Advanced Predictive Model and Real-World Results for Medium Concentration CPV

2011 ◽  
Author(s):  
Bruce Karney ◽  
Marc Finot ◽  
Frank Dimroth ◽  
Sarah Kurtz ◽  
Gabriel Sala ◽  
...  
Keyword(s):  
Predictive Model ◽  
Real World ◽  
Medium Concentration

scholarly journals Predictive Model Evaluation for PHM

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Chunsheng Yang ◽  
Yanni Zou ◽  
Jie Liu ◽  
Kyle R Mulligan
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Real World ◽  
Predictive Models ◽  
Model Evaluation ◽  
High Performance ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Real World Applications ◽  
Prognostic Application

In the past decades, machine learning techniques or algorithms, particularly, classifiers have been widely applied to various real-world applications such as PHM. In developing high-performance classifiers, or machine learning-based models, i.e. predictive model for PHM, the predictive model evaluation remains a challenge. Generic methods such as accuracy may not fully meet the needs of models evaluation for prognostic applications. This paper addresses this issue from the point of view of PHM systems. Generic methods are first reviewed while outlining their limitations or deficiencies with respect to PHM. Then, two approaches developed for evaluating predictive models are presented with emphasis on specificities and requirements of PHM. A case of real prognostic application is studies to demonstrate the usefulness of two proposed methods for predictive model evaluation. We argue that predictive models for PHM must be evaluated not only using generic methods, but also domain-oriented approaches in order to deploy the models in real-world applications.

Leveraging real world data and AIML to inform smart patient care in oncology.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e20002-e20002
Author(s):  
Li Zhou ◽  
Rob Steen ◽  
Lynn Lu
Keyword(s):  
Patient Care ◽  
Predictive Model ◽  
Real World ◽  
Model Performance ◽  
Treatment Decision ◽  
Treatment Patterns ◽  
Real World Data ◽  
World Data ◽  
Co Morbidity ◽  
Low Probability

e20002 Background: Identifying optimal therapy options can help maximize treatment outcomes. Finding ways to help improve treatment decision is of great value to achieve better patient care. With the availability of robust patient real world data and the application of state of the art Artificial Intelligence and Machine Learning (AIML) technology, new opportunities have emerged for a broad spectrum of research needs from oncology R&D to commercialization. To illustrate the above advancements, this study identified patients diagnosed with CLL who may progress to next line of treatment in the near future (e.g. future 3 months). More importantly, we can identify treatment patterns which are more effective in treating different types of CLL patients. Methods: This study includes multiple steps which have already been analyzed for feasibility: 1. Collect CLL patients. IQVIA's real world data contains ~60,000 active CLL treated patients. ~2,000 patients have progressed line of treatment in 3 month. 2. Define patients into positive and negative cohorts based on those who have/have not advanced to line L2+. 3. Determine patient profiles based on treatment regimens, symptoms, lab tests, doctor visits, hospital visits, and co-morbidity, etc. 4. Select patient and treatment features to fit an AIML predictive model. 5. Test different algorithms to achieve best model results and validate model performance. 6. Score and classify CLL patients into high and low probability based on the predictive model. 7. Match patients based on feature importance and compare regimens between positive and negative cohort. Results: Model accuracy is above 90%. Top clinical features are calculated for each patient. Optimum treatment patterns between high and low probability patients are identified, with controlling patient key features. Conclusions: Conclusions from this study is expected to yield deeper insight into more tailored treatments by patient type. CLL patients started with oral therapy(targeting) have better response than other treatments.

scholarly journals Early Death in Multiple Myeloma. Analysis of Patients in Real-World Practice

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 5595-5595
Author(s):  
Sunil Lakhwani Lakhwani ◽  
Miguel T Hernández ◽  
Pablo Lorenzo-Barreto ◽  
Deborah Cabrera-Brito ◽  
Marta Fernández-González ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
Predictive Model ◽  
Real World ◽  
Rating Scale ◽  
Conflicts Of Interest ◽  
New Drugs ◽  
Response To Treatment ◽  
First Line ◽  
Biological Variables

