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Communicating the results of risk-based breast cancer screening through visualizations of risk: a participatory design approach BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-18 Inge S. van Strien-Knippenberg, Hannah Arjangi-Babetti, Danielle R. M. Timmermans, Laura Schrauwen, Mirjam P. Fransen, Marijke Melles, Olga C. Damman
Risk-based breast cancer (BC) screening raises new questions regarding information provision and risk communication. This study aimed to: 1) investigate women’s beliefs and knowledge (i.e., mental models) regarding BC risk and (risk-based) BC screening in view of implications for information development; 2) develop novel informational materials to communicate the screening result in risk-based BC screening
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An automated ICU agitation monitoring system for video streaming using deep learning classification BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-18 Pei-Yu Dai, Yu-Cheng Wu, Ruey-Kai Sheu, Chieh-Liang Wu, Shu-Fang Liu, Pei-Yi Lin, Wei-Lin Cheng, Guan-Yin Lin, Huang-Chien Chung, Lun-Chi Chen
To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence and deep learning. We collected the video recordings of ICU patients and cut them into 30-second (30-s) and 2-second (2-s) segments. All of the segments were annotated with the status of agitation
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SurvInt: a simple tool to obtain precise parametric survival extrapolations BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-14 Daniel Gallacher
Economic evaluation of emerging health technologies is mandated by agencies such as the National Institute for Health and Care Excellence (NICE) to ensure their cost is proportional to their benefit. To avoid bias, NICE stipulate that the benefit of a treatment is assessed across the lifetime of the patient population, which can be many decades. Unfortunately, follow-up from a clinical trial will not
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Exploring the potential of ChatGPT in medical dialogue summarization: a study on consistency with human preferences BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-14 Yong Liu, Shenggen Ju, Junfeng Wang
Telemedicine has experienced rapid growth in recent years, aiming to enhance medical efficiency and reduce the workload of healthcare professionals. During the COVID-19 pandemic in 2019, it became especially crucial, enabling remote screenings and access to healthcare services while maintaining social distancing. Online consultation platforms have emerged, but the demand has strained the availability
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Deep learning of movement behavior profiles and their association with markers of cardiometabolic health BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-13 Vahid Farrahi, Paul J Collings, Mourad Oussalah
Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and patterns of accumulation of movement behaviors, that altogether
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KGSCS—a smart care system for elderly with geriatric chronic diseases: a knowledge graph approach BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-12 Aihua Li, Che Han, Xinzhu Xing, Qinyan Wei, Yuxue Chi, Fan Pu
The increasing aging population has led to a shortage of geriatric chronic disease caregiver, resulting in inadequate care for elderly people. In this global context, many older people rely on nonprofessional family care. The credibility of existing health websites cannot meet the needs of care. Specialized health knowledge bases such as SNOMED—CT and UMLS are also difficult for nonprofessionals to
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Assessing the research landscape and clinical utility of large language models: a scoping review BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-12 Ye-Jean Park, Abhinav Pillai, Jiawen Deng, Eddie Guo, Mehul Gupta, Mike Paget, Christopher Naugler
Large language models (LLMs) like OpenAI’s ChatGPT are powerful generative systems that rapidly synthesize natural language responses. Research on LLMs has revealed their potential and pitfalls, especially in clinical settings. However, the evolving landscape of LLM research in medicine has left several gaps regarding their evaluation, application, and evidence base. This scoping review aims to (1)
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Implementation difficulties and solutions for a smart-clothes assisted home nursing care program for older adults with dementia or recovering from hip fracture BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-12 Chung-Chih Lin, Ching-Tzu Yang, Pei-Ling Su, Jung-Ling Hsu, Yea-Ing L. Shyu, Wen-Chuin Hsu
