{"results":[{"id":"ss_35b17550a0ec14cfbff1a308a097a654dd4bd7ea","title":"Prompt Engineering Paradigms for Medical Applications: Scoping Review","authors":[{"name":"Jamil Zaghir"},{"name":"M. Naguib"},{"name":"Mina Bjelogrlic"},{"name":"Aurélie Névéol"},{"name":"Xavier Tannier"},{"name":"Christian Lovis"}],"abstract":"Background Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain due to its specialized terminology and language technicity. Clinical natural language processing applications must navigate complex language and ensure privacy compliance. Prompt engineering offers a novel approach by designing tailored prompts to guide models in exploiting clinically relevant information from complex medical texts. Despite its promise, the efficacy of prompt engineering in the medical domain remains to be fully explored. Objective The aim of the study is to review research efforts and technical approaches in prompt engineering for medical applications as well as provide an overview of opportunities and challenges for clinical practice. Methods Databases indexing the fields of medicine, computer science, and medical informatics were queried in order to identify relevant published papers. Since prompt engineering is an emerging field, preprint databases were also considered. Multiple data were extracted, such as the prompt paradigm, the involved LLMs, the languages of the study, the domain of the topic, the baselines, and several learning, design, and architecture strategies specific to prompt engineering. We include studies that apply prompt engineering–based methods to the medical domain, published between 2022 and 2024, and covering multiple prompt paradigms such as prompt learning (PL), prompt tuning (PT), and prompt design (PD). Results We included 114 recent prompt engineering studies. Among the 3 prompt paradigms, we have observed that PD is the most prevalent (78 papers). In 12 papers, PD, PL, and PT terms were used interchangeably. While ChatGPT is the most commonly used LLM, we have identified 7 studies using this LLM on a sensitive clinical data set. Chain-of-thought, present in 17 studies, emerges as the most frequent PD technique. While PL and PT papers typically provide a baseline for evaluating prompt-based approaches, 61% (48/78) of the PD studies do not report any nonprompt-related baseline. Finally, we individually examine each of the key prompt engineering–specific information reported across papers and find that many studies neglect to explicitly mention them, posing a challenge for advancing prompt engineering research. Conclusions In addition to reporting on trends and the scientific landscape of prompt engineering, we provide reporting guidelines for future studies to help advance research in the medical field. We also disclose tables and figures summarizing medical prompt engineering papers available and hope that future contributions will leverage these existing works to better advance the field.","source":"Semantic Scholar","year":2024,"language":"en","subjects":["Computer Science","Medicine"],"doi":"10.2196/60501","url":"https://www.semanticscholar.org/paper/35b17550a0ec14cfbff1a308a097a654dd4bd7ea","pdf_url":"https://doi.org/10.2196/60501","is_open_access":true,"citations":86,"published_at":"","score":70.58},{"id":"doaj_10.1016/j.dib.2025.111823","title":"PCPAm - A dataset of histopathological images of penile cancer for classification tasksZenodo","authors":[{"name":"Marcos Gabriel Mendes Lauande"},{"name":"Geraldo Braz Júnior"},{"name":"João Dallyson Sousa de Almeida"},{"name":" Vandecia Rejane Monteiro Fernandes"},{"name":"Anselmo Cardoso de Paiva"},{"name":"Rui Miguel Gil da Costa"},{"name":"Amanda Mara Teles"},{"name":"Leandro Lima da Silva"},{"name":"Haissa Oliveira Brito"},{"name":"Flávia Castello Branco Vidal"}],"abstract":"Penile cancer has an incidence strongly linked to sociocultural factors, being more common in underdeveloped countries like Brazil, where it represents approximately 2% of cancers affecting men. This dataset was created to address the scarcity of publicly available resources for classifying histopathological images in penile cancer research. The images were collected in 2021 from tissue samples obtained through biopsies of patients undergoing treatment for penile cancer. After staining with Hematoxylin and Eosin (H\u0026E), the tissue samples were photographed using a Leica ICC50 HD camera attached to a bright-field microscope (Leica DM500). The dataset comprises 194 high-resolution images (2048 × 1536 pixels), categorized by magnification (40X and 100X) and pathological classification (Tumor or Non-Tumor). Metadata includes additional information such as histological grade and, for some images, HPV status. Although previous works have focused primarily on binary classification tasks, the dataset includes additional labels, such as histological grade and HPV (Human Papilloma Virus) presence, which provide opportunities for multi-label classification or other types of predictive modelling. These extended labels enhance the dataset’s versatility for more complex tasks in medical image analysis. The dataset holds significant reuse potential for machine learning tasks beyond binary classification, allowing researchers to explore additional layers of analysis, such as HPV detection and histological grading. It can also be used for model benchmarking and comparative studies in cancer research, contributing to developing new diagnostic tools. The dataset and metadata are available for further research