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Bioengineering
Special Issues
Artificial Intelligence for Better Healthcare and Precision Medicine
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Artificial Intelligence for Better Healthcare and Precision Medicine

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4541

Special Issue Editor

Dr. Yu Tian

E-Mail Website
Guest Editor
College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310058, China
Interests: medical informatics; clinical decision support system; knowledge graph; clinical data privacy computing

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has emerged as a disruptive technology in healthcare and precision medicine, offering immense potential to revolutionize the field. With the growing availability of patient data and the increasing complexity of medical decision-making processes, AI presents opportunities to enhance patient care, improve treatment outcomes, and facilitate precision medicine approaches. This Special Issue explores the applications of AI in healthcare and precision medicine, highlighting its impact on disease diagnosis, treatment selection, medical imaging, drug discovery, and healthcare resource management.

Disease Diagnosis and Prognosis:

AI algorithms excel in analyzing large and diverse datasets, enabling accurate disease identification, risk assessment, and prognostic predictions. By leveraging machine learning techniques, AI systems can analyze electronic health records, genomic data, and sensor readings, facilitating early detection, precise diagnoses, and personalized prognosis for various diseases.

Treatment Selection and Optimization:

AI algorithms assist healthcare professionals in selecting the most effective treatment strategies for individual patients. By integrating patient-specific data with clinical guidelines and medical knowledge, AI systems can provide tailored and evidence-based treatment recommendations, leading to improved outcomes and minimized adverse effects.

Medical Imaging and Diagnostics:

AI has transformed medical imaging interpretation by enabling the automated analysis of radiological images. Deep learning algorithms can detect anomalies, identify patterns, and assist in the early detection of diseases such as cancer. This enhances the accuracy of diagnoses, reduces human error, and speeds up the interpretation process.

Drug Discovery and Development:

AI accelerates the drug discovery and development process by expediting the analysis of vast chemical and biological datasets. Machine learning algorithms can predict drug–target interactions, identify potential drug candidates, and optimize drug design, helping researchers and pharmaceutical companies convey new therapies to the market more rapidly.

Healthcare Resource Management:

AI plays a crucial role in optimizing healthcare resource utilization, improving efficiency, and reducing costs. AI algorithms can analyze patient data, predict disease trends, optimize hospital workflows, and assist in resource allocation, ensuring that healthcare resources are allocated effectively and equitably based on patient needs.

Large language models for better healthcare:

Large Language Model is a deep learning-based AI technology that can understand and generate natural language to enable intelligent interaction with medical data and human users. Large language models have broad prospects and potential in clinical applications (such as clinical Q&A and clinical text analysis), and can help doctors and patients improve medical quality and efficiency.

Overall, this Special Issue explores the potential of AI to transform healthcare and precision medicine by leveraging vast amounts of data and sophisticated algorithms. From disease diagnosis and treatment selection to medical imaging analysis and drug discovery, AI-driven solutions have the capacity to improve patient care, enhance precision medicine approaches, and optimize healthcare resource management. While there are challenges and ethical considerations to address, the integration of AI in healthcare holds great promise for enabling enhanced patient outcomes, improved efficiency, and personalized care.

Dr. Yu Tian
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. Bioengineering 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 2700 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

  • artificial intelligence
  • healthcare
  • precision medicine
  • disease diagnosis
  • prognosis
  • drug discovery
  • personalized treatment selection
  • healthcare resource management
  • large language models

Published Papers (4 papers)

