scholarly journals Predictive usefulness of PCR testing in different patterns of Covid-19 symptomatology - Analysis of a French cohort of 12,810 outpatients

2020 ◽  
Author(s):  
◽  
Apra Caroline ◽  
Caucheteux Charlotte ◽  
Mensch Arthur ◽  
Mansour Jenny ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Presentation ◽  
Learning Algorithm ◽  
Diagnostic Value ◽  
Machine Learning Algorithm ◽  
Chain Reaction ◽  
Average Precision ◽  
Polymerase Chain ◽  
French Cohort ◽  
Clinical Strategy

AbstractPolymerase Chain reaction (PCR) is a key tool to diagnose Covid-19. Yet access to PCR is often limited. In this paper, we develop a clinical strategy for prescribing PCR to patients based on data from COVIDOM, a French cohort of 54,000 patients with clinically suspected Covid-19 including 12,810 patients tested by PCR. Using a machine learning algorithm (a decision tree), we show that symptoms alone are sufficient to predict PCR outcome with a mean average precision of 86%. We identify combinations of symptoms that are predictive of PCR positivity (90% for anosmia/ageusia) or negativity (only 30% of PCR+ for a subgroup with cardiopulmonary symptoms): in both cases, PCR provides little added diagnostic value. We deduce a prescribing strategy based on clinical presentation that can improve the global efficiency of PCR testing.

scholarly journals Predictive usefulness of RT-PCR testing in different patterns of Covid-19 symptomatology: analysis of a French cohort of 12,810 outpatients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
◽  
Caroline Apra ◽  
Charlotte Caucheteux ◽  
Arthur Mensch ◽  
Jenny Mansour ◽  
...  
Keyword(s):  
Clinical Presentation ◽  
Learning Algorithm ◽  
Diagnostic Value ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Average Precision ◽  
Efficient Test ◽  
Polymerase Chain ◽  
French Cohort ◽  
Clinical Strategy

AbstractReverse transcriptase polymerase chain reaction (RT-PCR) is a key tool to diagnose Covid-19. Yet it may not be the most efficient test in all patients. In this paper, we develop a clinical strategy for prescribing RT-PCR to patients based on data from COVIDOM, a French cohort of 54,000 patients with clinically suspected Covid-19, including 12,810 patients tested by RT-PCR. We use a machine-learning algorithm (decision tree) in order to predict RT-PCR results based on the clinical presentation. We show that symptoms alone are sufficient to predict RT-PCR outcome with a mean average precision of 86%. We identify combinations of symptoms that are predictive of RT-PCR positivity (90% for anosmia/ageusia) or negativity (only 30% of RT-PCR+ for a subgroup with cardiopulmonary symptoms): in both cases, RT-PCR provides little added diagnostic value. We propose a prescribing strategy based on clinical presentation that can improve the global efficiency of RT-PCR testing.

scholarly journals QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants

2020 ◽  
Vol 10 (7) ◽  
pp. 2411-2421
Author(s):  
Fan Lin ◽  
Elena Z. Lazarus ◽  
Seung Y. Rhee
Keyword(s):  
Machine Learning ◽  
Linkage Mapping ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Causal Gene ◽  
Training Set ◽  
Average Precision ◽  
Trait Improvement ◽  
Causal Genes ◽  
Mapping Process

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as the training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

scholarly journals Clinical presentation of COVID-19 – a model derived by a machine learning algorithm

2021 ◽  
Vol 18 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Malik Yousef ◽  
Louise C. Showe ◽  
Izhar Ben Shlomo
Keyword(s):  
Machine Learning ◽  
Clinical Presentation ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Risk Groups ◽  
Verbal Description ◽  
Machine Learning Algorithm ◽  
Proper Selection ◽  
Selection Of

Abstract COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, infected, and hospitalized is fragmentary making it difficult to easily select those most likely to be infected. The Israeli Ministry of Health made public its registry of immediate clinical data and the respective status of infected/not infected for all viral DNA tests performed up to Apr. 18th, 2020 including almost 120,000 tests. We used a machine-learning algorithm to find out which immediate clinical elements mattered the most in identifying the true status of the tested persons including age or gender matter, to enable future better allocation of surveillance policy for those belonging to high-risk groups. In addition to the analyses applied on the first batch of the available data (Apr. 11th), we further tested the algorithm on the independent second batch (Apr. 12th to 18th). Fever, cough and headache were the most diagnostic, differing in degree of importance in different subgroups. Higher percentage of men were found positive (9.3 vs. 7.3%), but gender did not matter for the clinical presentation. The prediction power of the model was high, with accuracy of 0.84 and area under the curve 0.92. We provide a hand-held short checklist with verbal description of importance for the leading symptoms, which should expedite the triage and enable proper selection of people for further follow-up.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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