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2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Rispoli, Michele Nobile, Marco S. Manzoni, Luca D’Onofrio, Alberto Confalonieri, Marco Salton, Francesco Confalonieri, Paola Ruaro, Barbara Gallese, Chiara Department of Mathematics Informatics and Geosciences University of Trieste Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy Pulmonology Unit University Hospital of Cattinara Trieste Italy Department of Medical Surgical and Health Sciences University of Trieste Italy Department of Law University of Turin Italy Bicocca Bioinformatics Biostatistics and Bioimaging research center Monza Italy

ISBN: (纸本)9798331508333

Artificial Intelligence, and Machine Learning systems in general, are becoming pervasive in our society, from the industry to the public administration. AI can often provide a very efficient means to support decision-making, but it can represent a danger for high-risk applications such as bio-medicine and healthcare. In particular, biased datasets might lead to inaccurate or discriminatory ML systems, undermining the accuracy of their predictions and putting patients’ health at risk. FanFAIR is a python tool that provides the community with a semiautomatic tool for datasets’ fairness assessment. FanFAIR is designed to integrate qualitative considerations – such as ethics, human rights assessment, and data protection – with quantitative indicators of dataset’s fairness, such as balance, the presence of invalid entries, or outliers. In this work, we extend FanFAIR to deal with categorical data, and introduce a new algorithm for outlier detection in the presence of missing values. We then provide a case study on the data collected from COVID patients admitted to pneumology departments in Italy. We show how the successive steps of data cleaning and variable selection improve the indicators provided by FanFAIR. This shows that data cleaning procedures are not only necessary to improve the performance of the machine learning algorithm using the data for learning, but are also a way to improve (a measure of) fairness. Hence, the proposed case study provides an example in which performance and fairness are not in contrast, like it is commonly believed to be, but they improve together. © 2025 IEEE.

关键词： Health risks

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In-Silico Analysis of the High-Risk Missense Variants in PTH1R Gene and Association with Primary Failure of Tooth Eruption (PFE)

In-Silico Analysis of the High-Risk Missense Variants in PTH...

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2023

作者： Ettaki, Imane Saih, Asmae Charoute, Hicham Baba, Hana Hamdi, Salsabil El Alloussi, Mustapha Barakat, Hamid Fellah, Hassan Wakrim, Lahcen Virology Unit Immunovirology Laboratory Institut Pasteur du Maroc Casablanca20360 Morocco Laboratory of Cellular and Molecular Pathology Research Team on Immunopathology of Infectious and Systemic Diseases Faculty of Medicine and Pharmacy Hassan II University of Casablanca Casablanca20000 Morocco Laboratory of Biology and Health URAC 34 Faculty of Sciences Ben M’Sik Hassan II University of Casablanca Casablanca Morocco Research Unit of Epidemiology Biostatistics and Bioinformatics Institut Pasteur du Maroc Casablanca Morocco Environmental Health Laboratory Institut Pasteur du Maroc Casablanca20360 Morocco International Faculty of Dental Medicine Campus UIR Technopolis Rocade Rabat Rabat Morocco Laboratoire de biologie moléculaire Institut Pasteur Casablanca Casablanca Morocco

ISBN: (纸本)9783031523847

Primary eruption failure (PFE) is a rare condition characterized by the cessation of tooth eruption posteriorly. Due to a mutation in the PTH1R gene, this dental phenotype is distinguished by a severe posterior open bite. The current study was used to investigate and evaluate the most deleterious nsSNPs in the dbSNP-extracted PTH1R gene using a variety of computational tools, including SIFT, PolyPhen-2, PROVEAN, Condel, Meat LR, Meat SVM, SWISS-MODEL, DUET, Yassara, ConSurf, and STRING. A total of 29 nsSNPs were found to be deleterious according to the ConSurf analysis. The majority of high-risk SNP amino acid positions (79.16%) were found in conserved regions. The Duet server determined that 16 of these variants decreased protein stability. Furthermore, hydrophobic and hydrogen interaction analyses and evaluation of structural effects of the deleterious nsSNPs on PTH1R by the YASARA program and Project HOPE server revealed significant changes between the wild-type and the 16 tested mutants. According to our results, 16 deleterious variants were identified as having an impact on the structure and probably function of the PTH1R protein involved in the PFE phenotype. These findings could contribute to the development of more efficient genetic tests for detecting mutations in the PTH1R gene. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Genes

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Investigating Fairness with FanFAIR: is Pre-Processing Useful Only for Performances?

Investigating Fairness with FanFAIR: is Pre-Processing Usefu...

