Intelligence-based medicine最新文献

{"title":"A multimodal machine learning model for bipolar disorder mania classification: Insights from acoustic, linguistic, and visual cues","authors":"Kiruthiga Devi Murugavel ,&nbsp;Parthasarathy R ,&nbsp;Sandeep Kumar Mathivanan ,&nbsp;Saravanan Srinivasan ,&nbsp;Basu Dev Shivahare ,&nbsp;Mohd Asif Shah","doi":"10.1016/j.ibmed.2025.100223","DOIUrl":"10.1016/j.ibmed.2025.100223","url":null,"abstract":"<div><div>Mood fluctuations that can vary from manic to depressive states are a symptom of a disease known as bipolar disorder, which affects mental health. Interviews with patients and gathering information from their families are essential steps in the diagnostic process for bipolar disorder. Automated approaches for treating bipolar disorder are also being explored. In mental health prevention and care, machine learning techniques (ML) are increasingly used to detect and treat diseases. With frequently analyzed human behaviour patterns, identified symptoms, and risk factors as various parameters of the dataset, predictions can be made for improving traditional diagnosis methods. In this study, A Multimodal Fusion System was developed based on an auditory, linguistic, and visual patient recording as an input dataset for a three-stage mania classification decision system. Deep Denoising Autoencoders (DDAEs) are introduced to learn common representations across five modalities: acoustic characteristics, eye gaze, facial landmarks, head posture, and Facial Action Units (FAUs). This is done in particular for the audio-visual modality. The distributed representations and the transient information during each recording session are eventually encoded into Fisher Vectors (FVs), which capture the representations. Once the Fisher Vectors (FVs) and document embeddings are integrated, a Multi-Task Neural Network is used to perform the classification task, while mitigating overfitting issues caused by the limited size of the bipolar disorder dataset. The study introduces Deep Denoising Autoencoders (DDAEs) for cross-modal representation learning and utilizes Fisher Vectors with Multi-Task Neural Networks, enhancing diagnostic accuracy while highlighting the benefits of multimodal fusion for mental health diagnostics. Achieving an unweighted average recall score of 64.8 %, with the highest AUC-ROC of 0.85 &amp; less interface time of 6.5 ms/sample scores the effectiveness of integrating multiple modalities in improving system performance and advancing feature representation and model interpretability.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100223"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regulating intelligence: a systematic analysis of safety, ethics, and equity in artificial intelligence driven healthcare","authors":"Muayyad Ahmad","doi":"10.1016/j.ibmed.2025.100320","DOIUrl":"10.1016/j.ibmed.2025.100320","url":null,"abstract":"<div><h3>Objective</h3><div>The growing use of artificial intelligence (AI) in healthcare demands robust regulatory frameworks to ensure safety, ethics, and legal compliance, particularly regarding algorithmic transparency, data privacy, and bias. This systematic review analyzes AI regulations and grey literature (2019–2024) from the Food and Drug Administration (FDA), the World Health Organization (WHO), the Organization for Economic Co-operation and Development (OECD), and the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC).</div></div><div><h3>Methods</h3><div>This systematic review, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analysed 26 studies (Peer-reviewed studies: n = 17 &amp; Grey literature: n = 9) on AI regulatory frameworks in healthcare between 2019 and 2024. A systematic literature search and rigorous inclusion criteria ensured relevance, with data consolidated into safety, effectiveness, and ethical themes. The analysis integrates a low-middle income countries (LMIC) perspective via WHO policy and a handful of studies, but the main academic body is drawn primarily from high-income contexts.</div><div>Bias risk was assessed systematically.</div></div><div><h3>Results</h3><div>The reviewed studies highlight the critical need for AI regulatory frameworks in healthcare, focusing on patient safety, ethics, and trust. Key findings stress the necessity for transparent, equitable integration and clear guidelines addressing bias, legal issues, and validation. Grey literature consistently emphasizes risk-based safety models and principles like transparency and human oversight. However, a significant gap remains in translating equity commitments into enforceable standards for bias mitigation, underscoring a critical need for future regulatory action.</div></div><div><h3>Conclusion</h3><div>This review identifies critical gaps in AI regulatory frameworks, particularly in equity, real-world validation, and liability, and proposes actionable, interdisciplinary strategies to ensure AI's safe, ethical, and equitable integration into healthcare.