Artificial Intelligence in Healthcare - 2nd Volume

Topic Information

Dear Colleagues,

The complexity and rise of data in healthcare means that artificial intelligence (AI), databases, and related technologies will be increasingly applied in the field. Interdisciplinary approaches involving these data and their applications are a challenging new field in artificial intelligence (AI), databases, bio- and medical research, etc. playing a growing role in society today. As such, research in these fields represents a promising and an important technical field with the potential to improve our health and quality of life. Therefore, the goal of this Special Issue is to explore how emerging technology solutions and real-world applications in daily life, disease, cancer, healthcare, and hospitals can help us to lead heathy lives as well as improve wellbeing. This issue seeks to present not only solutions that combine state-of-the-art devices, computer software, and model-based approaches for exploiting the huge health and bio data, as well as the Internet of Things resources available (while ensuring that these systems are explainable to domain experts), but also new methods that more generally describe the successful application of emerging technologies and spectra, and science and engineering to issues such as disease, cancer, databases, sensor device and user interfaces, software design, and system implementation in healthcare, as well as the medical domain, biology, and wellbeing. The main goal is to cover the applications of artificial intelligence (AI) and related technologies and engineering issues addressing all facets of solutions in the real world in databases, disease, and human health technology from a wellbeing and healthy life perspective. This issue welcomes the submission of papers on technical, experimental, methodological, and data analytical approaches that develop and implement solutions focused on real-world problems and systems, as well as on general applications of AI and data mining. It also invites papers on emerging technology solutions and real-world applications related to life, disease, cancer, healthcare, and hospitals that can help humans to lead heathy lives. The topics of interest include but are not limited to the following:

• Data mining in healthcare;
• Machine and deep learning approaches for disease, and health data;
• Decision support systems for healthcare and wellbeing;
• Regression and forecasting for medical and/or biomedical signals;
• Healthcare and wellness information systems;
• Medical signal and image processing and techniques;
• Applications of AI techniques in healthcare and wellbeing systems;
• Intelligent computing and platforms in medicine and healthcare;
• Biomedical applications;
• Biomedical text mining;
• Deep learning and methods to explain disease prediction;
• Big data frameworks and architectures for applied medical and health data;
• Visualization and interactive interfaces related to healthcare systems;
• Recommending and decision-making models and systems based on AI and data mining technologies;
• Machine learning and deep learning applications for life, disease, cancer, healthcare, and hospitals;
• Querying and filtering on heterogeneous, multi-source streaming life, and health data;
• Internet of things and data management for human life;
• Data and applications for human life; data and applications for technology improvement;
• Emerging technologies and applications of data, database, big data, and data mining, AI, models.

Prof. Dr. Keun Ho Ryu
Prof. Dr. Nipon Theera-Umpon
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400
Diagnostics diagnostics	3.6	3.6	2011	20.7 Days	CHF 2600
Healthcare healthcare	2.8	2.7	2013	19.5 Days	CHF 2700
International Journal of Environmental Research and Public Health ijerph	-	5.4	2004	29.6 Days	CHF 2500
Symmetry symmetry	2.7	4.9	2009	16.2 Days	CHF 2400

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Published Papers (16 papers)

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20 pages, 490 KiB  

Open AccessArticle

Assessment for Alzheimer’s Disease Advancement Using Classification Models with Rules

by Fadi Thabtah and David Peebles

Appl. Sci. 2023, 13(22), 12152; https://doi.org/10.3390/app132212152 - 08 Nov 2023

Cited by 1 | Viewed by 964

Abstract

Pre-diagnosis of common dementia conditions such as Alzheimer’s disease (AD) in the initial stages is crucial to help in early intervention, treatment plan design, disease management, and for providing quicker healthcare access. Current assessments are often stressful, invasive, and unavailable in most countries [...] Read more.

