IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics最新文献_第2页

Evaluating Generative Models in Medical Imaging. 评估医学成像中的生成模型。

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2024-06-01 Epub Date: 2024-08-22 DOI: 10.1109/ichi61247.2024.00084

Liyue Fan, Ashley Bang, Luca Bonomi

引用次数: 0

Fine-grained Patient Similarity Measuring using Contrastive Graph Similarity Networks. 使用对比图相似度网络的细粒度患者相似度测量。

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2024-06-01 Epub Date: 2024-08-22 DOI: 10.1109/ichi61247.2024.00009

Yuxi Liu, Zhenhao Zhang, Shaowen Qin, Flora D Salim, Jiang Bian, Antonio Jimeno Yepes

{"title":"Fine-grained Patient Similarity Measuring using Contrastive Graph Similarity Networks.","authors":"Yuxi Liu, Zhenhao Zhang, Shaowen Qin, Flora D Salim, Jiang Bian, Antonio Jimeno Yepes","doi":"10.1109/ichi61247.2024.00009","DOIUrl":"10.1109/ichi61247.2024.00009","url":null,"abstract":"Predictive analytics using Electronic Health Records (EHRs) have become an active research area in recent years, especially with the development of deep learning techniques. A popular EHR data analysis paradigm in deep learning is patient representation learning, which aims to learn a condensed mathematical representation of individual patients. However, EHR data are often inherently irregular, i.e., data entries were captured at different times as well as with different contents due to the individualized needs of each patient. Most of the work focused on the provision of deep neural networks with attention mechanisms that generate complete patient representations that can be readily used for downstream prediction tasks. However, such approaches fail to take patient similarity into account, which is generally used in clinical reasoning scenarios. This study presents a new Contrastive Graph Similarity Network for similarity calculation among patients in large EHR datasets. Particularly, we apply graph-based similarity analysis that explicitly extracts the clinical characteristics of each patient and aggregates the information of similar patients to generate rich patient representations. Experimental results on real-world EHR databases demonstrate the effectiveness and superiority of our method for the task of vital signs imputation and ICU patient deterioration prediction.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":"2024 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857143","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

An Ethical Approach to Genomic Privacy Preserving Technology Development.

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2024-06-01 Epub Date: 2024-08-22 DOI: 10.1109/ichi61247.2024.00102

Lynette Hammond Gerido, Erman Ayday

{"title":"An Ethical Approach to Genomic Privacy Preserving Technology Development.","authors":"Lynette Hammond Gerido, Erman Ayday","doi":"10.1109/ichi61247.2024.00102","DOIUrl":"https://doi.org/10.1109/ichi61247.2024.00102","url":null,"abstract":"Demand for genomic research data and genetic testing results from cancer patients has grown exponentially. When a patient is diagnosed with a hereditary cancer syndrome, standard practice is for providers to encourage patients to discuss their results with their relatives and encourage those relatives to have clinical genetic testing and possibly participate in genetic research. Genomic research data and genetic testing results are being shared and connected in ways never imagined. Genomic data sharing is critical for advancing precision health and increasing diversity in global genome databases. However, these advancements often come with undesirable consequences, which call for additional privacy safeguards and research practices to protect hereditary cancer patients and their families because relatives who may have genomic information in common with the patient causing privacy risks to ripple throughout a kinship network. We propose to address this gap using an interdisciplinary approach integrating bioethical principles (autonomy, non-maleficence, beneficence, respect for persons, and equity) with data science techniques to mitigate privacy risk challenges.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":"2024 ","pages":"638-641"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11976530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812955","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

Mitigating Membership Inference in Deep Survival Analyses with Differential Privacy. 利用差异隐私减轻深度生存分析中的成员推断。

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2023-06-01 Epub Date: 2023-12-11 DOI: 10.1109/ichi57859.2023.00022