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100320"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open-source small language models for personal medical assistant chatbots","authors":"Matteo Magnini ,&nbsp;Gianluca Aguzzi ,&nbsp;Sara Montagna","doi":"10.1016/j.ibmed.2024.100197","DOIUrl":"10.1016/j.ibmed.2024.100197","url":null,"abstract":"<div><div>Medical chatbots are becoming essential components of telemedicine applications as tools to assist patients in the self-management of their conditions. This trend is particularly driven by advancements in natural language processing techniques with pre-trained language models (LMs). However, the integration of LMs into clinical environments faces challenges related to reliability and privacy concerns.</div><div>This study seeks to address these issues by exploiting a <em>privacy by design</em> architectural solution that utilises the fully local deployment of open-source LMs. Specifically, to mitigate any risk of information leakage, we focus on evaluating the performance of open-source language models (SLMs) that can be deployed on personal devices, such as smartphones or laptops, without stringent hardware requirements.</div><div>We assess the effectiveness of this solution adopting hypertension management as a case study. Models are evaluated across various tasks, including intent recognition and empathetic conversation, using Gemini Pro 1.5 as a benchmark. The results indicate that, for certain tasks such as intent recognition, Gemini outperforms other models. However, by employing the “large language model (LLM) as a judge” approach for semantic evaluation of response correctness, we found several models that demonstrate a close alignment with the ground truth. In conclusion, this study highlights the potential of locally deployed SLMs as components of medical chatbots, while addressing critical concerns related to privacy and reliability.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using big data to predict young adult ischemic vs. non-ischemic heart disease risk factors: An artificial intelligence based model","authors":"Salam Bani Hani ,&nbsp;Muayyad Ahmad","doi":"10.1016/j.ibmed.2025.100207","DOIUrl":"10.1016/j.ibmed.2025.100207","url":null,"abstract":"","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100207"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting COPD admissions using machine learning and SHAP: An exploratory multi-hospital study in Riyadh, Saudi Arabia","authors":"Anas Ali Alhur ,&nbsp;Jamilu Sani ,&nbsp;Mohamed Mustaf Ahmed","doi":"10.1016/j.ibmed.2025.100312","DOIUrl":"10.1016/j.ibmed.2025.100312","url":null,"abstract":"<div><h3>Background</h3><div>Chronic obstructive pulmonary disease (COPD) is a leading cause of hospitalization and mortality globally, placing a substantial burden on healthcare systems. In Saudi Arabia, COPD admissions are rising due to demographic shifts and environmental exposures. Accurate prediction of COPD-related hospitalizations is essential for timely intervention and resource planning. This study applied machine learning (ML) techniques to predict COPD admissions using routine hospital data from major healthcare facilities in Riyadh.</div></div><div><h3>Methods</h3><div>A cross-sectional analysis was conducted using 41,544 patient admission records from eight major hospitals in Saudi Arabia between 2022 and 2024. The dataset included demographic, clinical, and healthcare utilization variables. Several ML classifiers: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, and XGBoost, were developed and evaluated. The primary outcome was inpatient admission for COPD. Model performance was assessed using accuracy, precision, recall, F1-score, AUROC, and confusion matrices. SHapley Additive exPlanations (SHAP) were used to interpret model outputs and rank feature importance.</div></div><div><h3>Results</h3><div>The Random Forest model outperformed other classifiers with an accuracy of 0.73, precision of 0.70, recall of 0.79, F1-score of 0.74, and AUROC of 0.79. Key predictors identified by SHAP analysis included hospital name, admission count, comorbid conditions, and disease severity. Features such as gender and seasonal variation showed minimal influence on prediction outcomes. SHAP visualizations provided interpretable insights into individual-level risk contributions.</div></div><div><h3>Conclusion</h3><div>Machine learning models, particularly Random Forest, demonstrated moderate but promising capacity for predicting COPD admissions using routine hospital data. Model interpretability through SHAP enhances clinical relevance and supports early identification of high-risk patients. Integration of these tools into hospital systems may facilitate proactive care and improve resource allocation for respiratory conditions.