Journal of Medical Systems最新文献

{"title":"Correction: Patient-Initiated Permanent Deletion of Their Electronic Health Record Data: implications for Artificial Intelligence and Big Data in Healthcare.","authors":"Clyde T Matava, Yvonne Fahy, Asad Siddiqui, Gregory Johnson, Melissa McCradden, Allan F Simpao","doi":"10.1007/s10916-026-02408-y","DOIUrl":"https://doi.org/10.1007/s10916-026-02408-y","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing Technology in Neuropsychological Assessments: A Scoping Review.","authors":"Elton H Lobo, Dashiell Young, Emily McCann, Deepa Sriram, Leander K Mitchell, Deborah Brooks, Nadeeka N Dissanayaka","doi":"10.1007/s10916-026-02407-z","DOIUrl":"https://doi.org/10.1007/s10916-026-02407-z","url":null,"abstract":"<p><p>Neuropsychological assessments are traditionally conducted in person using standardised pen-and-paper methods. Technology has enabled computerised, tablet-based, and videoconference-based alternatives that improve accessibility for individuals in rural areas or with mobility issues. Despite promising developments, implementation has been slow. This scoping review was conducted to identify the barriers and facilitators associated with implementing technology in neuropsychological assessments. The review followed Arksey and O'Malley's framework with Levac refinements and reported according to PRISMA-ScR guidelines. A comprehensive search was performed across eight electronic databases. Studies investigating technology-based neuropsychological or cognitive assessments examining barriers and/or facilitators in clinical and research settings were included. Two reviewers independently screened titles, abstracts, and full text. Data were analysed using the Theoretical Domains Framework (TDF). The review included 48 studies spanning 1998 to 2025. These studies were predominantly focused on Dementia and cognitive disorders. Findings were mapped across 13 of 14 TDF domains and synthesised into six themes: user capabilities, technological infrastructure, user experience design, support systems, interest holder perceptions and preferences, and implementation context. Barriers included technology anxiety, physical/cognitive impairments, poor connectivity, and clinician concerns about clinical observation. The identified facilitators include comprehensive training, technical reliability, personalised support systems, and consideration of interest holder perspectives. These findings highlight that successful implementation requires systematic approaches prioritising human and organisational factors beyond technology validation alone. Implementation efforts should emphasise capacity building and interest holder perspectives. Further research is needed to develop evidence-based frameworks focusing on co-design and systematic change management.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Effects of tACS Duration on Resting-State EEG: An Exploratory Within-Subject Study in Healthy Volunteers.","authors":"Yun-Sung Lee, Ga-Young Choi, Chang-Hee Han, Han-Jeong Hwang","doi":"10.1007/s10916-026-02398-x","DOIUrl":"https://doi.org/10.1007/s10916-026-02398-x","url":null,"abstract":"<p><p>Transcranial alternating current stimulation (tACS) is a noninvasive brain stimulation method that modulates neural activity by applying low-intensity alternating current to the scalp. Although tACS has shown promise in enhancing cognitive and motor functions and alleviating neuropsychiatric symptoms, variations in stimulation parameters led to inconsistent outcomes. While stimulation frequency and electrode montage have been extensively explored, systematic analyses focusing on stimulation duration remain limited. Resting-state EEG, recorded under relaxed conditions without specific tasks, minimizes variability due to individual performance and external factors, thus providing a stable measure of tACS-induced neuromodulation. Therefore, we aimed to investigate the differences in neuromodulatory effects between three tACS durations for effective neuromodulation using resting-state electroencephalography (EEG). Ten participants completed three randomized tACS sessions on different days, each with a duration of 10, 20, or 30 min. Resting-state EEG was recorded before and after stimulation under eyes-open (EO) and eyes-closed (EC) states. Power spectral density (PSD) and network indices were analyzed for neuromodulatory effects. The omnibus analysis revealed no significant main effect of stimulation duration on neuromodulatory outcomes. However, within-condition