and model development.","source":"DOAJ","year":2025,"language":"","subjects":["Computer applications to medicine. Medical informatics","Science (General)"],"doi":"10.1016/j.dib.2025.111823","url":"http://www.sciencedirect.com/science/article/pii/S2352340925005505","is_open_access":true,"published_at":"","score":69},{"id":"ss_ed31a876d8bdb20fd775d0ea30f0246f5e13105c","title":"Two Decades Of Collaboration Between Medicine And Informatics","authors":[{"name":"Irena Roterman-Konieczna"}],"abstract":"This editorial article outlines the origins, development and scientific mission of Bio-Algorithms and Med-Systems on the occasion of its 20th anniversary. It reconstructs the historical context of early 21st-century Poland, when interdisciplinary collaboration between medicine, computer science and engineering was still uncommon and often meet with scepticism. The text describes the pioneering role of the Jagiellonian University Medical College and AGH University of Krakow in promoting biomedical informatics, cybernetics and biomedical engineering fields that would later become essential to modern healthcare. It also recounts the establishment of the journal in 2005 as a response to the lack of publication venues for interdisciplinary work combining bio-phenomena, technical sciences and medical applications. The article presents the journal’s contribution to shaping the newly emerging discipline of biomedical engineering in Poland, its early publishing philosophy, and its evolution through various editorial and publishing stages. Finally, the authors reflect on the journal’s legacy, emphasising the importance of interdisciplinary cooperation, technological innovation and ethical frameworks as prerequisites for scientific progress.","source":"Semantic Scholar","year":2025,"language":"en","subjects":null,"doi":"10.5604/01.3001.0055.5405","url":"https://www.semanticscholar.org/paper/ed31a876d8bdb20fd775d0ea30f0246f5e13105c","is_open_access":true,"published_at":"","score":69},{"id":"ss_3d4605112f3275eb6adf77f7b374241906014ede","title":"Artificial Intelligence in Medicine","authors":[{"name":"Muath Aldergham"},{"name":"Areeg Alfouri"},{"name":"Rasha Al Madat"}],"abstract":"Artificial intelligence in medicine refers to the use of machine learning models to help process medical data and provide medical professionals with important insights, improving health outcomes and patient experience. Thanks to recent advances in computer science and informatics, artificial intelligence (AI) is rapidly becoming an integral part of modern healthcare. Therefore, artificial intelligence algorithms and other AI-powered applications are now used to support medical professionals in clinical settings and in ongoing research. There are several applications of Artificial intelligence in medicine, including applications to help detect and diagnose diseases; applications to treat diseases with the help of an AI-powered virtual assistant; AI applications in medical imaging; applications to increase the efficiency of clinical trials; and applications to accelerate drug development. The benefits of Artificial intelligence in medicine can be summarized in providing informed patient care, reducing errors, reducing care costs, and increasing doctor-patient engagement.","source":"Semantic Scholar","year":2024,"language":"en","subjects":null,"doi":"10.70135/seejph.vi.1561","url":"https://www.semanticscholar.org/paper/3d4605112f3275eb6adf77f7b374241906014ede","pdf_url":"https://seejph.com/index.php/seejph/article/download/1561/1115","is_open_access":true,"citations":6,"published_at":"","score":68.18},{"id":"ss_6a1b949e284e26cdb12bbd162a21fb80307dd664","title":"Development of Male Skeletal 3D Model for Applications in Medicine","authors":[{"name":"Marek Klimo"},{"name":"M. Kvaššay"},{"name":"N. Kvassayová"}],"abstract":"Three-dimensional (3D) models have significantly transformed medical practice, education, and research in the healthcare field. This study examines the diverse applications of 3D models in the field of medicine, encompassing a wide range of applications such as basic organ simulations, intricate surgical procedures, and personalized medicine. These models provide a comprehensive depiction of anatomical features and clinical settings using sophisticated imaging techniques and Computer-Aided Design (CAD) tools. Furthermore, the integration of Virtual Reality (VR) and Augmented Reality (AR) technology has significantly improved the usefulness of 3D models, offering immersive experiences and innovative opportunities for studying and executing complex tasks. However, one of the key issues in the development of all these applications connecting informatics and medicine is the quality of 3D models of human anatomy. In this paper, we compare two generic male models, analyze them quantitatively and introduce one more model that takes the best of both. We then compare newly modified model and quantitatively compare it with the previous models. In the development of the model, we focus on human male skeletal model.","source":"Semantic Scholar","year":2024,"language":"en","subjects":null,"doi":"10.1109/Informatics62280.2024.10900897","url":"https://www.semanticscholar.org/paper/6a1b949e284e26cdb12bbd162a21fb80307dd664","is_open_access":true,"citations":1,"published_at":"","score":68.03},{"id":"doaj_10.1186/s12911-024-02827-2","title":"Identification of confounders and estimating the causal effect of place of birth on age-specific childhood vaccination","authors":[{"name":"Ashagrie