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Algorithms
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Artificial Intelligence Algorithms in Healthcare
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Artificial Intelligence Algorithms in Healthcare

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 6751

Special Issue Editor

Dr. Gabriella Trucco

E-Mail Website
Guest Editor
Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, 20133 Milano, Italy
Interests: bioinformatics; computational biology; artificial intelligence in medicine; electronics

Special Issue Information

Dear Colleagues,

We invite you to submit your research to this Special Issue, “Artificial Intelligence Algorithms in Healthcare”, which will focus on the area of artificial intelligence methods and algorithms applied to the field of healthcare. Artificial intelligence in healthcare aims to support decision-based medical tasks through knowledge- and/or data-intensive computer-based solutions that ultimately support and improve the performance of a human care provider. The articles have to present new methods related to artificial intelligence and computer science algorithms with a high impact on the medical or healthcare domain. Potential topics include, but are not limited to: AI-based clinical decision making; natural language processing in medicine; data analytics and mining for biomedical decision support; new computational platforms and models for biomedicine; intelligent exploitation of heterogeneous data sources aimed at supporting decision-based and data-intensive clinical tasks; machine learning and deep learning in medicine and healthcare; artificial intelligence methods in computer-aided diagnostic tools and decision support analytics for clinical informatics; and deep learning in precision medicine.

Dr. Gabriella Trucco
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI-based clinical decision making
  • computational intelligence in bio- and clinical medicine
  • intelligent information systems in healthcare and medicine
  • natural language processing in medicine
  • data analytics and mining for biomedical decision support
  • new computational platforms and models for biomedicine
  • intelligent exploitation of heterogeneous data sources aimed at supporting decision-based and data-intensive clinical tasks
  • intelligent devices and instruments
  • automated reasoning and meta-reasoning in medicine
  • machine learning in medicine, medically oriented human biology, and healthcare
  • AI and data science in medicine, medically oriented human biology, and healthcare
  • AI-based modeling and management of healthcare pathways and clinical guidelines
  • models and systems for AI-based population health
  • AI in medical and healthcare education
  • artificial intelligence methods and algorithms in computer-aided diagnostic tools and decision support analytics for clinical informatics
  • deep learning in precision medicine
  • artificial intelligence algorithms in precision health
  • rule-based expert systems
  • robotic process automation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

