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Appendix 1 Self-medication study questionnaire
<p>Self-medication study questionnaire</p>
Perception of Artificial Intelligence Among Japanese Medical Technologists and the Significance of Seminars on Emerging Technologies: An Online Survey of the Nara Association of Medical Technologists
<p><span>This study aimed to investigate Japanese medical technologists' perceptions of AI in laboratory medicine and evaluate the significance of seminars on emerging technologies. An online survey was conducted before and after a seminar titled " Artificial Intelligence and Clinical Laboratory: A Tale of the Encounter between Deep Tech and Medical Technicians" held on August 19, 2023. Responses were analyzed using Fisher's exact test, and free-text responses were analyzed using a co-occurrence network. Of the 278 pre-seminar respondents, 62.9% had positive attitudes towards AI. The AI-positive group showed significantly higher usage of AI-related web services (p<0.01) and more proactive information-seeking behavior (p<0.05) compared to the non-positive group. Post-seminar, support for "AI results must always be verified by medical technologists" significantly decreased (p<0.05). Additionally, 63.9% of the 169 participants expressed intent to actively gather information in the future. Free-text responses revealed expectations for work improvement, quality enhancement, and technological advancement, alongside concerns about AI implementation. The results suggest that while many respondents view AI positively, their expectations are predicated on maintaining traditional roles. The seminar appeared to catalyze information-seeking behavior. This study revealed Japanese medical technologists' positive perceptions of AI and concluded that seminars including explanations of emerging technologies and discussions are valuable. These findings may contribute to discussions on AI implementation in laboratory medicine and the design of continuing education programs for medical technologists.</span></p>
Critical Gaps in Medical Research Reporting by Online News Media: Dataset
<p>Dataset for <strong>Critical Gaps in Medical Research Reporting by Online News Media</strong></p>
ANALYSING THE MULTIFACETED DETERMINANTS OF EXORBITANT MEDICAL EXPENSES AMONG SENIOR CITIZENS IN MALAYSIA
Open the record for dataset details and reuse information.
Supplementary material:medication usage of Participants with in US Adults with Metabolic Syndrome
<p>Supplementary Figure 1 The Kaplan-Meier survival curves of Cholesterol medicine and all-cause mortality in metabolic syndrome participants</p> <p>Supplementary Figure 2 The Kaplan-Meier survival curves of hypertension medicine and all-cause mortality in metabolic syndrome participants</p> <p>Supplementary Table 1 The medication usage of Participants with Metabolic Syndrome Classified by CYSC</p> <p>Supplementary Table 2 Baseline Characteristics of Participants with metabolic syndrome classified by mortality</p>
Risk-generating conditions in medication administration during the COVID-19 pandemic: a cross-sectional study
<h2><span>This is a database collected in two periods: pre and post COVID-19 pandemic, which was used to construct Juliane Silveira Lobo Lage's master's thesis in nursing, supervised by Dr Flávia Giron Camerini.</span></h2>
Research integrity in instructions for authors in Japanese medical journals using ICMJE recommendations: A descriptive literature study
<p>The goals of this project were to compare research integrity content in Instructions for Authors in ICMJE member journals with those in the English- and Japanese-language journals of the Japanese Association of Medical Sciences (JAMS).</p> <p>Therefore, this dataset contents include the following three categories: 1) journals' background information, 2) description numbers of research integrity topics, and 3) journal numbers that required ICMJE description forms in ICMJE member journals, English- and Japanese-language journals.</p>
A Big Data Analysis Algorithm for Massive Sensor Medical Images
