Search results

1

A different way to diagnosis acute appendicitis: machine learning

100%

Harmantepe A. T., Dikicier E., Gönüllü E., Ozdemir K., Kamburoğlu M. B., Yigit M.

Polish Journal of Surgery

|

2023

|

vol. 96

|

issue 2

38-42

EN

Indroduction: Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. Aim: Our aim is to predict acute appendicitis, which is the most common indication for emergency surgery, using machine learning algorithms with an easy and inexpensive method. Materials and methods: Patients who were treated surgically with a prediagnosis of acute appendicitis in a single center between 2011 and 2021 were analyzed. Patients with right lower quadrant pain were selected. A total of 189 positive and 156 negative appendectomies were found. Gender and hemogram were used as features. Machine learning algorithms and data analysis were made in Python (3.7) programming language. Results: Negative appendectomies were found in 62% (n = 97) of the women and in 38% (n = 59) of the men. Positive appendectomies were present in 38% (n = 72) of the women and 62% (n = 117) of the men. The accuracy in the test data was 82.7% in logistic regression, 68.9% in support vector machines, 78.1% in k-nearest neighbors, and 83.9% in neural networks. The accuracy in the voting classifier created with logistic regression, k-nearest neighbor, support vector machines, and artificial neural networks was 86.2%. In the voting classifier, the sensitivity was 83.7% and the specificity was 88.6%. Conclusions: The results of our study show that machine learning is an effective method for diagnosing acute appendicitis. This study presents a practical, easy, fast, and inexpensive method to predict the diagnosis of acute appendicitis.

2

Design of a Machine Learning Based Predictive Analytics System for Spam Problem

100%

Yüksel A., Çankaya Ş., Üncü İ.

Acta Physica Polonica A

|

2017

|

vol. 132

|

issue 3

500-504

EN

Spamming is the act of abusing an electronic messaging system by sending unsolicited bulk messages. Filtering of these messages is merely another line of defence and does not prevent spam messages from circulating in email systems. This problem causes users to distrust email systems, suspect even legitimate emails and leads to substantial investment in technologies to counter the spam problem. Spammers threaten users by abusing the lack of accountability and verification features of communicating entities. To contribute to the fight against spamming, a cloud-based system that analyses the email server logs and uses predictive analytics with machine learning to build trust identities that model the email messaging behavior of spamming and legitimate servers has been designed. The system constructs trust models for servers, updating them regularly to tune the models. This study proposed that this approach will not only minimize the circulation of spam in email messaging systems, but will also be a novel step in the direction of trust identities and accountability in email infrastructure.

3

Artificial intelligence as a tool for addressing racial and socioeconomic inequalities in cardiac arrest: a systematic review

100%

Burdett D., Furey P.

Critical Care Innovations

|

2025

|

vol. 8

|

issue 1

16-32

EN

INTRODUCTION: To determine whether artificial intelligence (AI) interventions have the potential to mitigate racial and socioeconomic disparities in out-of-hospital cardiac arrests (OHCA).OHCA remains a leading cause of mortality globally, with survival rates significantly influenced by systemic healthcare inequalities. Research has identified significant disparities in OHCAs, ethnic minorities have double the risk of OHCA mortality in comparison to Caucasians, and this data has not changed in over 30 years. The AI revolution has captivated physicians with its ability to improve outcomes. The question now pivots to whether it can ameliorate disparities which have been ever present in medical history. This study aims to explore the role of AI in identifying these disparities in the context of OHCA focused on potential benefits and limitations of AI when applied. MATERIALS AND METHODS: A systematic search was conducted in CINAHL and Medline databases, restricted to studies published in the last five years. The search found 164 papers, narrowed to 20 after applying inclusion and exclusion criteria. Seven studies were identified as relevant to the question, focused on AI’s role in addressing disparities in OHCA outcomes. RESULTS: Two overarching themes were identified to encapsulate current research that can be utilised to promote awareness and mitigate ensuring disparities in OHCAs: racial and socioeconomic disparities identified by AI, and implications of AI interventions in OHCA. AI demonstrates the capacity to identify disparities through geographic and demographic predictors but is limited in its ability due to gaps in implementation and ethical programming practices. CONCLUSIONS: The intersectionality of high-risk socioeconomic status and ethnic group minorities in OHCA prevalence is evident, where low-income black sociodemographic contend with incomparable vulnerability. AI presents many opportunities for emergency medicine. However, at present AI should not be implemented as it would only widen disparities and not mitigate due to a lack of relational ethics in the programming process and evolving AI research failing to acknowledge race and socioeconomic characteristics as influential variables on OHCA outcomes.

4

Artificial intelligence as a tool for addressing racial and socioeconomic inequalities in cardiac arrest: a systematic review.

