JMIR AI最新文献

{"title":"Survey on Pain Detection Using Machine Learning Models: Narrative Review.","authors":"Ruijie Fang, Elahe Hosseini, Ruoyu Zhang, Chongzhou Fang, Setareh Rafatirad, Houman Homayoun","doi":"10.2196/53026","DOIUrl":"10.2196/53026","url":null,"abstract":"<p><strong>Background: </strong>Pain, a leading reason people seek medical care, has become a social issue. Automated pain assessment has seen notable advancements over recent decades, addressing a critical need in both clinical and everyday settings.</p><p><strong>Objective: </strong>The objective of this survey was to provide a comprehensive overview of pain and its mechanisms, to explore existing research on automated pain recognition modalities, and to identify key challenges and future directions in this field.</p><p><strong>Methods: </strong>A literature review was conducted, analyzing studies focused on various modalities for automated pain recognition. The modalities reviewed include facial expressions, physiological signals, audio cues, and pupil dilation, with a focus on their efficacy and application in pain assessment.</p><p><strong>Results: </strong>The survey found that each modality offers unique contributions to automated pain recognition, with facial expressions and physiological signals showing particular promise. However, the reliability and accuracy of these modalities vary, often depending on factors such as individual variability and environmental conditions.</p><p><strong>Conclusions: </strong>While automated pain recognition has progressed considerably, challenges remain in achieving consistent accuracy across diverse populations and contexts. Future research directions are suggested to address these challenges, enhancing the reliability and applicability of automated pain assessment in clinical practice.</p>","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"4 ","pages":"e53026"},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494900","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":"Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study.","authors":"Yanjun Gao, Ruizhe Li, Emma Croxford, John Caskey, Brian W Patterson, Matthew Churpek, Timothy Miller, Dmitriy Dligach, Majid Afshar","doi":"10.2196/58670","DOIUrl":"10.2196/58670","url":null,"abstract":"<p><strong>Background: </strong>Electronic health records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives can overwhelm health care providers, increasing the risk of diagnostic inaccuracies. While large language models (LLMs) have showcased their potential in diverse language tasks, their application in health care must prioritize the minimization of diagnostic errors and the prevention of patient harm. Integrating knowledge graphs (KGs) into LLMs offers a promising approach because structured knowledge from KGs could enhance LLMs' diagnostic reasoning by providing contextually relevant medical information.</p><p><strong>Objective: </strong>This study introduces DR.KNOWS (Diagnostic Reasoning Knowledge Graph System), a model that integrates Unified Medical Language System-based KGs with LLMs to improve diagnostic predictions from EHR data by retrieving contextually relevant paths aligned with patient-specific information.</p><p><strong>Methods: </strong>DR.KNOWS combines a stack graph isomorphism network for node embedding with an attention-based path ranker to identify and rank knowledge paths relevant to a patient's clinical context. We evaluated DR.KNOWS on 2 real-world EHR datasets from different geographic locations, comparing its performance to baseline models, including QuickUMLS and standard LLMs (Text-to-Text Transfer Transformer and ChatGPT). To assess diagnostic reasoning quality, we designed and implemented a human evaluation framework grounded in clinical safety metrics.</p><p><strong>Results: </strong>DR.KNOWS demonstrated notable improvements over baseline models, showing higher accuracy in extracting diagnostic concepts and enhanced diagnostic prediction metrics. Prompt-based fine-tuning of Text-to-Text Transfer Transformer with DR.KNOWS knowledge paths achieved the highest ROUGE-L (Recall-Oriented Understudy for Gisting Evaluation-Longest Common Subsequence) and concept unique identifier F₁-scores, highlighting the benefits of KG integration. Human evaluators found the diagnostic rationales of DR.KNOWS to be aligned strongly with correct clinical reasoning, indicating improved abstraction and reasoning. Recognized limitations include potential biases within the KG data, which we addressed by emphasizing case-specific path selection and proposing future bias-mitigation strategies.</p><p><strong>Conclusions: </strong>DR.KNOWS offers a robust approach for enhancing diagnostic accuracy and reasoning by integrating structured KG knowledge into LLM-based clinical workflows. Although further work is required to address KG biases and extend generalizability, DR.KNOWS represents progress toward trustworthy artificial intelligence-driven clinical decision support, with a human evaluation framework focused on diagnostic safety and alignment with clinical standards.</p>","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"4 ","pages":"e58670"},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494873","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 Satisfaction With Chat-Counseling at a 24/7 Chat Hotline for the Youth: Natural Language Processing Study.","authors":"Silvan Hornstein, Ulrike Lueken, Richard Wundrack, Kevin Hilbert","doi":"10.2196/63701","DOIUrl":"10.2196/63701","url":null,"abstract":"<p><strong>Background: </strong>Chat-based counseling services are popular for the low-threshold provision of mental health support to youth. In addition, they are particularly suitable for the utilization of natural language processing (NLP) for improved provision of care.