International Journal of Medical Informatics最新文献

{"title":"Persuasive strategies in digital interventions to combat internet addiction: A systematic review","authors":"Yansen Theopilus ,&nbsp;Abdullah Al Mahmud ,&nbsp;Hilary Davis ,&nbsp;Johanna Renny Octavia","doi":"10.1016/j.ijmedinf.2024.105725","DOIUrl":"10.1016/j.ijmedinf.2024.105725","url":null,"abstract":"<div><h3>Background</h3><div>The internet provides valuable benefits in supporting our lives. However, concerns arise regarding internet addiction, a behavioural disorder due to excessive and uncontrolled internet use that has harmful effects on human health and wellbeing. Studies highlighted the potential of digital behaviour change interventions to address health behaviour problems. However, little is known about how and to what extent persuasive strategies can be utilised in digital interventions to combat internet addiction. Accordingly, this systematic review aims to investigate the design and implementation of persuasive strategies in digital interventions to combat internet addiction, including their contexts, features, and outcomes.</div></div><div><h3>Methods</h3><div>We searched for peer-reviewed articles from four databases (Scopus, Web of Science, ACM, and PubMed). The Persuasive Systems Design (PSD) model and Behaviour Change Technique (BCT) taxonomy were used to identify persuasive strategies. We included 14 primary studies discussing digital interventions to address the problem and their outcomes.</div></div><div><h3>Results</h3><div>Four persuasion contexts were identified, including 1) self-management systems to reduce internet use, 2) analytics systems to examine use patterns and provide behavioural suggestions, 3) parental control systems to manage children’s internet use, and 4) unattractive settings to discourage internet use. The promising interventions used the following persuasion route: help the user determine behaviour goals, facilitate actions to accomplish behaviour goals, and reinforce the user to perform behaviour goals. Potential persuasive strategies were also identified, including goal-setting, action planning, task reduction, tunnelling how to perform a behaviour, tailored and personalised suggestions/prompts, reminders, trustworthiness, anticipated regret, and social support strategies.</div></div><div><h3>Conclusion</h3><div>Our findings shed light on the promising persuasive contexts and strategies to combat internet addiction using digital interventions. We suggest future research and practices to utilise our findings to develop effective digital interventions, especially for combatting internet addiction in vulnerable populations like children or people from developing regions.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105725"},"PeriodicalIF":3.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active learning for extracting rare adverse events from electronic health records: A study in pediatric cardiology","authors":"Sophie Quennelle ,&nbsp;Sophie Malekzadeh-Milani ,&nbsp;Nicolas Garcelon ,&nbsp;Hassan Faour ,&nbsp;Anita Burgun ,&nbsp;Carole Faviez ,&nbsp;Rosy Tsopra ,&nbsp;Damien Bonnet ,&nbsp;Antoine Neuraz","doi":"10.1016/j.ijmedinf.2024.105761","DOIUrl":"10.1016/j.ijmedinf.2024.105761","url":null,"abstract":"<div><h3>Objective</h3><div>Automate the extraction of adverse events from the text of electronic medical records of patients hospitalized for cardiac catheterization.</div></div><div><h3>Methods</h3><div>We focused on events related to cardiac catheterization as defined by the NCDR-IMPACT registry. These events were extracted from the Necker Children’s Hospital data warehouse. Electronic health records were pre-screened using regular expressions. The resulting datasets contained numerous false positives sentences that were annotated by a cardiologist using an active learning process. A deep learning text classifier was then trained on this active learning-annotated dataset to accurately identify patients who have suffered a serious adverse event.</div></div><div><h3>Results</h3><div>The dataset included 2,980 patients. Regular expression based extraction of adverse events related to cardiac catheterization achieved a perfect recall. Due to the rarity of adverse events, the dataset obtained from this initial pre-screening step was imbalanced, containing a significant number of false positives. The active learning annotation enabled the acquisition of a representative dataset suitable for training a deep learning model. The deep learning text-classifier identified patients who underwent adverse events after cardiac catheterization with a recall of 0.78 and a specificity of 0.94.