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Best practices for multimodal clinical data management and integration: An atopic dermatitis research case

ALLERGOLOGY INTERNATIONAL 2024年第2期73卷 255-263页

作者： Ohta, Tazro Hananoe, Ayaka Fukushima-Nomura, Ayano Ashizaki, Koichi Sekita, Aiko Seita, Jun Kawakami, Eiryo Sakurada, Kazuhiro Amagai, Masayuki Koseki, Haruhiko Kawasaki, Hiroshi RIKEN Adv Data Sci Project Med Data Math Reasoning Team RIKEN Informat R&D & Strategy Headquarters Yokohama Kanagawa Japan Chiba Univ Inst Adv Acad Res Chiba Japan Chiba Univ Grad Sch Med Dept Artificial Intelligence Med Chiba Japan RIKEN Ctr Integrat Med Sci Lab Dev Genet Yokosuka Kanagawa Japan Keio Univ Sch Med Dept Dermatol Tokyo Japan RIKEN Adv Data Sci Project RIKEN Informat R&D & Strategy Headquarters Yokohama Kanagawa Japan RIKEN Ctr Integrat Med Sci Lab Integrat Genom Yokohama Kanagawa Japan RIKEN Adv Data Sci Project Med Data Math Reasoning Team RIKEN Informat R&D & Strategy Headquarters Yokohama Japan RIKEN Infrastruct Res & Dev Div Med Data Sharing Unit RIKEN Informat R&D& Strategy Headquarters Saitama Japan Keio Univ Dept Extended Intelligence Med Ishii Ishibashi Lab Sch Med Tokyo Japan RIKEN Lab Skin Homeostasis Ctr Integrat Med Sci Yokohama Kanagawa Japan RIKEN RIKEN Ctr Integrat Med Sci 1-7-22 Suehiro choTsurumi ku Yokohama Kanagawa 2300045 Japan

Background: In clinical research on multifactorial diseases such as atopic dermatitis, data -driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multicenter, causes dif ficulties in data integration and management, which limits productivity in clinical data science. Methods: We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis. Results: MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA. Conclusions: The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data -driven medical research. (c) 2023 Japanese Society of Allergology. Published by Elsevier B.V. This is an open access article under the CC BY -NC -ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Data management Atopic dermatitis medical informatics applications medical informatics computing Precision medicine

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Internet Search for Illicit Drugs in Italy: Infodemiological Analysis of Six Years of Research

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GLOBAL HEALTH EPIDEMIOLOGY AND GENOMICS 2025年第1期2025卷

作者： Di Martino, Giuseppe Gianfredi, Vincenza Catalini, Alessandro Fonzo, Marco Menegon, Tiziana Pennisi, Flavia Russo, Gianluca Stacchini, Lorenzo Santangelo, Omar Enzo Cedrone, Fabrizio G Annunzio Univ Chieti Pescara Dept Med & Ageing Sci I-66100 Chieti Italy ASL Pescara Unit Hyg Epidemiol & Prevent Med I-65100 Pescara Italy Univ Milan Dept Biomed Sci Hlth I-20133 Milan Italy Local Hlth Author Macerata Food Safety & Nutr Unit I-62100 Macerata Italy Univ Padua Dept Cardiac Thorac Vasc Sci & Publ Hlth Hyg & Publ Hlth Unit I-35131 Padua Italy Azienda ULSS Marca Trevigiana Hlth Promot Unit I-31100 Treviso Italy Univ Vite Salute San Raffaele Fac Med Milan Italy Osped St Spirito UOC Oncol Med I-65100 Pescara Italy Univ Florence Dept Hlth Sci I-50134 Florence Italy ASST Lodi Reg Hlth Care & Social Agcy Lodi I-26900 Lodi Italy Univ Milan I-20122 Milan Italy Local Hlth Author Pescara Hosp Management I-65100 Pescara Italy