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Computational Intelligence in Health and Medicine (CIHM), IEEE Symposium on

作者： Michele Rispoli Marco S. Nobile Luca Manzoni Alberto D'Onofrio Marco Confalonieri Francesco Salton Paola Confalonieri Barbara Ruaro Chiara Gallese Department of Mathematics Informatics and Geosciences University of Trieste Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy Bicocca Bioinformatics Biostatistics and Bioimaging research center Monza Italy Pulmonology Unit University Hospital of Cattinara Trieste Italy Department of Medical Surgical and Health Sciences University of Trieste Italy Department of Law University of Turin Italy

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

Artificial Intelligence, and Machine Learning systems in general, are becoming pervasive in our society, from the industry to the public administration. AI can often provide a very efficient means to support decision-making, but it can represent a danger for high-risk applications such as bio-medicine and healthcare. In particular, biased datasets might lead to inaccurate or discriminatory ML systems, undermining the accuracy of their predictions and putting patients' health at risk. FanFAIR is a python tool that provides the community with a semi-automatic tool for datasets' fairness assessment. FanFAIR is designed to integrate qualitative considerations - such as ethics, human rights assessment, and data protection - with quantitative indicators of dataset's fairness, such as balance, the presence of invalid entries, or outliers. In this work, we extend FanFAIR to deal with categorical data, and introduce a new algorithm for outlier detection in the presence of missing values. We then provide a case study on the data collected from COVID patients admitted to pneumology departments in Italy. We show how the successive steps of data cleaning and variable selection improve the indicators provided by FanFAIR. This shows that data cleaning procedures are not only necessary to improve the performance of the machine learning algorithm using the data for learning, but are also a way to improve (a measure of) fairness. Hence, the proposed case study provides an example in which performance and fairness are not in contrast, like it is commonly believed to be, but they improve together.

关键词： COVID-19 Pulmonology Machine learning algorithms Portable computers Medical services Machine learning Data models Cleaning Libraries Anomaly detection

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Real-world walking behaviors are associated with early-stage heart failure: a Project Baseline Health Study

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Journal of Cardiac Failure 2024年第11期30卷 1423-1433页

作者： Shin, Sooyoon Kowahl, Nathan Hansen, Taylor Ling, Albee Y. Barman, Poulami Cauwenberghs, Nicholas Rainaldi, Erin Short, Sarah Dunn, Jessilyn Shandhi, Md Mobashir Hasan Shah, Svati H. Mahaffey, Kenneth W. Kuznetsova, Tatiana Daubert, Melissa A. Douglas, Pamela S. Haddad, Francois Kapur, Ritu Verily Life Sciences South San Francisco CA United States Research Unit Hypertension and Cardiovascular Epidemiology KU Leuven Department of Cardiovascular Sciences University of Leuven Leuven Belgium Duke University Department of Biomedical Engineering Durham NC Duke University Department of Biostatistics & Bioinformatics Durham NC Duke Clinical Research Institute Durham NC Division of Cardiology Duke University Medical School Duke University Durham NC Stanford Center for Clinical Research Department of Medicine Stanford School of Medicine Stanford CA Division of Cardiovascular Medicine Department of Medicine Stanford University Stanford CA Stanford Cardiovascular Institute Stanford University Stanford CA Department of Neurology Radboud University Medical Center Nijmegen Netherlands

Background: Data collected via wearables may complement in-clinic assessments to monitor subclinical heart failure (HF). Objectives: Evaluate the association of sensor-based digital walking measures with HF stage and characterize their correlation with in-clinic measures of physical performance, cardiac function and participant reported outcomes (PROs) in individuals with early HF. Methods: The analyzable cohort included participants from the Project Baseline Health Study (PBHS) with HF stage 0, A, or B, or adaptive remodeling phenotype (without risk factors but with mild echocardiographic change, termed RF-/ECHO+) (based on available first-visit in-clinic test and echocardiogram results) and with sufficient sensor data. We computed daily values per participant for 18 digital walking measures, comparing HF subgroups vs stage 0 using multinomial logistic regression and characterizing associations with in-clinic measures and PROs with Spearman's correlation coefficients, adjusting all analyses for confounders. Results: In the analyzable cohort (N=1265;50.6% of the PBHS cohort), one standard deviation decreases in 17/18 walking measures were associated with greater likelihood for stage-B HF (multivariable-adjusted odds ratios [ORs] vs stage 0 ranging from 1.18-2.10), or A (ORs vs stage 0, 1.07-1.45), and lower likelihood for RF-/ECHO+ (ORs vs stage 0, 0.80-0.93). Peak 30-minute pace demonstrated the strongest associations with stage B (OR vs stage 0=2.10;95% CI:1.74-2.53) and A (OR vs stage 0=1.43;95% CI:1.23-1.66). Decreases in 13/18 measures were associated with greater likelihood for stage-B HF vs stage A. Strength of correlation with physical performance tests, echocardiographic cardiac-remodeling and dysfunction indices and PROs was greatest in stage B, then A, and lowest for 0. Conclusions: Digital measures of walking captured by wearable sensors could complement clinic-based testing to identify and monitor pre-symptomatic HF. © 2024 Verily Life Sciences LLC