analyses revealed significant increases in PSD in the post-tACS EO recording after 10 min (r = 0.79) and 20 min (r = 0.66), whereas in the post-tACS EC recording significant increases were observed only at 10 min (r = 0.63). Network efficiency also increased significantly in the EO state after 10 (r = 0.79) and 20 min (r = 0.85) for clustering coefficient and after 10 (r = 0.73) and 20 min (r = 0.85) for path length, respectively. Moreover, the observed patterns differed between brain states, with more consistent effects observed in the EO state. These findings suggest that neuromodulatory responses may vary depending on both tACS duration and brain state, highlighting the importance of considering both factors in the design of tACS protocols and interpretation of neuromodulatory effects.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147839030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic Modeling Based on Post-Endovascular Thrombectomy Systolic Blood Pressure Trajectories Using Explainable Artificial Intelligence: A Secondary Analysis of the OPTIMAL-BP Trial.","authors":"Rim Yu, JoonNyung Heo, Eunjeong Park, Haram Joo, Jae Wook Jung, Kwang Hyun Kim, Jaeseob Yun, Hyungwoo Lee, Jin Kyo Choi, Il Hyung Lee, In Hwan Lim, Soon-Ho Hong, Minyoul Baik, Byung Moon Kim, Dong Joon Kim, Na-Young Shin, Bang-Hoon Cho, Seong Hwan Ahn, Hyungjong Park, Sung-Il Sohn, Jeong-Ho Hong, Tae-Jin Song, Yoonkyung Chang, Gyu Sik Kim, Kwon-Duk Seo, Kijeong Lee, Jun Young Chang, Jung Hwa Seo, Sukyoon Lee, Jang-Hyun Baek, Han-Jin Cho, Dong Hoon Shin, Jinkwon Kim, Joonsang Yoo, Yo Han Jung, Yang-Ha Hwang, Chi Kyung Kim, Jae Guk Kim, Chan Joo Lee, Sungha Park, Hye Sun Lee, Sun U Kwon, Oh Young Bang, Ji Hoe Heo, Young Dae Kim, Hyo Suk Nam","doi":"10.1007/s10916-026-02362-9","DOIUrl":"https://doi.org/10.1007/s10916-026-02362-9","url":null,"abstract":"<p><p>Blood pressure (BP) management following successful reperfusion after endovascular thrombectomy (EVT) is critical in achieving favorable clinical outcomes. Individualized BP management using predictive modeling by machine learning may further improve prediction of functional outcomes. This study was a retrospective analysis of data from the Outcome in Patients Treated with Intra-Arterial Thrombectomy-Optimal Blood Pressure Control (OPTIMAL-BP) trial, a randomized controlled trial comparing between intensive and conventional BP management during the 24 h after successful recanalization by EVT from June 18, 2020, to November 28, 2022. The trial was conducted across 19 centers in South Korea. Machine learning models were developed to predict functional independence (90-day modified Rankin Scale 0 to 2). Model performance was compared between clinical variables only and systolic blood pressure (SBP) metrics in addition to clinical variables. In addition, the Shapley additive explanations (SHAP) analysis was performed to provide model explanation and understand the importance of SBP metrics. A total of 288 patients (61.1% men, median age 75 years [interquartile range, 65-81]) were included. Among the six algorithms, the deep neural network model incorporating SBP metrics performed best on validation, achieving an area under the curve of 0.86 (95% confidence interval, 0.76-0.92) which was significantly better than the model using only clinical variables (area under the curve 0.80 [95% confidence interval, 0.69-0.88], P = .037). Among SBP metrics, SHAP analysis identified time rate of SBP and minimum SBP as important features, with time rate showing greater influence in the intensive group and minimum SBP in the conventional group. Integrating SBP metrics with clinical variables significantly improved machine learning performance in predicting functional outcomes after successful EVT. Explainable artificial intelligence (AI) identified time rate and minimum SBP as key predictors of outcome. Trial Registration Information: ClinicalTrials.gov (NCT04205305; registered December 17, 2019).</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147815480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Minimum Safety Case for Record-Connected Consumer Health Assistants.","authors":"Henry Bair","doi":"10.1007/s10916-026-02401-5","DOIUrl":"https://doi.org/10.1007/s10916-026-02401-5","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147816386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Machine Learning to Anticipate the Diagnostic Yield of EEG in the Emergency department. The EMINENCE study.","authors":"Maenia Scarpino, Ester Marra, Piergiuseppe Liuzzi, Benedetta Piccardi, Peiman Nazerian, Ilaria Sgrilli, Andrea Mannini, Andrea Nencioni, Antonello Grippo","doi":"10.1007/s10916-026-02397-y","DOIUrl":"https://doi.org/10.1007/s10916-026-02397-y","url":null,"abstract":"<p><strong>Introduction: </strong>Emergent electroencephalography (emEEG) is increasingly employed in the emergency department (ED) for evaluating altered consciousness and seizure-related conditions, yet standardized criteria guiding its use remain limited.