scholarly journals Machine learning models to predict length of stay and discharge destination in complex head and neck surgery

Head & Neck ◽  
2020 ◽  
Author(s):  
Khodayar Goshtasbi ◽  
Tyler M. Yasaka ◽  
Mehdi Zandi‐Toghani ◽  
Hamid R. Djalilian ◽  
William B. Armstrong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Head And Neck ◽  
Neck Surgery ◽  
Head And Neck Surgery ◽  
Discharge Destination ◽  
Learning Models ◽  
Machine Learning Models

A contemporary review of machine learning in otolaryngology–head and neck surgery

2019 ◽  
Vol 130 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Matthew G. Crowson ◽  
Jonathan Ranisau ◽  
Antoine Eskander ◽  
Aaron Babier ◽  
Bin Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Head And Neck ◽  
Neck Surgery ◽  
Head And Neck Surgery

scholarly journals Above and Beyond Age: Prediction of Major Postoperative Adverse Events in Head and Neck Surgery

2021 ◽  
pp. 000348942110412
Author(s):  
Marco A. Mascarella ◽  
Nikesh Muthukrishnan ◽  
Farhad Maleki ◽  
Marie-Jeanne Kergoat ◽  
Keith Richardson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Adverse Events ◽  
Head And Neck ◽  
Multivariate Regression ◽  
Free Tissue Transfer ◽  
Neck Surgery ◽  
Attributable Fraction ◽  
Head And Neck Surgery ◽  
Supervised Machine Learning ◽  
C Statistic

Objective: Major postoperative adverse events (MPAEs) following head and neck surgery are not infrequent and lead to significant morbidity. The objective of this study was to ascertain which factors are most predictive of MPAEs in patients undergoing head and neck surgery. Methods: A cohort study was carried out based on data from patients registered in the National Surgical Quality Improvement Program (NSQIP) from 2006 to 2018. All patients undergoing non-ambulatory head and neck surgery based on Current Procedural Terminology codes were included. Perioperative factors were evaluated to predict MPAEs within 30-days of surgery. Age was classified as both a continuous and categorical variable. Retained factors were classified by attributable fraction and C-statistic. Multivariate regression and supervised machine learning models were used to quantify the contribution of age as a predictor of MPAEs. Results: A total of 43 701 operations were analyzed with 5106 (11.7%) MPAEs. The results of supervised machine learning indicated that prolonged surgeries, anemia, free tissue transfer, weight loss, wound classification, hypoalbuminemia, wound infection, tracheotomy (concurrent with index head and neck surgery), American Society of Anesthesia (ASA) class, and sex as most predictive of MPAEs. On multivariate regression, ASA class (21.3%), hypertension on medication (15.8%), prolonged operative time (15.3%), sex (13.1%), preoperative anemia (12.8%), and free tissue transfer (9%) had the largest attributable fractions associated with MPAEs. Age was independently associated with MPAEs with an attributable fraction ranging from 0.6% to 4.3% with poor predictive ability (C-statistic 0.60). Conclusion: Surgical, comorbid, and frailty-related factors were most predictive of short-term MPAEs following head and neck surgery. Age alone contributed a small attributable fraction and poor prediction of MPAEs. Level of evidence: 3

scholarly journals On Missingness Features in Machine Learning Models for Critical Care: Observational Study (Preprint)

2020 ◽  
Author(s):  
Janmajay Singh ◽  
Masahiro Sato ◽  
Tomoko Ohkuma
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Electronic Health Records ◽  
Prediction Models ◽  
External Validation ◽  
Model Performance ◽  
Learning Models ◽  
Health Records ◽  
Electronic Health ◽  
Machine Learning Models

