scholarly journals Accessing and applying molecular history

2015 ◽  
Author(s):  
Joshua G Stern ◽  
Eric A Gaucher
Keyword(s):  
Machine Learning ◽  
Molecular Evolution ◽  
Real World ◽  
Evolutionary History ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
History Of ◽  
Evolution And Medicine ◽  
Real World Problems

Studying the evolutionary history of life’s molecules - DNA, RNA, and protein - reveals nature-based solutions to real-world problems. We discuss an approach to applied molecular evolution that is well-known within the field but may be unfamiliar to a wider audience. Using a case study at the intersection of molecular evolution and medicine, we introduce the fundamental concepts of orthology and paralogy. We also explain a practical entry point to molecular evolution named STORI: Selectable Taxon Ortholog Retrieval Iteratively. STORI is a machine learning algorithm designed to clear a bottleneck that researchers encounter when studying evolution.

scholarly journals Accessing and applying molecular history

2015 ◽  
Author(s):  
Joshua G Stern ◽  
Eric A Gaucher
Keyword(s):  
Machine Learning ◽  
Molecular Evolution ◽  
Real World ◽  
Evolutionary History ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
History Of ◽  
Evolution And Medicine ◽  
Real World Problems

Studying the evolutionary history of life’s molecules - DNA, RNA, and protein - reveals nature-based solutions to real-world problems. We discuss an approach to applied molecular evolution that is well-known within the field but may be unfamiliar to a wider audience. Using a case study at the intersection of molecular evolution and medicine, we introduce the fundamental concepts of orthology and paralogy. We also explain a practical entry point to molecular evolution named STORI: Selectable Taxon Ortholog Retrieval Iteratively. STORI is a machine learning algorithm designed to clear a bottleneck that researchers encounter when studying evolution.

Empirical Comparison of Various Discretization Procedures

1998 ◽  
Vol 12 (07) ◽  
pp. 1017-1032 ◽  
Author(s):  
Petr Berka ◽  
Ivan Bruha
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Machine Learning Algorithm ◽  
Empirical Comparison ◽  
Numerical Attributes ◽  
Real World Problems ◽  
Discretization Procedure

The genuine symbolic machine learning (ML) algorithms are capable of processing symbolic, categorial data only. However, real-world problems, e.g. in medicine or finance, involve both symbolic and numerical attributes. Therefore, there is an important issue of ML to discretize (categorize) numerical attributes. There exist quite a few discretization procedures in the ML field. This paper describes two newer algorithms for categorization (discretization) of numerical attributes. The first one is implemented in the KEX (Knowledge EXplorer) as its preprocessing procedure. Its idea is to discretize the numerical attributes in such a way that the resulting categorization corresponds to KEX knowledge acquisition algorithm. Since the categorization for KEX is done "off-line" before using the KEX machine learning algorithm, it can be used as a preprocessing step for other machine learning algorithms, too. The other discretization procedure is implemented in CN4, a large extension of the well-known CN2 machine learning algorithm. The range of numerical attributes is divided into intervals that may form a complex generated by the algorithm as a part of the class description. Experimental results show a comparison of performance of KEX and CN4 on some well-known ML databases. To make the comparison more exhibitory, we also used the discretization procedure of the MLC++ library. Other ML algorithms such as ID3 and C4.5 were run under our experiments, too. Then, the results are compared and discussed.

Twitter Users' Classification Based on Interest

2020 ◽  
Vol 10 (1) ◽  
pp. 1-12
Author(s):  
Noura A. AlSomaikhi ◽  
Zakarya A. Alzamil
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Knowledge Sharing ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Software System ◽  
Web Based ◽  
Twitter Users

Microblogging platforms, such as Twitter, have become a popular interaction media that are used widely for different daily purposes, such as communication and knowledge sharing. Understanding the behaviors and interests of these platforms' users become a challenge that can help in different areas such as recommendation and filtering. In this article, an approach is proposed for classifying Twitter users with respect to their interests based on their Arabic tweets. A Multinomial Naïve Bayes machine learning algorithm is used for such classification. The proposed approach has been developed as a web-based software system that is integrated with Twitter using Twitter API. An experimental study on Arabic tweets has been investigated on the proposed system as a case study.

scholarly journals Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals

2020 ◽  
Vol 27 (1) ◽  
pp. e100109 ◽  
Author(s):  
Hoyt Burdick ◽  
Eduardo Pino ◽  
Denise Gabel-Comeau ◽  
Andrea McCoy ◽  
Carol Gu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Severe Sepsis ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Clinical Outcomes ◽  
Real World ◽  
Learning Algorithm ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Machine Learning Algorithm

