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Artificial Intelligence for Better Healthcare and Precision Medicine

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 12326

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Dr. Yu Tian

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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

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Keywords

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

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Published Papers (8 papers)

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Research

12 pages, 2635 KiB

Open AccessArticle

Storage and Query of Drug Knowledge Graphs Using Distributed Graph Databases: A Case Study

by Xingjian Han and Yu Tian

Bioengineering 2025, 12(2), 115; https://doi.org/10.3390/bioengineering12020115 - 26 Jan 2025

Viewed by 332

Abstract

Background: Distributed graph databases are a promising method for storing and conducting complex pathway queries on large-scale drug knowledge graphs to support drug research. However, there is a research gap in evaluating drug knowledge graphs’ storage and query performance based on distributed graph databases. This study evaluates the feasibility and performance of distributed graph databases in managing large-scale drug knowledge graphs. Methods: First, a drug knowledge graph storage and query system is designed based on the Nebula Graph database. Second, the system’s writing and query performance is evaluated. Finally, two drug repurposing benchmarks are used to provide a more extensive and reliable assessment. Results: The performance of distributed graph databases surpasses that of single-machine databases, including data writing, regular queries, constrained queries, and concurrent queries. Additionally, the advantages of distributed graph databases in writing performance become more pronounced as the data volume increases. The query performance benefits of distributed graph databases also improve with the complexity of query tasks. The drug repurposing evaluation results show that 78.54% of the pathways are consistent with currently approved drug treatments according to repoDB. Additionally, 12 potential pathways for new drug indications are found to have literature support according to DrugRepoBank. Conclusions: The proposed system is able to construct, store, and query a large graph of multisource drug knowledge and provides reliable and explainable drug–disease paths for drug repurposing. Full article

(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine)

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27 pages, 11482 KiB

Open AccessArticle

Clean Self-Supervised MRI Reconstruction from Noisy, Sub-Sampled Training Data with Robust SSDU

by Charles Millard and Mark Chiew

Bioengineering 2024, 11(12), 1305; https://doi.org/10.3390/bioengineering11121305 - 23 Dec 2024

Viewed by 634

Abstract

Most existing methods for magnetic resonance imaging (MRI) reconstruction with deep learning use fully supervised training, which assumes that a fully sampled dataset with a high signal-to-noise ratio (SNR) is available for training. In many circumstances, however, such a dataset is highly impractical or even technically infeasible to acquire. Recently, a number of self-supervised methods for MRI reconstruction have been proposed, which use sub-sampled data only. However, the majority of such methods, such as Self-Supervised Learning via Data Undersampling (SSDU), are susceptible to reconstruction errors arising from noise in the measured data. In response, we propose Robust SSDU, which provably recovers clean images from noisy, sub-sampled training data by simultaneously estimating missing k-space samples and denoising the available samples. Robust SSDU trains the reconstruction network to map from a further noisy and sub-sampled version of the data to the original, singly noisy, and sub-sampled data and applies an additive Noisier2Noise correction term upon inference. We also present a related method, Noiser2Full, that recovers clean images when noisy, fully sampled data are available for training. Both proposed methods are applicable to any network architecture, are straightforward to implement, and have a similar computational cost to standard training. We evaluate our methods on the multi-coil fastMRI brain dataset with novel denoising-specific architecture and find that it performs competitively with a benchmark trained on clean, fully sampled data. Full article

(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine)

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19 pages, 1364 KiB

Open AccessArticle

A New Breast Cancer Discovery Strategy: A Combined Outlier Rejection Technique and an Ensemble Classification Method

by Shereen H. Ali and Mohamed Shehata

Bioengineering 2024, 11(11), 1148; https://doi.org/10.3390/bioengineering11111148 - 15 Nov 2024