Abstract Background. During the last years survival of myeloma patients has improve due mainly to the introduction of new drugs. Despite this, there are patients who die early: before two years from diagnosis. There are few data published of this group of patients with early mortality (EM) and most of them analyzing patients included in clinical trials. Methods. We reviewed myeloma patients from our center from 1990 to 2017 and identified patients with EM. We recorded many clinical and biological variables, both at diagnosis and in evolution. We compared data of patients with EM with the rest and we analyze which variables confer worse prognosis of this group in a multivariate analysis. We also create a predictive model at diagnosis of patients with EM through a discriminant analysis. Results. We identified 186 patients with less than two years of survival from diagnosis, 87 women and 99 men. Mean age was 70,3 years old (range 42-99). The most common cause of death was infection (42,33%) followed by progession (39,26%). We could compare data of these patients with those without EM from 1990 to 2009 (n=255) and we found that patients with EM were older (p=0,039), with more comorbidities (p=0,004) and worse ECOG (p<0,001). They had a disease more agressive and more evolved with worse bone disease (p=0,019), lower hemoglobine (p<0,001) and albumin (p<0,001); higher calcium (p=0,007), creatinine (p<0,001), LDH (p<0,001), β2microglobuline (p<0,001), immunoparesis (p=0,007) and percentage of plasma cells in bone marrow (p<0,001) and consequently worse Durie-Salmon (p<0,001) and ISS (p<0,001) prognosis score. We also found a worse response to first line treatment (p<0,001). In a multivariate analysis, variables that confer a worst prognosis (overall survival) of patients with EM myeloma were β2-microglobuline ≥5,5mg/l (HR: 2,251, p=0,004), >4 points in Cumullative Illness Rating Scale (CIRS) (HR: 1,760, p=0,036) and progressive disease to first line tratment (HR: 2,230, p=0,007). We create a mathematic predictive model at diagnosis through a discriminant analysis using serum calcium, ISS score and CIRS score to identify patients with EM. With this method we could classify correctly 68,4% of our patients. Conclusions. In our real-world series patients with EM myeloma die mainly because infection or progression. They have worse basal situation (age, comorbidity, ECOG) an a more agressive and evolved disease as wel as worse response to treatment. We present a model to try to identify these patients at diagnosis, which could involve changing the management like giving antibiotic profilaxis to try to improve survival. Disclosures No relevant conflicts of interest to declare.

A predictive model for survival in non-small cell lung cancer (NSCLC) based on electronic health record (EHR) and tumor sequencing data at the Department of Veterans Affairs (VA).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 109-109
Author(s):  
Nathanael Fillmore ◽  
Jamie Ramos-Cejudo ◽  
David Cheng ◽  
David P. Tuck ◽  
Ayesha Rizwan Sheikh ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Predictive Model ◽  
Real World ◽  
Survival Models ◽  
Data Repository ◽  
Precision Oncology ◽  
Newly Diagnosed ◽  
Sequencing Data ◽  
Tumor Sequencing ◽  
Nsclc Patients