关键词： Digital biomarkers Heart failure subclinical heart failure fitness walking capacity

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Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes

arXiv

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arXiv 2023年

作者： Liu, Xueqing Qian, Tianchen Bell, Lauren Chakraborty, Bibhas Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Statistics University of California Irvine IrvineCA United States Medical Research Council Biostatistics Unit University of Cambridge Cambridge United Kingdom Department of Medical Statistics The London School of Hygiene and Tropical Medicine London United Kingdom Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

In mobile health, tailoring interventions for real-time delivery is of paramount importance. Microrandomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion effect", a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data. © 2023, CC BY.

关键词： Binary alloys

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Improving the Predictive Ability of Radiomics-Based Regression Survival Models Through Incorporating Multiple Regions of Interest 23rd

Improving the Predictive Ability of Radiomics-Based Regressi...

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Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering, PCBEE 2023

作者： Wilk, Agata Malgorzata Kozlowska, Emilia Borys, Damian D’Amico, Andrea Gorczewska, Izabela Debosz-Suwińska, Iwona Galecki, Seweryn Fujarewicz, Krzysztof Suwiński, Rafal Świerniak, Andrzej Department of Systems Biology and Engineering Silesian University of Technology Gliwice44-100 Poland Department of Biostatistics and Bioinformatics Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Gliwice44-102 Poland Department of Nuclear Medicine and Endocrine Oncology PET Diagnostics Unit Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Gliwice44-102 Poland Department of Radiotherapy Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Gliwice44-102 Poland II-nd Radiotherapy and Chemotherapy Clinic and Teaching Hospital Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Gliwice44-102 Poland

ISBN: (纸本)9783031384295

Radiomic features, numeric values extracted from a region of interest (ROI) in medical images, can be used to train prognostic models for various types of cancer. However, in locally advanced diseases, more than one lesion may be present. Using the information contained in multiple regions increases the complexity and necessitates additional processing. Here, we tested seven strategies of handling multiple regions in radiomic-based regularized Cox regression for predicting metastasis-free survival using a cohort of 115 non-small cell lung cancer patients. We have found that using all ROIs to fit the model allowed for better results than using only the largest ROI, achieving c-indexes of 0.617 and 0.581, respectively. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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OP0109 EXPRESSION QUANTITATIVE TRAIT LOCI IN EARLY TREATMENT-NAÏVE RHEUMATOID ARTHRITIS

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Annals of the Rheumatic Diseases 2022年 81卷 71-72页

作者： K. Goldmann E. Vigorito C. Wallace M. Barnes A. Barton C. Pitzalis M. Lewis William Harvey Research Institute Centre for Experimental Medicine and Rheumatology London United Kingdom William Harvey Research Institute Centre for Translational Bioinformatics London United Kingdom University of Cambridge MRC Biostatistics Unit Cambridge United Kingdom Manchester Academic Health Sciences Centre Centre of Genetics & Genomics Versus Arthritis Manchester United Kingdom

Background To date more than 100 genetic loci have been associated with rheumatoid arthritis (RA), particularly in the human leukocyte antigen (HLA) region. Our understanding of the functional consequences of genetic variation in RA causality, however, is limited and it has been shown that a substantial portion of complex disease risk alleles modify gene expression in a cell-specific manner [1]. The Pathobiology of Early Arthritis Cohort (PEAC) is a longitudinal study looking at treatment-naiv̈e RA patients with genotyped data as well as both synovial and blood RNA-sequenced biopsies prior to treatment with disease modifying anti-rheumatic drugs (DMARDs). Objectives To explore expression quantitative trait loci (eQTL) in synovium and blood within PEAC and characterise the effects of genetic variation on gene expression measured by RNA-sequencing. A further goal was to investigate the role of these variants in RA disease severity and response variables. Methods Genotypes were generated by Illumina Human CoreExome-24 version 1-0 array in 118 RA patients. Single nucleotide polymorphisms (SNPs) in the HLA region were imputed using HLA-TAPAS. A candidate gene study was performed on variants within the HLA region using Plink v2.0. Synovial (n=85) and blood (n=51) RNA-sequenced samples then underwent cis -eQTL analysis (loci within ±5x10 5 Mb of the variant) based on linear regression models with the matrixeQTL R package using PEER [2] and PCA eigenvectors as covariates. Differences in eQTL between tissues were determined using a linear interaction term. Results The candidate gene study determined several amino acids around HLA-DRB1 acting as markers for seropositivity, which replicated findings by Raychaudhuri et. al. [3]. Using eQTL analysis, around 33,000 synovial SNPs were found with genome-wide significance (p ≤ 5x10 -8 ) and around 29,000 in blood. This corresponded to 279 unique significant genes in synovium and 417 in blood ( Figure 1 ). There were 100 genes common