100%

Burdett D., Furey P.

Critical Care Innovations

|

2025

|

vol. 8

|

issue 1

16-32

EN

INTRODUCTION: To determine whether artificial intelligence (AI) interventions have the potential to mitigate racial and socioeconomic disparities in out-of-hospital cardiac arrests (OHCA).OHCA remains a leading cause of mortality globally, with survival rates significantly influenced by systemic healthcare inequalities. Research has identified significant disparities in OHCAs, ethnic minorities have double the risk of OHCA mortality in comparison to Caucasians, and this data has not changed in over 30 years. The AI revolution has captivated physicians with its ability to improve outcomes. The question now pivots to whether it can ameliorate disparities which have been ever present in medical history. This study aims to explore the role of AI in identifying these disparities in the context of OHCA focused on potential benefits and limitations of AI when applied. MATERIALS AND METHODS: A systematic search was conducted in CINAHL and Medline databases, restricted to studies published in the last five years. The search found 164 papers, narrowed to 20 after applying inclusion and exclusion criteria. Seven studies were identified as relevant to the question, focused on AI’s role in addressing disparities in OHCA outcomes. RESULTS: Two overarching themes were identified to encapsulate current research that can be utilised to promote awareness and mitigate ensuring disparities in OHCAs: racial and socioeconomic disparities identified by AI, and implications of AI interventions in OHCA. AI demonstrates the capacity to identify disparities through geographic and demographic predictors but is limited in its ability due to gaps in implementation and ethical programming practices. CONCLUSIONS: The intersectionality of high-risk socioeconomic status and ethnic group minorities in OHCA prevalence is evident, where low-income black sociodemographic contend with incomparable vulnerability. AI presents many opportunities for emergency medicine. However, at present AI should not be implemented as it would only widen disparities and not mitigate due to a lack of relational ethics in the programming process and evolving AI research failing to acknowledge race and socioeconomic characteristics as influential variables on OHCA outcomes.

PL

WSTĘP: Aby określić, czy interwencje sztucznej inteligencji (AI) mają potencjał łagodzenia różnic rasowych i społeczno-ekonomicznych w przypadku pozaszpitalnych zatrzymań krążenia (OHCA). OHCA pozostaje główną przyczyną śmiertelności na świecie, a wskaźniki przeżywalności są w znacznym stopniu uzależnione od systemowych nierówności w opiece zdrowotnej. Badania wykazały znaczne różnice w przypadku OHCA, mniejszości etniczne mają dwukrotnie większe ryzyko śmiertelności z powodu OHCA w porównaniu do rasy białej, a dane te nie zmieniły się od ponad 30 lat. Rewolucja AI zafascynowała lekarzy swoją zdolnością do poprawy wyników. Pytanie teraz dotyczy tego, czy może ona złagodzić różnice, które zawsze były obecne w historii medycyny. Niniejsze badanie ma na celu zbadanie roli AI w identyfikowaniu tych różnic w kontekście OHCA, skupiając się na potencjalnych korzyściach i ograniczeniach AI w przypadku zastosowania. MATERIAŁY I METODY: Przeprowadzono systematyczne przeszukanie baz danych CINAHL i Medline, ograniczone do badań opublikowanych w ciągu ostatnich pięciu lat. Przeszukanie wykazało 164 prace, zawężając je do 20 po zastosowaniu kryteriów włączenia i wykluczenia. Siedem badań zostało zidentyfikowanych jako istotne dla pytania, skupiających się na roli AI w rozwiązywaniu dysproporcji w wynikach OHCA. WYNIKI: Zidentyfikowano dwa nadrzędne tematy, aby ująć bieżące badania, które można wykorzystać do promowania świadomości i łagodzenia zapewniających dysproporcje w OHCA: dysproporcje rasowe i społeczno-ekonomiczne zidentyfikowane przez AI oraz implikacje interwencji AI w OHCA. AI wykazuje zdolność do identyfikowania dysproporcji za pomocą predyktorów geograficznych i demograficznych, ale jest ograniczona w swoich możliwościach ze względu na luki w implementacji i etycznych praktykach programowania. WNIOSKI: Interseksualność wysokiego ryzyka statusu społeczno-ekonomicznego i mniejszości etnicznych w rozpowszechnieniu OHCA jest oczywista, podczas gdy czarnoskórzy o niskich dochodach zmagają się z nieporównywalną podatnością. AI stwarza wiele możliwości dla medycyny ratunkowej. Jednakże obecnie nie należy wdrażać sztucznej inteligencji, ponieważ pogłębiłoby to tylko dysproporcje, a nie zmniejszyło ich ze względu na brak etyki relacyjnej w procesie programowania oraz fakt, że w prowadzonych badaniach nad sztuczną inteligencją nie bierze się pod uwagę rasy i cech społeczno-ekonomicznych jako zmiennych wpływających na wyniki OHCA.