This study explores the potential of Internet search data from Google Trends and Wikipedia as innovative tools for monitoring public interest and behavior related to illicit drug information. Employing a cross-sectional study design, this research investigates the correlations between search patterns on these platforms and provides insights into public health monitoring. Our analysis indicates significant correlations between searches for various illicit drugs, with correlation coefficients ranging from 0.71 to 0.83, suggesting that these searches can not only reflect but also potentially predict public interest and shifts in drug use patterns. This approach highlights specific periods and demographics showing increased interest in drugs, thereby allowing for targeted public health interventions. The study found that searches related to drugs like cannabis, cocaine, and opioids are not only frequent but also show a tendency to be conducted in tandem, possibly due to synergistic effects. Moreover, the data underscore the importance of providing timely and accurate information to the public through authoritative online sources. With the vast reach of the Internet, ensuring that institutional websites and social networks offer correct information becomes crucial to prevent the spread of misinformation. This study supports Internet-based data as a complementary tool to traditional drug monitoring systems, which can enhance public health response capabilities by providing real-time, nonintrusive insights. This research advocates for integrating big data analytics into public health strategy, emphasizing the role of Internet searches in shaping public health policies and practices. The findings suggest that properly leveraged, Internet search data could greatly assist in the precision and effectiveness of public health interventions and preparedness.

关键词： digital epidemiology dope drug epidemiology Google infodemiology infoveillance Italy medical informatics computing narcotic Wikipedia

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Towards Structuring Clinical Texts: Joint Entity and Relation Extraction from Japanese Case Report Corpus

Towards Structuring Clinical Texts: Joint Entity and Relatio...

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19th World Congress on medical and Health informatics (MEDINFO)

作者： Shibata, Daisaku Shinohara, Emiko Shimamoto, Kiminori Kawazoe, Yoshimasa Univ Tokyo Grad Sch Med Tokyo Japan

ISBN: (纸本)9781643684574;9781643684567

Important pieces of information related to patient symptoms and diagnosis are often written in free-text form in clinical texts. To utilize these texts, information extraction using natural language processing is required. This study evaluated the performance of named entity recognition (NER) and relation extraction (RE) using machine-learning methods. The Japanese case report corpus was used for this study, which had 113 types of entities and 36 types of relations that were manually annotated. There were 183 cases comprising 2,194 sentences after preprocessing. In addition, a machine learning model based on bidirectional encoder representations from transformers was used. The results revealed that the maximum micro-averaged F1 scores of NER and RE were 0.912 and 0.759, respectively. The results of this study are comparable to those of previous studies. Hence, these results could be of substantial baseline accuracy.

关键词： Natural language processing machine learning medical informatics computing

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Computational fluid dynamics modeling approaches to assess lower urinary tract hydraulic dynamics in posterior urethral valve before and after endoscopic valve ablation: a pilot study

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WORLD JOURNAL OF UROLOGY 2022年第2期40卷 505-511页

作者： Chen, Hong-Song Liu, Xing Zhang, Zhi-Cheng Ye, Zi-Han Lin, Tao He, Da-Wei Wei, Guang-Hui Chongqing Med Univ Dept Urol Childrens Hosp Chongqing 400014 Peoples R China Minist Educ Key Lab Child Dev & Disorders Chongqing 400014 Peoples R China Natl Clin Res Ctr Child Hlth & Disorders Chongqing 400014 Peoples R China China Int Sci & Technol Cooperat Base Child Dev & Chongqing 400014 Peoples R China Chongqing Med Univ Childrens Hosp Chongqing 400014 Peoples R China Chongqing Key Lab Pediat Chongqing 400014 Peoples R China Chongqing Key Lab Children Urogenital Dev & Tissu Chongqing 400014 Peoples R China

Purpose Computational fluid dynamics (CFD) has been used successfully in cardiovascular system research to analyze the physiological processes inside vessels. We evaluated the hydraulic information of urine through the lower urinary tract in a patient with posterior urethral valve (PUV) before and after valve ablation by CFD. Methods A set of models of the lower urinary tract were developed based on geometrical data obtained by cystoscopy and voiding cystourethrography. Simulated assumptions and conditions were applied according to prior studies and urodynamic results. We used Fluent CFD 19.0 (Ansys Inc., USA) to compute the velocity and pressure of the fluid regions. The simplification of Bernoulli's formula was applied afterward to calculate the hydraulic energy of different positions. Results The urine flow rates of the NORMALst, the PUVst, and the POSTst at 5000 Pa were 18.08 ml/s, 11.14 ml/s, and 12.16 ml/s, respectively. Precipitous pressure change was observed around the valve in the PUVst, and the abnormal change was concentrated in the dilated urethra in the POSTst. Major energy dissipations were generated around the valve and the dilated urethra in the PUVst. The energy loss that occurred in the dilated urethra did not improve after the operation. Conclusions Our findings are probably indicative of the hydrodynamics changes in the dilated urethra in PUV and need to be confirmed through more improved CFD models in the future. CFD may revolutionize pediatric urologists' perception in the management of urinary disease.

