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

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.

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Proof of concept: Calibration of an overhead line conductors’ movements simulation model using Ensemble-based machine learning model

IEEE Access ◽

10.1109/access.2021.3131391 ◽

2021 ◽

pp. 1-1

Author(s):

Hamdi Amroun ◽

Mehdi Ammi ◽

Fikri Hafid

Keyword(s):

Machine Learning ◽

Simulation Model ◽

Learning Model ◽

Proof Of Concept ◽

Overhead Line ◽

Machine Learning Model

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Automated Machine Learning Overview

Research Papers Faculty of Materials Science and Technology Slovak University of Technology ◽

10.2478/rput-2019-0033 ◽

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.

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Development of a machine learning model to predict bleed in esophageal varices in compensated advanced chronic liver disease: A proof of concept.

Journal of Gastroenterology and Hepatology ◽

10.1111/jgh.15560 ◽

2021 ◽

Author(s):

Samagra Agarwal ◽

Sanchit Sharma ◽

Manoj Kumar ◽

Shantan Venishetty ◽

Ankit Bhardwaj ◽

...

Keyword(s):

Machine Learning ◽

Liver Disease ◽

Chronic Liver Disease ◽

Esophageal Varices ◽

Learning Model ◽

Chronic Liver ◽

Proof Of Concept ◽

Machine Learning Model

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

Research Ideas and Outcomes ◽

10.3897/rio.7.e68513 ◽

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.

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

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2021-0044-oa ◽

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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