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Novel Approaches for Machine Learning in Healthcare Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 4528

Special Issue Editor

Special Issue Information

Dear Colleagues,

Conventional research covers a limited range of predictors for healthcare applications, using statistical models with an unrealistic assumption of ceteris paribus, i.e., “all the other variables staying constant”. For this reason, emerging literature employs artificial intelligence for healthcare applications. It is free from unrealistic assumptions of “all the other variables staying constant”. It delivers important values and rankings of predictors for healthcare applications (e.g., SHAP plots). Moreover, the notions of generative artificial intelligence and reinforcement learning are enjoying immense popularity now. Given a sequence of words, generative artificial intelligence generates a sequence of their probabilities based on BERT or GPT. Its astonishing performance comes from the attention mechanism (in which different input words receive different weights based on their similarity with the output word). And reinforcement learning is a branch of machine learning in which (1) the environment presents a series of rewards, (2) an agent takes a series of actions to maximize the cumulative reward in response, and (3) the environment moves to the next period with the given transition probabilities. In fact, it has been reinforcement learning that has brought the notion of artificial intelligence to worldwide popularity since the publication of a seminal article on Alpha-Go in 2016. Two revolutionary ideas behind reinforcement learning were that artificial intelligence (e.g., Alpha-Go) starts like a human player, i.e., takes a series of actions and maximizes the cumulative reward (chance of victory) from the limited information available in limited periods only, and that it moves far beyond the best human player ever based on the sheer power of big data covering all human players to date. Little examination has been conducted and more investigation is needed on these important issues. In this context, this Special Issue invites original and review articles on novel approaches for machine learning in healthcare applications.

Dr. Kwang-Sig Lee
Guest Editor

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Keywords

  • machine learning
  • deep learning
  • explainable artificial intelligence
  • SHAP
  • generative artificial intelligence
  • BERT
  • GPT
  • reinforcement learning

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Published Papers (2 papers)

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Research

20 pages, 1193 KiB  
Article
Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach
by Eduardo Maekawa, Mariana Mendes de Sá Martins, Carina Akemi Nakamura, Ricardo Araya, Tim J. Peters, Pepijn Van de Ven and Marcia Scazufca
Appl. Sci. 2024, 14(16), 7258; https://doi.org/10.3390/app14167258 - 18 Aug 2024
Viewed by 793
Abstract
Anxiety in older individuals is understudied despite its prevalence. Investigating its occurrence can be challenging, yet understanding the factors influencing its recurrence is important. Gaining insights into these factors through an explainable, probabilistic approach can enhance improved management. A Bayesian network (BN) is [...] Read more.
Anxiety in older individuals is understudied despite its prevalence. Investigating its occurrence can be challenging, yet understanding the factors influencing its recurrence is important. Gaining insights into these factors through an explainable, probabilistic approach can enhance improved management. A Bayesian network (BN) is well-suited for this purpose. This study aimed to model the recurrence of anxiety symptomatology in an older population within a five-month timeframe. Data included baseline socio-demographic and general health information for older adults aged 60 years or older with at least mild depressive symptoms. A BN model explored the relationship between baseline data and recurrent anxiety symptomatology. Model evaluation employed the Area Under the Receiver Operating Characteristic Curve (AUC). The BN model was also compared to four machine learning models. The model achieved an AUC of 0.821 on the test data, using a threshold of 0.367. The model demonstrated generalisation abilities while being less complex and more explainable than other machine learning models. Key factors associated with recurrence of anxiety symptomatology were: “Not being able to stop or control worrying”; “Becoming easily annoyed or irritable”; “Trouble relaxing”; and “depressive symptomatology severity”. These findings indicate a prioritised sequence of predictors to identify individuals most likely to experience recurrent anxiety symptomatology. Full article
(This article belongs to the Special Issue Novel Approaches for Machine Learning in Healthcare Applications)
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12 pages, 4509 KiB  
Article
Application of Artificial Intelligence in the Mammographic Detection of Breast Cancer in Saudi Arabian Women
by Rowa Aljondi, Salem Saeed Alghamdi, Abdulrahman Tajaldeen, Shareefah Alassiri, Monagi H. Alkinani and Thomas Bertinotti
Appl. Sci. 2023, 13(21), 12087; https://doi.org/10.3390/app132112087 - 6 Nov 2023
Cited by 1 | Viewed by 3149
Abstract
Background: Breast cancer has a 14.8% incidence rate and an 8.5% fatality rate in Saudi Arabia. Mammography is useful for the early detection of breast cancer. Researchers have been developing artificial intelligence (AI) algorithms for early breast cancer diagnosis and reducing false-positive mammography [...] Read more.
Background: Breast cancer has a 14.8% incidence rate and an 8.5% fatality rate in Saudi Arabia. Mammography is useful for the early detection of breast cancer. Researchers have been developing artificial intelligence (AI) algorithms for early breast cancer diagnosis and reducing false-positive mammography results. The aim of this study was to examine the performance and accuracy of an AI system in breast cancer screening among Saudi women. Materials and Methods: This is a retrospective cross-sectional study that included 378 mammograms collected from 2017 to 2021 from government hospitals in Jeddah, Saudi Arabia. The patients’ demographic and clinical information were collected from files and electronic medical records. The radiologists’ assessments of the mammograms were based on Breast Imaging Reporting and Data System (BIRADS) scores. Follow-up or biopsy reports verified the radiologists’ findings. The MammoScreen system was the AI tool used in this study. Data were analyzed using SPSS Version 25. Results: The patients’ mean age was 50.31 years. Most patients had breast density B (42.3%) followed by A (27.2%) and C (25.9%). Most malignant cases were invasive ductal carcinomas (37.3%). Of the 181 cancer cases, 36.9% were BIRADS category V. The area under the curve for the AI detection (0.923; 95% confidence interval [CI], 0.893–0.954) was greater than that for the radiologists’ interpretation (0.838; 95% CI, 0.796–0.881). The AI detection agreed with the histopathological result in 167 positive (91.3%) and 182 negative cases (93.3%). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the AI system were 92.8%, 91.9%, 91.3%, 93.3%, and 92.3%, respectively. The radiologist’s interpretation agreed with the pathology report in 180 positive (73.8%) and 134 negative cases (100%). Its sensitivity, specificity, PPV, NPV, and accuracy were 100%, 67.7%, 73.8%, 100%, and 83.1%, respectively. Conclusions: The AI system tested in this study had better accuracy and diagnostic performance than the radiologists and thus could be used as a support diagnostic tool for breast cancer detection in clinical practice and to reduce false-positive recalls. Full article
(This article belongs to the Special Issue Novel Approaches for Machine Learning in Healthcare Applications)
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