BACKGROUND Missing data in electronic health records is inevitable and considered to be nonrandom. Several studies have found that features indicating missing patterns (missingness) encode useful information about a patient’s health and advocate for their inclusion in clinical prediction models. But their effectiveness has not been comprehensively evaluated. OBJECTIVE The goal of the research is to study the effect of including informative missingness features in machine learning models for various clinically relevant outcomes and explore robustness of these features across patient subgroups and task settings. METHODS A total of 48,336 electronic health records from the 2012 and 2019 PhysioNet Challenges were used, and mortality, length of stay, and sepsis outcomes were chosen. The latter dataset was multicenter, allowing external validation. Gated recurrent units were used to learn sequential patterns in the data and classify or predict labels of interest. Models were evaluated on various criteria and across population subgroups evaluating discriminative ability and calibration. RESULTS Generally improved model performance in retrospective tasks was observed on including missingness features. Extent of improvement depended on the outcome of interest (area under the curve of the receiver operating characteristic [AUROC] improved from 1.2% to 7.7%) and even patient subgroup. However, missingness features did not display utility in a simulated prospective setting, being outperformed (0.9% difference in AUROC) by the model relying only on pathological features. This was despite leading to earlier detection of disease (true positives), since including these features led to a concomitant rise in false positive detections. CONCLUSIONS This study comprehensively evaluated effectiveness of missingness features on machine learning models. A detailed understanding of how these features affect model performance may lead to their informed use in clinical settings especially for administrative tasks like length of stay prediction where they present the greatest benefit. While missingness features, representative of health care processes, vary greatly due to intra- and interhospital factors, they may still be used in prediction models for clinically relevant outcomes. However, their use in prospective models producing frequent predictions needs to be explored further.

scholarly journals Comparisons of forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Six Machine Learning Models Based on Multi-Omics

2021 ◽  
Author(s):  
Liying Mo ◽  
Yuangang Su ◽  
Jianhui Yuan ◽  
Zhiwei Xiao ◽  
Ziyan Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Survival Outcome ◽  
Learning Models ◽  
Machine Learning Methods ◽  
Machine Learning Models ◽  
Better Than

Abstract Background: Machine learning methods showed excellent predictive ability in a wide range of fields. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics influence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and find the most suitable machine learning prediction method. Results: For omics of HNSC, the results of the six models all showed that the performance of multi-omics was better than each single-omic alone. Results were presented which showed that the BN model played a good prediction performance (area under the curve [AUC] 0.8250) in HNSC multi-omics data. The other machine learning models RF (AUC = 0.8002), NN (AUC = 0.7200), and GLM (AUC = 0.7145) also showed high predictive performance except for DT(AUC = 0.5149) and SVM(AUC = 0.6981). And the results of a vitro qPCR were consistent with the Random forest algorithm. Conclusion: Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the Bayesian network was the most superior. Moreover, the forecast result of multi-omics was better than single-omic alone in HNSC.

Enhanced recovery after surgery in head and neck surgery: Reduced opioid use and length of stay

2019 ◽  
Vol 130 (5) ◽  
pp. 1227-1232 ◽  
Author(s):  
Danny B. Jandali ◽  
Deborah Vaughan ◽  
Michael Eggerstedt ◽  
Ashwin Ganti ◽  
Holly Scheltens ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Head And Neck ◽  
Enhanced Recovery ◽  
Enhanced Recovery After Surgery ◽  
Neck Surgery ◽  
Head And Neck Surgery ◽  
Opioid Use

Discharge Destination after Head and Neck Surgery

2016 ◽  
Vol 155 (6) ◽  
pp. 997-1004 ◽  
Author(s):  
John D. Cramer ◽  
Urjeet A. Patel ◽  
Sandeep Samant ◽  
Stephanie Shintani Smith
Keyword(s):  
Head And Neck ◽  
Neck Surgery ◽  
Head And Neck Surgery ◽  
Discharge Destination

scholarly journals Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3723
Author(s):  
Xiaoyang Liu ◽  
Farhad Maleki ◽  
Nikesh Muthukrishnan ◽  
Katie Ovens ◽  
Shao Hui Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Head And Neck ◽  
Squamous Cell ◽  
Model Performance ◽  
Tumor Location ◽  
Nodal Metastasis ◽  
P Value ◽  
Learning Models ◽  
Hpv Status ◽  
Machine Learning Models

Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences (p-value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.

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