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Research

20 pages, 1712 KiB  
Article
Patch-Level Feature Selection for Thoracic Disease Classification by Chest X-ray Images Using Information Bottleneck
by Manh Hung-Nguyen
Bioengineering 2024, 11(4), 316; https://doi.org/10.3390/bioengineering11040316 - 26 Mar 2024
Viewed by 560
Abstract
Chest X-ray (CXR) examination serves as a widely employed clinical test in medical diagnostics. Many studied have tried to apply artificial intelligence (AI) programs to analyze CXR images. Despite numerous positive outcomes, assessing the applicability of AI models for comprehensive diagnostic support remains [...] Read more.
Chest X-ray (CXR) examination serves as a widely employed clinical test in medical diagnostics. Many studied have tried to apply artificial intelligence (AI) programs to analyze CXR images. Despite numerous positive outcomes, assessing the applicability of AI models for comprehensive diagnostic support remains a formidable challenge. We observed that, even when AI models exhibit high accuracy on one dataset, their performance may deteriorate when tested on another. To address this issue, we propose incorporating a variational information bottleneck (VIB) at the patch level to enhance the generalizability of diagnostic support models. The VIB introduces a probabilistic model aimed at approximating the posterior distribution of latent variables given input data, thereby enhancing the model’s generalization capabilities on unseen data. Unlike the conventional VIB approaches that flatten features and use a re-parameterization trick to sample a new latent feature, our method applies the trick to 2D feature maps. This design allows only important pixels to respond, and the model will select important patches in an image. Moreover, the proposed patch-level VIB seamlessly integrates with various convolutional neural networks, offering a versatile solution to improve performance. Experimental results illustrate enhanced accuracy in standard experiment settings. In addition, the method shows robust improvement when training and testing on different datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine)
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17 pages, 2642 KiB  
Article
Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label
by Guangya Yu, Qi Ye and Tong Ruan
Bioengineering 2024, 11(3), 225; https://doi.org/10.3390/bioengineering11030225 - 27 Feb 2024
Viewed by 895
Abstract
The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in [...] Read more.
The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledge graphs via intrinsic labEL (EMKGEL). Considering the absence of hyper-view KG, we establish a hyper-view KG and a triplet-level KG for implicit label information and neighborhood information, respectively. Inspired by the success of graph attention networks (GATs), we introduce the hyper-view GAT to incorporate label messages and neighborhood information into representation learning. We leverage a confidence score that combines local and global trustworthiness to estimate the triplets. To validate the effectiveness of our approach, we conducted experiments on three publicly available MKGs, namely PharmKG-8k, DiseaseKG, and DiaKG. Compared with the baseline models, the Precision@K value improved by 0.7%, 6.1%, and 3.6%, respectively, on these datasets. Furthermore, our method empirically showed that it significantly outperformed the baseline on a general knowledge graph, Nell-995. Full article
(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine)
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20 pages, 4294 KiB  
Article
DSP-KD: Dual-Stage Progressive Knowledge Distillation for Skin Disease Classification
by Xinyi Zeng, Zhanlin Ji, Haiyang Zhang, Rui Chen, Qinping Liao, Jingkun Wang, Tao Lyu and Li Zhao
Bioengineering 2024, 11(1), 70; https://doi.org/10.3390/bioengineering11010070 - 10 Jan 2024
Viewed by 1226
Abstract
The increasing global demand for skin disease diagnostics emphasizes the urgent need for advancements in AI-assisted diagnostic technologies for dermatoscopic images. In current practical medical systems, the primary challenge is balancing lightweight models with accurate image analysis to address constraints like limited storage [...] Read more.
The increasing global demand for skin disease diagnostics emphasizes the urgent need for advancements in AI-assisted diagnostic technologies for dermatoscopic images. In current practical medical systems, the primary challenge is balancing lightweight models with accurate image analysis to address constraints like limited storage and computational costs. While knowledge distillation methods hold immense potential in healthcare applications, related research on multi-class skin disease tasks is scarce. To bridge this gap, our study introduces an enhanced multi-source knowledge fusion distillation framework, termed DSP-KD, which improves knowledge transfer in a dual-stage progressive distillation approach to maximize mutual information between teacher and student representations. The experimental results highlight the superior performance of our distilled ShuffleNetV2 on both the ISIC2019 dataset and our private skin disorders dataset. Compared to other state-of-the-art distillation methods using diverse knowledge sources, the DSP-KD demonstrates remarkable effectiveness with a smaller computational burden. Full article
