scholarly journals Attribution Markers and Data Mining in Art Authentication

Molecules ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 70
Author(s):  
Barbara I. Łydżba-Kopczyńska ◽  
Janusz Szwabiński
Keyword(s):  
Continuous Time ◽  
Decision Process ◽  
Time Pressure ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Classification Models ◽  
Promising Technique ◽  
Tree Model ◽  
Global Art

Today’s global art market is a billion-dollar business, attracting not only investors but also forgers. The high number of forged works requires reliable authentication procedures to mitigate the risk of investments. However, with the developments in the methodology, continuous time pressure and the threat of litigation, authenticating artwork is becoming increasingly complex. In this paper, we examined whether the decision process involved in the authenticity examination may be supported by machine learning algorithms. The idea is motivated by existing clinical decision support systems. We used a set of 55 artworks (including 12 forged ones) with determined attribution markers to train a decision tree model. From our preliminary results, it follows that it is a very promising technique able to support art experts. Decision trees are able to summarize the existing knowledge about all investigations and may also be used as a classifier for new paintings with known markers. However, larger datasets with artworks of known provenance are needed to build robust classification models. The method can also utilize the most important markers and, consequently, reduce the costs of investigations.

Using machine learning to optimize selection of elderly patients for endovascular thrombectomy

2019 ◽  
Vol 11 (8) ◽  
pp. 847-851 ◽  
Author(s):  
Ali Alawieh ◽  
Fadi Zaraket ◽  
Mohamed Baker Alawieh ◽  
Arindam Rano Chatterjee ◽  
Alejandro Spiotta
Keyword(s):  
Machine Learning ◽  
Elderly Patients ◽  
Characteristic Curve ◽  
Clinical Decision ◽  
Functional Independence ◽  
Machine Learning Algorithms ◽  
Tree Model ◽  
Vessel Occlusion ◽  
Endovascular Thrombectomy ◽  
Symptomatic Intracerebral Hemorrhage

BackgroundEndovascular thrombectomy (ET) is the standard of care for treatment of acute ischemic stroke (AIS) secondary to large vessel occlusion. The elderly population has been under-represented in clinical trials on ET, and recent studies have reported higher morbidity and mortality in elderly patients than in their younger counterparts.ObjectiveTo use machine learning algorithms to develop a clinical decision support tool that can be used to select elderly patients for ET.MethodsWe used a retrospectively identified cohort of 110 patients undergoing ET for AIS at our institution to train a regression tree model that can predict 90-day modified Rankin Scale (mRS) scores. The identified algorithm, termed SPOT, was compared with other decision trees and regression models, and then validated using a prospective cohort of 36 patients.ResultsWhen predicting rates of functional independence at 90 days, SPOT showed a sensitivity of 89.36% and a specificity of 89.66% with an area under the receiver operating characteristic curve of 0.952. Performance of SPOT was significantly better than results obtained using National Institutes of Health Stroke Scale score, Alberta Stroke Program Early CT score, or patients’ baseline deficits. The negative predictive value for SPOT was >95%, and in patients who were SPOT-negative, we observed higher rates of symptomatic intracerebral hemorrhage after thrombectomy. With mRS scores prediction, the mean absolute error for SPOT was 0.82.ConclusionsSPOT is designed to aid clinical decision of whether to undergo ET in elderly patients. Our data show that SPOT is a useful tool to determine which patients to exclude from ET, and has been implemented in an online calculator for public use.

scholarly journals Kunstmatige intelligentie voor klinische toepassing van beeldvorming in de psychiatrie

2020 ◽  
Author(s):  
Guido van Wingen
Keyword(s):  
Machine Learning ◽  
Psychological Symptoms ◽  
Predictive Biomarkers ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Clinical Applications ◽  
Machine Learning Algorithms ◽  
Brain Scans ◽  
Learning Analysis ◽  
The Brain

The clinical application of neuroimaging for psychological complaints has so far been limited to the exclusion of somatic pathology. Radiological assessment of brain scans usually does not explain the psychological symptoms. However, that does not mean that psychological symptoms have no neurobiological basis. Hope has therefore been placed on functional MRI, which measures the activity of the brain. However, this has not yet resulted in clinical applications. A multivariate approach using machine learning analysis now appears to be changing this. Machine learning algorithms can already automate various tasks in radiology. Recent studies show that machine learning analysis of MRI images can also provide diagnostic, prognostic and predictive biomarkers for psychiatry. Larger studies are needed to develop clinical applications, such as clinical decision support systems to support personalized treatment choices.

scholarly journals Deep Learning, a Not so Magical Problem Solver: A Case Study with Predicting the Complexity of Breast Cancer Cases

2021 ◽  
Author(s):  
My-Anh Le Thien ◽  
Akram Redjdal ◽  
Jacques Bouaud ◽  
Brigitte Seroussi
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Patient Management ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Problem Solver ◽  
Multi Layer Perceptron ◽  
Complex Cases

Using guideline-based clinical decision support systems (CDSSs) has improved clinical practice, especially during multidisciplinary tumour boards (MTBs) in cancer patient management. However, MTBs have been reported to be overcrowded, with limited time to discuss all cases. Complex breast cancer cases that need further MTB discussions should have priority in the organization of MTBs. In order to optimize MTB workflow, we attempted to predict complex cases defined as non-compliant cases despite the use of the decision support system OncoDoc. After previously obtaining insufficient performance with machine learning algorithms, we tested Multi Layer Perceptron for classification, compared various samplers to compensate data imbalance combined with cross- validation, and optimized all models with hyperparameter tuning and feature selection with no improvement and lacklustre results (F1-score: 31.4%).

