Smart Health最新文献_第8页

ChatDiet: Empowering personalized nutrition-oriented food recommender chatbots through an LLM-augmented framework ChatDiet：通过 LLM 增强框架增强以营养为导向的个性化食品推荐聊天机器人的能力

Smart Health Pub Date : 2024-03-23 DOI: 10.1016/j.smhl.2024.100465

Zhongqi Yang , Elahe Khatibi , Nitish Nagesh , Mahyar Abbasian , Iman Azimi , Ramesh Jain , Amir M. Rahmani

{"title":"ChatDiet: Empowering personalized nutrition-oriented food recommender chatbots through an LLM-augmented framework","authors":"Zhongqi Yang , Elahe Khatibi , Nitish Nagesh , Mahyar Abbasian , Iman Azimi , Ramesh Jain , Amir M. Rahmani","doi":"10.1016/j.smhl.2024.100465","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100465","url":null,"abstract":"<div><p>The profound impact of food on health necessitates advanced nutrition-oriented food recommendation services. Conventional methods often lack the crucial elements of personalization, explainability, and interactivity. While Large Language Models (LLMs) bring interpretability and explainability, their standalone use falls short of achieving true personalization. In this paper, we introduce ChatDiet, a novel LLM-powered framework designed specifically for personalized nutrition-oriented food recommendation chatbots. ChatDiet integrates personal and population models, complemented by an orchestrator, to seamlessly retrieve and process pertinent information. The personal model leverages causal discovery and inference techniques to assess personalized nutritional effects for a specific user, whereas the population model provides generalized information on food nutritional content. The orchestrator retrieves, synergizes and delivers the output of both models to the LLM, providing tailored food recommendations designed to support targeted health outcomes. The result is a dynamic delivery of personalized and explainable food recommendations, tailored to individual user preferences. Our evaluation of ChatDiet includes a compelling case study, where we establish a causal personal model to estimate individual nutrition effects. Our assessments, including a food recommendation test showcasing a 92<span><math><mtext>%</mtext></math></span> effectiveness rate, coupled with illustrative dialogue examples, underscore ChatDiet’s strengths in explainability, personalization, and interactivity.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100465"},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000217/pdfft?md5=a7d3359763fd8d1a7e233cdecf118a1b&pid=1-s2.0-S2352648324000217-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140308587","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}

引用次数: 0

Reconstructing human gaze behavior from EEG using inverse reinforcement learning 利用反强化学习从脑电图重构人类注视行为

Smart Health Pub Date : 2024-03-21 DOI: 10.1016/j.smhl.2024.100480

Jiaqi Gong , Shengting Cao , Soroush Korivand , Nader Jalili

{"title":"Reconstructing human gaze behavior from EEG using inverse reinforcement learning","authors":"Jiaqi Gong , Shengting Cao , Soroush Korivand , Nader Jalili","doi":"10.1016/j.smhl.2024.100480","DOIUrl":"10.1016/j.smhl.2024.100480","url":null,"abstract":"<div><p>Decoding eye movements from non-invasive electroencephalography (EEG) data is a challenging yet vital task for both scientific and practical purposes, especially for identifying neurodegenerative disorders like Alzheimer’s disease (AD). Our research tackles this complexity by adapting inverse reinforcement learning (IRL), a machine learning method, to infer decision-making strategies from observed behaviors. We implement this to understand the processes driving eye direction and movements during diverse cognitive tasks, providing new insights into this field. Our paper begins with a detailed description of the procedures for collecting and preprocessing EEG data related to gaze behavior. We then elaborate on the development of an IRL framework designed to predict the spatial and temporal dynamics of eye movements (scanpaths) in participants engaged in cognitive tasks of varying complexity. Our model is tailored to accommodate the complexities inherent in neural signals and the stochastic nature of human gaze patterns. Our research findings underscore IRL’s effectiveness in precisely forecasting gaze patterns based on a combination of EEG and image data. The correlation between the model’s predictions and the actual gaze behavior observed in controlled experiments reinforces the utility of IRL in cognitive neuroscience research. Notably, our IRL-EEG models demonstrated superior performance, especially in more complex cognitive tasks. We further delve into the implications of our results for enhancing the understanding of neural mechanisms that govern gaze behavior.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100480"},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140272780","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}