</div></div><div><h3>Conclusion</h3><div>Our model effectively identified patients who experienced adverse events related to cardiac catheterization using real clinical data. Enabled by an active learning annotation process, it shows promise for large language model applications in clinical research, especially for rare diseases with limited annotated databases. Our model’s strength lies in its development by physicians for physicians, ensuring its relevance and applicability in clinical practice.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105761"},"PeriodicalIF":3.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preliminary findings regarding the association between patient demographics and ED experience scores across a regional health system: A cross sectional study using natural language processing of patient comments","authors":"Diane Kuhn ,&nbsp;Nicholas E. Harrison ,&nbsp;Paul I. Musey Jr ,&nbsp;David J. Crandall ,&nbsp;Peter S. Pang ,&nbsp;Julie L. Welch ,&nbsp;Christopher A Harle","doi":"10.1016/j.ijmedinf.2024.105748","DOIUrl":"10.1016/j.ijmedinf.2024.105748","url":null,"abstract":"<div><h3>Objective</h3><div>Existing literature shows associations between patient demographics and reported experiences of care, but this relationship is poorly understood. Our objective was to use natural language processing of patient comments to gain insight into associations between patient demographics and experiences of care.</div></div><div><h3>Methods</h3><div>This is a cross-sectional study of 14,848 unique emergency department (ED) patient visits from 1/1/2020 to 12/31/2020. Patients discharged from one of 16 ED sites in a regional health system who filled out a patient experience survey with comments were included. This study had two outcome variables: (1) positive vs. non-positive (negative/neutral) comment sentiment, and (2) promoter vs. non-promoter status (based on NRCHealth’s Net Promoter Score; likelihood to recommend of 9 or 10 are considered “promoters”, while scores of 8 or below are “non-promoters”). We used natural language processing to sort patient comments into topics and sentiments. Logistic regression with mediation analysis was used to estimate the associations between patient demographics and the following: (1) comments about compassion vs. other topics, (2) positive comments, and (3) patient experience, defined as likelihood to recommend.</div></div><div><h3>Results</h3><div>Comments about care and compassion (51 % of total comments) had highly positive sentiment (97 %), compared to mixed sentiment for other topics. Older, male, and Asian patients were more likely to comment on compassion and most likely to make positive comments. Our mediation analysis suggests that the demographic association with positive patient comments and net promoter scores was mediated by their focus on care and compassion as a primary comment theme for their visit. Notably, the overall percentage of patients providing comments was only 1.8 %, raising concerns about whether data currently used for hospital and physician feedback has adequate validity to yield meaningful insights.</div></div><div><h3>Conclusions</h3><div>The increased likelihood of specific patient sub-groups to comment on compassionate care may explain previously reported differences in experience by patient demographics.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105748"},"PeriodicalIF":3.7,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the fasting blood glucose response to basal insulin adjustment in type 2 diabetes: An explainable machine learning approach on real-world data","authors":"Camilla Heisel Nyholm Thomsen ,&nbsp;Thomas Kronborg ,&nbsp;Stine Hangaard ,&nbsp;Peter Vestergaard ,&nbsp;Ole Hejlesen ,&nbsp;Morten Hasselstrøm Jensen","doi":"10.1016/j.ijmedinf.2024.105758","DOIUrl":"10.1016/j.ijmedinf.2024.105758","url":null,"abstract":"<div><h3>Introduction</h3><div>Optimal basal insulin titration for people with type 2 diabetes is vital to effectively reducing the risk of complications. However, a sizeable proportion of people (30–50 %) remain in suboptimal glycemic control six months post-initiation of basal insulin. This indicates a clear need for novel titration methods that account for individual patient variability in real-world settings.</div></div><div><h3>Objective</h3><div>This study aims to investigate the use of real-world data and explainable machine learning in modeling fasting glucose responses to basal insulin adjustments, focusing on identifying factors influencing fasting glucose variability.</div></div><div><h3>Methods</h3><div>A three-step explanatory approach was used to develop models using multiple linear regression, forward feature selection, and three-fold cross-validation. The models were built progressively, starting with a baseline model incorporating fasting blood glucose and insulin dose adjustments, followed by iterative models that in turn included biometric data, social factors, and biochemistry data, and lastly, a comprehensive model without constraints on the feature pool.