Journal of healthcare informatics research最新文献

{"title":"Chronic Cough: Characterizing and Quantifying Burden in Adults Using a Nationwide Electronic Health Records Database","authors":"Lindsey E. Scierka, Brooklyn A. Bradley, Earl Glynn, Sierra Davis, Mark Hoffman, Jade B. Tam-Williams, Carlos Mena-Hurtado, Kim G. Smolderen","doi":"10.1007/s41666-023-00150-5","DOIUrl":"https://doi.org/10.1007/s41666-023-00150-5","url":null,"abstract":"","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535655","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}

{"title":"Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme","authors":"Gabriel Cerono, Ombretta Melaiu, Davide Chicco","doi":"10.1007/s41666-023-00138-1","DOIUrl":"https://doi.org/10.1007/s41666-023-00138-1","url":null,"abstract":"Abstract Glioblastoma multiforme (GM) is a malignant tumor of the central nervous system considered to be highly aggressive and often carrying a terrible survival prognosis. An accurate prognosis is therefore pivotal for deciding a good treatment plan for patients. In this context, computational intelligence applied to data of electronic health records (EHRs) of patients diagnosed with this disease can be useful to predict the patients’ survival time. In this study, we evaluated different machine learning models to predict survival time in patients suffering from glioblastoma and further investigated which features were the most predictive for survival time. We applied our computational methods to three different independent open datasets of EHRs of patients with glioblastoma: the Shieh dataset of 84 patients, the Berendsen dataset of 647 patients, and the Lammer dataset of 60 patients. Our survival time prediction techniques obtained concordance index (C-index) = 0.583 in the Shieh dataset, C-index = 0.776 in the Berendsen dataset, and C-index = 0.64 in the Lammer dataset, as best results in each dataset. Since the original studies regarding the three datasets analyzed here did not provide insights about the most predictive clinical features for survival time, we investigated the feature importance among these datasets. To this end, we then utilized Random Survival Forests, which is a decision tree-based algorithm able to model non-linear interaction between different features and might be able to better capture the highly complex clinical and genetic status of these patients. Our discoveries can impact clinical practice, aiding clinicians and patients alike to decide which therapy plan is best suited for their unique clinical status.","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308334","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}

{"title":"Natural Language Processing to Classify Caregiver Strategies Supporting Participation Among Children and Youth with Craniofacial Microsomia and Other Childhood-Onset Disabilities.","authors":"Vera C Kaelin, Andrew D Boyd, Martha M Werler, Natalie Parde, Mary A Khetani","doi":"10.1007/s41666-023-00149-y","DOIUrl":"10.1007/s41666-023-00149-y","url":null,"abstract":"<p><p>Customizing participation-focused pediatric rehabilitation interventions is an important but also complex and potentially resource intensive process, which may benefit from automated and simplified steps. This research aimed at applying natural language processing to develop and identify a best performing predictive model that classifies caregiver strategies into participation-related constructs, while filtering out non-strategies. We created a dataset including 1,576 caregiver strategies obtained from 236 families of children and youth (11-17 years) with craniofacial microsomia or other childhood-onset disabilities. These strategies were annotated to four participation-related constructs and a non-strategy class. We experimented with manually created features (i.e., speech and dependency tags, predefined likely sets of words, dense lexicon features (i.e., Unified Medical Language System (UMLS) concepts)) and three classical methods (i.e., logistic regression, naïve Bayes, support vector machines (SVM)). We tested a series of binary and multinomial classification tasks applying 10-fold cross-validation on the training set (80%) to test the best performing model on the held-out test set (20%). SVM using term frequency-inverse document frequency (TF-IDF) was the best performing model for all four classification tasks, with accuracy ranging from 78.10 to 94.92% and a macro-averaged F1-score ranging from 0.58 to 0.83. Manually created features only increased model performance when filtering out non-strategies. Results suggest pipelined classification tasks (i.e., filtering out non-strategies; classification into intrinsic and extrinsic strategies; classification into participation-related constructs) for implementation into participation-focused pediatric rehabilitation interventions like Participation and Environment Measure Plus (PEM+) among caregivers who complete the Participation and Environment Measure for Children and Youth (PEM-CY).</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s41666-023-00149-y.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10620347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71490987","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":"Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery","authors":"Mike Holmes, Miquel Perelló Nieto, Hao Song, Emma Tonkin, Sabrina Grant, Peter A. Flach","doi":"10.1007/s41666-020-00072-6","DOIUrl":"https://doi.org/10.1007/s41666-020-00072-6","url":null,"abstract":"","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141207157","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}

{"title":"Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery","authors":"Mike Holmes, Miquel Perelló Nieto, Hao Song, Emma Tonkin, Sabrina Grant, Peter A. Flach","doi":"10.1007/s41666-020-00072-6","DOIUrl":"https://doi.org/10.1007/s41666-020-00072-6","url":null,"abstract":"","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141207208","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}

