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Advances in AI for Health and Medical Applications
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Advances in AI for Health and Medical Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 45045

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Sidong Liu

E-Mail Website
Guest Editor
Centre for Health Informatics, Macquarie University, Sydney, NSW 2019, Australia
Interests: medical informatics; medical image computing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Cristián Castillo Olea

E-Mail Website
Guest Editor
School of Medicine and Psychology, Autonomous University of Baja California, Mexicali 21100, Mexico
Interests: machine learning; deep learning; big data mining; health applications
Dr. Shlomo Berkovsky

E-Mail Website
Guest Editor
Centre for Health Informatics, Macquarie University, Sydney, NSW 2019, Australia
Interests: health informatics; personalised technologies; intelligent user interfaces

Special Issue Information

Dear Colleagues,

The MDPI Information Journal invites submissions to a Special Issue on “Advances in AI for Health and Medical Applications”.

The design and use of Artificial Intelligence (AI) and digital technologies is driving fundamental changes in healthcare. AI allows us to imagine new and improved ways of delivering care. It has the capacity to make significant contributions to solving modern healthcare challenges, such as the growing burden of chronic illness, over-treatment and diagnostic error, resulting in potential patient harm and resource waste. Through AI, we may exploit patterns in large-scale clinical datasets and build advanced computational reasoning methods that support human decision-making.

Encouraging results have been reported, suggesting that AI has become so powerful that it outreasons human experts in areas such as radiology or dermatology. It will soon be routine to see AI applied to every aspect of healthcare and medicine, from screening through to diagnosis, treatment and population health. However, clinical specialties like radiology might not disappear, but they will certainly be heavily transformed, and clinicians will have a major new role in the time of AI.

This Special Issue is concerned with groundbreaking topics on recent advances in AI for health and medical applications. The areas of interest include (but are not limited to):

  • Applications of AI in healthcare;
  • Clinician interaction with AI;
  • AI-augmented decision support;
  • Trust and automation bias in medical applications;
  • AI-powered behavioural change support systems;
  • AI and HCI methods for health big data mining;
  • AI for enhancing safety, quality, access and efficiency of care;
  • Personal health information management;
  • Network processes and digital epidemiology;
  • Health misinformation on social media;
  • Sensing technologies and wearable devices for health applications.

Paper length must be 9–15 pages and papers should be formatted according to the MDPI template. Complete instructions for authors can be found at: https://www.mdpi.com/journal/information/instructions.

Dr. Sidong Liu
Dr. Cristián Castillo Olea
Dr. Shlomo Berkovsky
Guest Editors

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. Information 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.

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

Published Papers (13 papers)

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Editorial

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4 pages, 181 KiB  
Editorial
Emerging Applications and Translational Challenges for AI in Healthcare
by Sidong Liu, Cristián Castillo-Olea and Shlomo Berkovsky
Information 2024, 15(2), 90; https://doi.org/10.3390/info15020090 - 6 Feb 2024
Cited by 1 | Viewed by 1957
Abstract
The past decade has witnessed an explosive growth in the development and use of artificial intelligence (AI) across diverse fields [...] Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)

