Progression prediction model for solid tumors with clinical and immunological parameters.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 2539-2539
Author(s):  
Aleksei Viktorovich Novik ◽  
Dmitrii Viktorovich Girdyuk ◽  
Tatyana Leonidovna Nekhaeva ◽  
Natalia Viktorovna Emelyanova ◽  
Anna Semenova ◽  
...  
Keyword(s):  
Immune System ◽  
Prediction Model ◽  
Disease Progression ◽  
Model Prediction ◽  
Clinical Benefit ◽  
Disease Stage ◽  
Gradient Boosting ◽  
Test Model ◽  
Primary Tumors ◽  
Progression Prediction

2539 Background: The immune system has well-known relation to tumor progression. Numerous immune-related parameters exist, but only a minor part could be used as biomarkers, especially dynamic ones. We trained a progression prediction model based on clinical features and peripheral immune system assessments. Methods: Patients with immunogenic (melanoma, 295, kidney cancer, 81), non-immunogenic (soft tissue sarcoma, 47, colorectal cancer, 26) and multiple primary tumors (29) with immunologic assessments before treatment (23.5%), on therapy (58.3), and in follow-up after the treatment (18.2%) were randomly divided in 7:3 ratio to the training and test groups. Counts of lymphocytes, T-, B, NK cells, cytotoxic lymphocytes, T-helpers were used as immunologic parameters. Age, sex, disease, stage, therapy, mutational status, last response on treatment, disease and therapy duration, previous treatments were used as clinical ones. The model was trained to predict disease progression in the next three months using “Catboost” gradient boosting. We used ROC AUC to test model performance and Yoden’s index for optimal cutoff calculation. We also studied the influence of model prediction on overall survival (OS) and time to progression (TTP) on the test dataset using the Kaplan-Meyer method and Cox regression. Results: We used 1682 assessments of immune parameters (immune status, IS) done in 354 patients (average 5 per patient) to train the model and 616 IS in 124 patients for validation. All IS of one patient were in the same group. The ROC AUC value of the model was 0.801. The model prediction of progression increased the probability of progressive disease from 37.5 to 62% and decreased the response rate from 37,5% to 8.4% (p = 0.016). The model prediction did not add information over known prognostic factors for OS in the multifactorial model but was an independent prognostic factor for TTP (HR 2.204, p = 0.011). False-positive results separate the group of patients with poor prognosis (OS 16 months, TTP 6 months) among patients with clinical benefit from patients with favorable prognosis (OS 61 months, TTP 18 months, p < 0.001), who had a truly negative model prediction. The possibility of prognosis improvement with therapy change was an essential factor for OS and TTP prediction (р < 0.001). The model was useful in predicting higher OS in patients with disease progression (p = 0.033) and shorter response duration in patients with clinical benefit (р = 0.03). Conclusions: Our progression prediction model provides clinically useful information and can be used for decision making in several clinical situations. Its utility should be tested in a prospective trial.

scholarly journals Clinical benefit of continuing ALK inhibition with crizotinib beyond initial disease progression in patients with advanced ALK-positive NSCLC

2014 ◽  
Vol 25 (2) ◽  
pp. 415-422 ◽  
Author(s):  
S.-H. I. Ou ◽  
P.A. Jänne ◽  
C.H. Bartlett ◽  
Y. Tang ◽  
D.-W. Kim ◽  
...  
Keyword(s):  
Disease Progression ◽  
Clinical Benefit ◽  
Alk Positive

scholarly journals Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

2021 ◽  
Vol 13 (21) ◽  
pp. 12302
Author(s):  
Xiwen Cui ◽  
Shaojun E ◽  
Dongxiao Niu ◽  
Bosong Chen ◽  
Jiaqi Feng
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Carbon Emission ◽  
Historical Data ◽  
Global Climate ◽  
Optimization Methods ◽  
Gradient Boosting ◽  
Target Values ◽  
Whale Optimization ◽  
Practical Implications

As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.

scholarly journals Identifying Children at Readmission Risk: At-Admission Versus Traditional At-Discharge Readmission Prediction Model

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1334
Author(s):  
Hasan Symum ◽  
José Zayas-Castro
Keyword(s):  
Prediction Model ◽  
Information Exchange ◽  
High Risk Patient ◽  
Machine Learning Algorithms ◽  
Polynomial Kernel ◽  
Gradient Boosting ◽  
Support Vector ◽  
Hospital Discharges ◽  
Discharge Model ◽  
Readmission Risk