Sharew Iyassu"},{"name":"Haile Mekonnen Fenta"},{"name":"Zelalem G. Dessie"},{"name":"Temesgen T. Zewotir"}],"abstract":"Abstract Background In causal analyses, some third factor may distort the relationship between the exposure and the outcome variables under study, which gives spurious results. In this case, treatment groups and control groups that receive and do not receive the exposure are different from one another in some other essential variables, called confounders. Method Place of birth was used as exposure variable and age-specific childhood vaccination status was used as outcome variables. Three approaches of confounder selection techniques such as all pre-treatment covariates, outcome cause covariates, and common cause covariates were proposed. Multiple logistic regression was used to estimate the propensity score for inverse probability treatment weighting (IPTW) confounder adjustment techniques. The proportional odds model was used to estimate the causal effect of place of birth on age-specific childhood vaccination. To validate the result obtained from observed data, we used a plasmode simulation of resampling 1000 samples from actual data 500 times. Result Outcome cause and common cause confounder identification techniques gave comparable results in terms of treatment effect in the plasmode data. However, outcome causes that contain common causes and predictors of the outcome confounder identification gave relatively better treatment effect results. The treatment effect result in the IPTW confounder adjustment method was better than that of the regression adjustment method. The effect of place of birth on log odds of cumulative probability of age-specific childhood vaccination was 0.36 with odds ratio of 1.43 for higher level vaccination status. Conclusion It is essential to use plasmode simulation data to validate the reproducibility of the proposed methods on the observed data. It is important to use outcome-cause covariates to adjust their confounding effect on the outcome. Using inverse probability treatment weighting gives unbiased treatment effect results as compared to the regression method of confounder adjustment. Institutional delivery increases the likelihood of childhood vaccination at the recommended schedule.","source":"DOAJ","year":2024,"language":"","subjects":["Computer applications to medicine. Medical informatics"],"doi":"10.1186/s12911-024-02827-2","url":"https://doi.org/10.1186/s12911-024-02827-2","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.1186/s12911-024-02652-7","title":"lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses","authors":[{"name":"Ahmed Medhat Zayed"},{"name":"Arne Janssens"},{"name":"Pavlos Mamouris"},{"name":"Nicolas Delvaux"}],"abstract":"Abstract Background The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and accuracy remains a significant challenge due to variations in data recording and reporting standards. Methods We developed lab2clean, a novel algorithm aimed at automating and standardizing the cleaning of retrospective clinical laboratory results data. lab2clean was implemented as two R functions specifically designed to enhance data conformance and plausibility by standardizing result formats and validating result values. The functionality and performance of the algorithm were evaluated using two extensive electronic medical record (EMR) databases, encompassing various clinical settings. Results lab2clean effectively reduced the variability of laboratory results and identified potentially erroneous records. Upon deployment, it demonstrated effective and fast standardization and validation of substantial laboratory data records. The evaluation highlighted significant improvements in the conformance and plausibility of lab results, confirming the algorithm’s efficacy in handling large-scale data sets. Conclusions lab2clean addresses the challenge of preprocessing and cleaning clinical laboratory data, a critical step in ensuring high-quality data for research outcomes. It offers a straightforward, efficient tool for researchers, improving the quality of clinical laboratory data, a major portion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models. Future developments aim to broaden its functionality and accessibility, solidifying its vital role in healthcare data management. Graphical Abstract","source":"DOAJ","year":2024,"language":"","subjects":["Computer applications to medicine. Medical informatics"],"doi":"10.1186/s12911-024-02652-7","url":"https://doi.org/10.1186/s12911-024-02652-7","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.1109/JTEHM.2024.3355432","title":"NeuroDiag: Software for Automated Diagnosis of Parkinson\u0026#x2019;s Disease Using Handwriting","authors":[{"name":"Quoc Cuong Ngo"},{"name":"Nicole McConnell"},{"name":"Mohammod Abdul Motin"},{"name":"Barbara Polus"},{"name":"Arup Bhattacharya"},{"name":"Sanjay Raghav"},{"name":"Dinesh Kant Kumar"}],"abstract":"Objective: A change in handwriting is an early sign of Parkinson\u0026#x2019;s disease (PD). However, significant inter-person differences in handwriting make it difficult to identify pathological handwriting, especially in the early stages. This paper reports the testing of NeuroDiag, a software-based medical device, for the automated detection of PD using handwriting patterns. NeuroDiag is designed to direct the user to perform six drawing and writing tasks, and the recordings are then uploaded onto a server for analysis. Kinematic information and pen pressure of handwriting