Save Face or Save Life: Physicians’ Dilemma in Using Clinical Decision Support Systems

2021 ◽  
Author(s):  
Huigang Liang ◽  
Yajiong Xue
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Time Pressure ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Care Quality ◽  
Machine Intelligence ◽  
Perceived Usefulness

Humans think both rationally and heuristically. So do physicians. Clinical decision support systems (CDSSs) provide advice to physicians that could save patients’ lives, but they could also make physicians feel face loss because of submission to machine intelligence, leading to a perplexing dilemma. Thinking rationally, physicians focus on fulfilling their professional duty to save patients and should follow advice from CDSS to improve care quality. Thinking heuristically, they focus on protecting their authoritative image to maintain face and are inclined to avoid embarrassment by resisting CDSS. Through a longitudinal survey and follow-up interviews with a group of Chinese physicians, we find that the dilemma does exist. Moreover, face loss has a stronger effect on CDSS resistance when physicians have high autonomy. When time pressure is high, perceived usefulness more strongly reduces, whereas face loss more strongly increases CDSS resistance, worsening the dilemma. As face is a universal social concern existing in both Eastern and Western cultures, this research generates insights regarding why physicians are slow in adopting information technology innovations.

scholarly journals Systematic Review on the Association of Radiomics with Tumor Biological Endpoints

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3015
Author(s):  
Agustina La Greca Saint-Esteven ◽  
Diem Vuong ◽  
Fabienne Tschanz ◽  
Janita E. van Timmeren ◽  
Riccardo Dal Bello ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Evidence ◽  
Tumor Biology ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Molecular Profile ◽  
Extensive Literature ◽  
Non Invasive ◽  
Surrogate Method

Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

Efficacy and cardiovascular safety of SGLT2 Inhibitors

2020 ◽  
Vol 15 ◽  
Author(s):  
Cornelius James Fernandez ◽  
Abisha Graciano Nevins ◽  
Shasta Nawaz ◽  
Tahir Nazir ◽  
Fahmy W F Hanna
Keyword(s):  
Decision Process ◽  
Cardiovascular Events ◽  
Unmet Need ◽  
Clinical Decision ◽  
Sglt2 Inhibitors ◽  
Renal Protection ◽  
Sodium Glucose Cotransporter ◽  
Patients With Diabetes ◽  
Glucagon Like Peptide ◽  
Glucagon Like Peptide 1

: Patients with diabetes continued to exhibit a high risk for cardiovascular and renal events despite achieving satisfactory glycemic, blood pressure and lipid targets. Studies evaluating new diabetes medications focused on cardiovascular events, largely overlooking heart failure (HF). The latter has recently been recognised as a major cause of morbidity and mortality in patients with diabetes mellitus. There had been an unmet need for drugs with cardiovascular (including HF) and renal protection, with an expectation that an ideal diabetic drug should improve these end points. Moreover, an ideal drug should have weight lowering benefits. Recently published outcome trials have shown that sodium glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like peptide 1 receptor agonists (GLP-1RAs) can reduce cardiovascular and renal events, together with statistically significant weight reduction. As a result, many recently published international guidelines have recommended SGLT2 inhibitors and GLP-1RAs in patients with diabetes and pre-existing cardiovascular disease (CVD). In this review we will critically analyse the efficacy and cardiovascular (CV) safety of SGLT2 inhibitors, based on the available literature to help position them in the clinical decision process.

scholarly journals Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1263
Author(s):  
Samy Ammari ◽  
Raoul Sallé de Chou ◽  
Tarek Assi ◽  
Mehdi Touat ◽  
Emilie Chouzenoux ◽  
...  
Keyword(s):  
Machine Learning ◽  
Therapeutic Option ◽  
Binary Classification ◽  
Progression Free Survival ◽  
Recurrent Glioblastoma ◽  
Machine Learning Algorithms ◽  
Survival Prediction ◽  
Classification Models ◽  
Angiogenic Therapy ◽  
Recurrent Gbm

Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18–80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients.

scholarly journals The acceptance of clinical decision support systems among clinicians in the treatment of neck and/or back pain

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S M Jansen-Kosterink ◽  
M Cabrita ◽  
I Flierman
Keyword(s):  
Back Pain ◽  
Decision Support ◽  
Focus Groups ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Online Questionnaire

Abstract Background Clinical Decision Support Systems (CDSSs) are computerized systems using case-based reasoning to assist clinicians in making clinical decisions. Despite the proven added value to public health, the implementation of CDSS clinical practice is scarce. Particularly, little is known about the acceptance of CDSS among clinicians. Within the Back-UP project (Project Number: H2020-SC1-2017-CNECT-2-777090) a CDSS is developed with prognostic models to improve the management of Neck and/or Low Back Pain (NLBP). Therefore, the aim of this study is to present the factors involved in the acceptance of CDSSs among clinicians. Methods To assess the acceptance of CDSSs among clinicians we conducted a mixed method analysis of questionnaires and focus groups. An online questionnaire with a low-fidelity prototype of a CDSS (TRL3) was sent to Dutch clinicians aimed to identify the factors influencing the acceptance of CDSSs (intention to use, perceived threat to professional autonomy, trusting believes and perceived usefulness). Next to this, two focus groups were conducted with clinicians addressing the general attitudes towards CDSSs, the factors determining the level of acceptance, and the conditions to facilitate use of CDSSs. Results A pilot-study of the online questionnaire is completed and the results of the large evaluation are expected spring 2020. Eight clinicians participated in two focus groups. After being introduced to various types of CDSSs, participants were positive about the value of CDSS in the care of NLBP. The clinicians agreed that the human touch in NLBP care must be preserved and that CDSSs must remain a supporting tool, and not a replacement of their role as professionals. Conclusions By identifying the factors hindering the acceptance of CDSSs we can draw implications for implementation of CDSSs in the treatment of NLBP.

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