{"title":"Comparative Analysis of Gait Speed Estimation Using Wideband and Narrowband Radars, Thermal Camera, and Motion Tracking Suit Technologies","authors":"P. Morita, A. S. Rocha, G. Shaker, D. Lee, J. Wei, B. Fong, A. Thatte, A. Karimi, L. Xu, A. Ma, A. Wong, J. Boger","doi":"10.1007/s41666-020-00071-7","DOIUrl":"https://doi.org/10.1007/s41666-020-00071-7","url":null,"abstract":"","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141211923","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}

{"title":"Comparative Analysis of Gait Speed Estimation Using Wideband and Narrowband Radars, Thermal Camera, and Motion Tracking Suit Technologies","authors":"P. Morita, A. S. Rocha, G. Shaker, D. Lee, J. Wei, B. Fong, A. Thatte, A. Karimi, L. Xu, A. Ma, A. Wong, J. Boger","doi":"10.1007/s41666-020-00071-7","DOIUrl":"https://doi.org/10.1007/s41666-020-00071-7","url":null,"abstract":"","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141211924","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}

{"title":"Fully Automated Approach for Early Detection of Pigmented Skin Lesion Diagnosis Using ABCD","authors":"M. Mabrouk, A. Y. Sayed, Heba M. Afifi, Mariam A. Sheha, A. Sharawy","doi":"10.1007/s41666-020-00067-3","DOIUrl":"https://doi.org/10.1007/s41666-020-00067-3","url":null,"abstract":"","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141225094","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}

{"title":"An Exploratory Study of the Readiness of Public Healthcare Facilities in Developing Countries to Adopt Health Information Technology (HIT)/e-Health: the Case of Ghana.","authors":"Salifu Yusif, Abdul Hafeez-Baig, Jeffrey Soar","doi":"10.1007/s41666-020-00070-8","DOIUrl":"https://doi.org/10.1007/s41666-020-00070-8","url":null,"abstract":"<p><p>There are myriad of factors used in assessing health information technology (HIT)/e-Health of healthcare institutions in developing countries and beyond. In this paper, we intended to identify and gain a deeper understanding of factors used in assessing HIT/e-Health readiness in developing countries through the identification of contextual attributes using Ghana as an exemplary developing country. Through in-depth interviews using <i>aide memoire</i> as interview guide, we explored <i>Core readiness</i>, <i>Engagement readiness</i>, <i>Technological readiness</i>, <i>HIT funding readiness</i>, <i>Regulatory and policy readiness</i>, <i>Workforce readiness and Change Management readiness.</i> We adapted the systematic thematic analysis of qualitative data guide suggested by Braun and Clarke (2013) and O'Connor and Gibson (Pimatisiwin 1: 63-90, 2003) in order to generate codes and build over-arching themes. While <i>Organizational cultural readiness</i> was found to be a more applicable theme/factor in place of <i>Engagement readiness</i> and <i>Change management readiness, Resource readiness</i> wasalso deemed a more appropriate theme for <i>HIT funding readiness</i> and <i>Workforce readiness</i> respectively. A total of 23 factors likely to promote HIT adoption in Ghana and 29 factors capable of impeding HIT adoption in Ghana and potentially in other developing countries were identified. For effective assessment of HIT readiness factors, there is a critical need for a deeper understanding of their applicability in differing settings. The outcome of this study offers a valuable insight into improving circumstances under which HIT/e-Health is adopted. When effectually carried out, assessment of this nature could be help side-step losses on large money, effort, time, delay and importantly, dissatisfaction among stakeholders while enabling change processes healthcare institutions and communities involved. This study also contributes to the limited literature on HIT/e-Health implementation scenarios while offering basis for theory-building.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982759/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057791","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":"Emotional Awareness and Decision-Making in the Context of Computer-Mediated Psychotherapy.","authors":"Ebrahim Oshni Alvandi, George Van Doorn, Mark Symmons","doi":"10.1007/s41666-019-00050-7","DOIUrl":"https://doi.org/10.1007/s41666-019-00050-7","url":null,"abstract":"<p><p>Emotional awareness has been previously investigated among clinicians. In this work, we bring to the fore of research the interest to uncover emotional awareness of clinicians during the tele-mental health session. The study reported here aimed at determining whether clinicians process their own emotions, as well as those of the client, in a computer-mediated context. Also, clinicians' decision-making process was assessed because such action appears to be related to the way they feel and recognise how those emotions may change their thinking and impact their interaction with clients. We estimated that such ability in clinicians' would be contrasted when the psychotherapy-session level is conducted via various technologies. Participant of the study were presented by stimuli in different modes of delivery (e.g. text, audio, and video). The experiment indicates that the ability to manage, perceive, and utilise emotions was as being satisfactory during all modes of delivery. In essence, the findings contribute to the field of remote therapy suggesting emotional awareness as a key cognitive factor in diagnosis.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2019-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057789","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}