Wearable devices have the advantage of always being with individuals, enabling easy detection of their movements. Smart clothing can provide feedback to family caregivers of older adults with disabilities who require in-home care. This study describes the process of setting up a smart technology-assisted (STA) home-nursing care program, the difficulties encountered, and strategies applied to improve
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Development and validation of ‘Patient Optimizer’ (POP) algorithms for predicting surgical risk with machine learning BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-11 Gideon Kowadlo, Yoel Mittelberg, Milad Ghomlaghi, Daniel K. Stiglitz, Kartik Kishore, Ranjan Guha, Justin Nazareth, Laurence Weinberg
Pre-operative risk assessment can help clinicians prepare patients for surgery, reducing the risk of perioperative complications, length of hospital stay, readmission and mortality. Further, it can facilitate collaborative decision-making and operational planning. To develop effective pre-operative risk assessment algorithms (referred to as Patient Optimizer or POP) using Machine Learning (ML) that
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Developing an integrated clinical decision support system for the early identification and management of kidney disease—building cross-sectoral partnerships BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-08 Gillian Gorham, Asanga Abeyaratne, Sam Heard, Liz Moore, Pratish George, Paul Kamler, Sandawana William Majoni, Winnie Chen, Bhavya Balasubramanya, Mohammad Radwanur Talukder, Sophie Pascoe, Adam Whitehead, Cherian Sajiv, Louise Maple Brown, Nadarajah Kangaharan, Alan Cass
The burden of chronic conditions is growing in Australia with people in remote areas experiencing high rates of disease, especially kidney disease. Health care in remote areas of the Northern Territory (NT) is complicated by a mobile population, high staff turnover, poor communication between health services and complex comorbid health conditions requiring multidisciplinary care. This paper aims to
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A framework for inferring and analyzing pharmacotherapy treatment patterns BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-08 Everett Rush, Ozgur Ozmen, Minsu Kim, Erin Rush Ortegon, Makoto Jones, Byung H. Park, Steven Pizer, Jodie Trafton, Lisa A. Brenner, Merry Ward, Jonathan R. Nebeker
To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data. We apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy fills, dispensed inpatient antidepressant medications, emergency
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Continual learning framework for a multicenter study with an application to electrocardiogram BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-06 Junmo Kim, Min Hyuk Lim, Kwangsoo Kim, Hyung-Jin Yoon
Deep learning has been increasingly utilized in the medical field and achieved many goals. Since the size of data dominates the performance of deep learning, several medical institutions are conducting joint research to obtain as much data as possible. However, sharing data is usually prohibited owing to the risk of privacy invasion. Federated learning is a reasonable idea to train distributed multicenter
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Feasibility of a wearable self-management application for patients with COPD at home: a pilot study BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-05 Robert Wu, Eyal de Lara, Daniyal Liaqat, Salaar Liaqat, Jun Lin Chen, Tanya Son, Andrea S. Gershon
Among people with COPD, smartphone and wearable technology may provide an effective method to improve care at home by supporting, encouraging, and sustaining self-management. The current study was conducted to determine if patients with COPD will use a dedicated smartphone and smartwatch app to help manage their COPD and to determine the effects on their self-management. We developed a COPD self-management
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MMIR: an open-source software for the registration of multimodal histological images BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-05 Rodrigo Escobar Díaz Guerrero, José Luis Oliveira, Juergen Popp, Thomas Bocklitz
Multimodal histology image registration is a process that transforms into a common coordinate system two or more images obtained from different microscopy modalities. The combination of information from various modalities can contribute to a comprehensive understanding of tissue specimens, aiding in more accurate diagnoses, and improved research insights. Multimodal image registration in histology
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Identification of health-related problems in youth: a mixed methods feasibility study evaluating the Youth Health Report System BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-05 Petra V. Lostelius, Catharina Gustavsson, Eva Thors Adolfsson, Anne Söderlund, Åsa Revenäs, Ann-Britt Zakrisson, Magdalena Mattebo