</div></div><div><h3>Results</h3><div>The baseline model yielded an average root mean squared error (RMSE) of 1.52 [95% CI: 1.33–1.71]. The iterative models resulted in an average RMSE of 1.49 [95% CI: 1.35–1.62] (biometric data), 1.47 [95% CI: 1.36–1.58] (social factors), and 1.52 [95% CI: 1.34–1.70] (biochemistry data). The comprehensive model yielded an average RMSE of 1.44 [95% CI: 1.41–1.48].</div></div><div><h3>Conclusion</h3><div>Developing explainable machine learning models using real-world data is possible for basal insulin titration. However, model performance is influenced by data’s ability to capture everyday behavior, underscoring the need for incorporating more detailed behavioral and social data to optimize future titration models.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105758"},"PeriodicalIF":3.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review on the impact of artificial intelligence on electrocardiograms in cardiology","authors":"Oluwafemi Ayotunde Oke ,&nbsp;Nadire Cavus","doi":"10.1016/j.ijmedinf.2024.105753","DOIUrl":"10.1016/j.ijmedinf.2024.105753","url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI) has revolutionized numerous industries, enhancing efficiency, scalability, and insight generation. In cardiology, particularly through electrocardiogram (ECG) analysis, AI has the potential to improve diagnostic accuracy and reduce the time needed for diagnosis. This systematic review explores the integration of AI, machine learning (ML), and deep learning (DL) in ECG analysis, focusing on their impact on predictive diagnostics and treatment support in cardiology.</div></div><div><h3>Methods</h3><div>A systematic literature review was conducted following the PRISMA 2020 framework, using four high-impact databases to identify studies from 2014 to -2024. The inclusion criteria included English-language journal articles and research papers that focused on AI applications in cardiology, specifically ECG analysis. Records were screened, duplicates were removed, and final selections were made on the basis of their relevance to AI-ECG integration for cardiac health.</div></div><div><h3>Results</h3><div>The review included 46 studies that met the inclusion criteria, covering diverse AI models such as CNNs, RNNs, and hybrid models. These models were applied to ECG data to detect and predict heart conditions such as arrhythmia, myocardial infarction, and heart failure. These findings indicate that AI-driven ECG analysis improves diagnostic accuracy and provides significant support for early diagnosis and personalized treatment.</div></div><div><h3>Conclusions</h3><div>AI technologies, especially ML and DL, enhance ECG-based cardiology diagnostics by increasing accuracy, reducing diagnosis time, and supporting timely interventions and personalized care. Continued research in this area is essential to refine algorithms and integrate AI tools into clinical practice for improved patient outcomes in cardiology.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105753"},"PeriodicalIF":3.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a low-cost training application to train microelectrode recording identification in deep brain stimulation surgeries","authors":"Ignacio Oropesa ,&nbsp;Marta Naranjo-Castresana ,&nbsp;Marta Colmenar ,&nbsp;Ainara Carpio ,&nbsp;Óscar Ansótegui ,&nbsp;María Elena Hernando","doi":"10.1016/j.ijmedinf.2024.105759","DOIUrl":"10.1016/j.ijmedinf.2024.105759","url":null,"abstract":"<div><h3>Objective</h3><div>Deep brain stimulation (DBS) is a surgical technique that alleviates motor symptoms in Parkinson’s disease. Surgically implanted microelectrodes stimulate the basal ganglia to improve patients’ symptoms. One of the training challenges for neurophysiologists is to identify during surgery the target area of the brain in which the electrodes must be implanted. Identification is based both on visual and auditory inspection of the microelectrode recordings (MERs) as they are lowered through the basal ganglia. We present the preliminary evaluation of DBSTrainer, a novel desktop application to train neurophysiologists in the identification of signals corresponding to different basal structures.</div></div><div><h3>Methods</h3><div>A pilot study was conducted with neurologists and neurophysiologists at the Hospital Universitario La Paz (Madrid, Spain). After completing a series of tasks with the application, they were asked to complete an evaluation questionnaire. Usability was assessed using the System Usability Scale (SUS). Functionality, contents, and perceived usefulness were assessed using an ad-hoc Likert questionnaire following the e-MIS framework for surgical learning platforms.