109 Background: Machine learning tools based on EHR data hold promise to help avoid unnecessary risks associated with lung cancer and its treatment. Additionally, molecular genetic profiling is becoming an integral tool for clinicians to individualize treatment for lung cancer. However, relatively few survival models have been built that integrate this data in individualized predictive models. Here, we combine real-world EHR and tumor sequencing data from the VA Precision Oncology Data Repository (PODR) to build accurate individualized survival predictions in newly-diagnosed NSCLC patients. Methods: We identified a cohort of 356 VA patients newly diagnosed with NSCLC for whom EHR, cancer registry, and targeted tumor sequencing data is available in PODR. We defined 41 features reflecting 15 baseline clinical and demographic characteristics from the EHR and registry, such as age, race, stage, histology, and therapy. We also defined features reflecting 206 clinically actionable somatic variants. We selected 5 important variants for inclusion in the model, as well as the total number of mutations. We trained a random forests algorithm to predict 1-year survival. Precision, recall, and area under the ROC curve (AUC) were assessed using 5-fold cross validation. Results: Mean age at diagnosis was 66 years. The majority of patients had late stage disease (15% stage I, 6% II, 15% III, 44% IV), and 59% of patients received systemic therapy. 45% died within 1 year of diagnosis, and 55% survived past 1 year. Our predictive model for 1-year survival achieves strong results. Cross-validated AUC is 0.79 (SD 0.08), precision is 0.79 (SD 0.07), recall is 0.74 (SD 0.07), suggesting that the trained model combining clinical and genomic features is effective at predicting 1-year survival. Conclusions: By integrating real-world EHR and sequencing data, we built a highly accurate predictive model of 1-year survival in NSCLC patients at the VA. Such a model, after ongoing validation in a larger cohort, offers the ability to make individualized predictions that could inform patient care to improve outcomes.

PH-0716: Radiomics pCR predictive model in rectal cancer: an intercontinental validation on real world data

2020 ◽  
Vol 152 ◽  
pp. S405
Author(s):  
L. Boldrini ◽  
J. Lenkowicz ◽  
L.C. Orlandini ◽  
N. Dinapoli ◽  
G. Yin ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Predictive Model ◽  
Real World ◽  
Real World Data ◽  
World Data

scholarly journals Evaluation of Breast Cancer HER2 Status Accuracy Using A Predictive Model Based on Her-France Real World National Database

2016 ◽  
Vol 19 (7) ◽  
pp. A685
Author(s):  
C Egele ◽  
D Pau ◽  
J Rabut ◽  
D Fetique ◽  
J Martin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Predictive Model ◽  
Real World ◽  
National Database ◽  
Her2 Status ◽  
Model Based

scholarly journals A Framework for Big Data Analytical Process and Mapping—BAProM: Description of an Application in an Industrial Environment

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6014
Author(s):  
Giovanni Gravito de Carvalho Chrysostomo ◽  
Marco Vinicius Bhering de Aguiar Vallim ◽  
Leilton Santos da Silva ◽  
Leandro A. Silva ◽  
Arnaldo Rabello de Aguiar Vallim Filho
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Predictive Model ◽  
Real World ◽  
Decision Support Tool ◽  
Exploratory Analysis ◽  
Process Mapping ◽  
Support Tool ◽  
Analytical Process ◽  
Real World Application

This paper presents an application of a framework for Big Data Analytical Process and Mapping—BAProM—consisting of four modules: Process Mapping, Data Management, Data Analysis, and Predictive Modeling. The framework was conceived as a decision support tool for industrial business, encompassing the whole big data analytical process. The first module incorporates in big data analytical a mapping of processes and variables, which is not common in such processes. This is a proposal that proved to be adequate in the practical application that was developed. Next, an analytical “workbench” was implemented for data management and exploratory analysis (Modules 2 and 3) and, finally, in Module 4, the implementation of artificial intelligence algorithm support predictive processes. The modules are adaptable to different types of industry and problems and can be applied independently. The paper presents a real-world application seeking as final objective the implementation of a predictive maintenance decision support tool in a hydroelectric power plant. The process mapping in the plant identified four subsystems and 100 variables. With the support of the analytical workbench, all variables have been properly analyzed. All underwent a cleaning process and many had to be transformed, before being subjected to exploratory analysis. A predictive model, based on a decision tree (DT), was implemented for predictive maintenance of equipment, identifying critical variables that define the imminence of an equipment failure. This DT model was combined with a time series forecasting model, based on artificial neural networks, to project those critical variables for a future time. The real-world application showed the practical feasibility of the framework, particularly the effectiveness of the analytical workbench, for pre-processing and exploratory analysis, as well as the combined predictive model, proving effectiveness by providing information on future events leading to equipment failures.

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