Pre-diagnosis of common dementia conditions such as Alzheimer’s disease (AD) in the initial stages is crucial to help in early intervention, treatment plan design, disease management, and for providing quicker healthcare access. Current assessments are often stressful, invasive, and unavailable in most countries worldwide. In addition, many cognitive assessments are time-consuming and rarely cover all cognitive domains involved in dementia diagnosis. Therefore, the design and implementation of an intelligent method for dementia signs of progression from a few cognitive items in a manner that is accessible, easy, affordable, quick to perform, and does not require special and expensive resources is desirable. This paper investigates the issue of dementia progression by proposing a new classification algorithm called Alzheimer’s Disease Class Rules (AD-CR). The AD-CR algorithm learns models from the distinctive feature subsets that contain rules with low overlapping among their cognitive items yet are easily interpreted by clinicians during clinical assessment. An empirical evaluation of the Disease Neuroimaging Initiative data repository (ADNI) datasets shows that the AD-CR algorithm offers good performance (accuracy, sensitivity, etc.) when compared with other machine learning algorithms. The AD-CR algorithm was superior in comparison to the other algorithms overall since it reached a performance above 92%, 92.38% accuracy, 91.30% sensitivity, and 93.50% specificity when processing data subsets with cognitive and demographic attributes. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

29 pages, 966 KiB  

Open AccessArticle

Machine Learning for Sarcopenia Prediction in the Elderly Using Socioeconomic, Infrastructure, and Quality-of-Life Data

by Minje Seok, Wooseong Kim and Jiyoun Kim

Healthcare 2023, 11(21), 2881; https://doi.org/10.3390/healthcare11212881 - 01 Nov 2023

Cited by 4 | Viewed by 1270

Abstract

Since the WHO’s 2021 aging redefinition emphasizes “healthy aging” by focusing on the elderly’s ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal muscle mass, is now becoming a critical health concern, especially in South Korea with a [...] Read more.

Since the WHO’s 2021 aging redefinition emphasizes “healthy aging” by focusing on the elderly’s ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal muscle mass, is now becoming a critical health concern, especially in South Korea with a rapidly aging population. Therefore, we develop a prediction model for sarcopenia by using machine learning (ML) techniques based on the Korea National Health and Nutrition Examination Survey (KNHANES) data 2008–2011, in which we focus on the role of socioeconomic status (SES), social infrastructure, and quality of life (QoL) in the prevalence of sarcopenia. We successfully identify sarcopenia with approximately 80% accuracy by using random forest (RF) and LightGBM (LGB), CatBoost (CAT), and a deep neural network (DNN). For prediction reliability, we achieve area under curve (AUC) values of 0.831, 0.868, and 0.773 for both genders, males, and females, respectively. Especially when using only male data, all the models consistently exhibit better performance overall. Furthermore, using the SHapley Additive exPlanations (SHAP) analysis, we find several common key features, which mainly contribute to model building. These include SES features, such as monthly household income, housing type, marriage status, and social infrastructure accessibility. Furthermore, the causal relationships of household income, per capita neighborhood sports facility area, and life satisfaction are analyzed to establish an effective prediction model for sarcopenia management in an aging population. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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12 pages, 3495 KiB  

Open AccessArticle

Improving Motion Intention Recognition for Trans-Radial Amputees Based on sEMG and Transfer Learning

by Chuang Lin, Xinyue Niu, Jun Zhang and Xianping Fu

Appl. Sci. 2023, 13(19), 11071; https://doi.org/10.3390/app131911071 - 08 Oct 2023

Cited by 1 | Viewed by 808

Abstract

Hand motion intentions can be detected by analyzing the surface electromyographic (sEMG) signals obtained from the remaining forearm muscles of trans-radial amputees. This technology sheds new light on myoelectric prosthesis control; however, fewer signals from amputees can be collected in clinical practice. The [...] Read more.

Hand motion intentions can be detected by analyzing the surface electromyographic (sEMG) signals obtained from the remaining forearm muscles of trans-radial amputees. This technology sheds new light on myoelectric prosthesis control; however, fewer signals from amputees can be collected in clinical practice. The collected signals can further suffer from quality deterioration due to the muscular atrophy of amputees, which significantly decreases the accuracy of hand motion intention recognition. To overcome these problems, this work proposed a transfer learning strategy combined with a long-exposure-CNN (LECNN) model to improve the amputees’ hand motion intention recognition accuracy. Transfer learning can leverage the knowledge acquired from intact-limb subjects to amputees, and LECNN can effectively capture the information in the sEMG signals. Two datasets with 20 intact-limb and 11 amputated-limb subjects from the Ninapro database were used to develop and evaluate the proposed method. The experimental results demonstrated that the proposed transfer learning strategy significantly improved the recognition performance ($78.1\%\pm 19.9\%$, p-value < 0.005) compared with the non-transfer case ($73.4\%\pm 20.8\%$). When the source and target data matched well, the after-transfer accuracy could be improved by up to 8.5%. Compared with state-of-the-art methods in two previous studies, the average accuracy was improved by 11.6% (from 67.5% to 78.1%, p-value < 0.005) and 12.1% (from 67.0% to 78.1%, p-value < 0.005). This result is also among the best from the contrast methods. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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23 pages, 997 KiB  