(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine)
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16 pages, 2163 KiB  
Article
Blood Urea Nitrogen-to-Albumin Ratio May Predict Mortality in Patients with Traumatic Brain Injury from the MIMIC Database: A Retrospective Study
by Yiran Guo, Yuxin Leng and Chengjin Gao
Bioengineering 2024, 11(1), 49; https://doi.org/10.3390/bioengineering11010049 - 02 Jan 2024
Viewed by 1214
Abstract
Traumatic brain injury (TBI), a major global health burden, disrupts the neurological system due to accidents and other incidents. While the Glasgow coma scale (GCS) gauges neurological function, it falls short as the sole predictor of overall mortality in TBI patients. This highlights [...] Read more.
Traumatic brain injury (TBI), a major global health burden, disrupts the neurological system due to accidents and other incidents. While the Glasgow coma scale (GCS) gauges neurological function, it falls short as the sole predictor of overall mortality in TBI patients. This highlights the need for comprehensive outcome prediction, considering not just neurological but also systemic factors. Existing approaches relying on newly developed biomolecules face challenges in clinical implementation. Therefore, we investigated the potential of readily available clinical indicators, like the blood urea nitrogen-to-albumin ratio (BAR), for improved mortality prediction in TBI. In this study, we investigated the significance of the BAR in predicting all-cause mortality in TBI patients. In terms of research methodologies, we gave preference to machine learning methods due to their exceptional performance in clinical support in recent years. Initially, we obtained data on TBI patients from the Medical Information Mart for Intensive Care database. A total of 2602 patients were included, of whom 2260 survived and 342 died in hospital. Subsequently, we performed data cleaning and utilized machine learning techniques to develop prediction models. We employed a ten-fold cross-validation method to obtain models with enhanced accuracy and area under the curve (AUC) (Light Gradient Boost Classifier accuracy, 0.905 ± 0.016, and AUC, 0.888; Extreme Gradient Boost Classifier accuracy, 0.903 ± 0.016, and AUC, 0.895; Gradient Boost Classifier accuracy, 0.898 ± 0.021, and AUC, 0.872). Simultaneously, we derived the importance ranking of the variable BAR among the included variables (in Light Gradient Boost Classifier, the BAR ranked fourth; in Extreme Gradient Boost Classifier, the BAR ranked sixth; in Gradient Boost Classifier, the BAR ranked fifth). To further evaluate the clinical utility of BAR, we divided patients into three groups based on their BAR values: Group 1 (BAR < 4.9 mg/g), Group 2 (BAR ≥ 4.9 and ≤10.5 mg/g), and Group 3 (BAR ≥ 10.5 mg/g). This stratification revealed significant differences in mortality across all time points: in-hospital mortality (7.61% vs. 15.16% vs. 31.63%), as well as one-month (8.51% vs. 17.46% vs. 36.39%), three-month (9.55% vs. 20.14% vs. 41.84%), and one-year mortality (11.57% vs. 23.76% vs. 46.60%). Building on this observation, we employed the Cox proportional hazards regression model to assess the impact of BAR segmentation on survival. Compared to Group 1, Groups 2 and 3 had significantly higher hazard ratios (95% confidence interval (CI)) for one-month mortality: 1.77 (1.37–2.30) and 3.17 (2.17–4.62), respectively. To further underscore the clinical potential of BAR as a standalone measure, we compared its performance to established clinical scores, like sequential organ failure assessment (SOFA), GCS, and acute physiology score III(APS-III), using receiver operator characteristic curve (ROC) analysis. Notably, the AUC values (95%CI) of the BAR were 0.67 (0.64–0.70), 0.68 (0.65–0.70), and 0.68 (0.65–0.70) for one-month mortality, three-month mortality, and one-year mortality. The AUC value of the SOFA did not significantly differ from that of the BAR. In conclusion, the BAR is a highly influential factor in predicting mortality in TBI patients and should be given careful consideration in future TBI prediction research. The blood urea nitrogen-to-albumin ratio may predict mortality in TBI patients. Full article
(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine)
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