引用次数: 0

Real-time dynamic analysis of EEG Response for Live Indian Classical Vocal Stimulus with Therapeutic Indications 实时动态分析现场印度古典声乐刺激的脑电图反应与治疗指征

Smart Health Pub Date : 2024-03-21 DOI: 10.1016/j.smhl.2024.100461

Satyam Panda , Dasari Shivakumar , Yagnyaseni Majumder , Cota Navin Gupta , Budhaditya Hazra

{"title":"Real-time dynamic analysis of EEG Response for Live Indian Classical Vocal Stimulus with Therapeutic Indications","authors":"Satyam Panda , Dasari Shivakumar , Yagnyaseni Majumder , Cota Navin Gupta , Budhaditya Hazra","doi":"10.1016/j.smhl.2024.100461","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100461","url":null,"abstract":"<div><p>Numerous studies have been conducted on the connection between music and the brain, and it has been established that listening to music directly affects brain activity and stimulation. The potential benefits of music therapy, which uses music as a tool for healing and fostering well-being, have come to light in a number of circumstances. However, there is a gap in understanding the effects of Indian classical music (ICM) on the brain and its therapeutic applications. Yaman and Puria Dhanashree were the two chosen ragas, which share same notes (swaras) and differs in two of their counter notes. The brain responses are captured from five volunteers through 24 channel Electroencephalogram (EEG) cap using a smartphone, which is utilized to allocate electrodes to different regions of the brain. In this work, different automated approaches for identifying brain regions evoked to live ICM stimuli are proposed, considering input and output uncertainties. These approaches are based on automated energy and Mahalanobis distance measurements, and, a region-specific real-time algorithm based on eigen perturbation, which provides a measure to capture the time evolution of brain activity. This identification is relevant in understanding dynamic changes in brain responses during musical experiences providing a more comprehensive perception and processing of ICM in the human brain. Also, significant change in beta band power reduction was observed after music. These approaches can help integrate it into evidence-based music therapy for cognitive, emotional, and psychological conditions. The findings of this study provide evidence indicating ragas activate different brain regions based on listener’s musical knowledge and is a first step for mhealth based applications.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100461"},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297003","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}

引用次数: 0

IoT-based vital sign monitoring: A literature review 基于物联网的生命体征监测：文献综述

Smart Health Pub Date : 2024-03-20 DOI: 10.1016/j.smhl.2024.100462

Alexandre Andrade, Arthur Tassinari Cabral, Bárbara Bellini, Vinicius Facco Rodrigues, Rodrigo da Rosa Righi, Cristiano André da Costa, Jorge Luis Victória Barbosa

{"title":"IoT-based vital sign monitoring: A literature review","authors":"Alexandre Andrade, Arthur Tassinari Cabral, Bárbara Bellini, Vinicius Facco Rodrigues, Rodrigo da Rosa Righi, Cristiano André da Costa, Jorge Luis Victória Barbosa","doi":"10.1016/j.smhl.2024.100462","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100462","url":null,"abstract":"<div><p>The Internet of Things (IoT) applied to the health area is in significant growth, with companies putting effort into developing specialized devices. Remote patient healthcare monitoring, in particular, benefits society as it unburdens hospitals and helps patients with chronic diseases. Analyzing the health status with IoT and Artificial Intelligence (AI) is new in the digital community. The literature yet presents a limited number of references explicitly concerning the topic of qualified data acquisition. In this sense, the present literature review aims to update the joint subject of IoT and vital signs, seeking to understand the state-of-the-art and future directions. We have analyzed 78 articles and IoT manufacturer websites that address vital signs collection to answer a group of primary and specific questions. In particular, we revisited architectures, communication protocols, data acquisition mechanisms, evaluation metrics, and how to efficiently transfer data through the lens of sensors, actuators, and healthcare. Currently, two themes are considered as promising directions for studies in the joint area of IoT and vital sign-based healthcare monitoring. The first is the connection promotion between third-party applications and IoT devices to collect and process time-critical data with the support of edge, fog, and cloud infrastructures. The second theme again brings the focus to data compression methodologies since monitoring vital signs in a smart city geographical area naturally requires strategies to optimize network bandwidth consumption and data storage on computational resources. Moreover, both themes are directly linked to energy-saving approaches and quality of service (QoS) for efficient patient healthcare checking.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100462"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140195632","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}