<p><span>The smart sensor based big data analysis recommendation system has significant privacy and security concerns when it comes to using sensor medical images for suggestions and monitoring. The danger of security breaches and unauthorized access which might lead to identity theft and privacy violations increases when sending and storing sensitive medical data on the cloud. Insufficient or erroneous patient data can lead to poor treatment decisions, misdiagnoses, and unreliable recommendations. By creating an anomaly detection system based on machine learning specifically for medical image and providing timely treatments and notifications, our effort will improve patient care and well-being. We infer the feature extraction, feature selection, attack detection, and data collection data processing procedures in order to anticipate the anomaly in patient data. We transfer the data, take care of any missing values, and sanitize it using the data pre-processing mechanism. We employed the RFE and DPCA algorithms for feature selection and extraction, respectively. In addition, we applied the AGRNN approach to identify abnormalities. Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and RMSE are some of the metrics used to evaluate the proposed task.</span></p>
Multi-modality medical image dataset for medical image processing in Python lesson
<p>This dataset contains a collection of medical imaging files for use in the <a href="https://github.com/esciencecenter-digital-skills/medical-image-processing">"Medical Image Processing with Python" lesson</a>, developed by the <a href="https://www.esciencecenter.nl/">Netherlands eScience Center</a>. </p> <p>The dataset includes:</p> <ol> <li>SimpleITK compatible files: MRI T1 and CT scans (<em>training_001_mr_T1.mha, training_001_ct.mha</em>), digital X-ray (<em>digital_xray.dcm</em> in DICOM format), neuroimaging data (<em>A1_grayT1.nrrd, A1_grayT2.nrrd</em>). Data have been downloaded from <a href="https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/00_Setup.html">here</a>. </li> <li>MRI data: a T2-weighted image (<em>OBJECT_phantom_T2W_TSE_Cor_14_1.nii</em> in NIfTI-1 format). Data have been downloaded from <a href="../records/6467772">here</a>. </li> <li>Example images for the machine learning lesson: chest X-rays (<em>rotatechest.png, other_op.png</em>), cardiomegaly example (<em>cardiomegaly_cc0.png</em>).</li> <li>Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from <a href="https://doi.org/10.1109/TNS.1974.6499235">the Schepp Logan phantom</a> and from the <a href="https://fastmri.med.nyu.edu/">NYU FastMRI dataset</a></li> <li>Data for the anonymization exercises: ultrasound (<em>identifiable_us.jpg</em>) dowloaded from <a href="https://www.flickr.com/photos/jcarter/2461223727">here</a>, and DICOM data (<em>our_sample_dicom.dcm</em>) shared for this course specifically by a colleague</li> <li>Histopathology data: histopathology slide images from <a href="https://openslide.org/">openslide</a> library samples in the freely distributable test data </li> </ol> <p>These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.</p>
Exploiting hierarchy in medical concept embedding
<p>Objective</p> <p>To construct and publicly release a set of medical concept embeddings for codes following the ICD-10 coding standard which explicitly incorporate hierarchical information from medical codes into the embedding formulation.</p> <p>Materials and Methods</p> <p>We trained concept embeddings using several new extensions to the Word2Vec algorithm using a dataset of approximately 600,000 patients from a major integrated healthcare organization in the Mid-Atlantic US. Our concept embeddings included additional entities to account for the medical categories assigned to codes by the Clinical Classification Software Revised (CCSR) dataset. We compare these results to sets of publicly-released pretrained embeddings and alternative training methodologies.</p> <p>Results</p> <p>We found that Word2Vec models which included hierarchical data outperformed ordinary Word2Vec alternatives on tasks which compared naïve clusters to canonical ones provided by CCSR. Our Skip-Gram model with both codes and categories achieved 61.4% Normalized Mutual Information with canonical labels in comparison to 57.5% with traditional Skip-Gram. In models operating on two different outcomes we found that including hierarchical embedding data improved classification performance 96.2% of the time. When controlling for all other variables, we found that co-training embeddings improved classification performance 66.7% of the time. We found that all models outperformed our competitive benchmarks.</p> <p>Discussion</p> <p>We found significant evidence that our proposed algorithms can express the hierarchical structure of medical codes more fully than ordinary Word2Vec models, and that this improvement carries forward into classification tasks. As part of this publication, we have released several sets of pretrained medical concept embeddings using the ICD-10 standard which significantly outperform other well-known pretrained vectors on our tested outcomes.</p>