BackgroundSevere sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat.ObjectiveThe purpose of this study was to evaluate the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay and 30-day readmission.DesignProspective clinical outcomes evaluation.SettingEvaluation was performed on a multiyear, multicentre clinical data set of real-world data containing 75 147 patient encounters from nine hospitals across the continental USA, ranging from community hospitals to large academic medical centres.ParticipantsAnalyses were performed for 17 758 adult patients who met two or more systemic inflammatory response syndrome criteria at any point during their stay (‘sepsis-related’ patients).InterventionsMachine learning algorithm for severe sepsis prediction.Outcome measuresIn-hospital mortality, length of stay and 30-day readmission rates.ResultsHospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay and a 22.7% reduction in 30-day readmission rate for sepsis-related patient stays when using the machine learning algorithm in clinical outcomes analysis.ConclusionsReductions of in-hospital mortality, hospital length of stay and 30-day readmissions were observed in real-world clinical use of the machine learning-based algorithm. The predictive algorithm may be successfully used to improve sepsis-related outcomes in live clinical settings.Trial registration numberNCT03960203

scholarly journals Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

2018 ◽  
Author(s):  
Hoyt Burdick ◽  
Eduardo Pino ◽  
Denise Gabel-Comeau ◽  
Andrea McCoy ◽  
Carol Gu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Severe Sepsis ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Retrospective Analysis ◽  
Real World ◽  
Learning Algorithm ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Machine Learning Algorithm

AbstractObjectiveTo validate performance of a machine learning algorithm for severe sepsis determination up to 48 hours before onset, and to evaluate the effect of the algorithm on in-hospital mortality, hospital length of stay, and 30-day readmission.SettingThis cohort study includes a combined retrospective analysis and clinical outcomes evaluation: a dataset containing 510,497 patient encounters from 461 United States health centers for retrospective analysis, and a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals for clinical outcomes evaluation.ParticipantsFor retrospective analysis, 270,438 adult patients with at least one documented measurement of five out of six vital sign measurements were included. For clinical outcomes analysis, 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay were included.ResultsAt severe sepsis onset, the MLA demonstrated an AUROC of 0.91 (95% CI 0.90, 0.92), which exceeded those of MEWS (0.71, P<001), SOFA (0.74; P<.001), and SIRS (0.62; P<.001). For severe sepsis prediction 48 hours in advance of onset, the MLA achieved an AUROC of 0.77 (95% CI 0.73, 0.80). For the clinical outcomes study, when using the MLA, hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.ConclusionsThe MLA accurately predicts severe sepsis onset up to 48 hours in advance using only readily available vital signs in retrospective validation. Reductions of in-hospital mortality, hospital length of stay, and 30-day readmissions were observed in real-world clinical use of the MLA. Results suggest this system may improve severe sepsis detection and patient outcomes over the use of rules-based sepsis detection systems.KEY POINTSQuestionIs a machine learning algorithm capable of accurate severe sepsis prediction, and does its clinical implementation improve patient mortality rates, hospital length of stay, and 30-day readmission rates?FindingsIn a retrospective analysis that included datasets containing a total of 585,644 patient encounters from 461 hospitals, the machine learning algorithm demonstrated an AUROC of 0.93 at time of severe sepsis onset, which exceeded those of MEWS (0.71), SOFA (0.74), and SIRS (0.62); and an AUROC of 0.77 for severe sepsis prediction 48 hours in advance of onset. In an analysis of real-world data from nine hospitals across 75,147 patient encounters, use of the machine learning algorithm was associated with a 39.5% reduction in in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.MeaningThe accurate and predictive nature of this algorithm may encourage early recognition of patients trending toward severe sepsis, and therefore improve sepsis related outcomes.STRENGTHS AND LIMITATIONS OF THIS STUDYA retrospective study of machine learning severe sepsis prediction from a dataset with 510,497 patient encounters demonstrates high accuracy up to 48 hours prior to onset.A multicenter clinical study of real-world data using this machine learning algorithm for severe sepsis alerts achieved reductions of in-hospital mortality, length of stay, and 30-day readmissions.The required presence of an ICD-9 code to classify a patient as severely septic in our retrospective analysis potentially limits our ability to accurately classify all patients.Only adults in US hospitals were included in this study.For the real-world section of the study, we cannot eliminate the possibility that implementation of a sepsis algorithm raised general awareness of sepsis within a hospital, which may lead to higher recognition of septic patients, independent of algorithm performance.

Sign in / Sign up

Export Citation Format

Share Document