引用次数: 0

Patient flow modeling and simulation to study HAI incidence in an Emergency Department 研究急诊科 HAI 发生率的患者流建模与模拟

Smart Health Pub Date : 2024-03-20 DOI: 10.1016/j.smhl.2024.100467

Sarawat Murtaza Sara , Ravi Chandra Thota , Md Yusuf Sarwar Uddin , Majid Bani-Yaghoub , Gary Sutkin , Mohamed Nezar Abourraja

{"title":"Patient flow modeling and simulation to study HAI incidence in an Emergency Department","authors":"Sarawat Murtaza Sara , Ravi Chandra Thota , Md Yusuf Sarwar Uddin , Majid Bani-Yaghoub , Gary Sutkin , Mohamed Nezar Abourraja","doi":"10.1016/j.smhl.2024.100467","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100467","url":null,"abstract":"<div><p>Healthcare-associated infections (HAIs), or nosocomial infections, refer to patients getting new infections while getting treatment for an existing condition in a healthcare facility. HAI poses a significant challenge in healthcare delivery that results in higher rates of mortality and morbidity as well as a longer duration of hospital stay. While the real cause of HAI in a hospital varies widely and in most cases untraceable, it is popularly believed that patient flow in a hospital—which hospital units patients visit and where they spend the most time since their admission into the hospital—can trace back to HAI incidence in the hospital. Based on this observation, we, in this paper, model and simulate patient flow in an emergency department of a hospital and then utilize the developed model to study HAI incidence therein. We obtain (a) a flowchart of patient movement (admission to discharge) and (b) anonymous patient data from University Health Medical Center for a duration of 11 months (Aug 2022–June 2023). Based on these data, we develop and validate the patient flow model. Our model captures patient movement in different areas of a typical emergency department, such as triage, waiting room, and minor procedure rooms. We employ the discrete-event simulation (DES) technique to model patient flow and associated HAI infections using the simulation software, Anylogic. Our simulation results show that the rates of HAI incidence are proportional to both the specific areas patients occupy and the duration of their stay. By utilizing our model, hospital administrators and infection control teams can implement targeted strategies to reduce the incidence of HAI and enhance patient safety, ultimately leading to improved healthcare outcomes and more efficient resource allocation.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100467"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297030","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}

引用次数: 0

EQS-Band Human Body Communication through frequency hopping and MCU-Based transmitter 通过跳频和基于 MCU 的发射器实现 EQS 波段人体通信

Smart Health Pub Date : 2024-03-20 DOI: 10.1016/j.smhl.2024.100471

Abdelhay Ali, Amr N. Abdelrahman, Abdulkadir Celik, Ahmed M. Eltawil

{"title":"EQS-Band Human Body Communication through frequency hopping and MCU-Based transmitter","authors":"Abdelhay Ali, Amr N. Abdelrahman, Abdulkadir Celik, Ahmed M. Eltawil","doi":"10.1016/j.smhl.2024.100471","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100471","url":null,"abstract":"<div><p>Human Body Communication (HBC) is an emerging technology that uses the human body as a communication channel. It offers significant advantages over traditional RF techniques in terms of power consumption and security. In recent developments, Electro-quasistatic HBC (EQS-HBC) in the frequency band below 1 MHz has been employed to enable communication without signal radiation beyond the body, effectively turning the body into a wired communication medium. This paper delves into the application of the EQS band for HBC. Experimental results show the determinantal effect of intermittent noise that sporadically disrupts communications across the band of interest. To address this challenge, we introduce an innovative frequency-hopping transceiver system, which allows the transmitter to seamlessly adapt to different frequencies. In addition, we present a miniature transmitter design, incorporating a simplified micro-controller unit (MCU) to facilitate the implementation of HBC. Furthermore, to validate this proposed design, we present a fully functional prototype of an HBC system that effectively employs frequency hopping techniques for practical applications.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100471"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140341331","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}

引用次数: 0

UbiHeart: A novel approach for non-invasive blood pressure monitoring through real-time facial video UbiHeart：通过实时面部视频进行无创血压监测的新方法