The timing of 30-day pediatric readmissions is skewed with approximately 40% of the incidents occurring within the first week of hospital discharges. The skewed readmission time distribution coupled with delay in health information exchange among healthcare providers might offer a limited time to devise a comprehensive intervention plan. However, pediatric readmission studies are thus far limited to the development of the prediction model after hospital discharges. In this study, we proposed a novel pediatric readmission prediction model at the time of hospital admission which can improve the high-risk patient selection process. We also compared proposed models with the standard at-discharge readmission prediction model. Using the Hospital Cost and Utilization Project database, this prognostic study included pediatric hospital discharges in Florida from January 2016 through September 2017. Four machine learning algorithms—logistic regression with backward stepwise selection, decision tree, Support Vector machines (SVM) with the polynomial kernel, and Gradient Boosting—were developed for at-admission and at-discharge models using a recursive feature elimination technique with a repeated cross-validation process. The performance of the at-admission and at-discharge model was measured by the area under the curve. The performance of the at-admission model was comparable with the at-discharge model for all four algorithms. SVM with Polynomial Kernel algorithms outperformed all other algorithms for at-admission and at-discharge models. Important features associated with increased readmission risk varied widely across the type of prediction model and were mostly related to patients’ demographics, social determinates, clinical factors, and hospital characteristics. Proposed at-admission readmission risk decision support model could help hospitals and providers with additional time for intervention planning, particularly for those targeting social determinants of children’s overall health.

A Data-Driven Two-Stage Prediction Model for Train Primary-Delay Recovery Time

2020 ◽  
Vol 30 (07) ◽  
pp. 921-940
Author(s):  
Bowen Gao ◽  
Dongxiu Ou ◽  
Decun Dong ◽  
Yusen Wu
Keyword(s):  
Prediction Model ◽  
Knowledge Base ◽  
Recovery Time ◽  
Prediction Accuracy ◽  
Gradient Boosting ◽  
Support Vector ◽  
Model Framework ◽  
Two Stage ◽  
Delay Propagation ◽  
Proposed Model

Accurate prediction of train delay recovery is critical for railway incident management and providing passengers with accurate journey time. In this paper, a two-stage prediction model is proposed to predict the recovery time of train primary-delay based on the real records from High-Speed Railway (HSR). In Stage 1, two models are built to study the influence of feature space and model framework on the prediction accuracy of buffer time in each section or station. It is found that explicitly inputting the attribute features of stations and sections to the model, instead of implicit simulation, will improve the prediction accuracy effectively. For validation purpose, the proposed model has been compared with several alternative models, namely, Logistic Regression (LR), Artificial Neutral Network (ANN), Support Vector Machine (SVM) and Gradient Boosting Tree (GBT). The results show that its remarkable performance is better than other schemes. Specifically, when the error is extended to 3[Formula: see text]min, the proposed model can achieve up to the accuracy of 94.63%. It proves that our method has high value in practical engineering application. Considering the delay propagation of trains is a complex process, our future study will focus on building delay propagation knowledge base and dispatcher experience knowledge base.

scholarly journals A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Daichi Shigemizu ◽  
Shintaro Akiyama ◽  
Yuya Asanomi ◽  
Keith A. Boroevich ◽  
Alok Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Prediction Model ◽  
Risk Prediction ◽  
Mirna Expression ◽  
Dementia With Lewy Bodies ◽  
Target Genes ◽  
Lewy Bodies ◽  
Risk Prediction Model ◽  
Gradient Boosting

Abstract Background Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer’s disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential biomarkers of prodromal DLB is still challenging. MicroRNAs (miRNAs) have recently received a lot of attention as a source of novel biomarkers. Methods In this study, using serum miRNA expression of 478 Japanese individuals, we investigated potential miRNA biomarkers and constructed an optimal risk prediction model based on several machine learning methods: penalized regression, random forest, support vector machine, and gradient boosting decision tree. Results The final risk prediction model, constructed via a gradient boosting decision tree using 180 miRNAs and two clinical features, achieved an accuracy of 0.829 on an independent test set. We further predicted candidate target genes from the miRNAs. Gene set enrichment analysis of the miRNA target genes revealed 6 functional genes included in the DHA signaling pathway associated with DLB pathology. Two of them were further supported by gene-based association studies using a large number of single nucleotide polymorphism markers (BCL2L1: P = 0.012, PIK3R2: P = 0.021). Conclusions Our proposed prediction model provides an effective tool for DLB classification. Also, a gene-based association test of rare variants revealed that BCL2L1 and PIK3R2 were statistically significantly associated with DLB.