Liyue Fan, Luca Bonomi

{"title":"Mitigating Membership Inference in Deep Survival Analyses with Differential Privacy.","authors":"Liyue Fan, Luca Bonomi","doi":"10.1109/ichi57859.2023.00022","DOIUrl":"10.1109/ichi57859.2023.00022","url":null,"abstract":"Deep neural networks have been increasingly integrated in healthcare applications to enable accurate predicative analyses. Sharing trained deep models not only facilitates knowledge integration in collaborative research efforts but also enables equitable access to computational intelligence. However, recent studies have shown that an adversary may leverage a shared model to learn the participation of a target individual in the training set. In this work, we investigate privacy-protecting model sharing for survival studies. Specifically, we pose three research questions. (1) Do deep survival models leak membership information? (2) How effective is differential privacy in defending against membership inference in deep survival analyses? (3) Are there other effects of differential privacy on deep survival analyses? Our study assesses the membership leakage in emerging deep survival models and develops differentially private training procedures to provide rigorous privacy protection. The experimental results show that deep survival models leak membership information and our approach effectively reduces membership inference risks. The results also show that differential privacy introduces a limited performance loss, and may improve the model robustness in the presence of noisy data, compared to non-private models.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":"2023 ","pages":"81-90"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10751041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139049861","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

An LSTM-based Gesture-to-Speech Recognition System. 基于 LSTM 的手势语音识别系统

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2023-06-01 Epub Date: 2023-12-11 DOI: 10.1109/ichi57859.2023.00062

Riyad Bin Rafiq, Syed Araib Karim, Mark V Albert

{"title":"An LSTM-based Gesture-to-Speech Recognition System.","authors":"Riyad Bin Rafiq, Syed Araib Karim, Mark V Albert","doi":"10.1109/ichi57859.2023.00062","DOIUrl":"10.1109/ichi57859.2023.00062","url":null,"abstract":"Fast and flexible communication options are limited for speech-impaired people. Hand gestures coupled with fast, generated speech can enable a more natural social dynamic for those individuals - particularly individuals without the fine motor skills to type on a keyboard or tablet reliably. We created a mobile phone application prototype that generates audible responses associated with trained hand movements and collects and organizes the accelerometer data for rapid training to allow tailored models for individuals who may not be able to perform standard movements such as sign language. Six participants performed 11 distinct gestures to produce the dataset. A mobile application was developed that integrated a bidirectional LSTM network architecture which was trained from this data. After evaluation using nested subject-wise cross-validation, our integrated bidirectional LSTM model demonstrates an overall recall of 91.8% in recognition of these pre-selected 11 hand gestures, with recall at 95.8% when two commonly confused gestures were not assessed. This prototype is a step in creating a mobile phone system capable of capturing new gestures and developing tailored gesture recognition models for individuals in speech-impaired populations. Further refinement of this prototype can enable fast and efficient communication with the goal of further improving social interaction for individuals unable to speak.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":"2023 ","pages":"430-438"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10894657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139974844","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

Benchmarking Transformer-Based Models for Identifying Social Determinants of Health in Clinical Notes. 在临床笔记中识别健康的社会决定因素的基于变压器的模型基准。

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2023-06-01 Epub Date: 2023-12-11 DOI: 10.1109/ichi57859.2023.00102

Xiaoyu Wang, Dipankar Gupta, Michael Killian, Zhe He

{"title":"Benchmarking Transformer-Based Models for Identifying Social Determinants of Health in Clinical Notes.","authors":"Xiaoyu Wang, Dipankar Gupta, Michael Killian, Zhe He","doi":"10.1109/ichi57859.2023.00102","DOIUrl":"10.1109/ichi57859.2023.00102","url":null,"abstract":"Electronic health records (EHR) have been widely used in building machine learning models for health outcomes prediction. However, many EHR-based models are inherently biased due to lack of risk factors on social determinants of health (SDoH), which are responsible for up to 40% preventive deaths. As SDoH information is often captured in clinical notes, recent efforts have been made to extract such information from notes with natural language processing and append it to other structured data. In this work, we benchmark 7 pre-trained transformer-based models, including BERT, ALBERT, BioBERT, BioClinicalBERT, RoBERTa, ELECTRA, and RoBERTa-MIMIC-Trial, for recognizing SDoH terms using a previously annotated corpus of MIMIC-III clinical notes. Our study shows that BioClinicalBERT model performs best on F-1 scores (0.911, 0.923) under both strict and relaxed criteria. This work shows the promise of using transformer-based models for recognizing SDoH information from clinical notes.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":"2023 ","pages":"570-574"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10795706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139492901","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