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100312"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing breast cancer diagnosis with convolutional autoencoders: Enhanced performance through modified loss functions","authors":"ArunaDevi Karuppasamy ,&nbsp;Hamza zidoum ,&nbsp;Majda Said Sultan Al-Rashdi ,&nbsp;Maiya Al-Bahri","doi":"10.1016/j.ibmed.2025.100248","DOIUrl":"10.1016/j.ibmed.2025.100248","url":null,"abstract":"<div><div>The Deep Learning (DL) has demonstrated a significant impact on a various pattern recognition applications, resulting in significant advancements in areas such as visual recognition, autonomous cars, language processing, and healthcare. Nowadays, deep learning was widely applied on the medical images to identify the diseases efficiently. Still, the use of applications in clinical settings is now limited to a small number. The main factors to this might be due to an inadequate annotated data, noises in the images and challenges related to collecting data. Our research proposed a convolutional autoencoder to classify the breast cancer tumors, using the Sultan Qaboos University Hospital(SQUH) and BreakHis datasets. The proposed model named Convolutional AutoEncoder with modified Loss Function (CAE-LF) achieved a good performance, by attaining a F1-score of 0.90, recall of 0.89, and accuracy of 91%. The results obtained are comparable to those obtained in earlier researches. Additional analyses conducted on the SQUH dataset demonstrate that it yields a good performance with an F1-score of 0.91, 0.93, 0.92, and 0.93 for 4x, 10x, 20x, and 40x magnifications, respectively. Our study highlights the potential of deep learning in analyzing medical images to classify breast tumors.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mobile application LukaKu as a tool for detecting external wounds with artificial intelligence","authors":"Dessy Novita ,&nbsp;Herika Hayurani ,&nbsp;Eva Krishna Sutedja ,&nbsp;Firdaus Ryan Pratomo ,&nbsp;Achmad Dino Saputra ,&nbsp;Zahra Ramadhanti ,&nbsp;Nuryadin Abutani ,&nbsp;Muhammad Rafi Triandi ,&nbsp;Aldin Mubarok Guferol ,&nbsp;Anindya Apriliyanti Pravitasari ,&nbsp;Fajar Wira Adikusuma ,&nbsp;Atiek Rostika Noviyanti","doi":"10.1016/j.ibmed.2025.100200","DOIUrl":"10.1016/j.ibmed.2025.100200","url":null,"abstract":"<div><div>This study was conducted due to the lack of applications that can assist people intreating common external wounds. Therefore, we proposed the application of image-based detection which takes external wounds and identifies them using Artificial Intelligence namely LukaKu. In addition to detecting the type of wound that occurs, the application is expected to be able to produce first aid and medicine for each existing external wound label. The model used is YOLOv5 with various versions, namely YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. By calculating the validation data, each version has its own precision, recall, f1-score, and Mean Average Precision (mAP) values which are the comparison factors in determining the best model version, where YOLOv5l with mAP value of 0.785 is the best result and YOLOv5n with mAP value of 0.588 is the result with the lowest value. In the model development process, datasets of external injuries are needed to be used during the training process and test datasets for each existing model version. After each version of the model has been successfully built and analysed, the model with the best value is implemented in the mobile application, making it easier for users to access.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nanotechnology and machine learning: a promising confluence for the advancement of precision medicine","authors":"Shuaibu Saidu Musa ,&nbsp;Adamu Muhammad Ibrahim ,&nbsp;Muhammad Yasir Alhassan ,&nbsp;Abubakar Hafs Musa ,&nbsp;Abdulrahman Garba Jibo ,&nbsp;Auwal Rabiu Auwal ,&nbsp;Olalekan John Okesanya ,&nbsp;Zhinya Kawa Othman ,&nbsp;Muhammad Sadiq Abubakar ,&nbsp;Mohamed Mustaf Ahmed ,&nbsp;Carina Joane V. Barroso ,&nbsp;Abraham Fessehaye Sium ,&nbsp;Manuel B. Garcia ,&nbsp;James Brian Flores ,&nbsp;Adamu Safiyanu Maikifi ,&nbsp;M.B.N. Kouwenhoven ,&nbsp;Don Eliseo Lucero-Prisno","doi":"10.1016/j.ibmed.2025.100267","DOIUrl":"10.1016/j.ibmed.2025.100267","url":null,"abstract":"<div><div>The fusion of molecular-scale engineering in nanotechnology with machine learning (ML) analytics is reshaping the field of precision medicine. Nanoparticles enable ultrasensitive diagnostics, targeted drug and gene delivery, and high-resolution imaging, whereas ML models mine vast multimodal datasets to optimize nanoparticle design, enhance predictive accuracy, and personalize treatment in real-time. Recent breakthroughs include ML-guided formulations of lipid, polymeric, and inorganic carriers that cross biological barriers; AI-enhanced nanosensors that flag early disease from breath, sweat, or blood; and nanotheranostic agents that simultaneously track and treat tumors. Comparative insights into Retrieval-Augmented Generation and supervised learning pipelines reveal distinct advantages for nanodevice engineering across diverse data environments. An expanded focus on explainable AI tools, such as SHAP, LIME, Grad-CAM, and Integrated Gradients, highlights their role in enhancing transparency, trust, and