Because poor health in youth risk affecting their entry in adulthood, improved methods for their early identification are needed. Health and welfare technology is widely accepted by youth populations, presenting a potential method for identifying their health problems. However, healthcare technology must be evidence-based. Specifically, feasibility studies contribute valuable information prior to more
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A qualitative analysis of algorithm-based decision support usability testing for symptom management across the trajectory of cancer care: one size does not fit all BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-05 Hayley Dunnack Yackel, Barbara Halpenny, Janet L. Abrahm, Jennifer Ligibel, Andrea Enzinger, David F. Lobach, Mary E. Cooley
Adults with cancer experience symptoms that change across the disease trajectory. Due to the distress and cost associated with uncontrolled symptoms, improving symptom management is an important component of quality cancer care. Clinical decision support (CDS) is a promising strategy to integrate clinical practice guideline (CPG)-based symptom management recommendations at the point of care. The objectives
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Seasonally adjusted laboratory reference intervals to improve the performance of machine learning models for classification of cardiovascular diseases BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-04 Victorine P. Muse, Davide Placido, Amalie D. Haue, Søren Brunak
Variation in laboratory healthcare data due to seasonal changes is a widely accepted phenomenon. Seasonal variation is generally not systematically accounted for in healthcare settings. This study applies a newly developed adjustment method for seasonal variation to analyze the effect seasonality has on machine learning model classification of diagnoses. Machine learning methods were trained and tested
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Natural language processing to identify lupus nephritis phenotype in electronic health records BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-03 Yu Deng, Jennifer A. Pacheco, Anika Ghosh, Anh Chung, Chengsheng Mao, Joshua C. Smith, Juan Zhao, Wei-Qi Wei, April Barnado, Chad Dorn, Chunhua Weng, Cong Liu, Adam Cordon, Jingzhi Yu, Yacob Tedla, Abel Kho, Rosalind Ramsey-Goldman, Theresa Walunas, Yuan Luo
Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit
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A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-03-01 Sergio Alejandro Holguin-Garcia, Ernesto Guevara-Navarro, Alvaro Eduardo Daza-Chica, Maria Alejandra Patiño-Claro, Harold Brayan Arteaga-Arteaga, Gonzalo A. Ruz, Reinel Tabares-Soto, Mario Alejandro Bravo-Ortiz
Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by a neurological doctor using an electroencephalogram (EEG), which is lengthy. To optimize these processes and make them more efficient, we have resorted to innovative
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Effect of early serum phosphate disorder on in-hospital and 28-day mortality in sepsis patients: a retrospective study based on MIMIC-IV database BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-26 Yinghao Luo, Yahui Peng, Yujia Tang, Pengfei Huang, Qianqian Zhang, Chunying Wang, Weiting Zhang, Jing Zhou, Longyu Liang, YuXin Zhang, Kaijiang Yu, Changsong Wang
This study aims to assess the influence of early serum phosphate fluctuation on the short-term prognosis of sepsis patients. This retrospective study used the Medical Information Mart for Intensive Care IV database to analyze serum phosphate levels in sepsis patients within 3 days of ICU admission. According to the absolute value of delta serum phosphate (the maximum value minus the minimum value of
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Conceptual design of a generic data harmonization process for OMOP common data model BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-26 Elisa Henke, Michele Zoch, Yuan Peng, Ines Reinecke, Martin Sedlmayr, Franziska Bathelt
To gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from the Observational Health Data Sciences and
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A novel estimator for the two-way partial AUC BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-20 Elias Chaibub Neto, Vijay Yadav, Solveig K. Sieberts, Larsson Omberg
The two-way partial AUC has been recently proposed as a way to directly quantify partial area under the ROC curve with simultaneous restrictions on the sensitivity and specificity ranges of diagnostic tests or classifiers. The metric, as originally implemented in the tpAUC R package, is estimated using a nonparametric estimator based on a trimmed Mann-Whitney U-statistic, which becomes computationally