are extracted and used as baseline parameters. NeuroDiag was trained based on 26 PD patients in the early stage of the disease and 26 matching controls. Methods: Twenty-three people with PD (PPD) in their early stage of the disease, 25 age-matched healthy controls (AMC), and 7 young healthy controls were recruited for this study. Under the supervision of a consultant neurologist or their nurse, the participants used NeuroDiag. The reports were generated in real-time and tabulated by an independent observer. Results: The participants were able to use NeuroDiag without assistance. The handwriting data was successfully uploaded to the server where the report was automatically generated in real-time. There were significant differences in the writing speed between PPD and AMC (P\u0026#x003C;0.001). NeuroDiag showed 86.96\u0026#x0025; sensitivity and 76.92\u0026#x0025; specificity in differentiating PPD from those without PD. Conclusion: In this work, we tested the reliability of NeuroDiag in differentiating between PPD and AMC for real-time applications. The results show that NeuroDiag has the potential to be used to assist neurologists and for telehealth applications. Clinical and Translational Impact Statement \u0026#x2014; This pre-clinical study shows the feasibility of developing a community-wide screening program for Parkinson\u0026#x2019;s disease using automated handwriting analysis software, NeuroDiag.","source":"DOAJ","year":2024,"language":"","subjects":["Computer applications to medicine. Medical informatics","Medical technology"],"doi":"10.1109/JTEHM.2024.3355432","url":"https://ieeexplore.ieee.org/document/10403837/","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.1016/j.health.2023.100286","title":"A flexible analytic wavelet transform and ensemble bagged tree model for electroencephalogram-based meditative mind-wandering detection","authors":[{"name":"Ajay Dadhich"},{"name":"Jaideep Patel"},{"name":"Rovin Tiwari"},{"name":"Richa Verma"},{"name":"Pratha Mishra"},{"name":"Jay Kumar Jain"}],"abstract":"Mind-wandering (MW) is when an individual’s concentration drifts away from the task or activity. Researchers found a greater variability in electroencephalogram (EEG) signals due to MW. Collecting more nuanced information from raw EEG data to examine the harmful effects of MW is time-consuming. This study proposes a multi-resolution assessment of EEG signals using the flexible analytic wavelet transform (FAWT). The FAWT algorithm decomposes raw EEG data into more representative sub-bands (SBs). Several statistical characteristics are derived from the obtained SBs, and the effects of MW during meditation on the EEG signals are investigated. A set of significant characteristics is chosen and fed into the machine learning modules using a 10-fold validation approach to detect MW subjects automatically. Our proposed framework attained the highest classification accuracy of 92.41%, the highest sensitivity of 93.56%, and the highest specificity of 91.97%. The proposed framework can be used to design a suitable brain-computer interface (BCI) system to reduce MW and increase meditation depth for holistic and long-term health in society.","source":"DOAJ","year":2024,"language":"","subjects":["Computer applications to medicine. Medical informatics"],"doi":"10.1016/j.health.2023.100286","url":"http://www.sciencedirect.com/science/article/pii/S2772442523001533","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.59476/mtt.v2i20.673","title":"Karinių pratybų poveikis profesionalių karių miego kokybei","authors":[{"name":"Ligita Mažeikė"},{"name":"Arminas Vareika"}],"abstract":"Tinkama miego trukmė ir kokybė yra būtini optimaliai psichinei ir fizinei sveikatai. Karinės profesijos susiduria su unikaliais iššūkiais, tokiais kaip 36 valandų darbo pamainos, fiziškai alinantis darbas ir situacijos, dėl kurių galima susižaloti arba žūti. Pervargę kariai kenčia nuo sumažėjusio budrumo, su sprendimais susijusio reakcijos laiko, trumpalaikės atminties, navigacijos įgūdžių ir, kai kuriais atvejais, taiklumo per treniruotes suprastėjimo. Didelio fizinio ir psichinio nuovargio derinys gali padidinti traumų riziką ir sumažinti gebėjimą priimti tinkamus sprendimus reikiamu laiku. Šio tyrimo tikslas – nustatyti karinių pratybų poveikį profesionalių karių miego kokybei, kadangi dažniausi miego nepakankamumo atvejai pasitaiko per karines pratybas. Į tyrimą buvo įtraukta 10 profesionalių Lietuvos kariuomenės žvalgų būrio karių, 32,8 ± 6,9 metų amžiaus, atitinkančių pačius aukščiausius karių fizinio parengimo testo reikalavimus (≥ 270 balų). Profesionalių karių miego kokybė buvo vertinama 7 paras prieš karines pratybas, 7 paras karinių pratybų metu ir 9 paras iš karto po karinių pratybų. Visi tiriamieji dėvėjo laikrodžius „Garmin Decent G1“, kuriais buvo fiksuojami miego parodymai. Buvo registruojama bendra miego trukmė, gilaus miego, lengvo miego, REM ir būdravimo fazių trukmė. Nustatyta, kad karinių pratybų metu, karių bendra miego trukmė buvo 5,3 ± 2,9 val. per dieną (toliau – val./d.), o prieš pratybas buvo 2,3 ± 1,1 val./d. ilgesnė (p \u003c 0,05). Po karinių pratybų karių bendra miego trukmė buvo 3,0 ± 1,1 val./d. ilgesnė nei karinių pratybų metu (p \u003c 0,05). Taip pat po karinių pratybų karių bendra miego trukmė buvo ilgesnė 0,7 ± 0,5 val./d. lyginat su miego trukme prieš pratybas (p \u003c 0,05). Karinių pratybų metu taip pat sutrumpėjo gilaus miego fazė. Gilaus miego fazės trukmė pratybų metu sutrumpėjo net 58 proc. (p \u003c 0,05) lyginant su