An End-to-end In-Silico and In-Vitro Drug Repurposing Pipeline for Glioblastoma. 针对胶质母细胞瘤的端到端硅内和体外药物再利用管道。

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2023-06-01 Epub Date: 2023-12-11 DOI: 10.1109/ichi57859.2023.00135

Ko-Hong Lin, Jay-Jiguang Zhu, Judith A Smith, Yejin Kim, Xiaoqian Jiang

{"title":"An End-to-end In-Silico and In-Vitro Drug Repurposing Pipeline for Glioblastoma.","authors":"Ko-Hong Lin, Jay-Jiguang Zhu, Judith A Smith, Yejin Kim, Xiaoqian Jiang","doi":"10.1109/ichi57859.2023.00135","DOIUrl":"10.1109/ichi57859.2023.00135","url":null,"abstract":"Our study aims to address the challenges in drug development for glioblastoma, a highly aggressive brain cancer with poor prognosis. We propose a computational framework that utilizes machine learning-based propensity score matching to estimate counterfactual treatment effects and predict synergistic effects of drug combinations. Through our in-silico analysis, we identified promising drug candidates and drug combinations that warrant further investigation. To validate these computational findings, we conducted in-vitro experiments on two GBM cell lines, U87 and T98G. The experimental results demonstrated that some of the identified drugs and drug combinations indeed exhibit strong suppressive effects on GBM cell growth. Our end-to-end pipeline showcases the feasibility of integrating computational models with biological experiments to expedite drug repurposing and discovery efforts. By bridging the gap between in-silico analysis and in-vitro validation, we demonstrate the potential of this approach to accelerate the development of novel and effective treatments for glioblastoma.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":"2023 ","pages":"738-745"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10956733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140186468","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

Improving Prediction of Late Symptoms using LSTM and Patient-reported Outcomes for Head and Neck Cancer Patients. 利用 LSTM 和患者报告结果改进头颈癌患者晚期症状的预测。

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2023-06-01 Epub Date: 2023-12-11 DOI: 10.1109/ichi57859.2023.00047

Yaohua Wang, Lisanne Van Dijk, Abdallah S R Mohamed, Mohamed Naser, Clifton David Fuller, Xinhua Zhang, G Elisabeta Marai, Guadalupe Canahuate

{"title":"Improving Prediction of Late Symptoms using LSTM and Patient-reported Outcomes for Head and Neck Cancer Patients.","authors":"Yaohua Wang, Lisanne Van Dijk, Abdallah S R Mohamed, Mohamed Naser, Clifton David Fuller, Xinhua Zhang, G Elisabeta Marai, Guadalupe Canahuate","doi":"10.1109/ichi57859.2023.00047","DOIUrl":"10.1109/ichi57859.2023.00047","url":null,"abstract":"Patient-Reported Outcomes (PRO) are collected directly from the patients using symptom questionnaires. In the case of head and neck cancer patients, PRO surveys are recorded every week during treatment with each patient's visit to the clinic and at different follow-up times after the treatment has concluded. PRO surveys can be very informative regarding the patient's status and the effect of treatment on the patient's quality of life (QoL). Processing PRO data is challenging for several reasons. First, missing data is frequent as patients might skip a question or a questionnaire altogether. Second, PROs are patient-dependent, a rating of 5 for one patient might be a rating of 10 for another patient. Finally, most patients experience severe symptoms during treatment which usually subside over time. However, for some patients, late toxicities persist negatively affecting the patient's QoL. These long-term severe symptoms are hard to predict and are the focus of this study. In this work, we model PRO data collected from head and neck cancer patients treated at the MD Anderson Cancer Center using the MD Anderson Symptom Inventory (MDASI) questionnaire as time series. We impute missing values with a combination of K nearest neighbor (KNN) and Long Short-Term Memory (LSTM) neural networks, and finally, apply LSTM to predict late symptom severity 12 months after treatment. We compare performance against clinical and ARIMA models. We show that the LSTM model combined with KNN imputation is effective in predicting late-stage symptom ratings for occurrence and severity under the AUC and F1 score metrics.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":"2023 ","pages":"292-300"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10853990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139725194","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