Open AccessReview

Wearables for Stress Management: A Scoping Review

by Maria Luisa González Ramírez, Juan Pablo García Vázquez, Marcela D. Rodríguez, Luis Alfredo Padilla-López, Gilberto Manuel Galindo-Aldana and Daniel Cuevas-González

Healthcare 2023, 11(17), 2369; https://doi.org/10.3390/healthcare11172369 - 22 Aug 2023

Cited by 1 | Viewed by 1924

Abstract

In recent years, wearable devices have been increasingly used to monitor people’s health. This has helped healthcare professionals provide timely interventions to support their patients. In this study, we investigated how wearables help people manage stress. We conducted a scoping review following the [...] Read more.

In recent years, wearable devices have been increasingly used to monitor people’s health. This has helped healthcare professionals provide timely interventions to support their patients. In this study, we investigated how wearables help people manage stress. We conducted a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) standard to address this question. We searched studies in Scopus, IEEE Explore, and Pubmed databases. We included studies reporting user evaluations of wearable-based strategies, reporting their impact on health or usability outcomes. A total of 6259 studies were identified, of which 40 met the inclusion criteria. Based on our findings, we identified that 21 studies report using commercial wearable devices; the most common are smartwatches and smart bands. Thirty-one studies report significant stress reduction using different interventions and interaction modalities. Finally, we identified that the interventions are designed with the following aims: (1) to self-regulate during stress episodes, (2) to support self-regulation therapies for long-term goals, and (3) to provide stress awareness for prevention, consisting of people’s ability to recall, recognize and understand their stress. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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50 pages, 4331 KiB  

Open AccessSystematic Review

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

by Alejandra Cuevas-Chávez, Yasmín Hernández, Javier Ortiz-Hernandez, Eduardo Sánchez-Jiménez, Gilberto Ochoa-Ruiz, Joaquín Pérez and Gabriel González-Serna

Healthcare 2023, 11(16), 2240; https://doi.org/10.3390/healthcare11162240 - 09 Aug 2023

Cited by 5 | Viewed by 2573

Abstract

According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, [...] Read more.

According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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18 pages, 5048 KiB  

Open AccessArticle

Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals

by Md. Nazmul Hasan and Insoo Koo

Diagnostics 2023, 13(14), 2358; https://doi.org/10.3390/diagnostics13142358 - 13 Jul 2023

Cited by 3 | Viewed by 1259

Abstract

Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health issues from human sleep data. Manual sleep staging requires human expertise, which is occasionally prone to error and variation. In recent times, availability of polysomnography data has aided progress in [...] Read more.

Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health issues from human sleep data. Manual sleep staging requires human expertise, which is occasionally prone to error and variation. In recent times, availability of polysomnography data has aided progress in automatic sleep-stage classification. In this paper, a hybrid deep learning model is proposed for classifying sleep and wake states based on a single-channel electroencephalogram (EEG) signal. The model combines an artificial neural network (ANN) and a convolutional neural network (CNN) trained using mixed-input features. The ANN makes use of statistical features calculated from EEG epochs, and the CNN operates on Hilbert spectrum images generated during each epoch. The proposed method is assessed using single-channel Pz-Oz EEG signals from the Sleep-EDF database Expanded. The classification performance on four randomly selected individuals shows that the proposed model can achieve accuracy of around 96% in classifying between sleep and wake states from EEG recordings. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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11 pages, 1407 KiB  

Open AccessArticle

A Machine Learning Approach for Knee Injury Detection from Magnetic Resonance Imaging

by Massimiliano Mangone, Anxhelo Diko, Luca Giuliani, Francesco Agostini, Marco Paoloni, Andrea Bernetti, Gabriele Santilli, Marco Conti, Alessio Savina, Giovanni Iudicelli, Carlo Ottonello and Valter Santilli

Int. J. Environ. Res. Public Health 2023, 20(12), 6059; https://doi.org/10.3390/ijerph20126059 - 06 Jun 2023

Cited by 1 | Viewed by 1437

Abstract

The knee is an essential part of our body, and identifying its injuries is crucial since it can significantly affect quality of life. To date, the preferred way of evaluating knee injuries is through magnetic resonance imaging (MRI), which is an effective imaging [...] Read more.

