Machine learning algorithm for predicting longer postoperative length of stay among older cancer patients.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e21536-e21536 ◽  
Author(s):  
Armin Shahrokni ◽  
Seyed Ali Rokni ◽  
Hassan Ghasemzadeh
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Variable Selection ◽  
Cancer Patients ◽  
Learning Model ◽  
Selection Algorithm ◽  
Machine Learning Model ◽  
Tree Classifier ◽  
Postoperative Length ◽  
Older Cancer Patients

e21536 Background: To predict longer postoperative length of stay (LLOS) among older cancer patients, we have used a novel method to assess factors associated with this outcome. Methods: Our study was based on 47-item electronic Rapid Fitness Assessment (eRFA) data presented last year (Shahrokni, et al. (J Clin Oncol 34, 2016 (suppl; abstr 10011). LLOS was defined as LOS ≥8days.We used machine learning model to explore the relationship between > 70 pre, intra, and postoperative variables with LLOS. A filter-based variable selection algorithm was utilized to only identify the most important variables as defined by information gain. We computed the mutual information between each variable and the LOS, and ranked them from high to low gain. To create an interpretable machine learning model, we used decision tree classifier. The tree started with the most informative variable as the root and branches based on a threshold value such that the entropy decreases by most. Results: In a cohort of 492 postoperative older cancer patients (median age 80), the variable selection algorithm showed that only 5 variables are sufficient for predicting LLOS with an accuracy of 77.2%, sensitivity 81.6% and specificity 61.31% in all patients. The algorithm started with operation time (OT) of < or > 4 hrs. For example, patients with OT > 4 hours and social activity limitation (SAL) > 7 were 78% likely to have LLOS. Patients with SAL ≤ 7 and OT > 5 hours were 75% likely to have LLOS. Patients were 71% likely not to have LLOS if OT < 4 hours and SAL ≤ 7. To avoid the effect of potential data collection errors and increase the accuracy of the model, we used the Bootstrap Aggregating (bagging) method which ensembles the decision of a set small trees. Particularly, we generated 3 datasets from randomly sampling patients from current dataset with replacement and built 3 different trees. As a result of combining 3 short trees we reach the accuracy of 83.2% with sensitivity 83.9% and specificity 82.4%. Conclusions: Machine learning model could be helpful for predicting LLOS among older cancer patients. Prospective studies are needed to validate this method in different academic and community settings.

scholarly journals Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145968-145983 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Ataollah Shirzadi ◽  
Bahram Choubin ◽  
Fereshteh Taromideh ◽  
Farzaneh Sajedi Hosseini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Susceptibility Mapping ◽  
Snow Avalanche ◽  
Random Subspace ◽  
Ensemble Machine Learning ◽  
Machine Learning Model ◽  
Tree Classifier

scholarly journals Machine Learning Based Clinical Diagnosis of Liver Patients with Instance Replacement

2021 ◽  
Author(s):  
J. V. D. Prasad ◽  
A. Raghuvira Pratap ◽  
Babu Sallagundla
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Research Work ◽  
Feature Selection Method ◽  
Learning Model ◽  
Disease Classification ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Huge Data ◽  
Machine Learning Model

With the rapid increase in number of clinical data and hence the prediction and analysing data becomes very difficult. With the help of various machine learning models, it becomes easy to work on these huge data. A machine learning model faces lots of challenges; one among the challenge is feature selection. In this research work, we propose a novel feature selection method based on statistical procedures to increase the performance of the machine learning model. Furthermore, we have tested the feature selection algorithm in liver disease classification dataset and the results obtained shows the efficiency of the proposed method.

scholarly journals A machine learning model to predict death outcome in severe COVID-19 patients

2021 ◽  
Author(s):  
Qiao Yang ◽  
Jixi Li ◽  
Zhijia Zhang ◽  
Xiaocheng Wu ◽  
Tongquan Liao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lactic Dehydrogenase ◽  
Learning Model ◽  
Severe Illness ◽  
Decision Tree Classifier ◽  
Risk Of Death ◽  
Global Pandemic ◽  
Machine Learning Model ◽  
Tree Classifier ◽  
Novel Coronavirus

Abstract BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between survivors and non-survivors. A decision tree classifier, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

scholarly journals A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

2020 ◽  
Author(s):  
Qiao Yang ◽  
Jixi Li ◽  
Zhijia Zhang ◽  
Xiaocheng Wu ◽  
Tongquan Liao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Area Under The Curve ◽  
Lactic Dehydrogenase ◽  
Learning Model ◽  
Severe Illness ◽  
Decision Tree Classifier ◽  
Risk Of Death ◽  
Machine Learning Model ◽  
Tree Classifier

Abstract BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.MethodsA total of 2,169 adult COVID-19 patients were enrolled from Wuhan, China between February 10th and April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups as well as between survivors and non-survivors. In addition, we developed a decision tree classifier to identify risk factors for death outcome.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. The most common system symptoms were respiratory, systemic and digestive symptoms. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A machine learning model was developed to predict death outcome in severe patients. The decision tree classifier included three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase. The area under the curve of the receiver operating characteristic of this model was 0.96. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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