Smart Health Pub Date : 2024-03-20 DOI: 10.1016/j.smhl.2024.100473

Kazi Shafiul Alam, Sayed Mashroor Mamun, Masud Rabbani, Parama Sridevi, Sheikh Iqbal Ahamed

{"title":"UbiHeart: A novel approach for non-invasive blood pressure monitoring through real-time facial video","authors":"Kazi Shafiul Alam, Sayed Mashroor Mamun, Masud Rabbani, Parama Sridevi, Sheikh Iqbal Ahamed","doi":"10.1016/j.smhl.2024.100473","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100473","url":null,"abstract":"<div><p>Monitoring blood pressure (BP) is an essential component of evaluating cardiovascular health, aiding in the early detection and management of hypertension-related complications. Traditional methods of BP measurement often involve invasive or cumbersome devices, leading to discomfort and reduced compliance. We propose a framework to monitor BP non-invasively, analyzing the face video captured by a webcam or smartphone camera leveraging the relationship of image-based Pulse Transit Time (iPTT) and Heart Rate Variability (HRV) with BP. We have built a dataset of 90 sets of collected videos using a mobile phone front camera and BP data from a standard digital BP monitor from 12 individuals from an approved Institutional Review Board (IRB) to evaluate our system. We have got a Mean Absolute Error (MAE) of <span><math><mrow><mn>10</mn><mo>.</mo><mn>35</mn><mo>+</mo><mo>/</mo><mo>−</mo><mn>2</mn><mo>.</mo><mn>5</mn></mrow></math></span> mmHg for systolic BP (SBP) and <span><math><mrow><mn>7</mn><mo>.</mo><mn>8</mn><mo>+</mo><mo>/</mo><mo>−</mo><mn>1</mn><mo>.</mo><mn>5</mn></mrow></math></span> mmHg for diastolic BP (DBP) while using the HRV representation RMSSD. On the other hand, an MAE of <span><math><mrow><mn>8</mn><mo>.</mo><mn>25</mn><mo>+</mo><mo>/</mo><mo>−</mo><mn>3</mn><mo>.</mo><mn>5</mn></mrow></math></span> mmHg for SBP and <span><math><mrow><mn>7</mn><mo>.</mo><mn>7</mn><mo>+</mo><mo>/</mo><mo>−</mo><mn>2</mn><mo>.</mo><mn>5</mn></mrow></math></span> mmHg for DBP while using the HRV representation SDRR. Finally, we have developed a framework and built a real-time system to monitor BP as a mobile and web-based application that can facilitate early detection of trends and anomalies, allowing healthcare providers to intervene promptly and personalize treatment plans.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100473"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297029","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}

引用次数: 0

Multi-level cancer profiling through joint cell-graph representations 通过联合细胞图谱表征进行多层次癌症特征分析

Smart Health Pub Date : 2024-03-20 DOI: 10.1016/j.smhl.2024.100470

Luis Carlos Rivera Monroy , Leonhard Rist , Frauke Wilm , Christian Ostalecki , Andreas Baur , Julio Vera , Katharina Breininger , Andreas Maier

{"title":"Multi-level cancer profiling through joint cell-graph representations","authors":"Luis Carlos Rivera Monroy , Leonhard Rist , Frauke Wilm , Christian Ostalecki , Andreas Baur , Julio Vera , Katharina Breininger , Andreas Maier","doi":"10.1016/j.smhl.2024.100470","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100470","url":null,"abstract":"<div><p>Computer-aided analysis of digitized pathology samples has significantly advanced with the rapid progression of machine and Deep Learning (DL) methods. However, most existing approaches primarily focus on features extracted from patches due to the large image sizes. This focus limits the ability of Convolutional Neural Networks (CNNs) to capture global information from the samples, resulting in an incomplete phenotypical and topological representation and thereby restricting the diagnostic capabilities of these methods. The recent emergence of Graph Neural Networks (GNNs) offers new opportunities to overcome these limitations through graph-driven representations of pathological samples. This work introduces a graph-based framework that encompasses diverse cancer types and integrates different imaging modalities. In this framework, histopathology samples are represented as graphs, and a pipeline facilitating cell-wise and disease classification is developed. The results support this motivation: for cell-wise classification, we achieved an average accuracy of <span><math><mrow><mn>88</mn><mtext>%</mtext></mrow></math></span>, and for disease-wise classification, an average accuracy of 83%, outperforming reference models such as XGBoost and standard CNNs. This approach not only provides flexibility in combining various diseases but also extends to integrating different staining techniques.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100470"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000266/pdfft?md5=7c470921f13bec160c26b55acac4f145&pid=1-s2.0-S2352648324000266-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140290856","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}