Research

Jump to: Editorial

23 pages, 16129 KiB  
Article
Public Health Implications for Effective Community Interventions Based on Hospital Patient Data Analysis Using Deep Learning Technology in Indonesia
by Lenni Dianna Putri, Ermi Girsang, I Nyoman Ehrich Lister, Hsiang Tsung Kung, Evizal Abdul Kadir and Sri Listia Rosa
Information 2024, 15(1), 41; https://doi.org/10.3390/info15010041 - 11 Jan 2024
Cited by 1 | Viewed by 2734
Abstract
Public health is an important aspect of community activities, making research on health necessary because it is a crucial field in maintaining and improving the quality of life in society as a whole. Research on public health allows for a deeper understanding of [...] Read more.
Public health is an important aspect of community activities, making research on health necessary because it is a crucial field in maintaining and improving the quality of life in society as a whole. Research on public health allows for a deeper understanding of the health problems faced by a population, including disease prevalence, risk factors, and other determinants of health. This work aims to explore the potential of hospital patient data analysis as a valuable tool for understanding community implications and deriving insights for effective community health interventions. The study recognises the significance of harnessing the vast amount of data generated within hospital settings to inform population-level health strategies. The methodology employed in this study involves the collection and analysis of deidentified patient data from a representative sample of a hospital in Indonesia. Various data analysis techniques, such as statistical modelling, data mining, and machine learning algorithms, are utilised to identify patterns, trends, and associations within the data. A program written in Python is used to analyse patient data in a hospital for five years, from 2018 to 2022. These findings are then interpreted within the context of public health implications, considering factors such as disease prevalence, socioeconomic determinants, and healthcare utilisation patterns. The results of the data analysis provide valuable insights into the public health implications of hospital patient data. The research also covers predictions for the patient data to the hospital based on disease, age, and geographical residence. The research prediction shows that, in the year 2023, the number of patients will not be considerably affected by the infection, but in March to April 2024 the number will increase significantly up to 10,000 patients due to the trend in the previous year at the end of 2022. These recommendations encompass targeted prevention strategies, improved healthcare delivery models, and community engagement initiatives. The research emphasises the importance of collaboration between healthcare providers, policymakers, and community stakeholders in implementing and evaluating these interventions. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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19 pages, 707 KiB  
Article
A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh
by Md. Jamal Uddin, Md. Martuza Ahamad, Md. Nesarul Hoque, Md. Abul Ala Walid, Sakifa Aktar, Naif Alotaibi, Salem A. Alyami, Muhammad Ashad Kabir and Mohammad Ali Moni
Information 2023, 14(7), 376; https://doi.org/10.3390/info14070376 - 2 Jul 2023
Cited by 19 | Viewed by 4181
Abstract
Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk [...] Read more.
Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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23 pages, 3250 KiB  
Article
NUMSnet: Nested-U Multi-Class Segmentation Network for 3D Medical Image Stacks
by Sohini Roychowdhury
Information 2023, 14(6), 333; https://doi.org/10.3390/info14060333 - 13 Jun 2023
Cited by 1 | Viewed by 2214
Abstract
The semantic segmentation of 3D medical image stacks enables accurate volumetric reconstructions, computer-aided diagnostics and follow-up treatment planning. In this work, we present a novel variant of the Unet model, called the NUMSnet, that transmits pixel neighborhood features across scans through nested layers [...] Read more.
The semantic segmentation of 3D medical image stacks enables accurate volumetric reconstructions, computer-aided diagnostics and follow-up treatment planning. In this work, we present a novel variant of the Unet model, called the NUMSnet, that transmits pixel neighborhood features across scans through nested layers to achieve accurate multi-class semantic segmentation with minimal training data. We analyzed the semantic segmentation performance of the NUMSnet model in comparison with several Unet model variants in the segmentation of 3–7 regions of interest using only 5–10% of images for training per Lung-CT and Heart-CT volumetric image stack. The proposed NUMSnet model achieves up to 20% improvement in segmentation recall, with 2–9% improvement in Dice scores for Lung-CT stacks