</p><p><strong>Objective: </strong>Consequently, this paper evaluates the feasibility of such a use case, namely, the NLP-based automated evaluation of satisfaction with the chat interaction. This preregistered approach could be used for evaluation and quality control procedures, as it is particularly relevant for those services.</p><p><strong>Methods: </strong>The consultations of 2609 young chatters (around 140,000 messages) and corresponding feedback were used to train and evaluate classifiers to predict whether a chat was perceived as helpful or not. On the one hand, we trained a word vectorizer in combination with an extreme gradient boosting (XGBoost) classifier, applying cross-validation and extensive hyperparameter tuning. On the other hand, we trained several transformer-based models, comparing model types, preprocessing, and over- and undersampling techniques. For both model types, we selected the best-performing approach on the training set for a final performance evaluation on the 522 users in the final test set.</p><p><strong>Results: </strong>The fine-tuned XGBoost classifier achieved an area under the receiver operating characteristic score of 0.69 (P<.001), as well as a Matthews correlation coefficient of 0.25 on the previously unseen test set. The selected Longformer-based model did not outperform this baseline, scoring 0.68 (P=.69). A Shapley additive explanations explainability approach suggested that help seekers rating a consultation as helpful commonly expressed their satisfaction already within the conversation. In contrast, the rejection of offered exercises predicted perceived unhelpfulness.</p><p><strong>Conclusions: </strong>Chat conversations include relevant information regarding the perceived quality of an interaction that can be used by NLP-based prediction approaches. However, to determine if the moderate predictive performance translates into meaningful service improvements requires randomized trials. Further, our results highlight the relevance of contrasting pretrained models with simpler baselines to avoid the implementation of unnecessarily complex models.</p><p><strong>Trial registration: </strong>Open Science Framework SR4Q9; https://osf.io/sr4q9.</p>","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"4 ","pages":"e63701"},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451173","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":"Investigating the Classification of Living Kidney Donation Experiences on Reddit and Understanding the Sensitivity of ChatGPT to Prompt Engineering: Content Analysis.","authors":"Joshua Nielsen, Xiaoyu Chen, LaShara Davis, Amy Waterman, Monica Gentili","doi":"10.2196/57319","DOIUrl":"10.2196/57319","url":null,"abstract":"<p><strong>Background: </strong>Living kidney donation (LKD), where individuals donate one kidney while alive, plays a critical role in increasing the number of kidneys available for those experiencing kidney failure. Previous studies show that many generous people are interested in becoming living donors; however, a huge gap exists between the number of patients on the waiting list and the number of living donors yearly.</p><p><strong>Objective: </strong>To bridge this gap, we aimed to investigate how to identify potential living donors from discussions on public social media forums so that educational interventions could later be directed to them.</p><p><strong>Methods: </strong>Using Reddit forums as an example, this study described the classification of Reddit content shared about LKD into three classes: (1) present (presently dealing with LKD personally), (2) past (dealt with LKD personally in the past), and (3) other (LKD general comments). An evaluation was conducted comparing a fine-tuned distilled version of the Bidirectional Encoder Representations from Transformers (BERT) model with inference using GPT-3.5 (ChatGPT). To systematically evaluate ChatGPT's sensitivity to distinguishing between the 3 prompt categories, we used a comprehensive prompt engineering strategy encompassing a full factorial analysis in 48 runs. A novel prompt engineering approach, dialogue until classification consensus, was introduced to simulate a deliberation between 2 domain experts until a consensus on classification was achieved.</p><p><strong>Results: </strong>BERT and GPT-3.5 exhibited classification accuracies of approximately 75% and 78%, respectively. Recognizing the inherent ambiguity between classes, a post hoc analysis of incorrect predictions revealed sensible reasoning and acceptable errors in the predictive models. Considering these acceptable mismatched predictions, the accuracy improved to 89.3% for BERT and 90.7% for GPT-3.5.</p><p><strong>Conclusions: </strong>Large language models, such as GPT-3.5, are highly capable of detecting and categorizing LKD-targeted content on social media forums. They are sensitive to instructions, and the introduced dialogue until classification consensus method exhibited superior performance over stand-alone reasoning, highlighting the merit in advancing prompt engineering methodologies. The models can produce appropriate contextual reasoning, even when final conclusions differ from their human counterparts.