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Correction: Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-19 Simone Maria Kagerbauer, Bernhard Ulm, Armin Horst Podtschaske, Dimislav Ivanov Andonov, Manfred Blobner, Bettina Jungwirth, Martin Graessner
Correction: Kagerbauer et al. BMC Medical Informatics and Decision Making (2024) 24:34 https://doi.org/10.1186/s12911-024-02428-z Following the publication of the original article [1], the authors reported typesetting errors and a typo. The first typesetting error was found in the Methods’ section of the Abstract. The numbering in the section was mistakenly linked to the reference, as follows: We trained
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Exploring the potential of ChatGPT as an adjunct for generating diagnosis based on chief complaint and cone beam CT radiologic findings BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-19 Yanni Hu, Ziyang Hu, Wenjing Liu, Antian Gao, Shanhui Wen, Shu Liu, Zitong Lin
This study aimed to assess the performance of OpenAI’s ChatGPT in generating diagnosis based on chief complaint and cone beam computed tomography (CBCT) radiologic findings. 102 CBCT reports (48 with dental diseases (DD) and 54 with neoplastic/cystic diseases (N/CD)) were collected. ChatGPT was provided with chief complaint and CBCT radiologic findings. Diagnostic outputs from ChatGPT were scored based
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Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-16 Mohamed El Azzouzi, Gouenou Coatrieux, Reda Bellafqira, Denis Delamarre, Christine Riou, Naima Oubenali, Sandie Cabon, Marc Cuggia, Guillaume Bouzillé
Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods have been proposed to remove personal identifiers, with deep learning-based
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Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-14 Yufeng Zhang, Jessica R. Golbus, Emily Wittrup, Keith D. Aaronson, Kayvan Najarian
Timely and accurate referral of end-stage heart failure patients for advanced therapies, including heart transplants and mechanical circulatory support, plays an important role in improving patient outcomes and saving costs. However, the decision-making process is complex, nuanced, and time-consuming, requiring cardiologists with specialized expertise and training in heart failure and transplantation
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Which risk factor best predicts coronary artery disease using artificial neural network method? BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-14 Nahid Azdaki, Fatemeh Salmani, Toba Kazemi, Neda Partovi, Saeede Khosravi Bizhaem, Masomeh Noori Moghadam, Yoones Moniri, Ehsan Zarepur, Noushin Mohammadifard, Hassan Alikhasi, Fatemeh Nouri, Nizal Sarrafzadegan, Seyyed Ali Moezi, Mohammad Reza Khazdair
Coronary artery disease (CAD) is recognized as the leading cause of death worldwide. This study analyses CAD risk factors using an artificial neural network (ANN) to predict CAD. The research data were obtained from a multi-center study, namely the Iran-premature coronary artery disease (I-PAD). The current study used the medical records of 415 patients with CAD hospitalized in Razi Hospital, Birjand
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Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-14 Lin Lawrence Guo, Keith E. Morse, Catherine Aftandilian, Ethan Steinberg, Jason Fries, Jose Posada, Scott Lanyon Fleming, Joshua Lemmon, Karim Jessa, Nigam Shah, Lillian Sung
Diagnostic codes are commonly used as inputs for clinical prediction models, to create labels for prediction tasks, and to identify cohorts for multicenter network studies. However, the coverage rates of diagnostic codes and their variability across institutions are underexplored. The primary objective was to describe lab- and diagnosis-based labels for 7 selected outcomes at three institutions. Secondary
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InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-14 Borum Nam, Beomjun Bark, Jeyeon Lee, In Young Kim
This study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (PPG) signals for sleep monitoring with wearable devices. Furthermore, our aim was to develop a more efficient sleep monitoring method by considering both the interpretability and uncertainty of
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Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-14 Michael Selle, Magdalena Kircher, Cornelia Schwennen, Christian Visscher, Klaus Jung
Unsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dimensional data after dimension reduction. Clustering and outlier detection may, however, become less robust or contradictory if multiple high-dimensional data sets per patient exist. Such a scenario