25 pages, 1690 KiB  
Article
Correlations between Social Isolation and Functional Decline in Older Adults after Lower Limb Fractures Using Multimodal Sensors: A Pilot Study
by Faranak Dayyani, Charlene H. Chu, Ali Abedi and Shehroz S. Khan
Algorithms 2024, 17(9), 383; https://doi.org/10.3390/a17090383 - 1 Sep 2024
Viewed by 862
Abstract
Older adults (OAs) recovering from lower limb fractures experience social isolation (SI) and functional decline (FD) after they are discharged from inpatient rehabilitation due to reduced physical mobility. Our research used MAISON (Multimodal AI-based Sensor platform for Older iNdividuals), a multimodal sensor system [...] Read more.
Older adults (OAs) recovering from lower limb fractures experience social isolation (SI) and functional decline (FD) after they are discharged from inpatient rehabilitation due to reduced physical mobility. Our research used MAISON (Multimodal AI-based Sensor platform for Older iNdividuals), a multimodal sensor system comprising various smart devices collecting acceleration, heart rate, step count, frequency of indoor motion, GPS, and sleep metrics. This study aimed to investigate the correlations between SI and FD with multimodal sensor data from OAs following lower limb fractures. Multimodal sensor data from eight OAs (8 weeks per person) living at home were collected. Five clinical metrics were obtained via biweekly video calls, including three clinical questionnaires (Social Isolation Scale (SIS), Oxford Hip Score, Oxford Knee score) and two physical mobility assessments (Timed Up and Go, 30 s chair stand). From the sensor data collected, 53 statistical and domain features were extracted. Spearman correlation coefficients were calculated between the extracted features and clinical data. The results indicated strong correlations between various items of SIS and sleep metrics in OAs and various items of Oxford Knee Score with GPS and acceleration data. Strong correlations between the questions of the Oxford scores and sensor data highlight the direct impact of physical health status on measurable daily physical activities. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms in Healthcare)
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24 pages, 8078 KiB  
Article
EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
by Mohammed Azmi Al-Betar, Zaid Abdi Alkareem Alyasseri, Noor Kamal Al-Qazzaz, Sharif Naser Makhadmeh, Nabeel Salih Ali and Christoph Guger
Algorithms 2024, 17(8), 346; https://doi.org/10.3390/a17080346 - 8 Aug 2024
Viewed by 1144
Abstract
Stroke is a major cause of mortality worldwide, disrupts cerebral blood flow, leading to severe brain damage. Hemiplegia, a common consequence, results in motor task loss on one side of the body. Many stroke survivors face long-term motor impairments and require great rehabilitation. [...] Read more.
Stroke is a major cause of mortality worldwide, disrupts cerebral blood flow, leading to severe brain damage. Hemiplegia, a common consequence, results in motor task loss on one side of the body. Many stroke survivors face long-term motor impairments and require great rehabilitation. Electroencephalograms (EEGs) provide a non-invasive method to monitor brain activity and have been used in brain–computer interfaces (BCIs) to help in rehabilitation. Motor imagery (MI) tasks, detected through EEG, are pivotal for developing BCIs that assist patients in regaining motor purpose. However, interpreting EEG signals for MI tasks remains challenging due to their complexity and low signal-to-noise ratio. The main aim of this study is to focus on optimizing channel selection in EEG-based BCIs specifically for stroke rehabilitation. Determining the most informative EEG channels is crucial for capturing the neural signals related to motor impairments in stroke patients. In this paper, a binary bat algorithm (BA)-based optimization method is proposed to select the most relevant channels tailored to the unique neurophysiological changes in stroke patients. This approach is able to enhance the BCI performance by improving classification accuracy and reducing data dimensionality. We use time–entropy–frequency (TEF) attributes, processed through automated independent component analysis with wavelet transform (AICA-WT) denoising, to enhance signal clarity. The selected channels and features are proved through a k-nearest neighbor (KNN) classifier using public BCI datasets, demonstrating improved classification of MI tasks and the potential for better rehabilitation outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms in Healthcare)
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16 pages, 1886 KiB  
Article
Convolutional Neural Network-Based Digital Diagnostic Tool for the Identification of Psychosomatic Illnesses
by Marta Narigina, Andrejs Romanovs and Yuri Merkuryev
Algorithms 2024, 17(8), 329; https://doi.org/10.3390/a17080329 - 30 Jul 2024
Viewed by 889
Abstract
This paper appraises convolutional neural network (CNN) models’ capabilities in emotion detection from facial expressions, seeking to aid the diagnosis of psychosomatic illnesses, typically made in clinical setups. Using the FER-2013 dataset, two CNN models were designed to detect six emotions with 64% [...] Read more.
This paper appraises convolutional neural network (CNN) models’ capabilities in emotion detection from facial expressions, seeking to aid the diagnosis of psychosomatic illnesses, typically made in clinical setups. Using the FER-2013 dataset, two CNN models were designed to detect six emotions with 64% accuracy—although not evenly distributed; they demonstrated higher effectiveness in identifying “happy” and “surprise.” The assessment was performed through several performance metrics—accuracy, precision, recall, and F1-scores—besides further validation with an additional simulated clinical environment for practicality checks. Despite showing promising levels for future use, this investigation highlights the need for extensive validation studies in clinical settings. This research underscores AI’s potential value as an adjunct to traditional diagnostic approaches while focusing on wider scope (broader datasets) plus focus (multimodal integration) areas to be considered among recommendations in forthcoming studies. This study underscores the importance of CNN models in developing psychosomatic diagnostics and promoting future development based on ethics and patient care. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms in Healthcare)
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28 pages, 1476 KiB  
Article
Enabling Decision Making with the Modified Causal Forest: Policy Trees for Treatment Assignment
by Hugo Bodory, Federica Mascolo and Michael Lechner
Algorithms 2024, 17(7), 318; https://doi.org/10.3390/a17070318 - 19 Jul 2024
Viewed by 1071
Abstract
Decision making plays a pivotal role in shaping outcomes across various disciplines, such as medicine, economics, and business. This paper provides practitioners with guidance on implementing a decision tree designed to optimise treatment assignment policies through an interpretable and non-parametric algorithm. Building upon [...] Read more.
Decision making plays a pivotal role in shaping outcomes across various disciplines, such as medicine, economics, and business. This paper provides practitioners with guidance on implementing a decision tree designed to optimise treatment assignment policies through an interpretable and non-parametric algorithm. Building upon the method proposed by Zhou, Athey, and Wager (2023), our policy tree introduces three key innovations: a different approach to policy score calculation, the incorporation of constraints, and enhanced handling of categorical and continuous variables. These innovations enable the evaluation of a broader class of policy rules, all of which can be easily obtained using a single module. We showcase the effectiveness of our policy tree in managing multiple, discrete treatments using datasets from diverse fields. Additionally, the policy tree is implemented in the open-source Python package mcf (modified causal forest), facilitating its application in both randomised and observational research settings. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms in Healthcare)
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16 pages, 5690 KiB  
Article
Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method
by Xiaonan Si, Lei Wang, Wenchang Xu, Biao Wang and Wenbo Cheng
Algorithms 2024, 17(3), 122; https://doi.org/10.3390/a17030122 - 15 Mar 2024
Viewed by 1427
Abstract
Gout is one of the most painful diseases in the world. Accurate classification of gout is crucial for diagnosis and treatment which can potentially save lives. However, the current methods for classifying gout periods have demonstrated poor performance and have received little attention. [...] Read more.
Gout is one of the most painful diseases in the world. Accurate classification of gout is crucial for diagnosis and treatment which can potentially save lives. However, the current methods for classifying gout periods have demonstrated poor performance and have received little attention. This is due to a significant data imbalance problem that affects the learning attention for the majority and minority classes. To overcome this problem, a resampling method called ENaNSMOTE-Tomek link is proposed. It uses extended natural neighbors to generate samples that fall within the minority class and then applies the Tomek link technique to eliminate instances that contribute to noise. The model combines the ensemble ’bagging’ technique with the proposed resampling technique to improve the quality of generated samples. The performance of individual classifiers and hybrid models on an imbalanced gout dataset taken from the electronic medical records of a hospital is evaluated. The results of the classification demonstrate that the proposed strategy is more accurate than some imbalanced gout diagnosis techniques, with an accuracy of 80.87% and an AUC of 87.10%. This indicates that the proposed algorithm can alleviate the problems caused by imbalanced gout data and help experts better diagnose their patients. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms in Healthcare)
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