Impact of Motivation, Learning Strategy and Intelligence Quotient on Medical Students Grades during first semester
<p>Rekapitulasi data MSLQ dan IQ </p>
Medical Equipment Image Data set
<p>This data set aims to realise the medical equipment recognition to aid visual search through three deep learning models. The data set contains ten medical equipment classes: commodes, wheelchairs, walking frames, blood pressure monitors, breast pumps, thermometers, rippled mattresses, oximeters, crutches, and therapeutic ultrasound machines. We collected from online resources around 220 images for each medical equipment class. Each image class in the test set has around 40 images.</p> <p>Refer to the paper <a href="http://doi.org/10.3233/JIFS-212786">here</a> or in research gate (preprint).</p>
Impact of COVID-19 on international medical education
<p>Purpose: To assess the impact of the coronavirus disease 2019 (COVID-19) pandemic on the current study methods and future plans of medical students compared to those in the pre-pandemic period.</p> <p>Materials and methods: Second-grade medical students reported their academic experiences, study methods, and future career plans before (between 2016 and 2019) and during the pandemic (2020) using a questionnaire-based survey at Hokkaido University, Japan (n = 534).</p> <p>Results: From 2016 to 2019, we found an increasing trend for participation in short-term international exchange programs, taking the United States Medical Licensing Examination (USMLE), clinical training, and undertaking research abroad among the students. However, these percentages significantly declined (35.5%) during the COVID-19 pandemic in 2020 for all the assessed future plans, including short-term exchange programs (-27.9%), taking USMLE (-19.8%), clinical training (-24.5%), and undertaking research abroad (-13.2%) compared to 2019, wherein 67.9% of the students wished to have at least one of these four above-mentioned academic activities.</p> <p>Conclusions: The COVID-19 pandemic adversely and significantly influenced our medical students plans to go abroad for clinical and research training. Future studies are warranted to assess the long-term influence of this pandemic on the career planning of medical students.</p>
RIGA+ Dataset for Unsupervised Domain Adaptation in Medical Image Segmentation
<p>Different from the previous combined multi-domain dataset for unsupervised domain adaptation (UDA) in medical image segmentation, this multi-domain fundus image dataset contains annotations made by the same group of ophthalmologists. Hence the annotator bias among different datasets can be mitigated. Therefore, this dataset can provide a relatively fair benchmark for evaluating UDA methods in fundus image segmentation.</p> <p>This dataset is based on the RIGA[1] dataset and MESSIDOR[2] dataset. We appreciate their efforts devoted by the authors of [1] and [2].</p> <p>The six duplicated cases in the RIGA dataset are filtered out according to the <a href="https://www.adcis.net/en/third-party/messidor/">Errata</a>. We also remove the duplicated cases that exist in both the RIGA dataset and the MESSIDOR dataset by hash value matching.</p> <table align="center"> <caption>Details of the RIGA+ dataset</caption> <thead> <tr> <th scope="row">Domain</th> <th scope="col">Dataset</th> <th scope="col"> <p>Labeled Samples</p> <p>(Train+Test)</p> </th> <th scope="col"> <p>Unlabeled</p> <p>Samples</p> </th> </tr> </thead> <tbody> <tr> <th scope="row">Source</th> <td>BinRushed</td> <td>195 (195+0)</td> <td>0</td> </tr> <tr> <th scope="row">Source</th> <td>Magrabia</td> <td>95 (95+0)</td> <td>0</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE1</td> <td>173 (138+35)</td> <td>227</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE2</td> <td>148 (118+30)</td> <td>238</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE3</td> <td>133 (106+27)</td> <td>252</td> </tr> </tbody> </table> <p>[1] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. International Society for Optics and Photonics, 2018, 10579: 105790B.</p> <p>[2] Decencière E, Zhang X, Cazuguel G, et al. Feedback on a publicly distributed image database: the Messidor database[J]. Image Analysis & Stereology, 2014, 33(3): 231-234.</p> <p>If you find this dataset useful for your research, please consider citing the paper as follows:</p> <pre><code>@inproceedings{hu2022domain, title={Domain Specific Convolution and High Frequency Reconstruction based Unsupervised Domain Adaptation for Medical Image Segmentation}, author={Shishuai Hu and Zehui Liao and Yong Xia}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, year={2022}, organization={Springer} }</code></pre> <p> </p>