The knee is an essential part of our body, and identifying its injuries is crucial since it can significantly affect quality of life. To date, the preferred way of evaluating knee injuries is through magnetic resonance imaging (MRI), which is an effective imaging technique that accurately identifies injuries. The issue with this method is that the high amount of detail that comes with MRIs is challenging to interpret and time consuming for radiologists to analyze. The issue becomes even more concerning when radiologists are required to analyze a significant number of MRIs in a short period. For this purpose, automated tools may become helpful to radiologists assisting them in the evaluation of these images. Machine learning methods, in being able to extract meaningful information from data, such as images or any other type of data, are promising for modeling the complex patterns of knee MRI and relating it to its interpretation. In this study, using a real-life imaging protocol, a machine-learning model based on convolutional neural networks used for detecting medial meniscus tears, bone marrow edema, and general abnormalities on knee MRI exams is presented. Furthermore, the model’s effectiveness in terms of accuracy, sensitivity, and specificity is evaluated. Based on this evaluation protocol, the explored models reach a maximum accuracy of 83.7%, a maximum sensitivity of 82.2%, and a maximum specificity of 87.99% for meniscus tears. For bone marrow edema, a maximum accuracy of 81.3%, a maximum sensitivity of 93.3%, and a maximum specificity of 78.6% is reached. Finally, for general abnormalities, the explored models reach 83.7%, 90.0% and 84.2% of maximum accuracy, sensitivity and specificity, respectively. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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20 pages, 3031 KiB  

Open AccessArticle

Are You Depressed? Analyze User Utterances to Detect Depressive Emotions Using DistilBERT

by Jaedong Oh, Mirae Kim, Hyejin Park and Hayoung Oh

Appl. Sci. 2023, 13(10), 6223; https://doi.org/10.3390/app13106223 - 19 May 2023

Cited by 3 | Viewed by 1381

Abstract

This paper introduces the Are u Depressed (AuD) model, which aims to detect depressive emotional intensity and classify detailed depressive symptoms expressed in user utterances. The study includes the creation of a BWS dataset using a tool for the Best-Worst Scaling annotation task [...] Read more.

This paper introduces the Are u Depressed (AuD) model, which aims to detect depressive emotional intensity and classify detailed depressive symptoms expressed in user utterances. The study includes the creation of a BWS dataset using a tool for the Best-Worst Scaling annotation task and a DSM-5 dataset containing nine types of depression annotations based on major depressive disorder (MDD) episodes in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed model employs the DistilBERT model for both tasks and demonstrates superior performance compared to other machine learning and deep learning models. We suggest using our model for real-time depressive emotion detection tasks that demand speed and accuracy. Overall, the AuD model significantly advances the accurate detection of depressive emotions in user utterances. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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16 pages, 1278 KiB  

Open AccessArticle

Accelerating UN Sustainable Development Goals with AI-Driven Technologies: A Systematic Literature Review of Women’s Healthcare

by Pin Lean Lau, Monomita Nandy and Sushmita Chakraborty

Healthcare 2023, 11(3), 401; https://doi.org/10.3390/healthcare11030401 - 31 Jan 2023

Cited by 5 | Viewed by 3312

Abstract

In this paper, we critically examine if the contributions of artificial intelligence (AI) in healthcare adequately represent the realm of women’s healthcare. This would be relevant for achieving and accelerating the gender equality and health sustainability goals (SDGs) defined by the United Nations. [...] Read more.