</div></div><div><h3>Results</h3><div>15 volunteers participated in the study. Obtained SUS score was 86.7 ± 0.47. Most positive aspects on functionality were platform design and interactivity. Contents were found realistic and aligned with learning outcomes. Minor problems were identified with signal loading times.</div></div><div><h3>Conclusions</h3><div>This study provides preliminary evidence on the usefulness of DBSTrainer. It is, to our knowledge, the first Technology Enhanced Learning application to train neurophysiologists outside the operating room, and thus its introduction can have a real impact on patient safety and surgical outcomes.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105759"},"PeriodicalIF":3.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimated carbon emissions and support cost savings to telemedicine for patients with tracheal diseases","authors":"Pedro Prosperi Desenzi Ciaralo ,&nbsp;Paulo Francisco Guerreiro Cardoso ,&nbsp;Helio Minamoto ,&nbsp;Benoit Jacques Bibas ,&nbsp;Carlos Roberto Ribeiro de Carvalho ,&nbsp;Paulo Manuel Pego-Fernandes","doi":"10.1016/j.ijmedinf.2024.105757","DOIUrl":"10.1016/j.ijmedinf.2024.105757","url":null,"abstract":"<div><h3>Objective</h3><div>The patient’s journey to the medical center for an outpatient visit can often mean hours of travel in their vehicle, leading to increased expenses and greater carbon dioxide (CO2) emissions into the environment. The study demonstrates the estimated carbon emission and cost savings associated with a telemedicine program dedicated to patients with tracheal disease in the Brazilian public health system.</div></div><div><h3>Methods</h3><div>Cross-sectional study of telemedicine visits for patients with tracheal disease referred to a public academic hospital between August 1, 2020, and December 30, 2023. The consultations occurred in a telemedicine department using the hospital’s proprietary platform. The analysis included the round-trip distance savings using home postal codes; CO2 emissions savings by transportation using the Greenhouse Gas Protocol (GHG Protocol) adapted to the Brazilian reality (“Programa Brasileiro GHG Protocol”); and the cost savings in transportation and support using the Brazil Ministry of Health program.</div></div><div><h3>Results</h3><div>1767 telemedicine visits with 680 patients were conducted, 363 (53.4 %) male and 317 (46.6 %) female, a median [IQR] age of 33 [12.0–51.0] years. Patients were from 170 Brazilian cities from 22 states. There were 2.219.544,3 round-trip kilometers saved (median per patient [IQR] 542,88km [190,36-2.672,6]), corresponding to an estimated 353.097,55kg of CO2 emissions savings (median per patient [IQR] 102,56kg [36,56-496,96]). The cost savings was 305.187,96 dollars (median per patient [IQR] $48,22 [24,97-162,51] dollars).</div></div><div><h3>Conclusion</h3><div>Telemedicine consultations, in addition to significantly reducing carbon emissions and costs, promote greater accessibility and sustainability in medical care. These findings may influence public policies to expand telemedicine programs, especially in remote regions, and strengthen environmental initiatives in healthcare.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105757"},"PeriodicalIF":3.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach to antimicrobial resistance: Machine learning predictions for carbapenem-resistant Klebsiella in intensive care units","authors":"V. Alparslan ,&nbsp;Ö. Güler ,&nbsp;B. İnner ,&nbsp;A. Düzgün ,&nbsp;N. Baykara ,&nbsp;A. Kuş","doi":"10.1016/j.ijmedinf.2024.105751","DOIUrl":"10.1016/j.ijmedinf.2024.105751","url":null,"abstract":"<div><div>This study was conducted at Kocaeli University Hospital in Turkey and aimed to predict carbapenem-resistant <em>Klebsiella pneumoniae</em> infection in intensive care units using the Extreme Gradient Boosting (XGBoost) algorithm, a form of artificial intelligence. This was a retrospective case-control study involving 289 patients, including 159 carbapenem-resistant and 130 carbapenem-susceptible individuals as controls. The model’s predictive analysis combined a diverse range of demographic, clinical, and laboratory data, resulting in an average accuracy of 83.0%, precision of 83%, sensitivity of 88%, F1 score of 85%, and Matthews Correlation Coefficient of 0.66. Prolonged hospitalization and intensive care unit stay were significant predictors of carbapenem-resistant <em>Klebsiella pneumoniae</em> infection. The role of artificial intelligence role in healthcare, particularly in ICUs for managing antibiotic-resistant infections, is a major development in medicine. This study emphasizes the potential of artificial intelligence to predict antimicrobial resistance and improve clinical decisions in resource-limited settings. The study was approved by <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span> (trial registration number NCT05985057 on 02.08.2023).