and 2.5–16% improvement in Dice scores for Heart-CT stacks when compared to the Unet++ model. The NUMSnet model needs to be trained with ordered images around the central scan of each volumetric stack. The propagation of image feature information from the six nested layers of the Unet++ model are found to have better computation and segmentation performance than the propagation of fewer hidden layers or all ten up-sampling layers in a Unet++ model. The NUMSnet model achieves comparable segmentation performance to previous works while being trained on as few as 5–10% of the images from 3D stacks. In addition, transfer learning allows faster convergence of the NUMSnet model for multi-class semantic segmentation from pathology in Lung-CT images to cardiac segmentation in Heart-CT stacks. Thus, the proposed model can standardize multi-class semantic segmentation for a variety of volumetric image stacks with a minimal training dataset. This can significantly reduce the cost, time and inter-observer variability associated with computer-aided detection and treatment. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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20 pages, 2527 KiB  
Article
A Fuzzy Knowledge Graph Pairs-Based Application for Classification in Decision Making: Case Study of Preeclampsia Signs
by Hai Van Pham, Cu Kim Long, Phan Hung Khanh and Ha Quoc Trung
Information 2023, 14(2), 104; https://doi.org/10.3390/info14020104 - 7 Feb 2023
Cited by 1 | Viewed by 2716
Abstract
Problems of preeclampsia sign diagnosis are mostly based on symptom data with the characteristics of data collected periodically in uncertain, ambiguous, and obstetrician opinions. To reduce the effects of preeclampsia, many studies have investigated the disease, prevention, and complication. Conventional fuzzy inference techniques [...] Read more.
Problems of preeclampsia sign diagnosis are mostly based on symptom data with the characteristics of data collected periodically in uncertain, ambiguous, and obstetrician opinions. To reduce the effects of preeclampsia, many studies have investigated the disease, prevention, and complication. Conventional fuzzy inference techniques can solve several diagnosis problems in health such as fuzzy inference systems (FIS), and Mamdani complex fuzzy inference systems with rule reduction (M-CFIS-R), however, the computation time is quite high. Recently, the research direction of approximate inference based on fuzzy knowledge graph (FKG) has been proposed in the M-CFIS-FKG model with the combination of regimens in traditional medicine and subclinical data gathered from medical records. The paper has presented a proposed model of FKG-Pairs3 to support patients’ disease diagnosis, together with doctors’ preferences in decision-making. The proposed model has been implemented in real-world applications for disease diagnosis in traditional medicine based on input data sets with vague information, quantified by doctor’s preferences. To validate the proposed model, it has been tested in a real-world case study of preeclampsia signs in a hospital for disease diagnosis with the traditional medicine approach. Experimental results show that the proposed model has demonstrated the model’s effectiveness in the decision-making of preeclampsia signs. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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15 pages, 2858 KiB  
Article
Predicting Emergency Department Utilization among Older Hong Kong Population in Hot Season: A Machine Learning Approach
by Huiquan Zhou, Hao Luo, Kevin Ka-Lun Lau, Xingxing Qian, Chao Ren and Puihing Chau
Information 2022, 13(9), 410; https://doi.org/10.3390/info13090410 - 29 Aug 2022
Viewed by 2181
Abstract
Previous evidence suggests that temperature is associated with the number of emergency department (ED) visits. A predictive system for ED visits, which takes local temperature into account, is therefore needed. This study aimed to compare the predictive performance of various machine learning methods [...] Read more.
Previous evidence suggests that temperature is associated with the number of emergency department (ED) visits. A predictive system for ED visits, which takes local temperature into account, is therefore needed. This study aimed to compare the predictive performance of various machine learning methods with traditional statistical methods based on temperature variables and develop a daily ED attendance rate predictive model for Hong Kong. We analyzed ED utilization among Hong Kong older adults in May to September from 2000 to 2016. A total of 103 potential predictors were derived from 1- to 14-day lag of ED attendance rate and meteorological and air quality indicators and 0-day lag of holiday indicator and month and day of week indicators. LASSO regression was used to identify the most predictive temperature variables. Decision tree regressor, support vector machine (SVM) regressor, and random forest regressor were trained on the selected optimal predictor combination. Deep neural network (DNN) and gated recurrent unit (GRU) models were performed on the extended predictor combination for the previous 14-day horizon. Maximum ambient temperature was identified as a better predictor in its own value than as an indicator defined by the cutoff. GRU achieved the best predictive accuracy. Deep learning methods, especially the GRU model, outperformed conventional machine learning methods and traditional statistical methods. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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11 pages, 457 KiB  
