Journal of Healthcare Informatics Research最新文献_第5页

The Impact on Ambulance Mobilisations of an Increasing Age Profile of Telecare Service Users Receiving Advanced Proactive, Personalised Telecare in Spain—a Longitudinal Study 2014–2018 西班牙接受高级主动式个性化远程护理的远程护理服务用户年龄增长对救护车动员的影响——2014-2018年纵向研究

IF 5.9

Journal of Healthcare Informatics Research Pub Date : 2021-11-06 DOI: 10.1007/s41666-021-00108-5

Wendy Hugoosgift Contreras, Ester Sarquella, Eva Binefa, Mar Entrambasaguas, Anette Stjerne, Peter Booth

引用次数: 1

Preconception and Diabetes Information (PADI) App for Women with Pregestational Diabetes: a Feasibility and Acceptability Study. 妊娠前糖尿病妇女的孕前和糖尿病信息（PADI）应用程序的可行性和可接受性研究

IF 5.9

Journal of Healthcare Informatics Research Pub Date : 2021-08-26 eCollection Date: 2021-12-01 DOI: 10.1007/s41666-021-00104-9

Chidiebere H Nwolise, Nicola Carey, Jill Shawe

{"title":"Preconception and Diabetes Information (PADI) App for Women with Pregestational Diabetes: a Feasibility and Acceptability Study.","authors":"Chidiebere H Nwolise, Nicola Carey, Jill Shawe","doi":"10.1007/s41666-021-00104-9","DOIUrl":"10.1007/s41666-021-00104-9","url":null,"abstract":"Diabetes mellitus increases the risk of adverse maternal and fetal outcomes. Preconception care is vital to minimise complications; however, preconception care service provision is hindered by inadequate knowledge, resources and care fragmentation. Mobile health technology, particularly smartphone apps, could improve preconception care and pregnancy outcomes for women with diabetes. The aim of this study is to co-create a preconception and diabetes information app with healthcare professionals and women with diabetes and explore the feasibility, acceptability and preliminary effects of the app. A mixed-methods study design employing questionnaires and semi-structured interviews was used to assess preliminary outcome estimates (preconception care knowledge, attitudes and behaviours), and user acceptability. Data analysis included thematic analysis, descriptive statistics and non-parametric tests. Improvements were recorded in knowledge and attitudes to preconception care and patient activation measure following the 3-month app usage. Participants found the app acceptable (satisfaction rating was 72%), useful and informative. The app's usability and usefulness facilitated usage while manual data input and competing priorities were barriers which participants felt could be overcome via personalisation, automation and use of daily reminders. This is the first study to explore the acceptability and feasibility of a preconception and diabetes information app for women with diabetes. Triangulated data suggest that the app has potential to improve preconception care knowledge, attitudes and behaviours. However, in order for women with DM to realise the full potential of the app intervention, particularly improved maternal and fetal outcomes, further development and evaluation is required.","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":"5 1","pages":"446-473"},"PeriodicalIF":5.9,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41666-021-00104-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47401041","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: 4

Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder 自闭症谱系障碍儿童基于面孔的注意识别模型

IF 5.9

Journal of Healthcare Informatics Research Pub Date : 2021-07-15 DOI: 10.1007/s41666-021-00101-y

Bilikis Banire, Dena Al Thani, M. Qaraqe, Bilal Mansoor

引用次数: 6

PolSIRD: Modeling Epidemic Spread Under Intervention Policies: Analyzing the First Wave of COVID-19 in the USA. PolSIRD：模拟干预政策下的流行病传播：分析美国第一波 COVID-19。

IF 5.9

Journal of Healthcare Informatics Research Pub Date : 2021-06-14 eCollection Date: 2021-09-01 DOI: 10.1007/s41666-021-00099-3

Nitin Kamra, Yizhou Zhang, Sirisha Rambhatla, Chuizheng Meng, Yan Liu

{"title":"PolSIRD: Modeling Epidemic Spread Under Intervention Policies: Analyzing the First Wave of COVID-19 in the USA.","authors":"Nitin Kamra, Yizhou Zhang, Sirisha Rambhatla, Chuizheng Meng, Yan Liu","doi":"10.1007/s41666-021-00099-3","DOIUrl":"10.1007/s41666-021-00099-3","url":null,"abstract":"Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in the absence of any intervention policies. In addition, these models assume full observability of disease cases and do not account for under-reporting. We present a mathematical model, namely PolSIRD, which accounts for the under-reporting by introducing an observation mechanism. It also captures the effects of intervention policies on the disease spread parameters by leveraging intervention policy data along with the reported disease cases. Furthermore, we allow our recurrent model to learn the initial hidden state of all compartments end-to-end along with other parameters via gradient-based training. We apply our model to the spread of the recent global outbreak of COVID-19 in the USA, where our model outperforms the methods employed by the CDC in predicting the spread. We also provide counterfactual simulations from our model to analyze the effect of lifting the intervention policies prematurely and our model correctly predicts the second wave of the epidemic.","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":"5 3","pages":"231-248"},"PeriodicalIF":5.9,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39257097","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

Real-world Patient Trajectory Prediction from Clinical Notes Using Artificial Neural Networks and UMLS-Based Extraction of Concepts 使用人工神经网络和基于uml的概念提取从临床记录中预测真实世界的患者轨迹

IF 5.9

Journal of Healthcare Informatics Research Pub Date : 2021-06-05 DOI: 10.1007/s41666-021-00100-z