scholarly journals SAT-LB121 Development of a Machine-Learning Method for Predicting New Onset of Diabetes Mellitus: A Retrospective Analysis of 509,153 Annual Specific Health Checkup Records

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Akihiro Nomura ◽  
Sho Yamamoto ◽  
Yuta Hayakawa ◽  
Kouki Taniguchi ◽  
Takuya Higashitani ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Prediction Model ◽  
Performance Test ◽  
Bootstrap Method ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Health Checkup ◽  
Specific Health

Abstract Diabetes mellitus (DM) is a chronic disorder, characterized by impaired glucose metabolism. It is linked to increased risks of several diseases such as atrial fibrillation, cancer, and cardiovascular diseases. Therefore, DM prevention is essential. However, the traditional regression-based DM-onset prediction methods are incapable of investigating future DM for generally healthy individuals without DM. Employing gradient-boosting decision trees, we developed a machine learning-based prediction model to identify the DM signatures, prior to the onset of DM. We employed the nationwide annual specific health checkup records, collected during the years 2008 to 2018, from Kanazawa city, Ishikawa, Japan. The data included the physical examinations, blood and urine tests, and participant questionnaires. Individuals without DM (at baseline), who underwent more than two annual health checkups during the said period, were included. The new cases of DM onset were recorded when the participants were diagnosed with DM in the annual check-ups. The dataset was divided into three subsets in a 6:2:2 ratio to constitute the training, tuning (internal validation), and testing datasets. Employing the testing dataset, the ability of our trained prediction model to calculate the area under the curve (AUC), precision, recall, F1 score, and overall accuracy was evaluated. Using a 1,000-iteration bootstrap method, every performance test resulted in a two-sided 95% confidence interval (CI). We included 509,153 annual health checkup records of 139,225 participants. Among them, 65,505 participants without DM were included, which constituted36,303 participants in the training dataset and 13,101 participants in each of the tuning and testing datasets. We identified a total of 4,696 new DM-onset patients (7.2%) in the study period. Our trained model predicted the future incidence of DM with the AUC, precision, recall, F1 score, and overall accuracy of 0.71 (0.69-0.72 with 95% CI), 75.3% (71.6-78.8), 42.2% (39.3-45.2), 54.1% (51.2-56.7), and 94.9% (94.5-95.2), respectively. In conclusion, the machine learning-based prediction model satisfactorily identified the DM onset prior to the actual incidence.

scholarly journals Emerging nanomedicines for effective breast cancer immunotherapy

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Amirhossein Bahreyni ◽  
Yasir Mohamud ◽  
Honglin Luo
Keyword(s):  
Breast Cancer ◽  
Immune System ◽  
Cancer Immunotherapy ◽  
Immune Cells ◽  
Tumor Antigens ◽  
Host Immune System ◽  
Primary Tumors ◽  
Novel Approach ◽  
Tumor Environment ◽  
Advanced Biomaterials

AbstractBreast cancer continues to be the most frequently diagnosed malignancy among women, putting their life in jeopardy. Cancer immunotherapy is a novel approach with the ability to boost the host immune system to recognize and eradicate cancer cells with high selectivity. As a promising treatment, immunotherapy can not only eliminate the primary tumors, but also be proven to be effective in impeding metastasis and recurrence. However, the clinical application of cancer immunotherapy has faced some limitations including generating weak immune responses due to inadequate delivery of immunostimulants to the immune cells as well as uncontrolled modulation of immune system, which can give rise to autoimmunity and nonspecific inflammation. Growing evidence has suggested that nanotechnology may meet the needs of current cancer immunotherapy. Advanced biomaterials such as nanoparticles afford a unique opportunity to maximize the efficiency of immunotherapy and significantly diminish their toxic side-effects. Here we discuss recent advancements that have been made in nanoparticle-involving breast cancer immunotherapy, varying from direct activation of immune systems through the delivery of tumor antigens and adjuvants to immune cells to altering immunosuppression of tumor environment and combination with other conventional therapies.

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