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Systematic design of health monitoring systems centered on older adults and ADLs BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-13 Francisco M. Garcia-Moreno, Maria Bermudez-Edo, José Manuel Pérez-Mármol, Jose Luis Garrido, María José Rodríguez-Fórtiz
Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors
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Development of a predictive machine learning model for pathogen profiles in patients with secondary immunodeficiency BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-13 Qianning Liu, Yifan Chen, Peng Xie, Ying Luo, Buxuan Wang, Yuanxi Meng, Jiaqian Zhong, Jiaqi Mei, Wei Zou
Secondary immunodeficiency can arise from various clinical conditions that include HIV infection, chronic diseases, malignancy and long-term use of immunosuppressives, which makes the suffering patients susceptible to all types of pathogenic infections. Other than HIV infection, the possible pathogen profiles in other aetiology-induced secondary immunodeficiency are largely unknown. Medical records
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Data resource profile of an online database system for forensic mental health services BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-13 Junko Koike, Toshiaki Kono, Koji Takeda, Yuji Yamada, Chiyo Fujii, Naotsugu Hirabayashi
This paper introduces a forensic psychiatry database established in Japan and discusses its significance and future issues. The purpose of this Database, created under the Medical Treatment and Supervision Act (MTSA) Database Project, is to improve the quality of forensic psychiatry treatment. It can collect monthly data on “basic information,” “Orders and hospitalizations under the MTSA,” “Treatment
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Varying (preferred) levels of involvement in treatment decision-making in the intensive care unit before and during the COVID-19 pandemic: a mixed-methods study among relatives BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-12 Sophie C. Renckens, H. Roeline Pasman, Zina Jorna, Hanna T. Klop, Chantal du Perron, Lia van Zuylen, Monique A.H. Steegers, Birkitt L. ten Tusscher, Margo M.C. van Mol, Lilian C.M. Vloet, Bregje D. Onwuteaka-Philipsen
In the intensive care unit (ICU) relatives play a crucial role as surrogate decision-makers, since most patients cannot communicate due to their illness and treatment. Their level of involvement in decision-making can affect their psychological well-being. During the COVID-19 pandemic, relatives’ involvement probably changed. We aim to investigate relatives’ involvement in decision-making in the ICU
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DeepVAQ : an adaptive deep learning for prediction of vascular access quality in hemodialysis patients BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-12 Sarayut Julkaew, Thakerng Wongsirichot, Kasikrit Damkliang, Pornpen Sangthawan
Chronic kidney disease is a prevalent global health issue, particularly in advanced stages requiring dialysis. Vascular access (VA) quality is crucial for the well-being of hemodialysis (HD) patients, ensuring optimal blood transfer through a dialyzer machine. The ultrasound dilution technique (UDT) is used as the gold standard for assessing VA quality; however, its limited availability due to high
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Usability and feasibility analysis of an mHealth-tool for supporting physical activity in people with heart failure BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-12 Andreas Blomqvist, Maria Bäck, Leonie Klompstra, Anna Strömberg, Tiny Jaarsma
Physical inactivity and a sedentary lifestyle are common among people with heart failure (HF), which may lead to worse prognosis. On an already existing mHealth platform, we developed a novel tool called the Activity coach, aimed at increasing physical activity. The aim of this study was to evaluate the usability of the Activity coach and assess feasibility of outcome measures for a future efficacy
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Prediction of Sjögren’s disease diagnosis using matched electronic dental-health record data BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-09 Jason Mao, Grace Gomez Felix Gomez, Mei Wang, Huiping Xu, Thankam P. Thyvalikakath
Sjögren’s disease (SD) is an autoimmune disease that is difficult to diagnose early due to its wide spectrum of clinical symptoms and overlap with other autoimmune diseases. SD potentially presents through early oral manifestations prior to showing symptoms of clinically significant dry eyes or dry mouth. We examined the feasibility of utilizing a linked electronic dental record (EDR) and electronic
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Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-08 Simran Saggu, Hirad Daneshvar, Reza Samavi, Paulo Pires, Roberto B. Sassi, Thomas E. Doyle, Judy Zhao, Ahmad Mauluddin, Laura Duncan
The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance
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Choice of refractive surgery types for myopia assisted by machine learning based on doctors’ surgical selection data BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-08 Jiajing Li, Yuanyuan Dai, Zhicheng Mu, Zhonghai Wang, Juan Meng, Tao Meng, Jimin Wang
In recent years, corneal refractive surgery has been widely used in clinics as an effective means to restore vision and improve the quality of life. When choosing myopia-refractive surgery, it is necessary to comprehensively consider the differences in equipment and technology as well as the specificity of individual patients, which heavily depend on the experience of ophthalmologists. In our study
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Evaluating MedDRA-to-ICD terminology mappings BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-07 Xinyuan Zhang, Yixue Feng, Fang Li, Jin Ding, Danyal Tahseen, Ezekiel Hinojosa, Yong Chen, Cui Tao
In this era of big data, data harmonization is an important step to ensure reproducible, scalable, and collaborative research. Thus, terminology mapping is a necessary step to harmonize heterogeneous data. Take the Medical Dictionary for Regulatory Activities (MedDRA) and International Classification of Diseases (ICD) for example, the mapping between them is essential for drug safety and pharmacovigilance
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Exploring the performance and explainability of fine-tuned BERT models for neuroradiology protocol assignment BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-07 Salmonn Talebi, Elizabeth Tong, Anna Li, Ghiam Yamin, Greg Zaharchuk, Mohammad R. K. Mofrad
Deep learning has demonstrated significant advancements across various domains. However, its implementation in specialized areas, such as medical settings, remains approached with caution. In these high-stake environments, understanding the model's decision-making process is critical. This study assesses the performance of different pretrained Bidirectional Encoder Representations from Transformers
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Establishment of a risk prediction model for bowel necrosis in patients with incarcerated inguinal hernia BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-06 Jiajie Zhou, Xiaoming Yuan
Incarceration occurred in approximately 5% to 15% of inguinal hernia patients, with around 15% of incarcerated cases progressing to intestinal necrosis, necessitating bowel resection surgery. Patients with intestinal necrosis had significantly higher mortality and complication rates compared to those without necrosis.The primary objective of this study was to design and validate a diagnostic model
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Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-06 Mitra Rahimi, Mohammad Reza Afrash, Shahin Shadnia, Babak Mostafazadeh, Peyman Erfan Talab Evini, Mohadeseh Sarbaz Bardsiri, Maral Ramezani
Hemodialysis is a life-saving treatment used to eliminate toxins and metabolites from the body during poisoning. Despite its effectiveness, there needs to be more research on this method precisely, with most studies focusing on specific poisoning. This study aims to bridge the existing knowledge gap by developing a machine-learning prediction model for forecasting the prognosis of the poisoned patient
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A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-06 Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Anthony S. Maida, Xiali Hei
The most common eye infection in people with diabetes is diabetic retinopathy (DR). It might cause blurred vision or even total blindness. Therefore, it is essential to promote early detection to prevent or alleviate the impact of DR. However, due to the possibility that symptoms may not be noticeable in the early stages of DR, it is difficult for doctors to identify them. Therefore, numerous predictive
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Machine learning based biomarker discovery for chronic kidney disease–mineral and bone disorder (CKD-MBD) BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-05 Yuting Li, Yukuan Lou, Man Liu, Siyi Chen, Peng Tan, Xiang Li, Huaixin Sun, Weixin Kong, Suhua Zhang, Xiang Shao
Chronic kidney disease-mineral and bone disorder (CKD-MBD) is characterized by bone abnormalities, vascular calcification, and some other complications. Although there are diagnostic criteria for CKD-MBD, in situations when conducting target feature examining are unavailable, there is a need to investigate and discover alternative biochemical criteria that are easy to obtain. Moreover, studying the
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Acute kidney injury comorbidity analysis based on international classification of diseases-10 codes BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-03 Menglu Wang, Guangjian Liu, Zhennan Ni, Qianjun Yang, Xiaojun Li, Zhisheng Bi