In this paper, we critically examine if the contributions of artificial intelligence (AI) in healthcare adequately represent the realm of women’s healthcare. This would be relevant for achieving and accelerating the gender equality and health sustainability goals (SDGs) defined by the United Nations. Following a systematic literature review (SLR), we examine if AI applications in health and biomedicine adequately represent women’s health in the larger scheme of healthcare provision. Our findings are divided into clusters based on thematic markers for women’s health that are commensurate with the hypotheses that AI-driven technologies in women’s health still remain underrepresented, but that emphasis on its future deployment can increase efficiency in informed health choices and be particularly accessible to women in small or underrepresented communities. Contemporaneously, these findings can assist and influence the shape of governmental policies, accessibility, and the regulatory environment in achieving the SDGs. On a larger scale, in the near future, we will extend the extant literature on applications of AI-driven technologies in health SDGs and set the agenda for future research. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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12 pages, 1625 KiB  

Open AccessArticle

Analysis of the Risk Factors for De Novo Subdural Hygroma in Patients with Traumatic Brain Injury Using Predictive Modeling and Association Rule Mining

by Kwang Hyeon Kim, Byung-Jou Lee and Hae-Won Koo

Appl. Sci. 2023, 13(3), 1243; https://doi.org/10.3390/app13031243 - 17 Jan 2023

Viewed by 1572

Abstract

The relationship between risk factors for de novo hygroma in patients with traumatic brain injury (TBI) was investigated. We collected data on 222 patients with TBI to determine the risk factors for de novo hygroma, including sex, age, centrum semiovale perivascular space (CSO-PVS) [...] Read more.

The relationship between risk factors for de novo hygroma in patients with traumatic brain injury (TBI) was investigated. We collected data on 222 patients with TBI to determine the risk factors for de novo hygroma, including sex, age, centrum semiovale perivascular space (CSO-PVS) grade, trauma cause, hypertension, and diabetes. The importance of the risk factors was analyzed, and the feature contribution of the risk factors to all patients and each patient was analyzed using predictive modeling. Additionally, association rule mining was performed to determine the relationship between all factors, and the performance metrics of the predictive model were calculated. The overall feature importance was analyzed in the order of age, CSO-PVS, hypertension, and trauma cause. However, trauma cause, underlying disease, age, and sex as risk factors were different for a specific patient through the individual feature analysis. The mean area under the curve for the predictive model was 0.80 ± 0.04 using K-fold cross validation. We analyzed the risk factors for de novo hygroma in TBI and identified detailed relationships. Age and CSO-PVS severity were strongly correlated with de novo hygroma. Furthermore, according to the results of feature importance analysis and association rule mining, the significance of the risk factors may vary in each individual patient. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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14 pages, 1245 KiB  

Open AccessArticle

DeepSmile: Anomaly Detection Software for Facial Movement Assessment

by Eder A. Rodríguez Martínez, Olga Polezhaeva, Félix Marcellin, Émilien Colin, Lisa Boyaval, François-Régis Sarhan and Stéphanie Dakpé

Diagnostics 2023, 13(2), 254; https://doi.org/10.3390/diagnostics13020254 - 10 Jan 2023

Cited by 1 | Viewed by 2462

Abstract

Facial movements are crucial for human interaction because they provide relevant information on verbal and non-verbal communication and social interactions. From a clinical point of view, the analysis of facial movements is important for diagnosis, follow-up, drug therapy, and surgical treatment. Current methods [...] Read more.

Facial movements are crucial for human interaction because they provide relevant information on verbal and non-verbal communication and social interactions. From a clinical point of view, the analysis of facial movements is important for diagnosis, follow-up, drug therapy, and surgical treatment. Current methods of assessing facial palsy are either (i) objective but inaccurate, (ii) subjective and, thus, depending on the clinician’s level of experience, or (iii) based on static data. To address the aforementioned problems, we implemented a deep learning algorithm to assess facial movements during smiling. Such a model was trained on a dataset that contains healthy smiles only following an anomaly detection strategy. Generally speaking, the degree of anomaly is computed by comparing the model’s suggested healthy smile with the person’s actual smile. The experimentation showed that the model successfully computed a high degree of anomaly when assessing the patients’ smiles. Furthermore, a graphical user interface was developed to test its practical usage in a clinical routine. In conclusion, we present a deep learning model, implemented on open-source software, designed to help clinicians to assess facial movements. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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18 pages, 4751 KiB  

Open AccessArticle

Research on the Recognition of Various Muscle Fatigue States in Resistance Strength Training

by Yinghao Wang, Chunfu Lu, Mingyu Zhang, Jianfeng Wu and Zhichuan Tang

Healthcare 2022, 10(11), 2292; https://doi.org/10.3390/healthcare10112292 - 15 Nov 2022

Cited by 1 | Viewed by 1455

Abstract

Instantly and accurately identifying the state of dynamic muscle fatigue in resistance training can help fitness trainers to build a more scientific and reasonable training program. By investigating the isokinetic flexion and extension strength training of the knee joint, this paper tried to [...] Read more.