prieš pratybas buvusia, tačiau po pratybų išliko 18 proc. trumpesnė nei prieš pratybas (p \u003c 0,05). Lengvo miego fazės trukmė buvo 93,7 ± 46,6 min. per dieną (toliau – min./d.) trumpesnė nei prieš pratybas (p \u003c 0,05), tačiau vertinant procentais lengvo miego fazė sudarė apie 60 proc. bendros miego trukmės visuose etapuose ir tik apie 2 proc. buvo trumpesnė po karinių pratybų (p \u003e 0,05). Karinių pratybų metu REM ir būdravimo miego fazių trukmė reikšmingai nesiskyrė nuo prieš pratybas buvusių trukmių (p \u003e 0,05).","source":"DOAJ","year":2024,"language":"","subjects":["Computer applications to medicine. Medical informatics","Social Sciences"],"doi":"10.59476/mtt.v2i20.673","url":"https://ojs.kaunokolegija.lt/index.php/mttlk/article/view/673","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.3390/tomography9060167","title":"The Role of Cone-Beam Computed Tomography CT Extremity Arthrography in the Preoperative Assessment of Osteoarthritis","authors":[{"name":"Marion Hamard"},{"name":"Marta Sans Merce"},{"name":"Karel Gorican"},{"name":"Pierre-Alexandre Poletti"},{"name":"Angeliki Neroladaki"},{"name":"Sana Boudabbous"}],"abstract":"Osteoarthritis (OA) is a prevalent disease and the leading cause of pain, disability, and quality of life deterioration. Our study sought to evaluate the image quality and dose of cone-beam computed tomography arthrography (CBCT-A) and compare them to digital radiography (DR) for OA diagnoses. Overall, 32 cases of CBCT-A and DR with OA met the inclusion criteria and were prospectively analyzed. The Kellgren and Lawrence classification (KLC) stage, sclerosis, osteophytes, erosions, and mean joint width (MJW) were compared between CBCT-A and DR. Image quality was excellent in all CBCT-A cases, with excellent inter-observer agreement. OA under-classification was noticed with DR for MJW (\u003ci\u003ep\u003c/i\u003e = 0.02), osteophyte detection (\u003c0.0001), and KLC (\u003ci\u003ep\u003c/i\u003e \u003c 0.0001). The Hounsfield Unit (HU) values obtained for the cone-beam computed tomography CBCT did not correspond to the values for multi-detector computed tomography (MDCT), with a greater mean deviation obtained with the MDCT HU for Modeled Based Iterative Reconstruction 1st (MBIR1) than for the 2nd generation (MBIR2). CBCT-A has been found to be more reliable for OA diagnosis than DR as revealed by our results using a three-point rating scale for the qualitative image analysis, with higher quality and an acceptable dose. Moreover, the use of this imaging technique permits the preoperative assessment of extremities in an OA diagnosis, with the upright position and bone microarchitecture analysis being two other advantages of CBCT-A.","source":"DOAJ","year":2023,"language":"","subjects":["Computer applications to medicine. Medical informatics"],"doi":"10.3390/tomography9060167","url":"https://www.mdpi.com/2379-139X/9/6/167","is_open_access":true,"published_at":"","score":67},{"id":"ss_b3c5e02c9d3fc611d9a7e01d6d4e982a2c64db34","title":"Practical Imaging Informatics","authors":null,"abstract":"","source":"Semantic Scholar","year":2022,"language":"en","subjects":null,"url":"https://www.semanticscholar.org/paper/b3c5e02c9d3fc611d9a7e01d6d4e982a2c64db34","is_open_access":true,"citations":7,"published_at":"","score":66.21000000000001},{"id":"doaj_10.1016/j.dib.2022.108367","title":"Large datasets of water vapor sorption, mass diffusion immersed in water, hygroscopic expansion and mechanical properties of flax fibre/shape memory epoxy hygromorph composites","authors":[{"name":"Qinyu Li"},{"name":"Rujie Sun"},{"name":"Antoine Le Duigou"},{"name":"Jianglong Guo"},{"name":"Jonathan Rossiter"},{"name":"Liwu Li"},{"name":"Jinsong Leng"},{"name":"Fabrizio Scarpa"}],"abstract":"This data article presents four experimental sets of results related to flax fibre composites with epoxy shape memory polymer matrix: water vapor absorption, mass diffusion immersed in water, hygroscopic expansion, mechanical properties. The water vapor absorption tests are described in raw data related to four types of laminates with weights measured at different relative humidity (0%, 9%, 33%, 44%,75%, 85% and 100%). The mass diffusion experiments are related to weights of immersed samples over time. The unidirectional composite hygroscopic expansion is also measured along the fibre longitude and transverse directions. The mechanical properties of flax composite at various temperatures (20°C, 40°C, 60°C, 80°C and 100°C) and humidity environments (50% and immersed) are also described. Load-displacement diagrams of the hygromorph composites are converted into stress-strain diagrams via a compliance calibration, from which the tensile moduli are extracted. The data presented in this article can provide a benchmark for the development of new models, or for the determination of other properties via post processing. The detailed interpretation of the data can be found in [1]. The data is available in the Mendeley Data repository at [2].","source":"DOAJ","year":2022,"language":"","subjects":["Computer applications to medicine. Medical informatics","Science (General)"],"doi":"10.1016/j.dib.2022.108367","url":"http://www.sciencedirect.com/science/article/pii/S2352340922005649","is_open_access":true,"published_at":"","score":66},{"id":"doaj_10.1177/21501327211000221","title":"The Effect of Health Literacy on a Brief Intervention to Improve Advance Directive Completion: A Randomized Controlled Study","authors":[{"name":"Paige C. Barker"},{"name":"Neal