Acute kidney injury (AKI) is a clinical syndrome that occurs as a result of a dramatic decline in kidney function caused by a variety of etiological factors. Its main biomarkers, serum creatinine and urine output, are not effective in diagnosing early AKI. For this reason, this study provides insight into this syndrome by exploring the comorbidities of AKI, which may facilitate the early diagnosis
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Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-02 Simone Maria Kagerbauer, Bernhard Ulm, Armin Horst Podtschaske, Dimislav Ivanov Andonov, Manfred Blobner, Bettina Jungwirth, Martin Graessner
Concept drift and covariate shift lead to a degradation of machine learning (ML) models. The objective of our study was to characterize sudden data drift as caused by the COVID pandemic. Furthermore, we investigated the suitability of certain methods in model training to prevent model degradation caused by data drift. We trained different ML models with the H2O AutoML method on a dataset comprising
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The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-02 Md Ashiqul Haque, Muditha Lakmali Bodawatte Gedara, Nathan Nickel, Maxime Turgeon, Lisa M. Lix
Smoking is a risk factor for many chronic diseases. Multiple smoking status ascertainment algorithms have been developed for population-based electronic health databases such as administrative databases and electronic medical records (EMRs). Evidence syntheses of algorithm validation studies have often focused on chronic diseases rather than risk factors. We conducted a systematic review and meta-analysis
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Patient values in patient-provider communication about participation in early phase clinical cancer trials: a qualitative analysis before and after implementation of an online value clarification tool intervention BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-02 Liza G. G. van Lent, Mirte van der Ham, Maja J. A. de Jonge, Eelke H. Gort, Marjolein van Mil, Jeroen Hasselaar, Carin C. D. van der Rijt, Jelle van Gurp, Julia C. M. van Weert
Patients with advanced cancer who no longer have standard treatment options available may decide to participate in early phase clinical trials (i.e. experimental treatments with uncertain outcomes). Shared decision-making (SDM) models help to understand considerations that influence patients’ decision. Discussion of patient values is essential to SDM, but such communication is often limited in this
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The development of evaluation scale of the patient satisfaction with telemedicine: a systematic review BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-02-01 Yifei Du, Yu Gu
Since the outbreak of the COVID-19 pandemic, telemedicine become more and more popular, patients attempt to use telemedicine to meet personal medical needs. Patient satisfaction is a key indicator of insight into the patient experience. This systematic review aims to explore the measurement factors of patient satisfaction with telemedicine and develop a more comprehensive and systematic scale of patient
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An evaluation of GPT models for phenotype concept recognition BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-01-31 Tudor Groza, Harry Caufield, Dylan Gration, Gareth Baynam, Melissa A. Haendel, Peter N. Robinson, Christopher J. Mungall, Justin T. Reese
Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods)
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O2 supplementation disambiguation in clinical narratives to support retrospective COVID-19 studies BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-01-31 Akhila Abdulnazar, Amila Kugic, Stefan Schulz, Vanessa Stadlbauer, Markus Kreuzthaler
Oxygen saturation, a key indicator of COVID-19 severity, poses challenges, especially in cases of silent hypoxemia. Electronic health records (EHRs) often contain supplemental oxygen information within clinical narratives. Streamlining patient identification based on oxygen levels is crucial for COVID-19 research, underscoring the need for automated classifiers in discharge summaries to ease the manual
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Development of a mobile application to represent food intake in inpatients: dietary data systematization BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-01-30 Alan Renier Jamal Occhioni Molter, Naise Oliveira da Rocha Carvalho, Paloma Ribeiro Torres, Marlete Pereira da Silva, Patrícia Dias de Brito, Pedro Emmanuel Alvarenga Americano do Brasil, Claudio Fico Fonseca, Adriana Costa Bacelo