Instantly and accurately identifying the state of dynamic muscle fatigue in resistance training can help fitness trainers to build a more scientific and reasonable training program. By investigating the isokinetic flexion and extension strength training of the knee joint, this paper tried to extract surface electromyogram (sEMG) features and establish recognition models to classify muscle states of the target muscles in the isokinetic strength training of the knee joint. First, an experiment was carried out to collect the sEMG signals of the target muscles. Second, two nonlinear dynamic indexes, wavelet packet entropy (WPE) and power spectrum entropy (PSE), were extracted from the obtained sEMG signals to verify the feasibility of characterizing muscle fatigue. Third, a convolutional neural network (CNN) recognition model was constructed and trained with the obtained sEMG experimental data to enable the extraction and recognition of EMG deep features. Finally, the CNN recognition model was compared with multiple support vector machines (Multi-SVM) and multiple linear discriminant analysis (Multi-LDA). The results showed that the CNN model had a better classification accuracy. The overall recognition accuracy of the CNN model applied to the test data (91.38%) was higher than that of the other two models, which verified that the CNN dynamic fatigue recognition model based on subjective and objective information feedback had better recognition performance. Furthermore, training on a larger dataset could further improve the recognition accuracy of the CNN recognition model. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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9 pages, 710 KiB  

Open AccessArticle

Validation of a Natural Language Processing Algorithm for the Extraction of the Sleep Parameters from the Polysomnography Reports

by Mahbubur Rahman, Sara Nowakowski, Ritwick Agrawal, Aanand Naik, Amir Sharafkhaneh and Javad Razjouyan

Healthcare 2022, 10(10), 1837; https://doi.org/10.3390/healthcare10101837 - 22 Sep 2022

Cited by 3 | Viewed by 2033

Abstract

Background: There is a need to better understand the association between sleep and chronic diseases. In this study we developed a natural language processing (NLP) algorithm to mine polysomnography (PSG) free-text notes from electronic medical records (EMR) and evaluated the performance. Methods: Using [...] Read more.

Background: There is a need to better understand the association between sleep and chronic diseases. In this study we developed a natural language processing (NLP) algorithm to mine polysomnography (PSG) free-text notes from electronic medical records (EMR) and evaluated the performance. Methods: Using the Veterans Health Administration EMR, we identified 46,093 PSG studies using CPT code 95,810 from 1 October 2000–30 September 2019. We randomly selected 200 notes to compare the accuracy of the NLP algorithm in mining sleep parameters including total sleep time (TST), sleep efficiency (SE) and sleep onset latency (SOL), wake after sleep onset (WASO), and apnea-hypopnea index (AHI) compared to visual inspection by raters masked to the NLP output. Results: The NLP performance on the training phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. The NLP performance on the test phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. Conclusions: This study showed that NLP is an accurate technique to extract sleep parameters from PSG reports in the EMR. Thus, NLP can serve as an effective tool in large health care systems to evaluate and improve patient care. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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13 pages, 6386 KiB  

Open AccessArticle

Deep Learning Model for Classifying Periodontitis Stages on Dental Panoramic Radiography

by Ho Sun Shon, Vungsovanreach Kong, Jae Sung Park, Wooyeong Jang, Eun Jong Cha, Sang-Yup Kim, Eun-Young Lee, Tae-Geon Kang and Kyung Ah Kim

Appl. Sci. 2022, 12(17), 8500; https://doi.org/10.3390/app12178500 - 25 Aug 2022

Cited by 4 | Viewed by 3276

Abstract

In this study, an integrated deep learning framework was developed for classifying the periodontitis stages of each individual tooth using dental panoramic radiographs. Based on actual patient panoramic radiographs data, the bone loss by periodontitis and cementoenamel junction boundaries were detected, while the [...] Read more.