</p><p><strong>Methods: </strong>We retrospectively analyzed 1,018 patients (mean age 66 ± 20 years; 48.4% female) undergoing emEEG at the ED of the Careggi Teaching Hospital (Florence, Italy) in 2023. Clinical, anamnestic, and neuroimaging data available at admission were used to train supervised machine-learning (ML) models. We evaluated tree-based ensembles (Random Forest and XGBoost) to predict abnormal and epileptiform emEEG, as well as confirmation or refutation of initial diagnosis. Ground-truth labels were derived from a multidisciplinary expert team including neurologists, neurophysiopathologists and intensivists. Model performance was assessed with 5 × 5 nested cross-validation, receiver operating characteristic (ROC) analysis, balanced accuracy, decision-curve analysis, and Shapley Additive Explanations (SHAP) interpretability.</p><p><strong>Results: </strong>Abnormal emEEG occurred in 691 cases (67.9%), epileptiform activity in 192 patients (18.9%). emEEG ruled out the initial diagnostic suspicion in 514 cases (50.5%) and confirmed it in 188 cases (18.5%). Best performance was obtained with Random Forest for abnormal emEEG (AUC 0.79, 95% CI: 0.76-0.82) and diagnosis rule-out (0.84, 0.81-0.86), and with XGBoost for epileptiform emEEG (0.82, 0.78-0.85) and diagnosis confirmation (0.82, 0.79-0.85). Performance varied by initial diagnostic suspicion, but subgroup-stratified analyses showed overall consistent patterns. Key predictive features included altered consciousness, prior brain lesions, antiseizure therapy, and seizure-related presentations. Interpretability analyses revealed seizure-centric features drove confirmation, while systemic or nonspecific features favored refutation.</p><p><strong>Conclusions: </strong>Interpretable ML models using only admission data can predict emEEG outcomes and anticipate their diagnostic contribution, supporting triage and decision-making in emergency neurology without replacing clinical judgment. Models and explanations were easily usable on a freely-accessible website ( www.emergencyeeg.com ), where tools return probabilistic outputs for all four prediction tasks together with per-patient explanation plots, enabling transparent and reproducible use.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13132943/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147816416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated data extraction from electronic medical records for pragmatic clinical trials.","authors":"Eduardo Messias Hirano Padrao, Anne Thu Nguyen, Kevin Nguyen, Ioana A Sopuch, Alper Gulluoglu, Maria Alejandra Alape, Samantha Harrison, Adrian Wong, Mehrnaz Sadrolashrafi, Gabrielle Cozzi, Kalaila Pais, David Furfaro, Margaret Hayes, Valerie Goodspeed, Daniel S Talmor, Elias N Baedorf-Kassis","doi":"10.1007/s10916-026-02380-7","DOIUrl":"https://doi.org/10.1007/s10916-026-02380-7","url":null,"abstract":"<p><p>Data collection in randomized trials is expensive and labor intensive. With the rise in ongoing pragmatic trials, the use of electronic medical records (EMR) as a source of data has increased. Although potentially faster and cheaper, EMR use can lead to errors. Therefore, to ensure accurate data collection and to avoid systematic errors we performed a study comparing automated data extraction (ADE) with manual data extraction (MDE). We performed a retrospective cohort study to compare the accuracy of ADE using Structured Query Language with MDE by blinded physicians from our EMR. We tested the interrater agreement and intraclass correlation coefficient of clinical baseline data and outcomes of a random sample of 30 patients admitted to the ICU, on mechanical ventilation, requiring opioids for analgosedation for an upcoming pragmatic clinical trial. Key data compared included, but not limited to, patient's demographics, laboratory and vital signs, daily morphine milligram equivalent (MME), days alive and free of mechanical ventilation, days alive and free of hospitalization, days alive and free of ICU, days alive