关键词： Urethral obstruction Pediatrics medical informatics computing Patient-specific modeling Hydrodynamics

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Development of population-level colon cancer pathway concordance measures and association with survival

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INTERNATIONAL JOURNAL OF CANCER 2022年第12期150卷 2046-2057页

作者： Ieraci, Luciano Eberg, Maria Forster, Katharina Murray, Paula M. Borg, Emmett Habbous, Steven Esensoy, Ali Vahit Kennedy, Erin Holloway, Claire M. B. Ontario Hlth Canc Care Ontario Data & Decis Sci 500-525 Univ Ave Toronto ON M5G 2L3 Canada Univ Toronto Inst Hlth Policy Management & Evaluat Toronto ON Canada IQVIA 16720 Rte Transcanadienne Kirkland PQ Canada Ontario Hlth Canc Care Ontario Dis Pathway Management Toronto ON Canada Childrens Hosp Los Angeles Los Angeles CA 90027 USA Hoffmann La Roche Mississauga ON Canada Ontario Hlth Canc Care Ontario Qual Measurement & Evaluat Toronto ON Canada Klick Hlth Klick Labs Toronto ON Canada Univ Toronto Dept Surg Toronto ON Canada

Clinical cancer pathways help standardize healthcare delivery to optimize patient outcomes and health system costs. However, population-level measurement of concordance between standardized pathways and actual care received is lacking. Two measures of pathway concordance were developed for a simplified colon cancer pathway map for Stage II-III colon cancer patients in Ontario, Canada: a cumulative count of concordant events (CCCE) and the Levenshtein algorithm. Associations of concordance with patient survival were estimated using Cox proportional hazards models adjusted for patient characteristics and time-dependent cancer-related activities. Models were compared and the impact of including concordance scores was quantified using the likelihood ratio chi-squared test. The ability of the measures to discriminate between survivors and decedents was compared using the C-index. Normalized concordance scores were significantly associated with patient survival in models for cancer stage-a 10% increase in concordance for Stage II patients resulted in a CCCE score adjusted hazard ratio (aHR) of death of 0.93, 95% CI 0.88-0.98 and a Levenshtein score aHR of 0.64, 95% CI 0.60-0.67. A similar relationship was found for Stage III patients-a 10% increase in concordance resulted in a CCCE aHR of 0.85, 95% CI 0.81-0.88 and a Levenshtein aHR of 0.78, 95% CI, 0.74-0.81. Pathway concordance can be used as a tool for health systems to monitor deviations from established clinical pathways. The Levenshtein score better characterized differences between actual care and clinical pathways in a population, was more strongly associated with survival and demonstrated better patient discrimination.

关键词： colonic neoplasms guideline adherence medical informatics computing pathway concordance survival rate

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Impact on online research on celebrities' uncommon diseases: the curious case of Justin Bieber and Ramsay Hunt syndrome

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JOURNAL OF PUBLIC HEALTH-HEIDELBERG 2024年第9期32卷 1707-1715页

作者： Santangelo, Omar Enzo Gianfredi, Vincenza Provenzano, Sandro ASST Lodi Reg Hlth Care & Social Agcy Lodi piazza Ospitale 10 I-26900 Lodi Italy Univ Milan Dept Biomed Sci Hlth Via Pascal 36 I-20133 Milan Italy ASP Trapani Local Hlth Unit Trapani I-91100 Trapani Italy