In this study, an integrated deep learning framework was developed for classifying the periodontitis stages of each individual tooth using dental panoramic radiographs. Based on actual patient panoramic radiographs data, the bone loss by periodontitis and cementoenamel junction boundaries were detected, while the tooth number and tooth length were identified using data from AIHub, an open database platform. The two factors were integrated to classify and to evaluate the periodontitis staging on dental panoramic radiography. Periodontitis is classified into four stages based on the criteria of the radiographic bone level, as suggested at the relevant international conference in 2017. For the integrated deep learning framework developed in this study, the classification performance was evaluated by comparing the results of dental specialists, which indicated that the integrated framework had an accuracy of 0.929, with a recall and precision of 0.807 and 0.724, respectively, in average across all four stages. The novel framework was thus shown to exhibit a relatively high level of performance, and the findings in this study are expected to assist dental specialists with detecting the periodontitis stage and subsequent effective treatment. A systematic application will be developed in the future, to provide ancillary data for diagnosis and basic data for the treatment and prevention of periodontal disease. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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23 pages, 3053 KiB  

Open AccessArticle

A Human Defecation Prediction Method Based on Multi-Domain Features and Improved Support Vector Machine

by Lin Li, Yuwei Ke, Tie Zhang, Jun Zhao and Zequan Huang

Symmetry 2022, 14(9), 1763; https://doi.org/10.3390/sym14091763 - 24 Aug 2022

Cited by 3 | Viewed by 1505

Abstract

The difficulty of defecation seriously affects the quality of life of the bedridden elderly. To solve the problem that it is difficult to know the defecation time of the bedridden elderly, this paper proposed a human pre-defecation prediction method based on multi-domain features [...] Read more.

The difficulty of defecation seriously affects the quality of life of the bedridden elderly. To solve the problem that it is difficult to know the defecation time of the bedridden elderly, this paper proposed a human pre-defecation prediction method based on multi-domain features and improved support vector machine (SVM) using bowel sound as the original signal. The method includes three stages: multi-domain features extraction, feature optimization, and defecation prediction. In the stage of multi-domain features extraction, statistical analysis, fast Fourier transform (FFT), and wavelet packet transform are used to extract feature information in the time domain, frequency domain, and time-frequency domain. The symmetry of the bowel sound signal in the time domain, frequency domain, and time-frequency domain will change when the human has the urge to defecate. In the feature optimization stage, the Fisher Score (FS) algorithm is introduced to select meaningful and sensitive features according to the importance of each feature, aiming to remove redundant information and improve computational efficiency. In the stage of defecation prediction, SVM is optimized by the gray wolf optimization (GWO) algorithm to realize human defecation prediction. Finally, experimental analysis of the bowel sound data collected during the study is carried out. The experimental result shows that the proposed method could achieve an accuracy of 92.86% in defecation prediction, which proves the effectiveness of the proposed method. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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24 pages, 1791 KiB  

Open AccessArticle

Operating Room Usage Time Estimation with Machine Learning Models

by Justin Chu, Chung-Ho Hsieh, Yi-Nuo Shih, Chia-Chun Wu, Anandakumar Singaravelan, Lun-Ping Hung and Jia-Lien Hsu

Healthcare 2022, 10(8), 1518; https://doi.org/10.3390/healthcare10081518 - 12 Aug 2022

Cited by 5 | Viewed by 1811

Abstract

Effectively handling the limited number of surgery operating rooms equipped with expensive equipment is a challenging task for hospital management such as reducing the case-time duration and reducing idle time. Improving the efficiency of operating room usage via reducing the idle time with [...] Read more.

Effectively handling the limited number of surgery operating rooms equipped with expensive equipment is a challenging task for hospital management such as reducing the case-time duration and reducing idle time. Improving the efficiency of operating room usage via reducing the idle time with better scheduling would rely on accurate estimation of surgery duration. Our model can achieve a good prediction result on surgery duration with a dozen of features. We have found the result of our best performing department-specific XGBoost model with the values 31.6 min, 18.71 min, 0.71, 28% and 27% for the metrics of root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination ($R^2$), mean absolute percentage error (MAPE) and proportion of estimated result within 10% variation, respectively. We have presented each department-specific result with our estimated results between 5 and 10 min deviation would be more informative to the users in the real application. Our study shows comparable performance with previous studies, and the machine learning methods use fewer features that are better suited for universal usability. Full article

(This article belongs to the Topic Artificial Intelligence in Healthcare - 2nd Volume)

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