interpretability in nano-enabled clinical decisions. A structured narrative review method was applied, and key ML model performances were synthesized to strengthen analytical clarity. Emerging biodegradable nanomaterials, autonomous micro-nanorobots, and hybrid lab-on-chip systems promise faster point-of-care decisions but raise pressing questions about data integrity, interpretability, scalability, regulation, ethics, and equitable access. Addressing these hurdles will require robust data standards, privacy safeguards, interdisciplinary R&amp;D networks, and flexible approval pathways to translate bench advances into bedside benefits for patients. This review synthesizes the current landscape, critical challenges, and future directions at the intersection of nanotechnology and ML in precision medicine.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100267"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing ResNet50 performance using stochastic gradient descent on MRI images for Alzheimer's disease classification","authors":"Mohamed Amine Mahjoubi ,&nbsp;Driss Lamrani ,&nbsp;Shawki Saleh ,&nbsp;Wassima Moutaouakil ,&nbsp;Asmae Ouhmida ,&nbsp;Soufiane Hamida ,&nbsp;Bouchaib Cherradi ,&nbsp;Abdelhadi Raihani","doi":"10.1016/j.ibmed.2025.100219","DOIUrl":"10.1016/j.ibmed.2025.100219","url":null,"abstract":"<div><div>The field of optimization is focused on the formulation, analysis, and resolution of problems involving the minimization or maximization of functions. A particular subclass of optimization problems, known as empirical risk minimization, involves fitting a model to observed data. These problems play a central role in various areas such as machine learning, statistical modeling, and decision theory, where the objective is to find a model that best approximates underlying patterns in the data by minimizing a specified loss or risk function. In deep learning (DL) systems, various optimization algorithms are utilized with the gradient descent (GD) algorithm being one of the most significant and effective. Research studies have improved the GD algorithm and developed various successful variants, including stochastic gradient descent (SGD) with momentum, AdaGrad, RMSProp, and Adam. This article provides a comparative analysis of these stochastic gradient descent algorithms based on their accuracy, loss, and training time, as well as the loss of each algorithm in generating an optimization solution. Experiments were conducted using Transfer Learning (TL) technique based on the pre-trained ResNet50 base model for image classification, with a focus on stochastic gradient (SG) for performances optimization. The case study in this work is based on a data extract from the Alzheimer's image dataset, which contains four classes such as Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented. The obtained results with the Adam and SGD momentum optimizers achieved the highest accuracy of 97.66 % and 97.58 %, respectively.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100219"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image-based machine learning model as a tool for classification of [18F]PR04.MZ PET images in patients with parkinsonian syndrome","authors":"Maria Jiménez ,&nbsp;Cristian Soza-Ried ,&nbsp;Vasko Kramer ,&nbsp;Sebastian A. Ríos ,&nbsp;Arlette Haeger ,&nbsp;Carlos Juri ,&nbsp;Horacio Amaral ,&nbsp;Pedro Chana-Cuevas","doi":"10.1016/j.ibmed.2025.100232","DOIUrl":"10.1016/j.ibmed.2025.100232","url":null,"abstract":"<div><div>Parkinsonian syndrome (PS) is characterized by bradykinesia, resting tremor, rigidity, and encapsulates the clinical manifestation observed in various neurodegenerative disorders. Positron emission tomography (PET) imaging plays an important role in diagnosing PS by detecting the progressive loss of dopaminergic neurons. This study aimed to develop and compare five machine-learning models for the automatic classification of 204 [¹⁸F]PR04.MZ PET images, distinguishing between patients with PS and subjects without clinical evidence for dopaminergic deficit (SWEDD). Previously analyzed and classified by three expert blind readers into PS compatible (1) and SWEDDs (0), the dataset was processed in both two-dimensional and three-dimensional formats. Five widely used pattern recognition algorithms were trained and validated their performance. These algorithms were compared against the majority reading of expert diagnosis, considered the gold standard. Comparing the accuracy of 2D and 3D format images suggests that, without the depth dimension, a single image may overemphasize specific regions. Overall, three models outperformed with an accuracy greater than 98 %, demonstrating that machine-learning models trained with [¹⁸F]PR04.MZ PET images can provide a highly accurate and precise tool to support clinicians in automatic PET image analysis. This approach may be a first step in reducing the time required for interpretation, as well as increase certainty in the diagnostic process.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}