurosurgery cohort, 31 (15.3%) were in the Cerebrovascular Neurosurgery cohort, 4 (2.0%) were in the Epilepsy/Functional Neurosurgery cohort, 12 (5.9%) were in the Trauma cohort, and 5 (2.5%) were in the Other cohort. External validation was performed in 22 studies (10.9%). Many reported algorithms achieved high model performance for clinical outcome prediction in neurosurgery and its subspecialties.CONCLUSION: The ML literature for clinical outcome precision in neurosurgery has grown rapidly in recent years. Few articles report external validation of developed ML algorithms, limiting their generalizability and clinical practicality. Future ML algorithms developed for clinical outcome prediction in neurosurgery should perform external validation of developed ML models.
■590 ▼aSchool code: 0265.
■650 4▼aSurgery
■650 4▼aNeurosciences
■653 ▼aML algorithms
■653 ▼aMachine learning
■653 ▼aNeurosurgery
■653 ▼aOutcome prediction
■653 ▼aRisk prediction
■690 ▼a0576
■690 ▼a0317
■690 ▼a0800
■70010▼aDiLuna, Michael▼ejoint author
■71020▼aYale University▼bYale School of Medicine.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0265
■791 ▼aM.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160609▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
