scholarly journals Challenges in interpreting allergen microarrays in relation to clinical symptoms: A machine learning approach

2013 ◽  
Vol 25 (1) ◽  
pp. 71-79 ◽  
Author(s):  
Mattia C. F. Prosperi ◽  
Danielle Belgrave ◽  
Iain Buchan ◽  
Angela Simpson ◽  
Adnan Custovic
Keyword(s):  
Machine Learning ◽  
Clinical Symptoms ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Connectomic Underpinnings of Working Memory Deficits in Schizophrenia: Evidence From a replication fMRI study

2020 ◽  
Vol 46 (4) ◽  
pp. 916-926 ◽  
Author(s):  
Jie Yang ◽  
Weidan Pu ◽  
Guowei Wu ◽  
Eric Chen ◽  
Edwin Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Working Memory ◽  
Negative Symptoms ◽  
Clinical Symptoms ◽  
Linear Regression Analysis ◽  
Symptom Burden ◽  
Learning Approach ◽  
Brain Connectome ◽  
Graph Properties ◽  
Machine Learning Approach

Abstract Background Working memory (WM) deficit is a key feature of schizophrenia that relates to a generalized neural inefficiency of extensive brain areas. To date, it remains unknown how these distributed regions are systemically organized at the connectome level and how the disruption of such organization brings about the WM impairment seen in schizophrenia. Methods We used graph theory to examine the neural efficiency of the functional connectome in different granularity in 155 patients with schizophrenia and 96 healthy controls during a WM task. These analyses were repeated in another independent dataset (81 patients and 54 controls). Linear regression analysis was used to test associations of altered graph properties, clinical symptoms, and WM accuracy in patients. A machine-learning approach was adopted to study the ability of multivariate connectome features from one dataset to discriminate patients from controls in the second dataset. Results Small-worldness of the whole-brain connectome was significantly increased in schizophrenia during the WM task; this increase is related to better (though subpar) WM accuracy in patients with more severe negative symptom burden. There was a shift in the degree distribution to a more homogeneous form in patients. The machine-learning approach classified a new set of patients from controls with 84.3% true-positivity rate for schizophrenia and 71.6% overall accuracy. Conclusions We demonstrate a putative mechanistic link between connectome topology, hub redistribution, and impaired n-back performance in schizophrenia. The task-dependent modulation of the connectome relates to, but remains inefficient in, improving the performance above par in the presence of severe negative symptoms.

scholarly journals COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach

2020 ◽  
Author(s):  
Yazeed Zoabi ◽  
Noam Shomron
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Symptoms ◽  
Close Contact ◽  
Infected Individual ◽  
Learning Approach ◽  
Test Results ◽  
Machine Learning Approach ◽  
Simple Features ◽  
Limited Testing

AbstractEffective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed in hopes of assisting medical staff worldwide in triaging patients when allocating limited healthcare resources. We established a machine learning approach that trained on records from 51,831 tested individuals (of whom 4,769 were confirmed COVID-19 cases) while the test set contained data from the following week (47,401 tested individuals of whom 3,624 were confirmed COVID-19 cases). Our model predicts COVID-19 test results with high accuracy using only 8 features: gender, whether the age is above 60, information about close contact with an infected individual, and 5 initial clinical symptoms. Overall, using nationwide data representing the general population, we developed a model that enables screening suspected COVID-19 patients according to simple features accessed by asking them basic questions. Our model can be used, among other considerations, to prioritize testing for COVID-19 when allocating limited testing resources.

scholarly journals Machine learning-based prediction of COVID-19 diagnosis based on symptoms

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yazeed Zoabi ◽  
Shira Deri-Rozov ◽  
Noam Shomron
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Symptoms ◽  
Infected Individual ◽  
Learning Approach ◽  
Test Results ◽  
Risk Of Infection ◽  
Machine Learning Approach ◽  
Binary Features ◽  
Simple Features

AbstractEffective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triaging patients, especially in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were confirmed to have COVID-19). Our model predicted COVID-19 test results with high accuracy using only eight binary features: sex, age ≥60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms. Overall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach
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