ACM transactions on computing for healthcare最新文献

{"title":"LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology.","authors":"Zhenyue Qin, Yang Liu, Y U Yin, Jinyu Ding, Haoran Zhang, Anran Li, Dylan Campbell, Xuansheng Wu, K E Zou, Tiarnan D L Keenan, Emily Y Chew, Zhiyong Lu, Yih Chung Tham, Ninghao Liu, Xiuzhen Zhang, Qingyu Chen","doi":"10.1145/3801746","DOIUrl":"10.1145/3801746","url":null,"abstract":"<p><p>The rising prevalence of vision-threatening eye diseases poses a major global health and economic burden, yet timely diagnosis remains limited by workforce shortages, diagnostic delays, and restricted access to specialized care. Artificial intelligence (AI) offers potential solutions. In particular, recent progress in foundation models and large language models-especially multimodal large language models (MLLMs)-has shown promise in medical image interpretation and automated clinical documentation. However, advancing MLLMs for ophthalmology is hindered by the lack of unified, comprehensive benchmark datasets for development and evaluation. Most existing benchmarks were designed for earlier models, which focused on narrow tasks or specific disease conditions. These benchmarks typically provide outputs in the form of disease labels rather than free-text responses. As a result, they are less suitable for assessing emerging generative models. In this work, we present LMOD+, a large-scale multimodal ophthalmology benchmark dataset comprising 32,633 instances with multi-granular annotations across 12 common ophthalmic conditions and 5 imaging modalities. The dataset integrates imaging, anatomical structures, demographics, and free-text annotations. It supports primary ophthalmic applications such as anatomical structure recognition, disease screening, disease staging, and demographic prediction for potential performance bias evaluation. Alongside the dataset, we introduce a systematic and unified data curation pipeline that repurposes existing or new datasets for MLLM development. LMOD+ extends our preliminary LMOD benchmark-the first multimodal ophthalmology benchmark for MLLMs-with three major enhancements. First, we expanded the dataset by nearly 50% (from 21,933 to 32,633 instances). The color fundus photography (CFP) modality, the most accessible imaging modality in ophthalmology, was significantly enlarged to cover a broader range of pathological conditions. Second, we broadened task coverage to include (a) 12 binary disease diagnosis tasks for prevalent conditions such as diabetic retinopathy, age-related macular degeneration, and retinal vein occlusion; (b) multi-class ophthalmic disease diagnosis; (c) disease severity classification, including a diabetic retinopathy staging task, which uses two internationally adopted grading standards: the international clinical diabetic retinopathy classification and the Scottish diabetic retinopathy grading scheme classification; and (d) demographic prediction (age and sex) to assess potential model bias. Third, we systematically evaluated 24 state-of-the-art MLLMs, including recent models from the InternVL, Qwen, and DeepSeek families. Our evaluations highlight both the promise and limitations of current MLLMs in ophthalmology. For example, Qwen-7B and InternVL achieved accuracies of 58.26% and 57.83% in disease screening under a zero-shot setting with a single model-a considerably more challenging p","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"7 3","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13353522/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148426941","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":"Fetal pHocus: A Novel Approach to Non-Invasive Fetal Arterial Blood pH Assessment via Near-Infrared Spectroscopy.","authors":"Randall Fowler, Begum Kasap, Weitai Qian, Rishad Joarder, Kourosh Vali, Siddharth Mani, Herman L Hedriana, Aijun Wang, Diana Farmer, Soheil Ghiasi","doi":"10.1145/3785412","DOIUrl":"10.1145/3785412","url":null,"abstract":"<p><p>Modern intrapartum fetal health assessments are currently limited to monitoring heart rate and spatial parameters, neglecting critical biomarkers that remain unmeasurable with today's clinical devices without performing surgery. Without precise evaluations of oxygen levels and blood acidity, clinicians are forced to rely on postnatal assessments to gauge fetal well-being, a delay that may obscure timely intervention. Fetal blood pH is a vital indicator of acid-base balance and cellular health, as any deviation could indicate potential health risks such as hypoxia and acidemia. In this study, we leverage the indirect relationship between pH and oxygen saturation to estimate fetal blood pH non-invasively using near-infrared (NIR) spectroscopy with wavelengths optimized for light transmission depth and oxygen saturation measurements. A convolutional neural network (CNN) extracts features from the acquired data, enabling accurate prediction of fetal blood pH using our machine learning (ML) model. Evaluation using hypoxic sheep models demonstrated an average prediction error of just 0.023 pH units, with all rounds maintaining errors below 0.05 pH units.</p>","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"7 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12994387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147482304","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":"HDFusion: Hierarchical Data Fusion for Robust Fetal Heart Rate Estimation using Transabdominal PPG Signals.","authors":"Tailai Lihe, Begum Kasap, Kourosh Vali, Soheil Ghiasi","doi":"10.1145/3785410","DOIUrl":"10.1145/3785410","url":null,"abstract":"<p><p>Evaluation of fetal health during pregnancy is highly dependent on monitoring of fetal heart rate (FHR). New technologies emerge, such as the transabdominal fetal pulse oximeter (TFO), a non-invasive, light-based measurement device, to provide obstetricians with additional fetal physiological markers such as fetal oxygen saturation. Estimation of FHR from TFO's acquired photoplethysmogram (PPG) signals is necessary for deriving oxygen saturation. Non-invasive optical sensing of deep fetal tissue is inherently challenged by low signal-to-noise ratio, and unpredictable anatomical and physiological dynamics, which render a particular sensor design suboptimal. Multiple sensors can conceptually enable the system to operate more robustly under such dynamics, assuming the data acquired by different sensors can be adaptively integrated to form a coherent view of the tissue. In this paper, we present an algorithm for data fusion at several levels of information abstraction, raw data, feature, and decision levels, to improve FHR estimation. We validate the proposed technique via in-vivo data collected in gold-standard pregnant ewe experiments using TFO. The root-mean-squared error of our three-level hierarchical data fusion compared to a single-level and two-level fusion improved by over 59% and 51%, respectively. This underscores the robustness of our approach in overcoming optical deep tissue sensing challenges.</p>","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"7 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13038302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596587","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":"Federated Choquet Regression with LASSO for Outcome Prediction in Multisite Longitudinal Trial Data.","authors":"Semyon Lomasov, Hua Fang, Honggang Wang","doi":"10.1145/3761824","DOIUrl":"10.1145/3761824","url":null,"abstract":"<p><p>Aggregating person-level data across multiple clinical study sites is often constrained by privacy regulations, necessitating the development of decentralized modeling approaches in biomedical research. To address this requirement, a federated nonlinear regression algorithm based on the Choquet integral has been introduced for outcome prediction. This approach avoids reliance on prior statistical assumptions about data distribution and captures feature interactions, reflecting the non-additive nature of biomedical data characteristics. This work represents the first theoretical application of Choquet integral regression to multisite longitudinal trial data within a federated learning framework. The Multiple Imputation Choquet Integral Regression with LASSO (MIChoquet-LASSO) algorithm is specifically designed to reduce overfitting and enable variable selection in federated learning settings. Its performance has been evaluated using synthetic datasets, publicly available biomedical datasets, and proprietary longitudinal randomized controlled trial data. Comparative evaluations were conducted against benchmark methods, including OLS regression and Choquet OLS regression, under various scenarios such as model misspecification and both linear and nonlinear data structures in non-federated and federated contexts. MSE was used as the primary performance metric. Results indicate that MIChoquet-LASSO outperforms compared models in handling nonlinear longitudinal data with missing values, particularly in scenarios prone to overfitting. In federated settings, Choquet OLS underperforms, whereas the federated variant of the model, FEDMIChoquet-LASSO, demonstrates consistently better performance. These findings suggest that FEDMIChoquet-LASSO offers a reliable solution for outcome prediction in multisite longitudinal trials, addressing challenges such as missing values, nonlinear relationships, and privacy constraints while maintaining strong performance within the federated learning framework.</p>","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"7 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13052512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635245","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":"\"I don't see anything specifically about Black/African Americans.\" Testing an Alzheimer-specific generative AI tool tailored for African American/Black communities.","authors":"Cristina Bosco, Fereshtehossadat Shojaei, Alec A Theisz, Vivian Nguyen, Haoru Song, Ruixiang Han, John Osorio Torres, Darshil Chheda, Jenny Lin, Xinran Peng, Nawal Z Waseem, Chelsea Simpkins, Bianca Cureton, Anna K Himes, Nenette M Jessup, Yvonne Lu Indiana, Hugh C Hendrie, Priscilla A Barnes, Carl V Hill, Patrick C Shih","doi":"10.1145/3712295","DOIUrl":"10.1145/3712295","url":null,"abstract":"<p><p>Low levels of health literacy concerning Alzheimer's Disease and related dementias (ADRD) impact African American/Black communities access to appropriate ADRD care. Additionally, a legacy of mistrust in medical research due to systemic racism, has resulted in insufficient participation in ADRD clinical trials among African American/Black adults. This study explores the potential of generative AI to improve ADRD literacy and encourage participation in clinical trials among African American/Black older adults. We designed a mobile health intervention featuring AI-driven conversational agents - a chatbot and a voice assistant - specifically developed for this population. We tested the quality of the intervention using heuristics methodology adapted to the target population along with inputs from African American/ Black medical professionals and UX designers. Key findings highlight the unique needs of the African American/Black communities for culturally relevant content that is accessible to users with varying language levels and tailored to users' geographical location. Concerning the interaction, high levels of personalization and control over the interaction can promote the use of the tool, by minimizing complexity and maximizing accessibility. These findings show the novel contribution offered by our study in the domain of designing health technology with generative AI, particularly LLMS, for African American/Black communities.</p>","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"7 3","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13345475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413869","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":"Domain-Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging.","authors":"Seungyeon Lee, Thai-Hoang Pham, Zhao Cheng, Ping Zhang","doi":"10.1145/3757066","DOIUrl":"10.1145/3757066","url":null,"abstract":"<p><p>Sleep staging has become a critical task in diagnosing and treating sleep disorders to prevent sleep-related diseases. With growing large-scale sleep databases, significant progress has been made toward automatic sleep staging. However, previous studies face critical problems in sleep studies; the heterogeneity of subjects' physiological signals, the inability to extract meaningful information from unlabeled data to improve predictive performances, the difficulty in modeling correlations between sleep stages, and the lack of an effective mechanism to quantify predictive uncertainty. In this study, we propose a neural network-based sleep staging model, DREAM, to learn domain generalized representations from physiological signals and model sleep dynamics. DREAM learns sleep-related and subject-invariant representations from diverse subjects' sleep signals and models sleep dynamics by capturing interactions between sequential signal segments and between sleep stages. We conducted a comprehensive empirical study to demonstrate the superiority of DREAM, including sleep stage prediction experiments, a case study, the usage of unlabeled data, and uncertainty. Notably, the case study validates DREAM's ability to learn the generalized decision function for new subjects, especially in case there are differences between testing and training subjects. Uncertainty quantification shows that DREAM provides prediction uncertainty, making the model reliable and helping sleep experts in real-world applications.</p>","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"6 4","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12629632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145566538","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":"HomeSenseALS: A Mobile Sensing and Self-Monitoring System to Monitor Patients with ALS in Everyday Life","authors":"Duarte Folgado, Pedro S. Rocha, Pedro Matias, Diana Liebeatrau, Francisco Nunes, Tiago Rocha, Inês Alves, Diana Lopes, Mamede de Carvalho, Bruno Miranda, André V. Carreiro","doi":"10.1145/3757930","DOIUrl":"https://doi.org/10.1145/3757930","url":null,"abstract":"Progressive neurological conditions like Amyotrophic Lateral Sclerosis (ALS) require regular and detailed monitoring to track functional decline and support timely clinical decisions. Traditional in-clinic assessments, however, can be burdensome and infrequent, often missing daily symptom variability. Smartphones and wearables offer a promising alternative for remote data collection. We present HomeSenseALS, a smartphone-based digital phenotyping application developed for ALS patients and caregivers, using a user-centered design approach involving patients, caregivers, and clinicians. HomeSenseALS collects multimodal data, including self-reported data from the Revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R), speech and respiratory recordings, and human movement passive data. To assess its potential for tracking ALS progression, we conducted a cross-sectional in-clinic study with 27 ALS patients and a longitudinal at-home study with 11 ALS patients over 21 weeks. Self-reported ALSFRS-R scores showed a strong correlation with in-clinic assessments. High-level physical activity features, such as time spent indoors, were associated with gross motor function. Speech analysis differentiated patients with and without bulbar dysfunction and correlated with respiratory parameters. Home spirometry aligned with clinical respiratory measures. These findings support the feasibility of using HomeSenseALS for remote, multimodal monitoring of ALS progression, potentially reducing patient burden while enabling more continuous and granular tracking of functional decline.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"6 4","pages":"1-26"},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://dl.acm.org/doi/pdf/10.1145/3757930","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147914074","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":"Video-based Intake Gesture Recognition Using Meal-length Context.","authors":"Zeyu Tang, Adam Hoover","doi":"10.1145/3709151","DOIUrl":"10.1145/3709151","url":null,"abstract":"<p><p>This article explores video analysis methods for monitoring eating behaviors, a critical factor in approximately 70% of global deaths due to illnesses like cancer, diabetes, and heart disease. Automated monitoring quantifies aspects such as meal duration, food types, and intake gestures (bite and drink gestures). Previous deep-learning methods segment videos into short clips (e.g., 16 frames at 8 Hz) for analysis, but this approach overlooks common meal-length patterns in gesture distribution across different individuals and sessions, which can enhance detection accuracy. Our study introduces a novel pipeline that analyzes the entire meal context (5-40 minutes). We propose a framework allowing a global detector to learn meal-length patterns with manageable computational demands. Additionally, we introduced a new augmentation technique to generate hundreds of meal-length feature samples per video, facilitating effective training of a global detector with limited video availability. Experimental results on two datasets (Clemson Cafeteria and EatSense) demonstrate that our pipeline significantly enhances the performance of state-of-the-art window-based networks, particularly in reducing false positives in gesture detection. On the Clemson Cafeteria dataset of 486 meal videos (the largest dataset to date), our method achieves F1 scores of 0.93 for bite gestures and 0.88 for drink gestures, substantially outperforming existing methodologies.</p>","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"6 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12872170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146127763","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":"CogProg: Utilizing Large Language Models to Forecast In-the-moment Health Assessment.","authors":"Gina Sprint, Maureen Schmitter-Edgecombe, Raven Weaver, Lisa Wiese, Diane J Cook","doi":"10.1145/3709153","DOIUrl":"10.1145/3709153","url":null,"abstract":"<p><p>Forecasting future health status is beneficial for understanding health patterns and providing anticipatory support for cognitive and physical health difficulties. In recent years, generative large language models (LLMs) have shown promise as forecasters. Though not traditionally considered strong candidates for numeric tasks, LLMs demonstrate emerging abilities to address various forecasting problems. They also provide the ability to incorporate unstructured information and explain their reasoning process. In this paper, we explore whether LLMs can effectively forecast future self-reported health state. To do this, we utilized in-the-moment assessments of mental sharpness, fatigue, and stress from multiple studies, utilizing daily responses (<i>N</i>=106 participants) and responses that are accompanied by text descriptions of activities (<i>N</i>=32 participants). With these data, we constructed prompt/response pairs to predict a participant's next answer. We fine-tuned several LLMs and applied chain-of-thought prompting evaluating forecasting accuracy and prediction explainability. Notably, we found that LLMs achieved the lowest mean absolute error (MAE) overall (0.851), while gradient boosting achieved the lowest overall root mean squared error (RMSE) (1.356). When additional text context was provided, LLM forecasts achieved the lowest MAE for predicting mental sharpness (0.862), fatigue (1.000), and stress (0.414). These multimodal LLMs further outperformed the numeric baselines in terms of RMSE when predicting stress (0.947), although numeric algorithms achieved the best RMSE results for mental sharpness (1.246) and fatigue (1.587). This study offers valuable insights for future applications of LLMs in health-based forecasting. The findings suggest that LLMs, when supplemented with additional text information, can be effective tools for improving health forecasting accuracy.</p>","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"6 2","pages":""},"PeriodicalIF":8.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12330958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144801058","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":"A Novel Multi-Modal Sensor Dataset and Benchmark to Detect Agitation in People Living with Dementia in a Residential Care Setting","authors":"Shehroz S. Khan, Pratik K. Mishra, Bing Ye, Smit Patel, Kristine Newman, Alex Mihailidis, Andrea Iaboni","doi":"10.1145/3720550","DOIUrl":"https://doi.org/10.1145/3720550","url":null,"abstract":"People living with dementia (PwD) in residential care settings often exhibit responsive behaviors, with agitation being the most common behavior. Automated systems to detect agitation events have the potential to improve patient care by helping to track symptoms and their response to interventions. We conducted a study from 2017 to 2019 in which we collected a novel multi-modal sensor dataset from 20 PwD living in a dementia care unit. Each participant wore an Empatica E4 watch for a maximum period of up to 60 days, leading to a large dataset worth 600 days. This wearable device collects raw acceleration, blood volume pulse, electro-dermal activity, and skin temperature data. The data are annotated with the start and end times of agitation events. Our previous analyses have shown that agitation behavior can be detected with a high area under receiver operating characteristic curve. We are now releasing this novel dataset for the research community to advance research in the field. In this article, we describe the study details, protocol used for annotation, improved agitation labelling, signal processing steps, and feature extraction approach. We present a new baseline on this dataset that can fuel new research in this important area of research.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"6 3","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://dl.acm.org/doi/pdf/10.1145/3720550","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147898046","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}