Article
Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning
by Abdullah Ahmed, Jayroop Ramesh, Sandipan Ganguly, Raafat Aburukba, Assim Sagahyroon and Fadi Aloul
Information 2022, 13(9), 406; https://doi.org/10.3390/info13090406 - 27 Aug 2022
Cited by 6 | Viewed by 2934
Abstract
Depression is one of the most common mental health disorders, affecting approximately 280 million people worldwide. This condition is defined as emotional dysregulation resulting in persistent feelings of sadness, loss of interest and inability to experience pleasure. Early detection can facilitate timely intervention [...] Read more.
Depression is one of the most common mental health disorders, affecting approximately 280 million people worldwide. This condition is defined as emotional dysregulation resulting in persistent feelings of sadness, loss of interest and inability to experience pleasure. Early detection can facilitate timely intervention in the form of psychological therapy and/or medication. With the widespread public adoption of wearable devices such as smartwatches and fitness trackers, it is becoming increasingly possible to gain insights relating the mental states of individuals in an unobtrusive manner within free-living conditions. This work presents a machine learning (ML) approach that utilizes retrospectively collected data-derived consumer-grade wearables for passive detection of depression severity. The experiments conducted in this work reveal that multimodal analysis of physiological signals in terms of their discrete wavelet transform (DWT) features exhibit considerably better performance than unimodal scenarios. Additionally, we conduct experiments to view the impact of severity on emotional valence-arousal detection. We believe that our work has implications towards guiding development in the domain of multimodal wearable-based screening of mental health disorders and necessitates appropriate treatment interventions. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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14 pages, 7724 KiB  
Article
Motivating Machines: The Potential of Modeling Motivation as MoA for Behavior Change Systems
by Fawad Taj, Michel C. A. Klein and Aart Van Halteren
Information 2022, 13(5), 258; https://doi.org/10.3390/info13050258 - 17 May 2022
Cited by 2 | Viewed by 3237
Abstract
The pathway through which behavior change techniques have an effect on the behavior of an individual is referred to as the Mechanism of Action (MoA). Digitally enabled behavior change interventions could potentially benefit from explicitly modelling the MoA to achieve more effective, adaptive, [...] Read more.
The pathway through which behavior change techniques have an effect on the behavior of an individual is referred to as the Mechanism of Action (MoA). Digitally enabled behavior change interventions could potentially benefit from explicitly modelling the MoA to achieve more effective, adaptive, and personalized interventions. For example, if ‘motivation’ is proposed as the targeted construct in any behavior change intervention, how can a model of this construct be used to act as a mechanism of action, mediating the intervention effect using various behavior change techniques? This article discusses a computational model for motivation based on the neural reward pathway with the aim to make it act as a mediator between behavior change techniques and target behavior. This model’s formal description and parametrization are described from a neurocomputational sciences prospect and elaborated with the help of a sub-question, i.e., what parameters/processes of the model are crucial for the generation and maintenance of motivation. An intervention scenario is simulated to show how an explicit model of ‘motivation’ and its parameters can be used to achieve personalization and adaptivity. A computational representation of motivation as a mechanism of action may also further advance the design, evaluation, and effectiveness of personalized and adaptive digital behavior change interventions. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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14 pages, 1613 KiB  
Article
An Attentive Multi-Modal CNN for Brain Tumor Radiogenomic Classification
by Ruyi Qu and Zhifeng Xiao