CareD: Caregiver's Experience with Cognitive Decline in Reddit Posts. CareD：照顾者在 Reddit 帖子中对认知能力衰退的体验。

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2023-06-01 Epub Date: 2023-12-11 DOI: 10.1109/ichi57859.2023.00104

Muskan Garg, Sunghwan Sohn

{"title":"CareD: Caregiver's Experience with Cognitive Decline in Reddit Posts.","authors":"Muskan Garg, Sunghwan Sohn","doi":"10.1109/ichi57859.2023.00104","DOIUrl":"10.1109/ichi57859.2023.00104","url":null,"abstract":"With advancements in analysis of cognitive decline in electronic health records, the research community witnesses a recent surge in social media posting by caregivers and/or loved ones of people with cognitive decline. The major challenges in this area are availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability. To this end, we construct a new dataset, Caregivers experiences with cognitive Decline (CareD), of 1005 posts with more than 194K words and 9541 sentences, highlighting discussions on people with dementia and Alzheimer's disease on Reddit. We discuss the changing trends of discussions on cognitive decline in social media and open challenges for natural language processing and social computing. We first identify the Reddit posts reflecting substantial information as candidate posts. We further formulate the annotation guidelines, handle perplexities to investigate the existence of experiences, self-reported articles and potential caregiver in candidate posts, resulting in the discovery of latent symptoms, firsthand information, and prospective source of longitudinal information about the patient, respectively.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":"2023 ","pages":"581-587"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10877621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139934508","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

End-to-End n-ary Relation Extraction for Combination Drug Therapies. 联合药物疗法的端到端 nary 关系提取。

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2023-06-01 Epub Date: 2023-12-11 DOI: 10.1109/ichi57859.2023.00021

Yuhang Jiang, Ramakanth Kavuluru

{"title":"End-to-End n-ary Relation Extraction for Combination Drug Therapies.","authors":"Yuhang Jiang, Ramakanth Kavuluru","doi":"10.1109/ichi57859.2023.00021","DOIUrl":"10.1109/ichi57859.2023.00021","url":null,"abstract":"Combination drug therapies are treatment regimens that involve two or more drugs, administered more commonly for patients with cancer, HIV, malaria, or tuberculosis. Currently there are over 350K articles in PubMed that use the combination drug therapy MeSH heading with at least 10K articles published per year over the past two decades. Extracting combination therapies from scientific literature inherently constitutes an n-ary relation extraction problem. Unlike in the general n-ary setting where n is fixed (e.g., drug-gene-mutation relations where n = 3), extracting combination therapies is a special setting where n ≥ 2 is dynamic, depending on each instance. Recently, Tiktinsky et al. (NAACL 2022) introduced a first of its kind dataset, CombDrugExt, for extracting such therapies from literature. Here, we use a sequence-to-sequence style end-to-end extraction method to achieve an F1-Score of 66.7% on the CombDrugExt test set for positive (or effective) combinations. This is an absolute ≈ 5% F1-score improvement even over the prior best relation classification score with spotted drug entities (hence, not end-to-end). Thus our effort introduces a state-of-the-art first model for end-to-end extraction that is already superior to the best prior non end-to-end model for this task. Our model seamlessly extracts all drug entities and relations in a single pass and is highly suitable for dynamic n-ary extraction scenarios.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":"2023 ","pages":"72-80"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10814995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139571682","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