250 related articles for article (PubMed ID: 33848833)
41. Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review.
Shung D; Simonov M; Gentry M; Au B; Laine L
Dig Dis Sci; 2019 Aug; 64(8):2078-2087. PubMed ID: 31055722
[TBL] [Abstract][Full Text] [Related]
42. Pediatric Surgical Risk Assessment Tools: A Systematic Review.
Ji D; Goudy SL; Raval MV; Raol N
J Surg Res; 2019 Feb; 234():277-282. PubMed ID: 30527485
[TBL] [Abstract][Full Text] [Related]
43. Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study.
van Niftrik CHB; van der Wouden F; Staartjes VE; Fierstra J; Stienen MN; Akeret K; Sebök M; Fedele T; Sarnthein J; Bozinov O; Krayenbühl N; Regli L; Serra C
Neurosurgery; 2019 Oct; 85(4):E756-E764. PubMed ID: 31149726
[TBL] [Abstract][Full Text] [Related]
44. Personalized Pancreatic Cancer Management: A Systematic Review of How Machine Learning Is Supporting Decision-making.
Bradley A; van der Meer R; McKay C
Pancreas; 2019; 48(5):598-604. PubMed ID: 31090660
[TBL] [Abstract][Full Text] [Related]
45. Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review.
Lee Y; Ragguett RM; Mansur RB; Boutilier JJ; Rosenblat JD; Trevizol A; Brietzke E; Lin K; Pan Z; Subramaniapillai M; Chan TCY; Fus D; Park C; Musial N; Zuckerman H; Chen VC; Ho R; Rong C; McIntyre RS
J Affect Disord; 2018 Dec; 241():519-532. PubMed ID: 30153635
[TBL] [Abstract][Full Text] [Related]
46. Artificial intelligence in knee arthroplasty: current concept of the available clinical applications.
Batailler C; Shatrov J; Sappey-Marinier E; Servien E; Parratte S; Lustig S
Arthroplasty; 2022 May; 4(1):17. PubMed ID: 35491420
[TBL] [Abstract][Full Text] [Related]
47. Machine learning for outcome prediction of neurosurgical aneurysm treatment: Current methods and future directions.
Velagapudi L; Saiegh FA; Swaminathan S; Mouchtouris N; Khanna O; Sabourin V; Gooch MR; Herial N; Tjoumakaris S; Rosenwasser RH; Jabbour P
Clin Neurol Neurosurg; 2023 Jan; 224():107547. PubMed ID: 36481326
[TBL] [Abstract][Full Text] [Related]
48. Prediction of sepsis patients using machine learning approach: A meta-analysis.
Islam MM; Nasrin T; Walther BA; Wu CC; Yang HC; Li YC
Comput Methods Programs Biomed; 2019 Mar; 170():1-9. PubMed ID: 30712598
[TBL] [Abstract][Full Text] [Related]
49. Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype.
El Hechi M; Ward TM; An GC; Maurer LR; El Moheb M; Tsoulfas G; Kaafarani HM
J Surg Res; 2021 Aug; 264():A1-A9. PubMed ID: 33743995
[TBL] [Abstract][Full Text] [Related]
50. A systematic review of machine learning models for predicting outcomes of stroke with structured data.
Wang W; Kiik M; Peek N; Curcin V; Marshall IJ; Rudd AG; Wang Y; Douiri A; Wolfe CD; Bray B
PLoS One; 2020; 15(6):e0234722. PubMed ID: 32530947
[TBL] [Abstract][Full Text] [Related]
51. Deep-learning model for predicting 30-day postoperative mortality.
Fritz BA; Cui Z; Zhang M; He Y; Chen Y; Kronzer A; Ben Abdallah A; King CR; Avidan MS
Br J Anaesth; 2019 Nov; 123(5):688-695. PubMed ID: 31558311
[TBL] [Abstract][Full Text] [Related]
52. Emergency department triage prediction of clinical outcomes using machine learning models.
Raita Y; Goto T; Faridi MK; Brown DFM; Camargo CA; Hasegawa K
Crit Care; 2019 Feb; 23(1):64. PubMed ID: 30795786
[TBL] [Abstract][Full Text] [Related]
53. Looking beyond historical patient outcomes to improve clinical models.
Chia CC; Rubinfeld I; Scirica BM; McMillan S; Gurm HS; Syed Z
Sci Transl Med; 2012 Apr; 4(131):131ra49. PubMed ID: 22539773
[TBL] [Abstract][Full Text] [Related]
54. The State of Machine Learning in Spine Surgery: A Systematic Review.
DelSole EM; Keck WL; Patel AA
Clin Spine Surg; 2022 Mar; 35(2):80-89. PubMed ID: 34121074
[TBL] [Abstract][Full Text] [Related]
55. Surgical Risk Is Not Linear: Derivation and Validation of a Novel, User-friendly, and Machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator.
Bertsimas D; Dunn J; Velmahos GC; Kaafarani HMA
Ann Surg; 2018 Oct; 268(4):574-583. PubMed ID: 30124479
[TBL] [Abstract][Full Text] [Related]
56. Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study.
Bhandari M; Nallabasannagari AR; Reddiboina M; Porter JR; Jeong W; Mottrie A; Dasgupta P; Challacombe B; Abaza R; Rha KH; Parekh DJ; Ahlawat R; Capitanio U; Yuvaraja TB; Rawal S; Moon DA; Buffi NM; Sivaraman A; Maes KK; Porpiglia F; Gautam G; Turkeri L; Meyyazhgan KR; Patil P; Menon M; Rogers C
BJU Int; 2020 Sep; 126(3):350-358. PubMed ID: 32315504
[TBL] [Abstract][Full Text] [Related]
57. Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data.
Mari T; Henderson J; Maden M; Nevitt S; Duarte R; Fallon N
J Pain; 2022 Mar; 23(3):349-369. PubMed ID: 34425248
[TBL] [Abstract][Full Text] [Related]
58. Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.
Brnabic A; Hess LM
BMC Med Inform Decis Mak; 2021 Feb; 21(1):54. PubMed ID: 33588830
[TBL] [Abstract][Full Text] [Related]
59. Machine learning applications for the prediction of surgical site infection in neurological operations.
Tunthanathip T; Sae-Heng S; Oearsakul T; Sakarunchai I; Kaewborisutsakul A; Taweesomboonyat C
Neurosurg Focus; 2019 Aug; 47(2):E7. PubMed ID: 31370028
[TBL] [Abstract][Full Text] [Related]
60. Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review.
Ogink PT; Groot OQ; Karhade AV; Bongers MER; Oner FC; Verlaan JJ; Schwab JH
Acta Orthop; 2021 Oct; 92(5):526-531. PubMed ID: 34109892
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