</p>","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"4 ","pages":"e57319"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11845884/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366909","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":"Advancing Privacy-Preserving Health Care Analytics and Implementation of the Personal Health Train: Federated Deep Learning Study.","authors":"Ananya Choudhury, Leroy Volmer, Frank Martin, Rianne Fijten, Leonard Wee, Andre Dekker, Johan van Soest","doi":"10.2196/60847","DOIUrl":"10.2196/60847","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The rapid advancement of deep learning in health care presents significant opportunities for automating complex medical tasks and improving clinical workflows. However, widespread adoption is impeded by data privacy concerns and the necessity for large, diverse datasets across multiple institutions. Federated learning (FL) has emerged as a viable solution, enabling collaborative artificial intelligence model development without sharing individual patient data. To effectively implement FL in health care, robust and secure infrastructures are essential. Developing such federated deep learning frameworks is crucial to harnessing the full potential of artificial intelligence while ensuring patient data privacy and regulatory compliance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objective is to introduce an innovative FL infrastructure called the Personal Health Train (PHT) that includes the procedural, technical, and governance components needed to implement FL on real-world health care data, including training deep learning neural networks. The study aims to apply this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer and present the results from a proof-of-concept experiment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The PHT framework addresses the challenges of data privacy when sharing data, by keeping data close to the source and instead bringing the analysis to the data. Technologically, PHT requires 3 interdependent components: \"tracks\" (protected communication channels), \"trains\" (containerized software apps), and \"stations\" (institutional data repositories), which are supported by the open source \"Vantage6\" software. The study applies this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer, with the introduction of an additional component called the secure aggregation server, where the model averaging is done in a trusted and inaccessible environment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We demonstrated the feasibility of executing deep learning algorithms in a federated manner using PHT and presented the results from a proof-of-concept study. The infrastructure linked 12 hospitals across 8 nations, covering 4 continents, demonstrating the scalability and global reach of the proposed approach. During the execution and training of the deep learning algorithm, no data were shared outside the hospital.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The findings of the proof-of-concept study, as well as the implications and limitations of the infrastructure and the results, are discussed. The application of federated deep learning to unstructured medical imaging data, facilitated by the PHT framework and Vantage6 platform, represents a significant advancement in the field. The proposed infrastructure addresses the challenges of data priva","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"4 ","pages":"e60847"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11843053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257561","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":"Urgency Prediction for Medical Laboratory Tests Through Optimal Sparse Decision Tree: Case Study With Echocardiograms.","authors":"Yiqun Jiang, Qing Li, Yu-Li Huang, Wenli Zhang","doi":"10.2196/64188","DOIUrl":"10.2196/64188","url":null,"abstract":"<p><strong>Background: </strong>In the contemporary realm of health care, laboratory tests stand as cornerstone components, driving the advancement of precision medicine. These tests offer intricate insights into a variety of medical conditions, thereby facilitating diagnosis, prognosis, and treatments. However, the accessibility of certain tests is hindered by factors such as high costs, a shortage of specialized personnel, or geographic disparities, posing obstacles to achieving equitable health care. For example, an echocardiogram is a type of laboratory test that is extremely important and not easily accessible. The increasing demand for echocardiograms underscores the imperative for more efficient scheduling protocols. Despite this pressing need, limited research has been conducted in this area.</p><p><strong>Objective: </strong>The study aims to develop an interpretable machine learning model for determining the urgency of patients requiring echocardiograms, thereby aiding in the prioritization of scheduling procedures. Furthermore, this study aims to glean insights into the pivotal attributes influencing the prioritization of echocardiogram appointments, leveraging the high interpretability of the machine learning model.</p><p><strong>Methods: </strong>Empirical and predictive analyses have been conducted to assess the urgency of patients based on a large real-world echocardiogram appointment dataset (ie, 34,293 appointments) sourced from electronic health records encompassing administrative information, referral diagnosis, and underlying patient conditions. We used a state-of-the-art interpretable machine learning algorithm, the optimal sparse decision tree (OSDT), renowned for its high accuracy and interpretability, to investigate the attributes pertinent to echocardiogram appointments.