P. Holland"},{"name":"Oliver Shore"},{"name":"Robert L. Cook"},{"name":"Yang Zhang"},{"name":"Carrie D. Warring"},{"name":"Melanie G. Hagen"}],"abstract":"Objective Completion of an advance directive (AD) document is one component of advanced care planning. We evaluated a brief intervention to enhance AD completion and assess whether the intervention effect varied according to health literacy. Methods A randomized controlled study was conducted in 2 internal medicine clinics. Participants were over 50, without documented AD, no diagnosis of dementia, and spoke English. Participants were screened for health literacy utilizing REALM-SF. Participants were randomized in a 1:1 ratio to the intervention, a 15-minute scripted introduction (grade 7 reading level) to our institution’s AD forms (grade 11 reading level) or to the control, in which subjects were handed blank AD forms without explanation. Both groups received reminder calls at 1, 3, and 5 months. The primary outcome was AD completion at 6 months. Results Five hundred twenty-nine subjects were enrolled; half were of limited and half were of adequate health literacy. The AD completion rate was 21.7% and was similar in the intervention vs. the control group (22.4% vs 22.2%, P  = .94).More participants with adequate health literacy completed an AD than those with limited health literacy (28.4% vs 16.2%, P  = .0008), although the effect of the intervention was no different within adequate or limited literacy groups. Conclusion A brief intervention had no impact on AD completion for subjects of adequate or limited health literacy. Practice Implications Our intervention was designed for easy implementation and to be accessible to patients of adequate or limited health literacy. This intervention was not more likely than the control (handing patients an AD form) to improve AD completion for patients of either limited or adequate health literacy. Future efforts and research to improve AD completion rates should focus on interventions that include: multiple inperson contacts with patients, contact with a trusted physician, documents at 5th grade reading level, and graphic/video decision aids. Trial Registration Number NCT02702284, Protocol ID IRB201500776","source":"DOAJ","year":2021,"language":"","subjects":["Computer applications to medicine. Medical informatics","Public aspects of medicine"],"doi":"10.1177/21501327211000221","url":"https://doi.org/10.1177/21501327211000221","is_open_access":true,"published_at":"","score":65},{"id":"doaj_10.1016/j.imu.2020.100452","title":"Bone fracture detection through the two-stage system of Crack-Sensitive Convolutional Neural Network","authors":[{"name":"Yangling Ma"},{"name":"Yixin Luo"}],"abstract":"Automated fracture detection is an essential part in a computer-aided tele-medicine system. Fractures often occur in human's arbitrary bone due to accidental injuries such as slipping. In fact, many hospitals lack experienced surgeons to diagnose fractures. Therefore, computer-aided diagnosis (CAD) reduces the burden on doctors and identifies fracture. We present a new classification network, Crack-Sensitive Convolutional Neural Network (CrackNet), which is sensitive to fracture lines. In this paper, we propose a new two-stage system to detect fracture. Firstly, we use Faster Region with Convolutional Neutral Network (Faster R-CNN) to detect 20 different types of bone regions in X-ray images, and then we recognize whether each bone region is fractured by using CrackNet. Total of 1052 images are used to test our system, of which 526 are fractured images and the rest are non-fractured images. We assess the performance of our proposed system with X-ray images from Haikou People's Hospital, achieving 90.11% accuracy and 90.14% F-measure. And our system is better than other two-stage systems.","source":"DOAJ","year":2021,"language":"","subjects":["Computer applications to medicine. Medical informatics"],"doi":"10.1016/j.imu.2020.100452","url":"http://www.sciencedirect.com/science/article/pii/S235291482030602X","is_open_access":true,"published_at":"","score":65},{"id":"doaj_10.1186/s13040-021-00271-w","title":"Taxonomy-based data representation for data mining: an example of the magnitude of risk associated with H. pylori infection","authors":[{"name":"Inese Polaka"},{"name":"Danute Razuka-Ebela"},{"name":"Jin Young Park"},{"name":"Marcis Leja"}],"abstract":"Abstract Background The amount of available and potentially significant data describing study subjects is ever growing with the introduction and integration of different registries and data banks. The single specific attribute of these data are not always necessary; more often, membership to a specific group (e.g. diet, social ‘bubble’, living area) is enough to build a successful machine learning or data mining model without overfitting it. Therefore, in this article we propose an approach to building taxonomies using clustering to replace detailed data from large heterogenous data sets from different sources, while improving interpretability. We used the GISTAR study data base that holds exhaustive self-assessment questionnaire data to demonstrate this approach in the task of differentiating between H. pylori positive and negative study participants, and assessing their potential risk factors. We have compared the results of taxonomy-based classification to the results of classification using raw data. Results Evaluation of our approach was carried out using 6 classification algorithms that induce rule-based