Nutritional risk situations related to decreased food intake in the hospital environment hinder nutritional care and increase malnutrition in hospitalized patients and are often associated with increased morbidity and mortality. The objective of this study is to develop and test the reliability and data similarity of a mobile application as a virtual instrument to assess the acceptability and quality
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Comparative assessment of synthetic time series generation approaches in healthcare: leveraging patient metadata for accurate data synthesis BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-01-30 Imanol Isasa, Mikel Hernandez, Gorka Epelde, Francisco Londoño, Andoni Beristain, Xabat Larrea, Ane Alberdi, Panagiotis Bamidis, Evdokimos Konstantinidis
Synthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with other privacy enhancing technologies. Generating uncompromised synthetic data could significantly benefit external researchers performing secondary analyses by providing unlimited access to information
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Application of machine learning models on predicting the length of hospital stay in fragility fracture patients BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-01-30 Chun-Hei Lai, Prudence Kwan-Lam Mok, Wai-Wang Chau, Sheung-Wai Law
The rate of geriatric hip fracture in Hong Kong is increasing steadily and associated mortality in fragility fracture is high. Moreover, fragility fracture patients increase the pressure on hospital bed demand. Hence, this study aims to develop a predictive model on the length of hospital stay (LOS) of geriatric fragility fracture patients using machine learning (ML) techniques. In this study, we use
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Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-01-26 Joon Yul Choi, Ik Hee Ryu, Jin Kuk Kim, In Sik Lee, Tae Keun Yoo
The epiretinal membrane (ERM) is a common retinal disorder characterized by abnormal fibrocellular tissue at the vitreomacular interface. Most patients with ERM are asymptomatic at early stages. Therefore, screening for ERM will become increasingly important. Despite the high prevalence of ERM, few deep learning studies have investigated ERM detection in the color fundus photography (CFP) domain. In
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Building gender-specific sexually transmitted infection risk prediction models using CatBoost algorithm and NHANES data BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-01-24 Mengjie Hu, Han Peng, Xuan Zhang, Lefeng Wang, Jingjing Ren
Sexually transmitted infections (STIs) are a significant global public health challenge due to their high incidence rate and potential for severe consequences when early intervention is neglected. Research shows an upward trend in absolute cases and DALY numbers of STIs, with syphilis, chlamydia, trichomoniasis, and genital herpes exhibiting an increasing trend in age-standardized rate (ASR) from 2010
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Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-01-24 Fatma M. Talaat, Shaker El-Sappagh, Khaled Alnowaiser, Esraa Hassan
Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance between overdiagnosis and early detection remains debated. Using Deep Learning (DL) algorithms for prostate cancer detection offers a promising solution for accurate and efficient diagnosis
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Participant characteristics and reasons for non-consent to health information linkage for research: experiences from the ATHENA COVID-19 study BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-01-23 Kim Greaves, Amanda King, Zoltan Bourne, Jennifer Welsh, Mark Morgan, M. Ximena Tolosa, Carissa Bonner, Tony Stanton, Michael Fryer, Rosemary Korda
The linkage of primary care, hospital and other health registry data is a global goal, and a consent-based approach is often used. Understanding the attitudes of why participants take part is important, yet little is known about reasons for non-participation. The ATHENA COVID-19 feasibility study investigated: 1) health outcomes of people diagnosed with COVID-19 in Queensland, Australia through primary
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Only the anxious ones? Identifying characteristics of symptom checker app users: a cross-sectional survey BMC Med. Inform. Decis. Mak. (IF 3.5) Pub Date : 2024-01-23 Anna-Jasmin Wetzel, Malte Klemmt, Regina Müller, Monika A. Rieger, Stefanie Joos, Roland Koch
Symptom checker applications (SCAs) may help laypeople classify their symptoms and receive recommendations on medically appropriate actions. Further research is necessary to estimate the influence of user characteristics, attitudes and (e)health-related competencies. The objective of this study is to identify meaningful predictors for SCA use considering user characteristics. An explorative cross-sectional