</p><p><strong>Results: </strong>The method demonstrated satisfactory performance (F₁-score=36.18% with an improvement of 1.7% and F₂-score=28.18% with an improvement of 0.79% by the best-performing baseline model) in comparison to the best-performing baseline model. Moreover, due to its high interpretability, the results provide valuable medical insights regarding the identification of urgent patients for tests through the extraction of decision rules from the OSDT model.</p><p><strong>Conclusions: </strong>The method demonstrated state-of-the-art predictive performance, affirming its effectiveness. Furthermore, we validate the decision rules derived from the OSDT model by comparing them with established medical knowledge. These interpretable results (eg, attribute importance and decision rules from the OSDT model) underscore the potential of our approach in prioritizing patient urgency for echocardiogram appointments and can be extended to prioritize other laboratory test appointments using electronic health record data.</p>","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"4 ","pages":"e64188"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11822316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143061551","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":"Identification of Use Cases, Target Groups, and Motivations Around Adopting Smart Speakers for Health Care and Social Care Settings: Scoping Review.","authors":"Sebastian Merkel, Sabrina Schorr","doi":"10.2196/55673","DOIUrl":"10.2196/55673","url":null,"abstract":"<p><strong>Background: </strong>Conversational agents (CAs) are finding increasing application in health and social care, not least due to their growing use in the home. Recent developments in artificial intelligence, machine learning, and natural language processing have enabled a variety of new uses for CAs. One type of CA that has received increasing attention recently is smart speakers.</p><p><strong>Objective: </strong>The aim of our study was to identify the use cases, user groups, and settings of smart speakers in health and social care. We also wanted to identify the key motivations for developers and designers to use this particular type of technology.</p><p><strong>Methods: </strong>We conducted a scoping review to provide an overview of the literature on smart speakers in health and social care. The literature search was conducted between February 2023 and March 2023 and included 3 databases (PubMed, Scopus, and Sociological Abstracts), supplemented by Google Scholar. Several keywords were used, including technology (eg, voice assistant), product name (eg, Amazon Alexa), and setting (health care or social care). Publications were included if they met the predefined inclusion criteria: (1) published after 2015 and (2) used a smart speaker in a health care or social care setting. Publications were excluded if they met one of the following criteria: (1) did not report on the specific devices used, (2) did not focus specifically on smart speakers, (3) were systematic reviews and other forms of literature-based publications, and (4) were not published in English. Two reviewers collected, reviewed, abstracted, and analyzed the data using qualitative content analysis.</p><p><strong>Results: </strong>A total of 27 articles were included in the final review. These articles covered a wide range of use cases in different settings, such as private homes, hospitals, long-term care facilities, and outpatient services. The main target group was patients, especially older users, followed by doctors and other medical staff members.</p><p><strong>Conclusions: </strong>The results show that smart speakers have diverse applications in health and social care, addressing different contexts and audiences. Their affordability and easy-to-use interfaces make them attractive to various stakeholders. It seems likely that, due to technical advances in artificial intelligence and the market power of the companies behind the devices, there will be more use cases for smart speakers in the near future.</p>","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"4 ","pages":"e55673"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11773277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973707","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":"Evaluating ChatGPT's Efficacy in Pediatric Pneumonia Detection From Chest X-Rays: Comparative Analysis of Specialized AI Models.","authors":"Nitin Chetla, Mihir Tandon, Joseph Chang, Kunal Sukhija, Romil Patel, Ramon Sanchez","doi":"10.2196/67621","DOIUrl":"10.2196/67621","url":null,"abstract":"","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"4 ","pages":"e67621"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962602","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":"Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study.","authors":"Sang Won Bae, Tammy Chung, Tongze Zhang, Anind K Dey, Rahul Islam","doi":"10.2196/52270","DOIUrl":"10.2196/52270","url":null,"abstract":"<p><strong>Background: </strong>Acute marijuana intoxication can impair motor skills and cognitive functions such as attention and information processing. However, traditional tests, like blood, urine, and saliva, fail to accurately detect acute marijuana intoxication in real time.