Uffe Kock Wiil

About Uffe Kock Wiil

Uffe Kock Wiil, With an exceptional h-index of 29 and a recent h-index of 14 (since 2020), a distinguished researcher at Syddansk Universitet, specializes in the field of health informatics, security informatics, social network analysis and mining, hypermedia, data-driven health technology.

His recent articles reflect a diverse array of research interests and contributions to the field:

Using data mining to discover new patterns of social media and smartphone use and emotional states

Pulmonologists-Level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques

Energy Expenditure Prediction from Accelerometry Data Using Long Short-Term Memory Recurrent Neural Networks

Special Issue" Recent Trends and Applications of Smart Systems for CyberSecurity"

Monkeypox detection using deep neural networks

A collection of multiregistry data on patients at high risk of lung cancer—a Danish retrospective cohort study of nearly 40,000 patients

A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders

Uffe Kock Wiil Information

University

Position

Professor

Citations(all)

2952

Citations(since 2020)

714

Cited By

2435

hIndex(all)

29

hIndex(since 2020)

14

i10Index(all)

72

i10Index(since 2020)

23

Email

University Profile Page

Google Scholar

Uffe Kock Wiil Skills & Research Interests

health informatics

security informatics

social network analysis and mining

hypermedia

data-driven health technology

Top articles of Uffe Kock Wiil

Title

Journal

Author(s)

Publication Date

Using data mining to discover new patterns of social media and smartphone use and emotional states

Social Network Analysis and Mining

Yeslam Al-Saggaf

Md Anisur Rahman

Uffe Kock Wiil

2024/4/17

Pulmonologists-Level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach

arXiv preprint arXiv:2402.09596

Ricco Noel Hansen Flyckt

Louise Sjodsholm

Margrethe Høstgaard Bang Henriksen

Claus Lohman Brasen

Ali Ebrahimi

...

2024/2/14

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques

Scientific Reports

Samin Babaei Rikan

Amir Sorayaie Azar

Amin Naemi

Jamshid Bagherzadeh Mohasefi

Habibollah Pirnejad

...

2024/1/29

Energy Expenditure Prediction from Accelerometry Data Using Long Short-Term Memory Recurrent Neural Networks

Sensors

Martin Vibæk

Abdolrahman Peimankar

Uffe Kock Wiil

Daniel Arvidsson

Jan Christian Brønd

2024/4/14

Special Issue" Recent Trends and Applications of Smart Systems for CyberSecurity"

Electronics

Asadullah Shaikh

Jawad Rasheed

Hani Alshahrani

Uffe Kock Wiil

2023

Monkeypox detection using deep neural networks

BMC Infectious Diseases

Amir Sorayaie Azar

Amin Naemi

Samin Babaei Rikan

Jamshid Bagherzadeh Mohasefi

Habibollah Pirnejad

...

2023/6/27

A collection of multiregistry data on patients at high risk of lung cancer—a Danish retrospective cohort study of nearly 40,000 patients

Translational Lung Cancer Research

Margrethe Bang Henriksen

Torben Frøstrup Hansen

Lars Henrik Jensen

Claus Lohman Brasen

Abdolrahman Peimankar

...

2023/12/12

A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders

Sensors

Abdolrahman Peimankar

Trine Straarup Winther

Ali Ebrahimi

Uffe Kock Wiil

2023/1

Data-driven technologies for future healthcare systems

Frontiers in Medical Technology

Md Anisur Rahman

Alireza Moayedikia

Uffe Kock Wiil

2023/5/24

Transformers for Detection of Distressed Cardiac Patients with an ICD Based on Danish Text Messages

Julie Dittmann Weimar Andersen

Marcus Lomstein Jensen

Uffe Kock Wiil

Søren Skovbakke

Ole Skov

...

2023/12/5

Lung cancer detection using smoking status and standard blood test analysis

Journal of Thoracic Oncology

MB Henriksen

O Hilberg

T Fr

LH Jensen

CL Brasen

...

2023/4/1

Efficacy of a web-based healthcare innovation to advance the quality of life and care of patients with an implantable cardioverter defibrillator (ACQUIRE-ICD): a randomized …

Europace

Ole Skov

Jens Brock Johansen

Jens Cosedis Nielsen

Charlotte E Larroudé

Sam Riahi

...

2023/12

Structured decision support to prevent hospitalisations of community-dwelling older adults in Denmark (PATINA): an open-label, stepped-wedge, cluster-randomised controlled trial

The Lancet Healthy Longevity

Anders Fournaise

Jørgen T Lauridsen

Søren K Nissen

Claire Gudex

Mickael Bech

...

2023/4/1

Important steps for artificial intelligence-based risk assessment of older adults

The Lancet Digital Health

Uffe Kock Wiil

2023/10/1

Explainable Intrusion Detection for Internet of Medical Things

Shafique Ahmed Memon

Uffe Kock Wiil

Mutiullah Shaikh

2023

AUD-DSS: a decision support system for early detection of patients with alcohol use disorder

BMC bioinformatics

Ali Ebrahimi

Uffe Kock Wiil

Ruben Baskaran

Abdolrahman Peimankar

Kjeld Andersen

...

2023/9/2

Special Issue" Recent Trends and Applications of Blockchain and IoT Technologies to Combat COVID-19"

Applied Sciences

Asadullah Shaikh

Uffe Kock Wiil

Yousef Asiri

2022

Analysis and Visualization Features in PEVNET

Amer Rasheed

Uffe Kock Wiil

Muhammad Mustansar Ali Khan

Muhammad Mustansar Ali Khan

Muhammad Mustansar Ali Khan

2022/10/29

Forecasting the COVID-19 Spread in Iran, Italy, and Mexico Using Novel Nonlinear Autoregressive Neural Network and ARIMA-Based Hybrid Models

Amin Naemi

Mostafa Naemi

Romina Zarrabi Ekbatani

Thomas Schmidt

Ali Ebrahimi

...

2022/2/25

Short-term atrial fibrillation detection using electrocardiograms: A comparison of machine learning approaches

International Journal of Medical Informatics

Masud Shah Jahan

Marjan Mansourvar

Sadasivan Puthusserypady

Uffe Kock Wiil

Abdolrahman Peimankar

2022/7/1

See List of Professors in Uffe Kock Wiil University(Syddansk Universitet)

Co-Authors

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