or tree-based classifiers. The taxonomy-based classification results show no significant loss in information, with similar and up to 2.5% better classification accuracy. Information held by 10 and more attributes can be replaced by one attribute demonstrating membership to a cluster in a hierarchy at a specific cut. The clusters created this way can be easily interpreted by researchers (doctors, epidemiologists) and describe the co-occurring features in the group, which is significant for the specific task. Conclusions While there are always features and measurements that must be used in data analysis as they are, the use of taxonomies for the description of study subjects in parallel allows using membership to specific naturally occurring groups and their impact on an outcome. This can decrease the risk of overfitting (picking attributes and values specific to the training set without explaining the underlying conditions), improve the accuracy of the models, and improve privacy protection of study participants by decreasing the amount of specific information used to identify the individual.","source":"DOAJ","year":2021,"language":"","subjects":["Computer applications to medicine. Medical informatics","Analysis"],"doi":"10.1186/s13040-021-00271-w","url":"https://doi.org/10.1186/s13040-021-00271-w","is_open_access":true,"published_at":"","score":65},{"id":"doaj_10.1016/j.imu.2021.100668","title":"Evaluation of the implementation of International Classification of Diseases, 11th revision for morbidity coding: Rationale and study protocol","authors":[{"name":"Reza Golpira"},{"name":"Zahra Azadmanjir"},{"name":"Javad Zarei"},{"name":"Nasim Hashemi"},{"name":"Zahra Meidani"},{"name":"Akram Vahedi"},{"name":"Hooman Bakhshandeh"},{"name":"Esmaeil Fakharian"},{"name":"Abbas Sheikhtaheri"}],"abstract":"International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) has been used for many years in many countries to manage health information. The World Health Organization (WHO) will soon replace ICD-10 with the International Classification of Diseases, 11th revision for Mortality and Morbidity Statistics (ICD-11 MMS or ICD-11). The transition to ICD-11 requires the acquisition of the right information for the right policymaking to manage the transitional period. As one of these countries, Iran has initiated a plan to implement ICD-11. The purpose of this paper is to describe the methodologies of our evaluation studies on the implementation of ICD-11 for morbidity coding. We established scientific and executive committees of the study at the Ministry of Health and Medical Education (MOHME) and coordinated our implementation with the WHO. The scientific committee developed the necessary training curriculum in the form of workshops and mentoring courses. We also developed five independent sub-studies with different and related goals to answer the questions considered important by the MOHME and the WHO. The purposes of these sub-studies are to compare the accuracy of coding with ICD-11 and ICD-10, to compare the coding time with ICD-11 and ICD-10, to evaluate the ICD-11 content to cover diagnoses documented in medical records and identify non-covered terms, to evaluate the quality of clinical documentation needed for coding with ICD-11 and the impact of training on clinical documentation, as well as to understand coders' perspectives on barriers, problems, and opportunities for ICD-11 implementation and its utility. During these sub-studies, over 2000 medical records in two teaching and non-teaching hospitals will be evaluated over a period of five months.In this paper, we discussed our model for conducting evaluation studies and the complete methodologies of these studies, the questions that will be answered during the implementation, and the scientific contribution and policy implication of these questions and sub-studies. Because other countries have started or will start implementing ICD-11 soon, they can use our protocol to tailor their pilot implementations concerning their circumstances and local considerations.","source":"DOAJ","year":2021,"language":"","subjects":["Computer applications to medicine. Medical informatics"],"doi":"10.1016/j.imu.2021.100668","url":"http://www.sciencedirect.com/science/article/pii/S2352914821001532","is_open_access":true,"published_at":"","score":65},{"id":"doaj_10.2196/23029","title":"A Brief Intervention to Increase Uptake and Adherence of an Internet-Based Program for Depression and Anxiety (Enhancing Engagement With Psychosocial Interventions): Randomized Controlled Trial","authors":[{"name":"Philip J Batterham"},{"name":"Alison L Calear"},{"name":"Matthew Sunderland"},{"name":"Frances Kay-Lambkin"},{"name":"Louise M Farrer"},{"name":"Helen Christensen"},{"name":"Amelia Gulliver"}],"abstract":"\n          \n            BackgroundPsychosocial, self-guided, internet-based programs are effective in treating depression and anxiety. However, the community uptake of these programs is poor. Recent approaches to increasing engagement (defined as both uptake and adherence) in internet-based programs include brief engagement facilitation interventions (EFIs). However, these programs require evaluation to assess their efficacy.\n            ObjectiveThe aims of this hybrid implementation effectiveness trial are to examine the effects of a brief internet-based EFI presented before an internet-based cognitive behavioral therapy self-help program (myCompass 2) in improving engagement (uptake and adherence) with that program (primary aim), assess the relative efficacy of the myCompass 2 program, and determine whether greater engagement was associated with improved efficacy (greater reduction in depression or anxiety symptoms) relative to the control (secondary aim).