</p><p><strong>Objective: </strong>This study aims to explore whether integrating smartphone-based sensors with readily accessible wearable activity trackers, like Fitbit, can enhance the detection of acute marijuana intoxication in naturalistic settings. No previous research has investigated the effectiveness of passive sensing technologies for enhancing algorithm accuracy or enhancing the interpretability of digital phenotyping through explainable artificial intelligence in real-life scenarios. This approach aims to provide insights into how individuals interact with digital devices during algorithmic decision-making, particularly for detecting moderate to intensive marijuana intoxication in real-world contexts.</p><p><strong>Methods: </strong>Sensor data from smartphones and Fitbits, along with self-reported marijuana use, were collected from 33 young adults over a 30-day period using the experience sampling method. Participants rated their level of intoxication on a scale from 1 to 10 within 15 minutes of consuming marijuana and during 3 daily semirandom prompts. The ratings were categorized as not intoxicated (0), low (1-3), and moderate to intense intoxication (4-10). The study analyzed the performance of models using mobile phone data only, Fitbit data only, and a combination of both (MobiFit) in detecting acute marijuana intoxication.</p><p><strong>Results: </strong>The eXtreme Gradient Boosting Machine classifier showed that the MobiFit model, which combines mobile phone and wearable device data, achieved 99% accuracy (area under the curve=0.99; F₁-score=0.85) in detecting acute marijuana intoxication in natural environments. The F₁-score indicated significant improvements in sensitivity and specificity for the combined MobiFit model compared to using mobile or Fitbit data alone. Explainable artificial intelligence revealed that moderate to intense self-reported marijuana intoxication was associated with specific smartphone and Fitbit metrics, including elevated minimum heart rate, reduced macromovement, and increased noise energy around participants.</p><p><strong>Conclusions: </strong>This study demonstrates the potential of using smartphone sensors and wearable devices for interpretable, transparent, and unobtrusive monitoring of acute marijuana intoxication in daily life. Advanced algorithmic decision-making provides valuable insight into behavioral, physiological, and environmental factors that could support timely interventions to reduce marijuana-related harm. Future real-world applications of these algorithms should be evaluated in collaboration with clinical experts to enhance their practicality and effectiveness.</p>","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"4 ","pages":"e52270"},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11739728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142923993","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":"Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study.","authors":"Butros M Dahu, Solaiman Khan, Imad Eddine Toubal, Mariam Alshehri, Carlos I Martinez-Villar, Olabode B Ogundele, Lincoln R Sheets, Grant J Scott","doi":"10.2196/64362","DOIUrl":"10.2196/64362","url":null,"abstract":"<p><strong>Background: </strong>The global obesity epidemic demands innovative approaches to understand its complex environmental and social determinants. Spatial technologies, such as geographic information systems, remote sensing, and spatial machine learning, offer new insights into this health issue. This study uses deep learning and spatial modeling to predict obesity rates for census tracts in Missouri.</p><p><strong>Objective: </strong>This study aims to develop a scalable method for predicting obesity prevalence using deep convolutional neural networks applied to satellite imagery and geospatial analysis, focusing on 1052 census tracts in Missouri.</p><p><strong>Methods: </strong>Our analysis followed 3 steps. First, Sentinel-2 satellite images were processed using the Residual Network-50 model to extract environmental features from 63,592 image chips (224×224 pixels). Second, these features were merged with obesity rate data from the Centers for Disease Control and Prevention for Missouri census tracts. Third, a spatial lag model was used to predict obesity rates and analyze the association between deep neural visual features and obesity prevalence. Spatial autocorrelation was used to identify clusters of obesity rates.</p><p><strong>Results: </strong>Substantial spatial clustering of obesity rates was found across Missouri, with a Moran I value of 0.68, indicating similar obesity rates among neighboring census tracts. The spatial lag model demonstrated strong predictive performance, with an R² of 0.93 and a spatial pseudo R² of 0.92, explaining 93% of the variation in obesity rates. Local indicators from a spatial association analysis revealed regions with distinct high and low clusters of obesity, which were visualized through choropleth maps.</p><p><strong>Conclusions: </strong>This study highlights the effectiveness of integrating deep convolutional neural networks and spatial modeling to predict obesity prevalence based on environmental features from satellite imagery. The model's high accuracy and ability to capture spatial patterns offer valuable insights for public health interventions. Future work should expand the geographical scope and include socioeconomic data to further refine the model for broader applications in obesity research.</p>","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"3 ","pages":"e64362"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840476","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}