Predictors of Success in the Proof-of-Concept Program of the European Research Council: An Automated Machine Learning Model of Application and Winning

2021 ◽  
Author(s):  
Marco Seeber ◽  
Ilan Alon ◽  
David Pina ◽  
Fredrik Niclas Piro ◽  
Michele Seeber
Keyword(s):  
Machine Learning ◽  
Research Council ◽  
Learning Model ◽  
European Research ◽  
Proof Of Concept ◽  
Predictors Of Success ◽  
European Research Council ◽  
Machine Learning Model ◽  
Automated Machine Learning

scholarly journals Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Milos Kotlar ◽  
Marija Punt ◽  
Zaharije Radivojevic ◽  
Milos Cvetanovic ◽  
Veljko Milutinovic
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Anomaly Detection ◽  
Learning Model ◽  
Machine Learning Model ◽  
Automated Machine Learning

scholarly journals Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2102
Author(s):  
Eyal Klang ◽  
Robert Freeman ◽  
Matthew A. Levin ◽  
Shelly Soffer ◽  
Yiftach Barash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Acute Diverticulitis ◽  
External Validation ◽  
White Blood Cells ◽  
Learning Model ◽  
Complicated Diverticulitis ◽  
Gradient Boosting ◽  
Proof Of Concept ◽  
Machine Learning Model

Background & Aims: We aimed at identifying specific emergency department (ED) risk factors for developing complicated acute diverticulitis (AD) and evaluate a machine learning model (ML) for predicting complicated AD. Methods: We analyzed data retrieved from unselected consecutive large bowel AD patients from five hospitals from the Mount Sinai health system, NY. The study time frame was from January 2011 through March 2021. Data were used to train and evaluate a gradient-boosting machine learning model to identify patients with complicated diverticulitis, defined as a need for invasive intervention or in-hospital mortality. The model was trained and evaluated on data from four hospitals and externally validated on held-out data from the fifth hospital. Results: The final cohort included 4997 AD visits. Of them, 129 (2.9%) visits had complicated diverticulitis. Patients with complicated diverticulitis were more likely to be men, black, and arrive by ambulance. Regarding laboratory values, patients with complicated diverticulitis had higher levels of absolute neutrophils (AUC 0.73), higher white blood cells (AUC 0.70), platelet count (AUC 0.68) and lactate (AUC 0.61), and lower levels of albumin (AUC 0.69), chloride (AUC 0.64), and sodium (AUC 0.61). In the external validation cohort, the ML model showed AUC 0.85 (95% CI 0.78–0.91) for predicting complicated diverticulitis. For Youden’s index, the model showed a sensitivity of 88% with a false positive rate of 1:3.6. Conclusions: A ML model trained on clinical measures provides a proof of concept performance in predicting complications in patients presenting to the ED with AD. Clinically, it implies that a ML model may classify low-risk patients to be discharged from the ED for further treatment under an ambulatory setting.

scholarly journals Automated Machine Learning Overview

2019 ◽  
Vol 27 (45) ◽  
pp. 107-112
Author(s):  
Roman Budjač ◽  
Marcel Nikmon ◽  
Peter Schreiber ◽  
Barbora Zahradníková ◽  
Dagmar Janáčová
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Learning Model ◽  
Machine Learning Method ◽  
Learning Method ◽  
Learning Tasks ◽  
Machine Learning Model ◽  
Automated Machine Learning ◽  
Expertise Level

Abstract This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several datasets, and demonstrated several automated machine learning features, as well as discussed the issue deeper.

scholarly journals ORKG: Facilitating the Transfer of Research Results with the Open Research Knowledge Graph

2021 ◽  
Vol 7 ◽  
Author(s):  
Sören Auer ◽  
Markus Stocker ◽  
Lars Vogt ◽  
Grischa Fraumann ◽  
Alexandra Garatzogianni
Keyword(s):  
Research Council ◽  
Knowledge Graph ◽  
European Research ◽  
Proof Of Concept ◽  
Research Knowledge ◽  
European Research Council ◽  
Research Results ◽  
Original Proposal ◽  
Open Research

This document is an edited version of the original funding proposal entitled 'ORKG: Facilitating the Transfer of Research Results with the Open Research Knowledge Graph' that was submitted to the European Research Council (ERC) Proof of Concept (PoC) Grant in September 2020 (https://erc.europa.eu/funding/proof-concept). The proposal was evaluated by five reviewers and has been placed after the evaluations on the reserve list. The main document of the original proposal did not contain an abstract.

scholarly journals A Physician-in-the-Loop Approach by Means of Machine Learning for the Diagnosis of Lymphocytosis in the Clinical Laboratory

2021 ◽  
Author(s):  
Laura Bigorra ◽  
Iciar Larriba ◽  
Ricardo Gutiérrez-Gallego
Keyword(s):  
Machine Learning ◽  
Clinical Laboratory ◽  
Complete Blood Count ◽  
Learning Model ◽  
Cost Ratio ◽  
Proof Of Concept ◽  
Data Set ◽  
Diagnostic Impact ◽  
Diagnostic Categories ◽  
Machine Learning Model

Context.— The goal of the lymphocytosis diagnosis approach is its classification into benign or neoplastic categories. Nevertheless, a nonnegligible percentage of laboratories fail in that classification. Objective.— To design and develop a machine learning model by using objective data from the DxH 800 analyzer, including cell population data, leukocyte and absolute lymphoid counts, hemoglobin concentration, and platelet counts, besides age and sex, with classification purposes for lymphocytosis diagnosis. Design.— A total of 1565 samples were included from 10 different lymphoid categories grouped into 4 diagnostic categories: normal controls (458), benign causes of lymphocytosis (567), neoplastic lymphocytosis (399), and spurious causes of lymphocytosis (141). The data set was distributed in a 60-20-20 scheme for training, testing, and validation stages. Six machine learning models were built and compared, and the selection of the final model was based on the minimum generalization error and 10-fold cross validation accuracy. Results.— The selected neural network classifier rendered a global 10-class classification validation accuracy corresponding to 89.9%, which, considering the aforementioned 4 diagnostic categories, presented a diagnostic impact accuracy corresponding to 95.8%. Finally, a prospective proof of concept was performed with 100 new cases with a global diagnostic accuracy corresponding to 91%. Conclusions.— The proposed machine learning model was feasible, with a high benefit-cost ratio, as the results were obtained within the complete blood count with differential. Finally, the diagnostic impact with high accuracies in both model validation and proof of concept encourages exploration of the model for real-world application on a daily basis.

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