scholarly journals A multimodality test to guide the management of patients with a pancreatic cyst

2019 ◽  
Vol 11 (501) ◽  
pp. eaav4772 ◽  
Author(s):  
Simeon Springer ◽  
David L. Masica ◽  
Marco Dal Molin ◽  
Christopher Douville ◽  
Christopher J. Thoburn ◽  
...  
Keyword(s):  
Management Practices ◽  
Pancreatic Cyst ◽  
Standard Of Care ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Pancreatic Cysts ◽  
Current Standard ◽  
Imaging Characteristics ◽  
Using Data ◽  
Multimodality Test

Pancreatic cysts are common and often pose a management dilemma, because some cysts are precancerous, whereas others have little risk of developing into invasive cancers. We used supervised machine learning techniques to develop a comprehensive test, CompCyst, to guide the management of patients with pancreatic cysts. The test is based on selected clinical features, imaging characteristics, and cyst fluid genetic and biochemical markers. Using data from 436 patients with pancreatic cysts, we trained CompCyst to classify patients as those who required surgery, those who should be routinely monitored, and those who did not require further surveillance. We then tested CompCyst in an independent cohort of 426 patients, with histopathology used as the gold standard. We found that clinical management informed by the CompCyst test was more accurate than the management dictated by conventional clinical and imaging criteria alone. Application of the CompCyst test would have spared surgery in more than half of the patients who underwent unnecessary resection of their cysts. CompCyst therefore has the potential to reduce the patient morbidity and economic costs associated with current standard-of-care pancreatic cyst management practices.

Management of Cystic Neoplasms of the Pancreas

2018 ◽  
Author(s):  
Victoria R Rendell ◽  
Walker A Julliard ◽  
Adam M Awe ◽  
Daniel E Abbott ◽  
Emily R Winslow ◽  
...  
Keyword(s):  
Pancreatic Cyst ◽  
Cystic Neoplasm ◽  
Pancreatic Cysts ◽  
Multidisciplinary Teams ◽  
Pancreatic Cystic Neoplasm ◽  
Cross Sectional ◽  
Management Guidelines ◽  
Imaging Characteristics ◽  
Cross Sectional Imaging ◽  
Sectional Imaging

The diagnosis of pancreatic cystic lesions is increasingly common. The majority of pancreatic cysts are now diagnosed incidentally on cross-sectional imaging. Lack of clear evidence-based guidelines and overall poor understanding of the natural history of pancreatic cysts contribute to complexity of managing patients with pancreatic cysts. Pancreatic cystic neoplasm types differ in their presentation, histologic features, imaging characteristics, and predisposition to develop invasive malignancy. The diagnostic strategies to determine cyst type and presence of malignancy—cross-sectional imaging, endoscopic ultrasonography, and analyses of pancreatic cyst fluid aspirates—have improved over time. However, accurate characterization of cysts remains challenging. Several large groups, including the American College of Radiology, the American Gastroenterological Association, the European Study Group on Cystic Tumours of the Pancreas, and the International Association of Pancreatology, have released cyst management guidelines or recommendations that have important differences. In this review, we provide an overview of the most common pancreatic cystic neoplasm, evaluate recent advancements in diagnostic techniques, and compare current management guidelines. This review contains 7 figures, 5 tables, and 77 references. Key Words: intraductal papillary mucinous neoplasm, management guidelines, multidisciplinary teams, mucinous cystic neoplasm, pancreatic cyst, pancreatic cystic neoplasm, serous cystadenoma, solid pseudopapillary neoplasm, surgical oncology 

scholarly journals Caregiver Support in Fibrodysplasia Ossificans Progressiva

2021 ◽  
Vol 6 (1) ◽  
pp. 6-12
Author(s):  
Mona Al Mukaddam ◽  
Kin Cheung ◽  
Sammi Kile ◽  
Michelle Davis ◽  
Frederick S. Kaplan ◽  
...  
Keyword(s):  
Soft Tissues ◽  
Functional Limitations ◽  
Standard Of Care ◽  
Fibrodysplasia Ossificans Progressiva ◽  
Full Time ◽  
Personal Care ◽  
Current Standard ◽  
Caregiver Support ◽  
Part Time ◽  
Using Data

Background:Fibrodysplasia ossificans progressiva (FOP) is an ultra-rare disease characterized by malformed great toes and progressive heterotopic ossification (HO) in soft tissues. Current standard-of-care is aimed at palliation of symptoms; there are no currently approved therapies to prevent HO. Recurrent episodes of HO starting in early life lead to cumulative disability, severe functional limitations, and shortened life span. Most individuals require assistive devices and extensive caregiver support before the second decade of life. Caregiver support is thought to be high, but the timing and extent of caregiver support in FOP has not been formally assessed. Methods: Using data from the International FOP Association (IFOPA) Global Registry on 299 patients (median age 21 years; range 0.1 to 78 years) from 54 countries, we characterized the extent of caregiver support by assessing the number of part-time and full-time caregivers and school aides reported by participants, based on age. Results: Over 50% of FOP Registry respondents reported a need for part-time or full-time home personal care attendants. The percentage of individuals who reported a requirement for bathing attendants and part- or full-time home personal care attendants increased with age (>1 part-time or full-time caregiver exceeded 30% for individuals >15 years of age), as did the number of part-time or full-time attendants. Support from school aides peaked between 9 and 15 years of age. Conclusion: Caregiver support in FOP is high in terms of time and amount of support needed, increases rapidly with age, and is substantial by the second decade of life. These findings highlight the urgent need for transformative treatments that will preserve the independence of individuals with FOP.