5

Adaptive controller design for electric drive with variable parameters by Reinforcement Learning method

88%

Pajchrowski T., Siwek P., Wójcik A., Pajchrowski T., Siwek P., Wójcik A.

Bulletin of the Polish Academy of Sciences: Technical Sciences

|

2020

|

issue 5

6

Physics-guided neural networks (PGNNs) to solve differential equations for spatial analysis

88%

Borzyszkowski B., Damaszke K., Romankiewicz J., Świniarski M., Moszyński M., Borzyszkowski B., Damaszke K., Romankiewicz J., Świniarski M., Moszyński M.

Bulletin of the Polish Academy of Sciences: Technical Sciences

|

2021

7

Prediction of PM2.5 hourly concentrations in Beijing based on machine learning algorithm and ground-based LiDAR

88%

Fang Z., Yang H., Li C., Cheng L., Zhao M., Xie C., Fang Z., Yang H., Li C., Cheng L., Zhao M., Xie C.

Archives of Environmental Protection

|

2021

8

Application of genetic programming for proton-proton interactions

88%

El-Dahshan E.-S.

Open Physics

|

2011

|

vol. 9

|

issue 3

874-883

EN

The aim of the present work is to use one of the machine learning techniques named the genetic programming (GP) to model the p-p interactions through discovering functions. In our study, GP is used to simulate and predict the multiplicity distribution of charged pions (P(n ch)), the average multiplicity (〈n ch〉) and the total cross section (σ tot) at different values of high energies. We have obtained the multiplicity distribution as a function of the center of mass energy ($$ \sqrt s $$) and charged particles (n ch). Also, both the average multiplicity and the total cross section are obtained as a function of $$ \sqrt s $$. Our discovered functions produced by GP technique show a good match to the experimental data. The performance of the GP models was also tested at non-trained data and was found to be in good agreement with the experimental data.

9

Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation

88%

Kun Q., Janott C., Zixing Z., Jun D., Baird A., Clemens H., Hohenhorst W., Herzog M., Werner H., Schuller B.

Archives of Acoustics

|

2018

|

vol. 43

|

issue 3

10

Artificial Intelligence, or just statistics done different?

88%

Wróbel M. J., Lewandowski B.

Polish Journal of Otolaryngology

|

2022

|

vol. 76

|

issue 6

1-5

PL

As interest in AI in medicine grows, so too does the need for education on the topic. Despite the technology itself being so close, our understanding of the essence of how it works remains remote. A greater, more judicious acceptance of AI tools can be fostered in medicine by a broader appreciation of what the technology can and cannot do.

11

Automatic speech based emotion recognition using paralinguistics features

88%

Hook J., Noroozi F., Toygar O., Anbarjafari G.

Bulletin of the Polish Academy of Sciences: Technical Sciences

|

2019

|

issue 3

12

Deep learning in pharmacology: opportunities and threats

88%

Kocić I., Kocić M., Rusiecka I., Kocić A., Kocić E.

|

vol. 5

|

issue 2

88-94

EN

Introduction This review aims to present briefly the new horizon opened to pharmacology by the deep learning (DL) technology, but also to underline the most important threats and limitations of this method. Material and Methods We searched multiple databases for articles published before May 2021 according to the preferred reported item related to deep learning and drug research. Out of the 267 articles retrieved, we included 50 in the final review. Results DL and other different types of artificial intelligence have recently entered all spheres of science, taking an increasingly central position in the decision-making processes, also in pharmacology. Hence, there is a need for better understanding of these technologies. The basic differences between AI (artificial intelligence), DL and ML (machine learning) are explained. Additionally, the authors try to highlight the role of deep learning methods in drug research and development as well as in improving the safety of pharmacotherapy. Finally, future directions of DL in pharmacology were outlined as well as possible misuses of it. Conclusions DL is a promising and powerful tool for comprehensive analysis of big data related to all fields of pharmacology, however it has to be used carefully.

13

Revolution in flow cytometry: using artificial intelligence for data processing and interpretation

88%

Bierzanowski S., Pietruczuk K.

|

vol. 8

|

issue 1

83-96

EN

Flow cytometry (FC) represents a pivotal technique in the domain of biomedical research, facilitating the analysis of the physical and biochemical properties of cells. The advent of artificial intelligence (AI) algorithms has marked a significant turning point in the processing and interpretation of cytometric data, facilitating more precise and efficient analysis. The application of key AI algorithms, including clustering techniques (unsupervised learning), classification (supervised learning) and advanced deep learning methods, is becoming increasingly prevalent. Similarly, multivariate analysis and dimension reduction are also commonly attempted. The integration of advanced AI algorithms with FC methods contributes to a better understanding and interpretation of biological data, opening up new opportunities in research and clinical diagnostics. However, challenges remain in optimising the algorithms for the specificity of the cytometric data and ensuring their interpretability and reliability.