Viewed by 741

Abstract

Annually, many people worldwide lose their lives due to breast cancer, making it one of the most prevalent cancers in the world. Since the disease is becoming more common, early detection of breast cancer is essential to avoiding serious complications and possibly death as well. This research provides a novel Breast Cancer Discovery (BCD) strategy to aid patients by providing prompt and sensitive detection of breast cancer. The two primary steps that form the BCD are the Breast Cancer Discovery Step (BCDS) and the Pre-processing Step (P²S). In the P²S, the needed data are filtered from any non-informative data using three primary operations: data normalization, feature selection, and outlier rejection. Only then does the diagnostic model in the BCDS for precise diagnosis begin to be trained. The primary contribution of this research is the novel outlier rejection technique known as the Combined Outlier Rejection Technique (CORT). CORT is divided into two primary phases: (i) the Quick Rejection Phase (QRP), which is a quick phase utilizing a statistical method, and (ii) the Accurate Rejection Phase (ARP), which is a precise phase using an optimization method. Outliers are rapidly eliminated during the QRP using the standard deviation, and the remaining outliers are thoroughly eliminated during ARP via Binary Harris Hawk Optimization (BHHO). The P²S in the BCD strategy indicates that data normalization is a pre-processing approach used to find numeric values in the datasets that fall into a predetermined range. Information Gain (IG) is then used to choose the optimal subset of features, and CORT is used to reject incorrect training data. Furthermore, based on the filtered data from the P²S, an Ensemble Classification Method (ECM) is utilized in the BCDS to identify breast cancer patients. This method consists of three classifiers: Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The Wisconsin Breast Cancer Database (WBCD) dataset, which contains digital images of fine-needle aspiration samples collected from patients’ breast masses, is used herein to compare the BCD strategy against several contemporary strategies. According to the outcomes of the experiment, the suggested method is very competitive. It achieves 0.987 accuracy, 0.013 error, 0.98 recall, 0.984 precision, and a run time of 3 s, outperforming all other methods from the literature. Full article

(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine)

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14 pages, 1765 KiB

Open AccessArticle

XAI-Augmented Voting Ensemble Models for Heart Disease Prediction: A SHAP and LIME-Based Approach

by Nermeen Gamal Rezk, Samah Alshathri, Amged Sayed, Ezz El-Din Hemdan and Heba El-Behery

Bioengineering 2024, 11(10), 1016; https://doi.org/10.3390/bioengineering11101016 - 12 Oct 2024

Cited by 1 | Viewed by 1668

Abstract

Ensemble Learning (EL) has been used for almost ten years to classify heart diseases, but it is still difficult to grasp how the “black boxes”, or non-interpretable models, behave inside. Predicting heart disease is crucial to healthcare, since it allows for prompt diagnosis and treatment of the patient’s true state. Nonetheless, it is still difficult to forecast illness with any degree of accuracy. In this study, we have suggested a framework for the prediction of heart disease based on Explainable artificial intelligence (XAI)-based hybrid Ensemble Learning (EL) models, such as LightBoost and XGBoost algorithms. The main goals are to build predictive models and apply SHAP (SHapley Additive expPlanations) and LIME (Local Interpretable Model-agnostic Explanations) analysis to improve the interpretability of the models. We carefully construct our systems and test different hybrid ensemble learning algorithms to determine which model is best for heart disease prediction (HDP). The approach promotes interpretability and transparency when examining these widespread health issues. By combining hybrid Ensemble learning models with XAI, the important factors and risk signals that underpin the co-occurrence of heart disease are made visible. The accuracy, precision, and recall of such models were used to evaluate their efficacy. This study highlights how crucial it is for healthcare models to be transparent and recommends the inclusion of XAI to improve interpretability and medical decisionmaking. Full article

(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine)

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20 pages, 1712 KiB

Open AccessArticle

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

Cited by 2 | Viewed by 1465

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1625

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 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

Open AccessArticle

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

Cited by 3 | Viewed by 2272

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 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

Open AccessArticle

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 - 2 Jan 2024

Cited by 1 | Viewed by 2282

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 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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