AimWe investigated how to use Internet user searches to gauge the impact of a celebrity illness on global public *** study design is cross-sectional. Data on Internet searches were obtained from Google Trends (GT) for the period between 2017-2022 using the search words "Ramsay Hunt syndrome" (RHS), "Ramsay Hunt syndrome type 2," "Herpes zoster," and "Justin Bieber." The frequency of specific page views for "Ramsay Hunt syndrome," "Ramsay Hunt syndrome type 1," Ramsay Hunt syndrome type 2," Ramsay Hunt syndrome type 3," "Herpes zoster," and "Justin Bieber" were collected via a Wikipedia analysis tool that shows the number of times a specific page is viewed. Statistical analyses were performed using the Pearson (r) and Spearman's rank correlation coefficient (rho).ResultsGT data, in 2022, show a strong correlation for Justin Bieber and RHS or RHS type 2 (r = 0.75);similarly, Wikipedia data show a strong correlation for Justin Bieber and the others explored terms (r > 0.75). Furthermore, the correlation was strong between GT and Wikipedia for RHS (rho = 0.89) and RHS type 2 (rho = 0.88).ConclusionsThe peak search times for the GT and Wikipedia pages were during the same period. Useful new tools and analyses of Internet traffic data may be effective in assessing the impact of announced celebrity uncommon illnesses on global public interest.

关键词： Ramsay Hunt syndrome Herpes zoster Google trends medical informatics computing Wikipedia Global public interest Celebrities

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SIMSAMU - A French medical dispatch dialog open dataset

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2025年 268卷 108857-108857页

作者： Nun, Aime Birot, Olivier Guibon, Gael Lapostolle, Frederic Lerner, Ivan Sorbonne Univ Univ Paris Cite Ctr Rech Cordeliers INSERM F-75006 Paris France Inria Paris HeKA F-75012 Paris France Georges Pompidou European Hosp AP HP Dept Med Informat Paris France Univ Lorraine LORIA CNRS F-54600 Villers Les Nancy France Avicenne Hosp AP HP SAMU 93 UF Rech Enseignement Qual Bobigny France Univ Paris 13 INSERM Unit 942 Sorbonne Paris Cite Bobigny France Univ Sorbonne Paris Nord CNRS Lab Informat Paris Nord F-93430 Villetaneuse France

Background: Dispatch Services (DS) are essential to Emergency medical Services (EMS). Dispatchers enable patients to access medical assistance in emergencies, anytime and anywhere, within limited time and resources. AIbased decision-support tools hold great promise for dispatchers. Developing these tools requires medical fieldspecific data. medical dispatch dialogue is unique: it is a brief phone exchange in an emergency, within a limited time frame, without a physical examination. Objective: Our main objective was to (i) create an open French dataset of medical dispatch dialogues. Our secondary objectives were to (ii) develop a detailed medical dispatch scheme from this dataset using an unsupervised method, and (iii) provide a baseline evaluation of diarization and speech recognition models for this domain in French. Methods: From 2022 to 2023, emergency medicine junior doctors simulated real-life medical dispatch calls. These calls were recorded and transcribed to form the SIMSAMU corpus. We developed a dispatch scheme based on (i) recording analysis, (ii) data-driven utterance typology, and (iii) domain expertise. Utterance typology was derived via hierarchical clustering of representations learned by finetuning BERT embeddings on SIMSAMU. Clusters were mapped to the Roter Interaction Analysis System (RIAS) and included in our dispatch scheme. SIMSAMU was used to train and evaluate state-of-the-art neural network models for diarization and speech recognition. Diarization used the PyaNet model, fine-tuned on the ESLO2 dataset. Speech recognition used a CTC model with pre-trained wav2vec 2.0 embedding, compared to the multilingual Whisper model. The CTCwav2vec model was further fine-tuned on SIMSAMU and evaluated by leave-one-speaker-out cross-validation. Results: The dataset consists of 61 audio recordings totaling 3 h 14 min. Four clusters were identified for callers and 3 for dispatchers. Two main dialogue phases were identified: interrogation and contractualiza

关键词： Communication medical dialogues medical informatics computing Cluster analysis Dialogue modeling medical emergencies dispatch

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Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging

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EUROPEAN RADIOLOGY 2021年第10期31卷 7865-7875页

作者： Tu, Shu-Ju Chen, Wei-Yuan Wu, Chen-Te Chang Gung Univ Dept Med Imaging & Radiol Sci Coll Med 259 Wen Hua First Rd Taoyuan 333 Taiwan Linkou Chang Gung Mem Hosp Dept Med Imaging & Intervent Taoyuan Taiwan