\n            MethodsA 3-arm randomized controlled trial (N=849; recruited via social media) assessed the independent efficacy of the EFI and myCompass 2. The myCompass 2 program was delivered with or without the EFI; both conditions were compared with an attention control condition. The EFI comprised brief (5 minutes), tailored audio-visual content on a series of click-through linear webpages.\n            ResultsUptake was high in all groups; 82.8% (703/849) of participants clicked through the intervention following the pretest survey. However, the difference in uptake between the EFI + myCompass 2 condition (234/280, 83.6%) and the myCompass 2 alone condition (222/285, 77.9%) was not significant (n=565; χ21=29.2; P=.09). In addition, there was no significant difference in the proportion of participants who started any number of modules (1-14 modules) versus those who started none between the EFI + myCompass 2 (214/565, 37.9%) and the myCompass 2 alone (210/565, 37.2%) conditions (n=565; χ21\u003c0.1; P=.87). Finally, there was no significant difference between the EFI + myCompass 2 and the myCompass 2 alone conditions in the number of modules started (U=39366.50; z=−0.32; P=.75) or completed (U=39494.0; z=−0.29; P=.77). The myCompass 2 program was not found to be efficacious over time for symptoms of depression (F4,349.97=1.16; P=.33) or anxiety (F4,445.99=0.12; P=.98). However, planned contrasts suggested that myCompass 2 may have been effective for participants with elevated generalized anxiety disorder symptoms (F4,332.80=3.50; P=.01).\n            ConclusionsThis brief internet-based EFI did not increase the uptake of or adherence to an existing internet-based program for depression and anxiety. Individuals’ motivation to initiate and complete internet-based self-guided interventions is complex and remains a significant challenge for self-guided interventions.\n            Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12618001565235; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375839","source":"DOAJ","year":2021,"language":"","subjects":["Computer applications to medicine. Medical informatics","Public aspects of medicine"],"doi":"10.2196/23029","url":"https://www.jmir.org/2021/7/e23029","is_open_access":true,"published_at":"","score":65},{"id":"ss_74c08c97fe4620b9ae983bce488a2b7a4233d8f6","title":"MEDAS: an open-source platform as a service to help break the walls between medicine and informatics","authors":[{"name":"Liang Zhang"},{"name":"Johann Li"},{"name":"Ping Li"},{"name":"Xiaoyuan Lu"},{"name":"Peiyi Shen"},{"name":"Guangming Zhu"},{"name":"Syed Afaq Ali Shah"},{"name":"Bennamoun"},{"name":"Kun Qian"},{"name":"Björn Schuller"}],"abstract":"In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields, such as computer vision and healthcare. Particularly, DL is experiencing an increasing development in advanced medical image analysis applications in terms of segmentation, classification, detection, and other tasks. On the one hand, tremendous needs that leverage DL’s power for medical image analysis arise from the research community of a medical, clinical, and informatics background to share their knowledge, skills, and experience jointly. On the other hand, barriers between disciplines are on the road for them, often hampering a full and efficient collaboration. To this end, we propose our novel open-source platform, i.e., MEDAS–the MEDical open-source platform As Service. To the best of our knowledge, MEDAS is the first open-source platform providing collaborative and interactive services for researchers from a medical background using DL-related toolkits easily and for scientists or engineers from informatics modeling faster. Based on tools and utilities from the idea of RINV (Rapid Implementation aNd Verification), our proposed platform implements tools in pre-processing, post-processing, augmentation, visualization, and other phases needed in medical image analysis. Five tasks, concerning lung, liver, brain, chest, and pathology, are validated and demonstrated to be efficiently realizable by using MEDAS. MEDAS is available at http://medas.bnc.org.cn/.","source":"Semantic Scholar","year":2020,"language":"en","subjects":["Computer Science","Medicine","Engineering"],"doi":"10.1007/s00521-021-06750-9","url":"https://www.semanticscholar.org/paper/74c08c97fe4620b9ae983bce488a2b7a4233d8f6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-021-06750-9.pdf","is_open_access":true,"citations":7,"published_at":"","score":64.21000000000001},{"id":"doaj_Missing+Values+in+PIMA+Diabetes+Dataset+and+Not+Considering+It+in+an+Article+Accepted+in+that+Journal","title":"Missing Values in PIMA Diabetes Dataset and Not Considering It in an Article Accepted in that Journal","authors":[{"name":"Fatemeh Ahouz"},{"name":"Amin Golabpour"}],"abstract":"The article is a letter to the editor, so it has no abstract.","source":"DOAJ","year":2020,"language":"","subjects":["Computer applications to medicine. Medical informatics","Medical technology"],"url":"http://jhbmi.ir/article-1-435-en.html","is_open_access":true,"published_at":"","score":64}],"total":12348531,"page":1,"page_size":20,"sources":["DOAJ","CrossRef","Semantic Scholar"],"query":"Computer applications to medicine. Medical informatics"}