14

The identification method of the coal mill motor power model with the use of machine learning techniques

75%

Łabęda-Grudziak Z. M., Lipiński M., Łabęda-Grudziak Z. M., Lipiński M.

Bulletin of the Polish Academy of Sciences: Technical Sciences

|

2021

|

issue 1

15

MIMO Beam Selection in 5G Using Neural Networks

75%

Ruseckas J., Molis G., Bogucka H., Ruseckas J., Molis G., Bogucka H.

International Journal of Electronics and Telecommunications

|

2021

|

vol. 67

|

issue 4

16

A High-Accuracy of Transmission Line Faults (TLFs) Classification Based on Convolutional Neural Network

75%

Fuada S., Shiddieqy H. A., Adiono T., Fuada S., Shiddieqy H. A., Adiono T.

International Journal of Electronics and Telecommunications

|

2020

|

vol. 66

|

issue 4

17

Stationary supercapacitor energy storage operation algorithm based on neural network learning system

75%

Jefimowski W., Nikitenko A., Drążek Z., Wieczorek M., Jefimowski W., Nikitenko A., Drążek Z., Wieczorek M.

Bulletin of the Polish Academy of Sciences: Technical Sciences

|

2020

|

issue 4

18

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

75%

Drzewiecki W.

Geodesy and Cartography

|

2017

|

vol. 66

|

issue 2

19

A machine learning-based mobile robot visual homing approach

75%

Zhu Q., Ji X., Cai C., Wang J.

Bulletin of the Polish Academy of Sciences: Technical Sciences

|

2018

|

issue 5

20

Artificial intelligence assistance in deciding management strategies for polytrauma and trauma patients

75%

Angthong C., Rungrattanawilai N., Pundee C.

Polish Journal of Surgery

|

2023

|

vol. 96

|

issue Suplement 1

114-117

EN

Introduction: Artificial intelligence (AI) is an emerging technology with vast potential for use in several fields of medicine. However, little is known about the application of AI in treatment decisions for patients with polytrauma. In this systematic review, we investigated the benefits and performance of AI in predicting the management of patients with polytrauma and trauma. Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were extracted from the PubMed and Google Scholar databases from their inception until November 2022, using the search terms “Artificial intelligence“ AND “polytrauma” AND “decision.” Seventeen articles were identified and screened for eligibility. Animal studies, review articles, systematic reviews, meta-analyses, and studies that did not involve polytrauma or severe trauma management decisions were excluded. Eight studies were eligible for final review. Results: Eight studies focusing on patients with trauma, including two on military trauma, were included. The AI applications were mainly implemented for predictions and/or decisions on shock, bleeding, and blood transfusion. Few studies predicted death/survival. The identification of trauma patients using AI was proposed in a previous study. The overall performance of AI was good (six studies), excellent (one study), and acceptable (one study). Discussion: AI demonstrated satisfactory performance in decision-making and management prediction in patients with polytrauma/severe trauma, especially in situations of shock/bleeding. Importance: The present study serves as a basis for further research to develop practical AI applications for the management of patients with trauma.

Refine search results

A different way to diagnosis acute appendicitis: machine learning

Design of a Machine Learning Based Predictive Analytics System for Spam Problem

Artificial intelligence as a tool for addressing racial and socioeconomic inequalities in cardiac arrest: a systematic review

Artificial intelligence as a tool for addressing racial and socioeconomic inequalities in cardiac arrest: a systematic review.

Adaptive controller design for electric drive with variable parameters by Reinforcement Learning method

Physics-guided neural networks (PGNNs) to solve differential equations for spatial analysis

Prediction of PM2.5 hourly concentrations in Beijing based on machine learning algorithm and ground-based LiDAR

Application of genetic programming for proton-proton interactions

Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation

Artificial Intelligence, or just statistics done different?

Automatic speech based emotion recognition using paralinguistics features

Deep learning in pharmacology: opportunities and threats

Revolution in flow cytometry: using artificial intelligence for data processing and interpretation

The identification method of the coal mill motor power model with the use of machine learning techniques

MIMO Beam Selection in 5G Using Neural Networks

A High-Accuracy of Transmission Line Faults (TLFs) Classification Based on Convolutional Neural Network

Stationary supercapacitor energy storage operation algorithm based on neural network learning system

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping