2023 IEEE International Conference on Digital Health (ICDH)最新文献

A Remotely Accessible Ecological Momentary Assessment and Actigraphy System for ALS Research 一种用于ALS研究的远程可访问生态瞬时评估与活动记录系统

2023 IEEE International Conference on Digital Health (ICDH) Pub Date : 2023-07-01 DOI: 10.1109/icdh60066.2023.00013

Nora Howard, Gina Sprint, D. Weeks, Elena Crooks

{"title":"A Remotely Accessible Ecological Momentary Assessment and Actigraphy System for ALS Research","authors":"Nora Howard, Gina Sprint, D. Weeks, Elena Crooks","doi":"10.1109/icdh60066.2023.00013","DOIUrl":"https://doi.org/10.1109/icdh60066.2023.00013","url":null,"abstract":"Healthcare-related research studies often deploy ecological momentary assessment techniques to sample information from participants in their natural environment. This paper presents an online system to support remote access of ecological self-report and actigraphy-based measurements for individuals with Amyotrophic Lateral Sclerosis (ALS) and their caregivers. The presented framework includes a custom mobile app and makes use of a web-based application programming interface for data collection with wrist-worn actigraphy devices. The system was evaluated with a research protocol to measure sleep, fatigue, and pain for individuals with ALS and their caregivers (N=8) over a consecutive seven-day period. Though daily self-report response rates were widespread (0%-100%) and the remote actigraphy collection varied in reliability, novel relationships between individuals with ALS and their caregivers were identified from the collected data. Online, ecological systems can support real-time remote monitoring and/or interventions to help understand diseases like ALS and advance healthcare research.","PeriodicalId":107307,"journal":{"name":"2023 IEEE International Conference on Digital Health (ICDH)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115717696","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

An Automated Digital Biomarker of Mobility 移动的自动数字生物标志物

2023 IEEE International Conference on Digital Health (ICDH) Pub Date : 2023-07-01 DOI: 10.1109/ICDH60066.2023.00022

Tian Hao, Yasunori Yamada, Jeffrey L. Rogers, Kaoru Shinakwa, M. Nemoto, K. Nemoto, T. Arai

{"title":"An Automated Digital Biomarker of Mobility","authors":"Tian Hao, Yasunori Yamada, Jeffrey L. Rogers, Kaoru Shinakwa, M. Nemoto, K. Nemoto, T. Arai","doi":"10.1109/ICDH60066.2023.00022","DOIUrl":"https://doi.org/10.1109/ICDH60066.2023.00022","url":null,"abstract":"The Timed Up and Go (TUG) test is a common clinical endpoint, but is limited by the need to conduct it in the presence of a trained evaluator, usually a clinician. Herein, we propose a sensor-agnostic automated pipeline based on machine learning to predict TUG scores using day-to-day walks captured using commonly used wearable sensors by generating a passive and continual stream of mobility biomarkers without the need of conducting scripted TUG tests. We validated our pipeline against data from 303 participants in three cohort datasets, each with a different primary focus population of healthy elderly adults, Parkinson’s disease patients, and patients with mild cognitive impairment or dementia. In addition to TUG scores, the three datasets include walking data collected from different wearable sensors, i.e., a lower-back-worn accelerometer, wrist-worn accelerometer, and in-sole pressure gait sensor, respectively. Our leave-one-subject-out validation using participants from all cohorts showed that a random-forest predictive model achieved an accuracy of 1.7 ± 1.7s (mean absolute error ± standard deviation) and 84.8% predictions within the minimal detectable change (± 3s) with reasonable generalization across cohorts. Through the validation on data collected using he three types of commonly used wearable sensors, we demonstrated the ability of our proposed pipeline to leverage heterogeneous inputs for predicting TUG scores from walking data, suggesting the feasibility to generate a continual stream of TUG predictions as a novel digital biomarker of mobility by leveraging naturally occurring walks in free-living scenario. Our investigation also suggests that, for certain cohorts (e.g., Parkinson’s disease population), applying a cohort-specific model instead of using a model trained with mixed cohorts might further improve performance.","PeriodicalId":107307,"journal":{"name":"2023 IEEE International Conference on Digital Health (ICDH)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115745119","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: 1

Copyright Page 版权页

2023 IEEE International Conference on Digital Health (ICDH) Pub Date : 2023-07-01 DOI: 10.1109/icdh60066.2023.00003

引用次数: 0

Wearable Technology to Quantify Patient Reported Outcome Measures to Guide Rehabilitation Following Anterior Cruciate Ligament Reconstruction 可穿戴技术量化患者报告的结果措施，指导前交叉韧带重建后的康复

2023 IEEE International Conference on Digital Health (ICDH) Pub Date : 2023-07-01 DOI: 10.1109/ICDH60066.2023.00060

Helina VanBibber, Andrew Moyal, Benjamin J. Geletka, Colin K. Drummond, Soham R. Patel, Jacob G. Calcei, J. Voos, Dhruv R. Seshadri

{"title":"Wearable Technology to Quantify Patient Reported Outcome Measures to Guide Rehabilitation Following Anterior Cruciate Ligament Reconstruction","authors":"Helina VanBibber, Andrew Moyal, Benjamin J. Geletka, Colin K. Drummond, Soham R. Patel, Jacob G. Calcei, J. Voos, Dhruv R. Seshadri","doi":"10.1109/ICDH60066.2023.00060","DOIUrl":"https://doi.org/10.1109/ICDH60066.2023.00060","url":null,"abstract":"The anterior cruciate ligament (ACL) stabilizes the knee joint to prevent internal rotation. ACL injuries are common for athletes in high-cutting sports, affecting female athletes at a greater incidence compared to male athletes. Wearable devices and digital health technologies, broadly speaking, have become increasingly utilized in clinical trials to quantify patient reported outcome measures to complement subjective assessments. In the context of sports medicine, wearable technology serves as an objective and continuous means to complement athlete ratings of perceived exertion. One such biomarker of interest is muscle oxygen saturation (SmO2), which is the ratio of oxygenated hemoglobin to total hemoglobin. A decrease in SmO2 is indicative of greater muscle exertion, higher energy output, and greater oxygen consumption. Current assessments for ACL rehabilitation employ subjective means and lack the integration of continuous, internal data. This study bridges this gap via the measurement of SmO2 to guide the return to play (RTP) process following ACL reconstruction (ACLR). Preliminary results from one patient demonstrate significant differences between the surgical and contralateral limbs during max-minute fan bike and Tabata fan bike exercises at both 6-and 9-month trials following ACLR. In the bilateral leg press exercise, significant differences were found between the surgical and contralateral lower extremities at 6-months but not at the 9-month trial. Data collection will also occur at 12-months post ACLR to further momtor differences between surgical and contralateral limbs in the RTP process. These results provide the impetus to enable the interoperability of data gathered from wearable devices into data management systems for optimizing performance and health of athletes following injury.","PeriodicalId":107307,"journal":{"name":"2023 IEEE International Conference on Digital Health (ICDH)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129851705","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

Transfer Learning Improves Unsupervised Assignment of ICD codes with Clinical Notes 迁移学习改进临床记录的ICD代码的无监督分配

2023 IEEE International Conference on Digital Health (ICDH) Pub Date : 2023-07-01 DOI: 10.1109/ICDH60066.2023.00047

Amit Kumar, Souparna Das, Suman Roy

{"title":"Transfer Learning Improves Unsupervised Assignment of ICD codes with Clinical Notes","authors":"Amit Kumar, Souparna Das, Suman Roy","doi":"10.1109/ICDH60066.2023.00047","DOIUrl":"https://doi.org/10.1109/ICDH60066.2023.00047","url":null,"abstract":"In healthcare industry, it is a standard practice to assign a set of International Classification of Diseases (ICD) to a clinical note (which can be a patient visit, a discharge summary and the like) as part of medical coding process mandated by medical care and patient billing. A supervised framework is adopted for most of the automated ICD coding assignment methods in which a subset of the clinical notes are a-priori labeled with ICD codes. But in lot of cases enough labeled texts are not available. These call for an unsupervised assignment of ICD codes. However, the quality of the data plays an important role in the performance of unsupervised coding, - low quality data leads to degradation of performance. In this paper, we explore a transfer learning approach for ICD coding using a combination of pre-training and supervised fine-tuning. We use a hierarchical BERT model comprising of a Bi-LSTM layered on top of BERT (this removes the restriction on the size of clinical texts)) as part of model architecture, and pre-train it on the total corpus (which include both labeled and unlabeled data). Next we transfer its weights to fine tune the model with labeled data (MIMIC data) in a supervised framework and then use this model to predict ICD code for unlabeled data using token similarity. This is the first use of using transfer learning in ICD prediction to our knowledge. Finally we show the efficacy of our transfer learning approach through rigorous experimentation, - there is 20% gain of sensitivity (recall) and 6% lift in specificity in ICD prediction compared to direct unsupervised prediction.","PeriodicalId":107307,"journal":{"name":"2023 IEEE International Conference on Digital Health (ICDH)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129857466","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

Policy Integrated Blockchain to Automate HIPAA Part 2 Compliance 政策集成区块链自动化HIPAA第2部分合规性

2023 IEEE International Conference on Digital Health (ICDH) Pub Date : 2023-07-01 DOI: 10.1109/ICDH60066.2023.00052

James R. Clavin, K. Joshi

{"title":"Policy Integrated Blockchain to Automate HIPAA Part 2 Compliance","authors":"James R. Clavin, K. Joshi","doi":"10.1109/ICDH60066.2023.00052","DOIUrl":"https://doi.org/10.1109/ICDH60066.2023.00052","url":null,"abstract":"Healthcare organizations exchange sensitive health records, including behavioral health data, across peer-to-peer networks, and it is challenging to find and fix compliance issues proactively. The Healthcare industry anticipates a growing need to audit substance use disorder patient data, commonly referred to as Part 2 data, having been shared without a release of information signed by the patient. To address this need, we developed and evaluated a novel methodology to detect Part 2 data exchanged between organizations that integrates Blockchain technologies with knowledge graphs. We detect substance use disorder data in patient encounters exchanged using clinical terminology based upon the value sets provided by the National Institutes of Health for the Substance Abuse and Mental Health Services Administration. Generally, we consider sharing Part 2 data without consent as Byzantine medical faults, as they represent data shared between known and trusted network participants, that is valid, but is not relevant, and sharing it causes a breach. In this paper, we present our methodology in detail along with the experiment results. We model a medical network of hospitals based upon the most recent healthcare legislation, TEFCA, and generate synthetic patient encounter data dynamically in HL7 format. We convert exchanged encounter data into a knowledge graph data model so that we can use SNOMED-CT for identifying Part 2 data. For cohorts of 1,000 patients, we detect Part 2 data in a subset of their encounter data shared between organizations and log that securely on an Ethereum-based blockchain.","PeriodicalId":107307,"journal":{"name":"2023 IEEE International Conference on Digital Health (ICDH)","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121308621","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

Towards Developing a Mobile-Based Care (KidneyCare) for Patients with Kidney Diseases Using Ten-second Fingertip Video and PPG with Machine Learning 利用10秒指尖视频和机器学习的PPG为肾病患者开发基于移动的护理(肾脏护理)

2023 IEEE International Conference on Digital Health (ICDH) Pub Date : 2023-07-01 DOI: 10.1109/ICDH60066.2023.00036

Parama Sridevi, Masud Rabbani, Sayed Mashroor Mamun, Mohammad Syam, Rumi Ahmed Khan, S. Ahamed

引用次数: 0

Personal Health Trajectory Traceability using Blockchain Technology 使用区块链技术追踪个人健康轨迹

2023 IEEE International Conference on Digital Health (ICDH) Pub Date : 2023-07-01 DOI: 10.1109/ICDH60066.2023.00054

J. Rojo, J. García-Alonso, Juan Hernández, J. M. Murillo, Abdelsalam Helal

{"title":"Personal Health Trajectory Traceability using Blockchain Technology","authors":"J. Rojo, J. García-Alonso, Juan Hernández, J. M. Murillo, Abdelsalam Helal","doi":"10.1109/ICDH60066.2023.00054","DOIUrl":"https://doi.org/10.1109/ICDH60066.2023.00054","url":null,"abstract":"Healthcare delivery transformations and the use of connected health devices are paving the way to a paradigm shift from current healthcare systems towards patient-centered systems. Many proposals successfully reorient health information systems so that data are still distributed among the institutions and services that generate them, while being accessed jointly from a single point of view per patient. However, this means that control over the operations involving this data is lost. Mechanisms to maintain the traceability of health data are needed. This will enable the verification of the integrity of the records and will provide assurances that they have not been compromised. This problem has already been addressed in other domains such as food supply chains, where traceability allows to know all interactions with a food supply from the time it is produced until it is consumed. This paper proposes a blockchain solution to achieve the traceability of health data in patient-centered distributed environments. To validate this proposal, a case study involving 50 sociosanitary institutions in Portugal have been chosen. Different performance tests have been conducted to demonstrate the suitability, feasibility and scalability of our proposal.","PeriodicalId":107307,"journal":{"name":"2023 IEEE International Conference on Digital Health (ICDH)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127579022","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

Remote Inference of Cognitive Scores in ALS Patients Using a Picture Description 使用图片描述的ALS患者认知评分的远程推断

2023 IEEE International Conference on Digital Health (ICDH) Pub Date : 2023-07-01 DOI: 10.1109/ICDH60066.2023.00017

C. Agurto, G. Cecchi, Bo Wen, E. Fraenkel, James D. Berry, I. Navar, R. Norel

{"title":"Remote Inference of Cognitive Scores in ALS Patients Using a Picture Description","authors":"C. Agurto, G. Cecchi, Bo Wen, E. Fraenkel, James D. Berry, I. Navar, R. Norel","doi":"10.1109/ICDH60066.2023.00017","DOIUrl":"https://doi.org/10.1109/ICDH60066.2023.00017","url":null,"abstract":"Amyotrophic lateral sclerosis (ALS) is a fatal disease that affects not only movement, speech, and breathing but also cognition. Recent studies have focused on the use of language analysis techniques to detect ALS and infer scales for monitoring functional progression. This paper focused on another important aspect, cognitive impairment, which affects 35-50% of the ALS population. In an effort to reach the ALS population, which frequently exhibits mobility limitations, we implemented the digital version of the Edinburgh Cognitive and Behavioral ALS Screen (ECAS) test for the first time. This test, designed to measure cognitive impairment, was remotely performed by 56 participants from the EverythingALS Speech Study1. As part of the study, participants (ALS and non-ALS) were asked to describe weekly one picture from a pool of many pictures with complex scenes displayed on their computer at home. We analyze the descriptions performed within +/− 60 days from the day the ECAS test was administered and extract different types of linguistic and acoustic features. We input those features into linear regression models to infer 5 ECAS sub-scores and the total score. Speech samples from the picture description are reliable enough to predict the ECAS subs-scores, achieving statistically significant Spearman correlation values between 0.32 and 0.51 for the model’s performance using 10-fold cross-validation.1https://www.everythingals.org/research","PeriodicalId":107307,"journal":{"name":"2023 IEEE International Conference on Digital Health (ICDH)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114341568","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

HCM-Dynamic-Echo: A Framework for Detecting Hypertrophic Cardiomyopathy (HCM) in Echocardiograms HCM-动态回声:在超声心动图中检测肥厚性心肌病(HCM)的框架

2023 IEEE International Conference on Digital Health (ICDH) Pub Date : 2023-07-01 DOI: 10.1109/ICDH60066.2023.00039

Abdulsalam Almadani, Emmanuel Agu, Atifa Sarwar, M. Ahluwalia, J. Kpodonu

{"title":"HCM-Dynamic-Echo: A Framework for Detecting Hypertrophic Cardiomyopathy (HCM) in Echocardiograms","authors":"Abdulsalam Almadani, Emmanuel Agu, Atifa Sarwar, M. Ahluwalia, J. Kpodonu","doi":"10.1109/ICDH60066.2023.00039","DOIUrl":"https://doi.org/10.1109/ICDH60066.2023.00039","url":null,"abstract":"Cardiovascular disease (CVD) is the leading cause of death worldwide. Hypertrophic Cardiomyopathy (HCM) is the most common genetic disease in which the heart’s Left Ventricular (LV) wall becomes thicker and stiffer, making it difficult to pump blood. HCM affects 1:200 to 1:500 people and can result in Sudden Cardiac Death (SCD), heart failure, and abnormal heart rhythms leading to stroke. Early diagnosis and treatment of HCM can improve outcomes. An echocardiogram, a heart ultrasound, is routinely performed on patients and is currently the gold standard for HCM diagnosis. However, expert analyses of echocardiograms can be inconsistent, resulting in missed diagnoses. Deep Video Action Recognition (VAR) models have achieved state-of-the-art performance for the task of recognizing human actions, such as running and walking, in a video. In this paper, we innovatively propose HCM-Dynamic-Echo, an end-to-end deep learning framework that uses the SlowFast VAR architecture, for the binary classification of echocardiogram videos as having HCM vs. normal. SlowFast has two arms: arm 1 (slow pathway) analyzes spatial features, while arm 2 (fast pathway) captures temporal structural information to increase video recognition accuracy. Furthermore, we employed transfer learning, pre-training HCM-Dynamic-Echo on the large Stanford EchoNet-Dynamic echocardiogram dataset, enabling HCM detection in a smaller echocardiogram video dataset. In rigorous evaluation, HCM-Dynamic-Echo outperformed state-of-the-art baselines, achieving an accuracy of 93.13%, a F1-score of 92.98%, Positive Predictive Value (PPV) of 94.64%, specificity of 94.87%, and an Area Under the Curve (AUC) of 93.13%. To the best of our knowledge, our work is the first that innovatively utilized the SlowFast VAR architecture for predicting HCM in racially and ethnically diverse echocardiogram videos.","PeriodicalId":107307,"journal":{"name":"2023 IEEE International Conference on Digital Health (ICDH)","volume":"40 43","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120811527","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