Data set supplementing "Determinants of Laypersons' Trust in Medical Decision Aids: Randomized Controlled Trial"
<p>This is the de-identified data set used to conduct the analyses in the preprint submitted to JMIR Human Factors under the title "Determinants of Laypersons’ Trust in Medical Decision Aids: Randomized Controlled Trial" (<a href="https://doi.org/10.2196/35219">https://doi.org/10.2196/35219</a>).</p> <p>This dataset contains 494 respondents' appraisals of a fictitious case vignette. They received support from a decision aid (that always disagreed with participants' first appraisal) showing a mock symptom checker logo, a decision aid framed as anthropomorphic or as an AI. Their second appraisal - taking into account the symptom checker advice - was collected again. </p> <p>Additionally, the data contains participants'</p> <ul> <li>age</li> <li>gender</li> <li>education</li> <li>medical training</li> <li>propensity to trust</li> <li>eHealth Literacy</li> <li>certainty in their appraisals</li> <li>trust in the decision aid</li> </ul>
Radiomics in medical imaging: pitfalls and challenges in clinical management
<p>We uploaded theimages of the manuscript "Radiomics in medical imaging: pitfalls and challenges in clinical management"</p>
Positive effects of e-learning tools in problem-based medical courses
<p>Data, generated or analysed during the study “Positive effects of e-learning tools in problem-based medical courses” at the University Witten/Herdecke, Germany</p>
Replication package for: Hours Constraints, Occupational Choice, and Gender: Evidence from Medical Residents
<p>This is the replication package for:</p> <p>Wasserman, Melanie (forthcoming) "Hours Constraints, Occupational Choice, and Gender: Evidence from Medical Residents" Review of Economic Studies</p> <p>It contains:</p> <ul> <li>README file</li> <li>do files to reproduce the main tables and figures</li> <li>data files for all reproducible analyses</li> <li>all reproducible results</li> </ul>
Medical data formatting to improve physician interpretation speed in the military healthcare system
<p><strong>Objective</strong>: The purpose of this project was to improve the ease and speed of physician comprehension when interpreting daily laboratory data for patients admitted within the Military Healthcare System (MHS).</p> <p><strong>Materials and Methods</strong>: A JavaScript program was created to convert the laboratory data obtained via the outpatient electronic medical record (EMR) into a "fishbone diagram" format that is familiar to most physicians. Using a balanced crossover design, 35 internal medicine trainees and staff were asked to complete timed comprehension tests for laboratory data sets formatted in the outpatient EMR's format and in fishbone diagram format. The number of responses per second and error rate per response were measured for each format. Participants were asked to rate relative ease of use for each format and indicate which format they preferred.</p> <p><strong>Results</strong>: Comprehension speed increased 37% (6.28 seconds per interpretation) with the fishbone diagram format with no observed increase in errors. Using a Likert scale of 1 to 5 (1 being hard, 5 easy), participants indicated the new format was easier to use (4.14 for fishbone vs 2.14 for table) with 89% expressing a preference for the new format. </p> <p><strong>Discussion</strong>: The publically available web application that converts tabular lab data to fishbone diagram format is currently used 10,000-12,000 times per month across the MHS, delivering significant benefit to the enterprise in terms of time saved and improved physician experience.</p> <p><strong>Conclusions</strong>: This study supports the use of fishbone diagram formatting for laboratory data for inpatients within the MHS.</p>
Research on The Fit of Head-face Medical Protective Equipment
<p>The database is from the research on the fit of head-face medical protective equipment. The Comfort Score is from the user comfort test and the Head-face Dimensions is from the 3D scanning headforms measurement.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.