</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105751"},"PeriodicalIF":3.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The digital prescription: A systematic review and meta-analysis of smartphone apps for blood pressure control","authors":"Emily Motta-Yanac ,&nbsp;Victoria Riley ,&nbsp;Naomi J. Ellis ,&nbsp;Aman Mankoo ,&nbsp;Christopher J. Gidlow","doi":"10.1016/j.ijmedinf.2024.105755","DOIUrl":"10.1016/j.ijmedinf.2024.105755","url":null,"abstract":"<div><h3>Objective</h3><div>Assess the effectiveness of digital health interventions (DHIs) in reducing blood pressure (BP) among individuals with high blood pressure and identify the impact of age, sex, and phone-based delivery methods on BP.</div></div><div><h3>Methods</h3><div>A systematic review and <em>meta</em>-analysis was undertaken according to the PRISMA and JBI. A comprehensive search was conducted across multiple databases. Randomised controlled trials (RCTs), mixed methods, descriptive, and experimental studies enrolling adult patients (≥<!--> <!-->18 years) with high BP and containing DHIs with blood pressure management aspect were included. We used a random-effects <em>meta</em>-analysis weighted mean difference (MD) between the comparison groups to pool data from the included studies. The outcome included the pooled MD reflecting systolic (SBP) or diastolic (DBP) change from baseline to 6-month period. Risk of bias was assessed using standardised tools.</div></div><div><h3>Results</h3><div>Thirty-six studies with 33,826 participants were included in the systematic review. The pooled estimate (26 RCTs) showed a significant reduction in SBP (MD = −1.45 mmHg, 95 % CI: −2.18 to −0.71) but not in DBP (MD = −0.50 mmHg, 95 % CI: −1.03 to 0.03), with evidence of some heterogeneity. Subgroup analysis indicated that smartphone app interventions were more effective in lowering SBP than short message services (SMS) or mobile phone calls. Additionally, the interventions significantly reduced the SBP compared with the control, regardless of participant sex.</div></div><div><h3>Conclusion</h3><div>Our findings indicate that DHIs, particularly smartphone apps, can lower SBP after 6 months in individuals with hypertension or high-risk factors, although changes might not be clinically significant. Further research is needed to understand the long-term impact and optimal implementation of DHIs for BP management across diverse populations.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105755"},"PeriodicalIF":3.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network","authors":"Thanh-Cong Do ,&nbsp;Hyung-Jeong Yang ,&nbsp;Soo-Hyung Kim ,&nbsp;Bo-Gun Kho ,&nbsp;Jin-Kyung Park","doi":"10.1016/j.ijmedinf.2024.105745","DOIUrl":"10.1016/j.ijmedinf.2024.105745","url":null,"abstract":"<div><h3>Objective</h3><div>In hospitals globally, the occurrence of clinical deterioration within the hospital setting poses a significant healthcare burden. Rapid clinical intervention becomes a crucial task in such cases. In this research, we propose an end-to-end deep learning architecture that interpolates high-dimensional sequential data for the early detection of clinical deterioration events.</div></div><div><h3>Materials and methods</h3><div>We consider the problem of detecting deterioration events with two stages: predicting the “detection” status, a pre-event state; and predicting the event from detection time. Our approach involves the development of dual-channel graph attention networks with multi-task learning strategy by jointly learning task relatedness with a shared model for multiple prediction in multivariate time-series.</div></div><div><h3>Results</h3><div>The experiments are conducted on two clinical time-series datasets collected from intensive care units (ICUs). Our model has shown the potential performance compared to other state-of-the-art methods, in terms of the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC).</div></div><div><h3>Discussion</h3><div>The proposed dual-channel graph attention networks can explicitly learn the correlations in both features and time domains of multivariate time-series. Our proposed objective function also can handle the problems of learning task relations and reducing task imbalance effects in multi-task learning.</div></div><div><h3>Conclusion</h3><div>Applying our proposed framework architecture could facilitate the implementation of early detecting in-hospital deterioration events.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105745"},"PeriodicalIF":3.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}