P-358 A machine learning approach for predicting bone metastases and its three-month prognostic risk factors in hepatocellular carcinoma patients using SEER data

Alkhawaldeh, Ibraheem M.; Al-Jafari, Mohammad; Abdel-Galil, M. M.; Tarawneh, Ahmad S.; Hassanat, Ahmad B. A.

doi:10.1016/j.annonc.2023.04.414

Cited by 3 publications

(5 citation statements)

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“…This study used feature correlation ( 29 ) and Permutation importance ( 30 ) with RF, Easy Ensemble (EE), and Artificial Neural Network (ANN) for feature selection. Features were categorized based on correlation coefficients: weak, moderate, and strong ( 31 , 32 ). Easy Ensemble identified 13 out of 19 variable (69%) and ranked them based on importance while and ANN and RF ranked all the variables based on importance ( Figure 3 ).…”

Section: Methodsmentioning

confidence: 99%

“…To assess classifiers’ true efficacy despite class imbalance, F1-score and area under the curve (AUC) were used ( 41 ). Both factorized and one-hot encoding methods were tested to determine which method enhances the learning process and improve data representation and understanding ( 32 ). Then, a final experiment was conducted to identify the best performing algorithm using the superior encoding method.…”

Section: Methodsmentioning

confidence: 99%

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Predicting 90-day prognosis for patients with stroke: a machine learning approach

Abujaber,

Alkhawaldeh,

Imam

et al. 2023

Front. Neurol.

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BackgroundStroke is a significant global health burden and ranks as the second leading cause of death worldwide.ObjectiveThis study aims to develop and evaluate a machine learning-based predictive tool for forecasting the 90-day prognosis of stroke patients after discharge as measured by the modified Rankin Score.MethodsThe study utilized data from a large national multiethnic stroke registry comprising 15,859 adult patients diagnosed with ischemic or hemorrhagic stroke. Of these, 7,452 patients satisfied the study’s inclusion criteria. Feature selection was performed using the correlation and permutation importance methods. Six classifiers, including Random Forest (RF), Classification and Regression Tree, Linear Discriminant Analysis, Support Vector Machine, and k-Nearest Neighbors, were employed for prediction.ResultsThe RF model demonstrated superior performance, achieving the highest accuracy (0.823) and excellent discrimination power (AUC 0.893). Notably, stroke type, hospital acquired infections, admission location, and hospital length of stay emerged as the top-ranked predictors.ConclusionThe RF model shows promise in predicting stroke prognosis, enabling personalized care plans and enhanced preventive measures for stroke patients. Prospective validation is essential to assess its real-world clinical performance and ensure successful implementation across diverse healthcare settings.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting 90-day prognosis for patients with stroke: a machine learning approach

Abujaber,

Alkhawaldeh,

Imam

et al. 2023

Front. Neurol.

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show abstract

“…This can be attributed to the categorical nature of the data, which was encoded using a standard factorization method (1, 2, 3, …) for each category. We might improve the performance of KNN by using another type of encoding, such as one-hot encoding, 27 and other distance measures, such as Hasanat distance, which was proven to be unaffected by outliers and data noise. 28 However, such improvement is beyond the scope of this manuscript.…”

Section: Discussionmentioning

confidence: 99%

“…It aids in assessing the consistency and reliability of the features chosen across different methodologies, as well as identifying features with good predictive value across numerous methods. 27 , 30 …”

Section: Discussionmentioning

confidence: 99%

Extended Spectrum beta-Lactamase Bacteria and Multidrug Resistance in Jordan are Predicted Using a New Machine-Learning system

Al-Khlifeh,

Alkhazi,

Alrowaily

et al. 2024

IDR

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Background The incidence of microorganisms with extended-spectrum beta-lactamase (ESBL) is on the rise, posing a significant public health concern. The current application of machine learning (ML) focuses on predicting bacterial resistance to optimize antibiotic therapy. This study employs ML to forecast the occurrence of bacteria that generate ESBL and demonstrate resistance to multiple antibiotics (MDR). Methods Six popular ML algorithms were initially trained on antibiotic resistance test patient reports (n = 489) collected from Al-Hussein/Salt Hospital in Jordan. Trained outcome models predict ESBL and multidrug resistance profiles based on microbiological and patients’ clinical data. The results were utilized to select the optimal ML method to predict ESBL’s most associated features. Results Escherichia coli ( E. coli , 82%) was the most commonly identified microbe generating ESBL, displaying multidrug resistance. Urinary tract infections (UTIs) constituted the most frequently observed clinical diagnosis (68.7%). Classification and Regression Trees (CART) and Random Forest (RF) classifiers emerged as the most effective algorithms. The relevant features associated with the emergence of ESBL include age and different classes of antibiotics, including cefuroxime, ceftazidime, cefepime, trimethoprim/ sulfamethoxazole, ciprofloxacin, and gentamicin. Fosfomycin nitrofurantoin, piperacillin/tazobactam, along with amikacin, meropenem, and imipenem, had a pronounced inverse relationship with the ESBL class. Conclusion CART and RF-based ML algorithms can be employed to predict the most important features of ESBL. The significance of monitoring trends in ESBL infections is emphasized to facilitate the administration of appropriate antibiotic therapy.

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“…The machine learning was applied after the data were converted to numerical input [13]. This technology was utilized for decision-making in infection control, risk of dementia, and predicting bone metastases of hepatocellular carcinoma [11,12,74]. Future work is required to compare the performance of various machine learning methods for the prediction of complications of DNI.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results

Chen,

Chin,

Chan

et al. 2023

Diagnostics

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Background: Deep neck infection (DNI) is a serious infectious disease, and descending mediastinitis is a fatal infection of the mediastinum. However, no study has applied artificial intelligence to assess progression to descending mediastinitis in DNI patients. Thus, we developed a model to assess the possible progression of DNI to descending mediastinitis. Methods: Between August 2017 and December 2022, 380 patients with DNI were enrolled; 75% of patients (n = 285) were assigned to the training group for validation, whereas the remaining 25% (n = 95) were assigned to the test group to determine the accuracy. The patients’ clinical and computed tomography (CT) parameters were analyzed via the k-nearest neighbor method. The predicted and actual progression of DNI patients to descending mediastinitis were compared. Results: In the training and test groups, there was no statistical significance (all p > 0.05) noted at clinical variables (age, gender, chief complaint period, white blood cells, C-reactive protein, diabetes mellitus, and blood sugar), deep neck space (parapharyngeal, submandibular, retropharyngeal, and multiple spaces involved, ≥3), tracheostomy performance, imaging parameters (maximum diameter of abscess and nearest distance from abscess to level of sternum notch), or progression to mediastinitis. The model had a predictive accuracy of 82.11% (78/95 patients), with sensitivity and specificity of 41.67% and 87.95%, respectively. Conclusions: Our model can assess the progression of DNI to descending mediastinitis depending on clinical and imaging parameters. It can be used to identify DNI patients who will benefit from prompt treatment.

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P-358 A machine learning approach for predicting bone metastases and its three-month prognostic risk factors in hepatocellular carcinoma patients using SEER data

Cited by 3 publications

References 0 publications

Predicting 90-day prognosis for patients with stroke: a machine learning approach

Predicting 90-day prognosis for patients with stroke: a machine learning approach

Extended Spectrum beta-Lactamase Bacteria and Multidrug Resistance in Jordan are Predicted Using a New Machine-Learning system

A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results

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