Jamil Zaghir, Jose F. Rodrigues-Jr, L. Goeuriot, S. Amer-Yahia

引用次数: 3

A Pilot Study to Detect Agitation in People Living with Dementia Using Multi-Modal Sensors 一项使用多模态传感器检测痴呆患者躁动的初步研究

IF 5.9

Journal of Healthcare Informatics Research Pub Date : 2021-05-01 DOI: 10.1007/s41666-021-00095-7

S. Spasojevic, Jacob Nogas, A. Iaboni, B. Ye, Alex Mihailidis, A. Wang, S. Li, L. Martin, Kristine Newman, Shehroz S. Khan

引用次数: 17

Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession Boards. 利用心理语言和众源词典预测匿名忏悔板中书面情感披露中的禁忌

IF 5.9

Journal of Healthcare Informatics Research Pub Date : 2021-04-30 eCollection Date: 2021-09-01 DOI: 10.1007/s41666-021-00092-w

Arindam Paul, Wei-Keng Liao, Alok Choudhary, Ankit Agrawal

{"title":"Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession Boards.","authors":"Arindam Paul, Wei-Keng Liao, Alok Choudhary, Ankit Agrawal","doi":"10.1007/s41666-021-00092-w","DOIUrl":"10.1007/s41666-021-00092-w","url":null,"abstract":"There have been many efforts in the last decade in the health informatics community to develop systems that can automatically recognize and predict disclosures on social media. However, a majority of such efforts have focused on simple topic prediction or sentiment classification. However, taboo disclosures on social media that people are not comfortable to talk with their friends represent an abstract theme dependent on context and background. Recent research has demonstrated the efficacy of injecting concept into the learning model to improve prediction. We present a vectorization scheme that combines corpus- and lexicon-based approaches for predicting taboo topics from anonymous social media datasets. The proposed vectorization scheme exploits two context-rich lexicons LIWC and Urban Dictionary. Our methodology achieves cross-validation accuracies of up to 78.1% for the supervised learning task on Facebook Confessions dataset, and 70.5% for the transfer learning task on the YikYak dataset. For both the tasks, supervised algorithms trained with features generated by the proposed vectorizer perform better than vanilla t f - i d f representation. This work presents a novel methodology for predicting taboos from anonymous emotional disclosures on confession boards.","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":"5 1","pages":"319-341"},"PeriodicalIF":5.9,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42181190","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

Using Graph Representation Learning to Predict Salivary Cortisol Levels in Pancreatic Cancer Patients 使用图表示学习预测胰腺癌患者唾液皮质醇水平

IF 5.9

Journal of Healthcare Informatics Research Pub Date : 2021-04-21 DOI: 10.1007/s41666-021-00098-4

Guimin Dong, M. Boukhechba, K. Shaffer, L. Ritterband, D. Gioeli, M. Reilley, T. Le, P. Kunk, T. Bauer, Philip I. Chow

引用次数: 6

Analyzing Patient Stories on Social Media Using Text Analytics. 使用文本分析分析社交媒体上的患者故事

IF 5.9

Journal of Healthcare Informatics Research Pub Date : 2021-03-24 eCollection Date: 2021-12-01 DOI: 10.1007/s41666-021-00097-5

Moutasem A Zakkar, Daniel J Lizotte

{"title":"Analyzing Patient Stories on Social Media Using Text Analytics.","authors":"Moutasem A Zakkar, Daniel J Lizotte","doi":"10.1007/s41666-021-00097-5","DOIUrl":"10.1007/s41666-021-00097-5","url":null,"abstract":"Patients can use social media to describe their healthcare experiences. Several social media platforms, such as the Care Opinion platform, host large volumes of patient stories. However, the large number of these stories and the healthcare system's workload make exploring these stories a difficult task for healthcare providers and administrators. This study uses text mining for analyzing patient stories on the Care Opinion platform and exploring healthcare experiences described in these stories. We collected 367,573 stories, which were posted between September 2005 and September 2019. Topic modeling (Latent Dirichlet Allocation) and sentiment analysis were used to analyze the stories. Sixteen topics were identified representing five aspects of the healthcare experience: communication between patients and providers, quality of clinical services, quality of non-clinical services, human aspects of healthcare experiences, and patient satisfaction. There was also a clear sentiment in 99% of the stories. More than 55% of the stories that describe the patient's request for information, the patient's description of treatment, or the patient's making of an appointment had a negative sentiment, which represents patient dissatisfaction. The study provides insights into the content of patient stories and demonstrates how topic modeling and sentiment analysis can be used to analyze large volumes of patient stories and provide insights into these stories. The findings suggest that these stories are not general social media posts; instead, they describe elements of healthcare experiences that can be helpful for quality improvement.Supplementary information: The online version contains supplementary material available at 10.1007/s41666-021-00097-5.","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":"5 1","pages":"382-400"},"PeriodicalIF":5.9,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41666-021-00097-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44778422","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: 10

A Cross-Sectional Study to Predict Mortality for Medicare Patients Based on the Combined Use of HCUP Tools 基于HCUP工具联合使用预测医疗保险患者死亡率的横断面研究

IF 5.9

Journal of Healthcare Informatics Research Pub Date : 2021-01-27 DOI: 10.1007/s41666-021-00091-x

D. Zikos, Aashara Shrestha, L. Fegaras

引用次数: 0