引用次数: 0

Adaptive attention-aware fusion for human-in-the-loop behavioral health detection 自适应注意力感知融合技术用于人在回路中的行为健康检测

Smart Health Pub Date : 2024-03-20 DOI: 10.1016/j.smhl.2024.100475

Martin Brown , Abm Adnan Azmee , Md. Abdullah Al Hafiz Khan , Dominic Thomas , Yong Pei , Monica Nandan

{"title":"Adaptive attention-aware fusion for human-in-the-loop behavioral health detection","authors":"Martin Brown , Abm Adnan Azmee , Md. Abdullah Al Hafiz Khan , Dominic Thomas , Yong Pei , Monica Nandan","doi":"10.1016/j.smhl.2024.100475","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100475","url":null,"abstract":"<div><p>Identifying behavioral health is paramount for law enforcement officers to provide appropriate follow-up community care. In the current practice, law enforcement offices manually identify these behavioral health cases to allow the designation of the relevant follow-up resources. In this work, we develop a tool to automatically detect behavioral health cases from police public narrative reports by identifying behavioral health indicator signals. We propose a novel adaptive attention-aware fusion model for detecting behavioral health signals in sensitive police reports. Our model leverages contextual and semantic information from the reports and relevant behavioral health cues as keywords from a pre-trained attention-weighted keyword-based model. Our model also employs label self-attention mechanisms to correlate label embeddings with the report and keyword representations. Furthermore, we propose a novel clustering-based uncertainty-enabled informative sampling query strategy to integrate humans-in-the-loop in the active learning framework to reduce required annotation from experts. This querying strategy selects the most informative and diverse samples for expert annotation. Our experimental results showed that the proposed model outperforms state-of-the-art classifiers on a dataset of 300 manually annotated ground truth police reports, achieving an accuracy of 87.58% and an F1-score of 85.67%. Applying our querying strategy to our proposed model increased the detection of behavioral health, achieving an accuracy of 92% and an F1-score of 91.1%. Also, our proposed model achieves an accuracy score of 93.75% and an F1-score of 93.61% on unseen samples. Lastly, our proposed model demonstrates its interpretability by extracting the keywords associated with each behavioral health category.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100475"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140343877","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}

引用次数: 0

Explainable AI and transformer models: Unraveling the nutritional influences on Alzheimer's disease mortality 可解释的人工智能和变压器模型：揭示阿尔茨海默病死亡率的营养影响因素

Smart Health Pub Date : 2024-03-20 DOI: 10.1016/j.smhl.2024.100478

Ziming Liu , Longjian Liu , Robert E. Heidel , Xiaopeng Zhao

{"title":"Explainable AI and transformer models: Unraveling the nutritional influences on Alzheimer's disease mortality","authors":"Ziming Liu , Longjian Liu , Robert E. Heidel , Xiaopeng Zhao","doi":"10.1016/j.smhl.2024.100478","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100478","url":null,"abstract":"<div><p>This pioneering study introduces the use of transformer-based machine learning models and explainable AI approaches to explore the impact of nutrition on Alzheimer's disease (AD) mortality. Using data from the Third National Health and Nutrition Examination Survey (Nhanes iii 1988 to 1994) and the NHANES III Mortality-Linked File (2019) databases, we investigate the intricate relationship between various nutritional factors and AD mortality. Our approach features a novel application of transformer models, which are then benchmarked against established methods like random forests and support vector machines. This comparison not only underscores the strengths of transformer models in handling complex medical datasets but also highlights their potential for providing deeper insights into disease progression. Key findings, such as the significant roles of Platelet distribution width in AD mortality in transformer and Serum Vitamin B12 in random forest, are enhanced by the use of Explainable Artificial Intelligence (XAI), particularly the Shapley Additive Explanations (SHAP) and the integrated gradient methods. This study serves as a vital step forward in applying advanced AI techniques to medical research, offering new perspectives in understanding and combating Alzheimer's Disease.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100478"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140320797","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}

引用次数: 0