scholarly journals Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak

2021 ◽  
Author(s):  
Massimiliano Greco ◽  
Giovanni Angelotti ◽  
Pier Francesco Caruso ◽  
Alberto Zanella ◽  
Niccolò Stomeo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Chronic Obstructive ◽  
Complex Data ◽  
Icu Admission ◽  
Lower Performance ◽  
Learning Techniques ◽  
Using Data

Abstract Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subsequently spread globally. COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making, considering that baseline comorbidities, age, and patient conditions at admission have been associated to poorer outcomes. Supervised machine learning techniques are increasingly diffuse in clinical medicine and can predict mortality and test associations reaching high predictive performance. We assessed performances of a machine learning approach to predict mortality in COVID-19 patients admitted to ICU using data from the Lombardy ICU Network.Methods: this is a secondary analysis of prospectively collected data from Lombardy ICU network. To predict survival at 7-,14- and 28 days we built two different models; model A included patient demographics, medications before admission and comorbidities, while model B also included the data of the first day since ICU admission. 10-fold cross validation was repeated 2500 times, to ensure optimal hyperparameter choice. The only constrain imposed to model optimization was the choice of logistic regression as final layer to increase clinical interpretability. Different imputation and over-sampling techniques were employed in model training.Results 1503 patients were included, with 766 deaths (51%). Exploratory analysis and Kaplan-Meier curves demonstrated mortality association with age and gender. Model A and B reached the greatest predictive performance at 28 days (AUC 0.77 and 0.79), with lower performance at 14 days (AUC 0.72 and 0.74) and 7 days (AUC 0.68 and 0.71). Male gender, age and number of comorbidities were strongly associated with mortality in both models. Among comorbidities, chronic kidney disease and chronic obstructive pulmonary disease demonstrated association. Mode of ventilatory assistance at ICU admission and Fraction of Inspired oxygen were associated with mortality in model B.Conclusions Supervised machine learning models demonstrated good performance in prediction of 28-day mortality. 7-days and 14-days predictions demonstrated lower performance. Machine learning techniques may be useful in emergency phases to reach higher predictive performance with reduced human supervision using complex data.

scholarly journals Optimization of PV Systems Using Data Mining and Regression Learner MPPT Techniques

2018 ◽  
Vol 10 (3) ◽  
pp. 1080
Author(s):  
Adedayo M. Farayola ◽  
Ali N Hasan ◽  
Ahmed Ali
Keyword(s):  
Gaussian Process Regression ◽  
Weather Conditions ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Improved Performance ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Power Point

<span>Supervised machine learning techniques such as artificial neural network (ANN) and ANFIS are powerful tools used to track the maximum power point (MPPT) in photovoltaic systems. However, these offline MPPT techniques still require large and accurate training data sets for successful tracking. This paper presents an innovative use of rational quadratic gaussian process regression (RQGPR) technique to generate the large and very accurate training data required for MPPT task. To confirm the effectiveness of the RQGPR technique, the combination of ANN and RQGPR as ANN-RQGPR technique results were compared with the conventional ANN technique results, and that of combined ANN and linear support vector machine regression as ANN-LSVM technique results under different weather conditions. Results show that ANN-RQGPR technique produced the overall best result and with an improved performance. </span>

Acute Rejection Following Kidney Transplantation: State-of-the-Art and Future Perspectives

2020 ◽  
Vol 26 (28) ◽  
pp. 3468-3496
Author(s):  
Emilio Rodrigo ◽  
Marcio F. Chedid ◽  
David San Segundo ◽  
Juan C.R. San Millán ◽  
Marcos López-Hoyos
Keyword(s):  
Kidney Transplantation ◽  
Acute Rejection ◽  
Rescue Therapy ◽  
Standard Of Care ◽  
Rejection Rate ◽  
New Drugs ◽  
High Dose ◽  
Current Standard ◽  
Antibody Mediated Rejection ◽  
Cellular Rejection

: Although acute renal graft rejection rate has declined in the last years, and because an adequate therapy can improve graft outcome, its therapy remains as one of the most significant challenges for pharmacists and physicians taking care of transplant patients. Due to the lack of evidence highlighted by the available metaanalyses, we performed a narrative review focused on the basic mechanisms and current and future therapies of acute rejection in kidney transplantation. : According to Kidney Disease/Improving Global Outcomes (KDIGO) guidelines, both clinical and subclinical acute rejection episodes should be treated. Usually, high dose steroids and basal immunosuppression optimization are the first line of therapy in treating acute cellular rejection. Rabbit antithymocytic polyclonal globulins are used as rescue therapy for recurrent or steroid-resistant cellular rejection episodes. Current standard-of-care (SOC) therapy for acute antibody-mediated rejection (AbMR) is the combination of plasma exchange with intravenous immunoglobulin (IVIG). Since a significant rate of AbMR does not respond to SOC, different studies have analyzed the role of new drugs such as Rituximab, Bortezomib, Eculizumab and C1 inhibitors. Lack of randomized controlled trials and heterogenicity among performed studies limit obtaining definite conclusions. Data about new direct and indirect B cell and plasma cell depleting agents, proximal and terminal complement blockers, IL-6/IL-6R pathway inhibitors and antibody removal agents, among other promising drugs, are reviewed.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

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