Information 2022, 13(3), 124; https://doi.org/10.3390/info13030124 - 2 Mar 2022
Cited by 13 | Viewed by 4201
Abstract
Medical images of brain tumors are critical for characterizing the pathology of tumors and early diagnosis. There are multiple modalities for medical images of brain tumors. Fusing the unique features of each modality of the magnetic resonance imaging (MRI) scans can accurately determine [...] Read more.
Medical images of brain tumors are critical for characterizing the pathology of tumors and early diagnosis. There are multiple modalities for medical images of brain tumors. Fusing the unique features of each modality of the magnetic resonance imaging (MRI) scans can accurately determine the nature of brain tumors. The current genetic analysis approach is time-consuming and requires surgical extraction of brain tissue samples. Accurate classification of multi-modal brain tumor images can speed up the detection process and alleviate patient suffering. Medical image fusion refers to effectively merging the significant information of multiple source images of the same tissue into one image, which will carry abundant information for diagnosis. This paper proposes a novel attentive deep-learning-based classification model that integrates multi-modal feature aggregation, lite attention mechanism, separable embedding, and modal-wise shortcuts for performance improvement. We evaluate our model on the RSNA-MICCAI dataset, a scenario-specific medical image dataset, and demonstrate that the proposed method outperforms the state-of-the-art (SOTA) by around 3%. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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17 pages, 2048 KiB  
Article
HIV Patients’ Tracer for Clinical Assistance and Research during the COVID-19 Epidemic (INTERFACE): A Paradigm for Chronic Conditions
by Antonella Cingolani, Konstantina Kostopoulou, Alice Luraschi, Aristodemos Pnevmatikakis, Silvia Lamonica, Sofoklis Kyriazakos, Chiara Iacomini, Francesco Vladimiro Segala, Giulia Micheli, Cristina Seguiti, Stathis Kanavos, Alfredo Cesario, Enrica Tamburrini, Stefano Patarnello, Vincenzo Valentini and Roberto Cauda
Information 2022, 13(2), 76; https://doi.org/10.3390/info13020076 - 5 Feb 2022
Cited by 1 | Viewed by 3554
Abstract
The health emergency linked to the SARS-CoV-2 pandemic has highlighted problems in the health management of chronic patients due to their risk of infection, suggesting the need of new methods to monitor patients. People living with HIV/AIDS (PLWHA) represent a paradigm of chronic [...] Read more.
The health emergency linked to the SARS-CoV-2 pandemic has highlighted problems in the health management of chronic patients due to their risk of infection, suggesting the need of new methods to monitor patients. People living with HIV/AIDS (PLWHA) represent a paradigm of chronic patients where an e-health-based remote monitoring could have a significant impact in maintaining an adequate standard of care. The key objective of the study is to provide both an efficient operating model to “follow” the patient, capture the evolution of their disease, and establish proximity and relief through a remote collaborative model. These dimensions are collected through a dedicated mobile application that triggers questionnaires on the basis of decision-making algorithms, tagging patients and sending alerts to staff in order to tailor interventions. All outcomes and alerts are monitored and processed through an innovative e-Clinical platform. The processing of the collected data aims into learning and evaluating predictive models for the possible upcoming alerts on the basis of past data, using machine learning algorithms. The models will be clinically validated as the study collects more data, and, if successful, the resulting multidimensional vector of past attributes will act as a digital composite biomarker capable of predicting HIV-related alerts. Design: All PLWH > 18 sears old and stable disease followed at the outpatient services of a university hospital (n = 1500) will be enrolled in the interventional study. The study is ongoing, and patients are currently being recruited. Preliminary results are yielding monthly data to facilitate learning of predictive models for the alerts of interest. Such models are learnt for one or two months of history of the questionnaire data. In this manuscript, the protocol—including the rationale, detailed technical aspects underlying the study, and some preliminary results—are described. Conclusions: The management of HIV-infected patients in the pandemic era represents a challenge for future patient management beyond the pandemic period. The application of artificial intelligence and machine learning systems as described in this study could enable remote patient management that takes into account the real needs of the patient and the monitoring of the most relevant aspects of PLWH management today. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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19 pages, 1065 KiB  