Objectives Quantum noise is a random process in X-ray-based imaging systems. We addressed and measured the uncertainty of radiomics features against this quantum noise in computed tomography (CT) images. Methods A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used. A solid tumor tissue removed from a male BALB/c mouse was included. We the placed phantom sets on the CT scanning table and repeated 20 acquisitions with identical imaging settings. Regions of interest were delineated for feature extraction. Statistical quantities-average, standard deviation, and percentage uncertainty-were calculated from these 20 repeated scans. Percentage uncertainty was used to measure and quantify feature stability against quantum noise. Twelve radiomics features were measured. Random noise was added to study the robustness of machine learning classifiers against feature uncertainty. Results We found the ranges of percentage uncertainties from homogeneous soft tissue phantoms, homogeneous bone phantoms, and solid tumor tissue to be 0.01-2138%, 0.02-15%, and 0.18-16%, respectively. Overall, it was found that the CT features ShortRunHighGrayLevelEmpha (SRHGE) (0.01-0.18%), ShortRunLowGrayLevelEmpha (SRLGE) (0.01-0.41%), LowGrayLevelRunEmpha (LGRE) (0.01-0.39%), and LongRunLowGrayLevelEmpha (LRLGE) (0.02-0.66%) were the most stable features against the inherent quantum noise. The most unstable features were cluster shade (1-2138%) and max probability (1-16%). The impact of random noise to the prediction accuracy by different machine learning classifiers was found to be between 0 and 12%. Conclusions Twelve features were used for uncertainty measurements. The upper and lower bounds of percentage uncertainties were determined. The quantum noise effect on machine learning classifiers is model dependent.

关键词： X-ray computed tomography Radiomics Uncertainty medical informatics computing Health care quality assurance

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Wikipedia searches and the epidemiology of infectious diseases: A systematic review

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DATA & KNOWLEDGE ENGINEERING 2022年 142卷

作者： Enzo Santangelo, Omar Gianfredi, Vincenza Provenzano, Sandro ASST Lodi Reg Hlth Care & Social Agcy Lodi Lodi Italy Univ Milan Dept Biomed Sci Hlth I-20132 Milan Italy Maastricht Univ CAPHRI Care & Publ Hlth Res Inst NL-6211 Maastricht Netherlands Azienda Osped Univ Policlin P Giaccone Palermo Italy Univ Milan Dept Biomed Sci Hlth Via Pascal 36 I-20132 Milan Italy

This review aims to collect, analyse and synthesize the available evidence that can be provided by Wikipedia for epidemiologic surveillance purposes. PRISMA guidelines were followed. PubMed/Medline and Scopus were consulted. Out of 238 retrieved articles, 16 articles were included in the systematic review. The most frequently assessed infectious disease was Influenza, followed by arboviruses and measles. Influenza studies show that Wikipedia could be consid-ered a scientifically valid surveillance system that fills the main gaps in existing traditional surveillance systems. As regards arboviruses, searches on the Web have positively mediated the relationship between epidemiological data and the number of Wikipedia page visualization. Regarding measles, studies showed a strong/moderate temporal correlation between infectious disease notification bulletins and Wikipedia search trends. Despite the type of infectious agents, three main aims can be detected: (i) understand the public's interest, (ii) explore the use of Wikipedia by organizations, and (iii) assess the accuracy of Wikipedia content. These new strategies for surveillance of infectious diseases should be implemented, to date they could be useful in supporting traditional surveillance.

关键词： Wikipedia Epidemiology Internet medical informatics computing Vaccine-preventable diseases medical informatics

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Comparison of Convolutional Neural Network Architectures and their Influence on Patient Classification Tasks Relating to Altered Mental Status

Comparison of Convolutional Neural Network Architectures and...

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IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Gagnon, Kevin Crawford, Tami L. Obeid, Jihad Univ South Carolina Comp Sci Columbia SC 29208 USA Med Univ South Carolina Biomed Informat Charleston SC 29425 USA

ISBN: (纸本)9781728162157

With the pervasiveness of Electronic Health Records in many hospital systems, the application of machine learning techniques to the field of health informatics has become much more feasible as large amounts of data become more accessible. In our experiment, we evaluated several different convolutional neural network architectures that are typically used in text classification tasks. We then tested those models based on 1,113 histories of present illness. (HPI) notes. This data was run over both sequential and multi-channel architectures, as well as a structure that implemented attention methods meant to focus the model on learning the influential data points within the text. We found that the multi-channel model performed the best with an accuracy of 92%, while the attention and sequential models performed worse with an accuracy of 90% and 89% respectively.

关键词： Machine Learning Deep Learning Bioinformatics Natural Language Processing medical informatics computing

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