and free of vasopressors, and death. Among 238 patients screened over 1-month period, 72 fulfilled inclusion criteria and 30 were randomly selected to be included in the evaluation. We blindly collected 1320 baseline data, 2160 categorical outcomes and 705 continuous outcomes for a total of 4185 data points. The intraclass correlation coefficient and the Cohen's Kappa were perfect or almost perfect for all data, including outcomes such as daily MME, days alive and free of mechanical ventilation, days alive and free of ICU and days alive and free of hospital with p < 0.001. Among all rechecked data, the ADE was correct in 53 (77.9%) of cases, while MDE in 15 (22.1%). The inaccurate data collected by ADE accounted for 0.36% of the total data-points. The performance of ADE had almost perfect agreement for all outcomes and when rechecking for disagreements, it was more accurate than MDE.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147816425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open-Source Large Language Models Distilled DeepSeek-R1 Pose Challenges for On-Premises Clinical Deployment in Medical Diagnosis: A Comparative Study of Performance.","authors":"Wei Zhong, Yiyao Fu, Dingchuan Peng, Yifan Liu, Yan Liu, Kai Yang, Huimin Gao, Huihui Yan, Wenjing Hao, Yousheng Yan, Chenghong Yin","doi":"10.1007/s10916-026-02390-5","DOIUrl":"https://doi.org/10.1007/s10916-026-02390-5","url":null,"abstract":"<p><p>The open-source reasoning large language model DeepSeek-R1 is increasingly being used in hospitals, but its multiple parameter versions, especially the distilled models, have not been fully evaluated for diagnostic performance. To address this, paired comparisons were conducted using five DeepSeek-R1 models and their respective base models. The models were tested on a diagnostic dataset of 110 simulated clinical cases from open access data, covering internal medicine, surgery, neurology, gynecology, and pediatrics, and categorized by incidence (frequent, less frequent, rare). The models were tasked with generating five preliminary diagnoses based on clinical symptoms, and a response was considered correct if the accurate diagnosis was included in the five generated. The model pairings were DeepSeek-R1-8B vs. Llama3.1-8B, DeepSeek-R1-14B vs. Qwen2.5-14B, DeepSeek-R1-32B vs. Qwen2.5-32B, DeepSeek-R1-70B vs. Llama3.3-70B, and DeepSeek-R1-671B vs. DeepSeek-V3. All reasoning models except DeepSeek-R1-671B were distilled versions. Diagnostic accuracy was assessed using McNemar's test for discordant pairs, with a significance threshold of 0.01. The results showed that DeepSeek-R1-671B significantly outperformed DeepSeek-V3 (95.45% vs. 88.18%; p = 0.008), while DeepSeek-R1-8B underperformed relative to Llama3.1-8B (47.27% vs. 64.54%; p = 0.003). No significant differences were observed for the mid-sized models. Subgroup analyses based on incidence and clinical specialties further supported these conclusions. Qualitative analysis of the chain-of-thought outputs in incorrect cases revealed three universally prevalent error modes across distilled models: Reasoning drift, Red-Flag recognition failure, and diagnostic priority inversion. The study concludes that the DeepSeek-R1-671B shows potential for medical diagnosis, but distilled models do not exceed their base models. Based on simulated clinical cases, our results do not support deploying distilled models for text-based diagnostic tasks without further validation on real patient data.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147816452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRSeqStudio: MRI Sequence Design and Simulation as a Service in a Free and Open-Source Web Platform.","authors":"Pablo Villacorta-Aylagas, Manuel Rodríguez-Cayetano, Carlos Castillo-Passi, Pablo Irarrazaval, Federico Simmross-Wattenberg, Carlos Alberola-López","doi":"10.1007/s10916-026-02394-1","DOIUrl":"10.1007/s10916-026-02394-1","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13128749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147773922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of Large Language Models and Machine Learning for ASA Classification Using Structured Electronic Health Record Data.","authors":"Shih-Yu Ko, Ting-Yun Huang, Ming-Siang Huang, Yi-Hsuan Lin, Yung-Cheng Su, Yung-Chun Chang","doi":"10.1007/s10916-026-02393-2","DOIUrl":"https://doi.org/10.1007/s10916-026-02393-2","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147773721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}