Article
Early Stage Identification of COVID-19 Patients in Mexico Using Machine Learning: A Case Study for the Tijuana General Hospital
by Cristián Castillo-Olea, Roberto Conte-Galván, Clemente Zuñiga, Alexandra Siono, Angelica Huerta, Ornela Bardhi and Eric Ortiz
Information 2021, 12(12), 490; https://doi.org/10.3390/info12120490 - 24 Nov 2021
Cited by 2 | Viewed by 3141
Abstract
Background: The current pandemic caused by SARS-CoV-2 is an acute illness of global concern. SARS-CoV-2 is an infectious disease caused by a recently discovered coronavirus. Most people who get sick from COVID-19 experience either mild, moderate, or severe symptoms. In order to help [...] Read more.
Background: The current pandemic caused by SARS-CoV-2 is an acute illness of global concern. SARS-CoV-2 is an infectious disease caused by a recently discovered coronavirus. Most people who get sick from COVID-19 experience either mild, moderate, or severe symptoms. In order to help make quick decisions regarding treatment and isolation needs, it is useful to determine which significant variables indicate infection cases in the population served by the Tijuana General Hospital (Hospital General de Tijuana). An Artificial Intelligence (Machine Learning) mathematical model was developed in order to identify early-stage significant variables in COVID-19 patients. Methods: The individual characteristics of the study subjects included age, gender, age group, symptoms, comorbidities, diagnosis, and outcomes. A mathematical model that uses supervised learning algorithms, allowing the identification of the significant variables that predict the diagnosis of COVID-19 with high precision, was developed. Results: Automatic algorithms were used to analyze the data: for Systolic Arterial Hypertension (SAH), the Logistic Regression algorithm showed results of 91.0% in area under ROC (AUC), 80% accuracy (CA), 80% F1 and 80% Recall, and 80.1% precision for the selected variables, while for Diabetes Mellitus (DM) with the Logistic Regression algorithm it obtained 91.2% AUC, 89.2% accuracy, 88.8% F1, 89.7% precision, and 89.2% recall for the selected variables. The neural network algorithm showed better results for patients with Obesity, obtaining 83.4% AUC, 91.4% accuracy, 89.9% F1, 90.6% precision, and 91.4% recall. Conclusions: Statistical analyses revealed that the significant predictive symptoms in patients with SAH, DM, and Obesity were more substantial in fatigue and myalgias/arthralgias. In contrast, the third dominant symptom in people with SAH and DM was odynophagia. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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14 pages, 2978 KiB  
Article
Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT
by You-Zhen Feng, Sidong Liu, Zhong-Yuan Cheng, Juan C. Quiroz, Dana Rezazadegan, Ping-Kang Chen, Qi-Ting Lin, Long Qian, Xiao-Fang Liu, Shlomo Berkovsky, Enrico Coiera, Lei Song, Xiao-Ming Qiu and Xiang-Ran Cai
Information 2021, 12(11), 471; https://doi.org/10.3390/info12110471 - 15 Nov 2021
Cited by 10 | Viewed by 3050
Abstract
Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic [...] Read more.
Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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13 pages, 16074 KiB  
Article
Deep Learning Models for Colorectal Polyps
by Ornela Bardhi, Daniel Sierra-Sosa, Begonya Garcia-Zapirain and Luis Bujanda
Information 2021, 12(6), 245; https://doi.org/10.3390/info12060245 - 10 Jun 2021
Cited by 9 | Viewed by 5053
Abstract
Colorectal cancer is one of the main causes of cancer incident cases and cancer deaths worldwide. Undetected colon polyps, be them benign or malignant, lead to late diagnosis of colorectal cancer. Computer aided devices have helped to decrease the polyp miss rate. The [...] Read more.
Colorectal cancer is one of the main causes of cancer incident cases and cancer deaths worldwide. Undetected colon polyps, be them benign or malignant, lead to late diagnosis of colorectal cancer. Computer aided devices have helped to decrease the polyp miss rate. The application of deep learning algorithms and techniques has escalated during this last decade. Many scientific studies are published to detect, localize, and classify colon polyps. We present here a brief review of the latest published studies. We compare the accuracy of these studies with our results obtained from training and testing three independent datasets using a convolutional neural network and autoencoder model. A train, validate and test split was performed for each dataset, 75%, 15%, and 15%, respectively. An accuracy of 0.937 was achieved for CVC-ColonDB, 0.951 for CVC-ClinicDB, and 0.967 for ETIS-LaribPolypDB. Our results suggest slight improvements compared to the algorithms used to date. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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