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Genetic identification and reiterated captures suggest that the Astyanax mexicanus El Pachón cavefish population is closed and declining

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Zoological Research 2023年第4期44卷 701-711页

作者： Laurent Legendre Julie Rode Isabelle Germon Marie Pavie Carla Quiviger Maxime Policarpo Julien Leclercq Stéphane Père Julien Fumey Carole Hyacinthe Patricia Ornelas-García Luis Espinasa Sylvie Rétaux Didier Casane UniversitéParis-Saclay CNRSIRDUMRÉvolutionGénomesComportement etÉcologieGif-sur-Yvette 91190France Objectif Sciences International NGO OSI-Panthera ProgramGeneva 99140Switzerland Institut de Neuroscience Paris-Saclay UniversitéParis-Saclay and CNRSSaclay 91400France Institut Pasteur UniversitéParis CitéCNRS UMR 2000Microbial Paleogenomics UnitParis 75015France Institut Pasteur UniversitéParis CitéBioinformatics and Biostatistics HubParis 75015France Department of Genetics Harvard Medical SchoolBlavatnik InstituteBoston MA 02115USA Colección Nacional de Peces Departamento de ZoologíaInstituto de BiologíaUniversidad Nacional Autónoma de MéxicoMéxico DF CP 04510México School of Science Marist CollegePoughkeepsieNew York 12601USA UniversitéParis Cité UFR Sciences du VivantParis 75013France Zoological Institute Department of Environmental SciencesUniversity of BaselBaselSwitzerland

The sizes of Astyanax mexicanus blind cavefish populations of North-East Mexico are demographic parameters of great importance for investigating a variety of ecological,evolutionary,and conservation ***,few estimates have been *** these mobile animals living in an environment difficult to explore as a whole,methods based on capture-mark-recapture are appropriate,but their feasibility and interpretation of results depend on several assumptions that must be carefully ***,we provide evidence that minimally invasive genetic identification from captures at different time intervals(three days and three years)can give insights into cavefish population size dynamics as well as other important demographic parameters of *** also provide tools to calibrate sampling and genotyping efforts necessary to reach a given level of *** results suggest that the El Pachón cave population is currently very small,of an order of magnitude of a few hundreds of individuals,and is distributed in a relatively isolated *** probable decline in population size in the El Pachón cave since the last census in 1971 raises serious conservation issues.

关键词： Cavefish Population size Conservation Swabbing Genetic identification

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Building clinically actionable models for predicting mechanical complications in postoperatively well-aligned adult spinal deformity patients using XGBoost algorithm

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Informatics in Medicine Unlocked 2023年 37卷

作者： Balaban, Baris Yilgor, Caglar Yucekul, Altug Zulemyan, Tais Obeid, Ibrahim Pizones, Javier Kleinstueck, Frank Perez-Grueso, Francisco Javier Sanchez Pellise, Ferran Alanay, Ahmet Sezerman, Osman Ugur Department of Bioinformatics and Biostatistics Acibadem University Istanbul Turkey Department of Orthopedics and Traumatology Acibadem University School of Medicine Istanbul Turkey Comprehensive Spine Center Acibadem Maslak Hospital Istanbul Turkey Clinique du Dos Elsan Jean Villar Private Hospital Bordeaux France Spine Surgery Unit Hospital Universitario La Paz Madrid Spain Spine Center Division Department of Orthopedics and Neurosurgery Schulthess Klinik Switzerland Spine Surgery Unit Hospital Universitari Vall d'Hebron Barcelona Spain

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Monoclonal antibodies against SARS-CoV-2 to prevent COVID-19 worsening in a large multicenter cohort

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Heliyon 2024年第16期10卷 e36102页

作者： Soria, Alessandro Graziano, Francesca Ghilardi, Giulia Lapadula, Giuseppe Gasperina, Daniela Dalla Benatti, Simone Vasilij Quiros-Roldan, Eugenia Milesi, Maurizio Bai, Francesca Merli, Marco Minisci, Davide Franzetti, Marco Asperges, Erika Chiabrando, Filippo Pocaterra, Daria Pandolfo, Alessandro Zanini, Fabio Lombardi, Domenico Cappelletti, Anna Rugova, Alban Borghesi, Maria Lucia Squillace, Nicola Pusterla, Luigi Piconi, Stefania Morelli, Paola Querini, Patrizia Rovere Bruno, Raffaele Rusconi, Stefano Casari, Salvatore Bandera, Alessandra Franzetti, Fabio Travi, Giovanna Marchetti, Giulia Pan, Angelo Castelli, Francesco Rizzi, Marco Dentali, Francesco Mallardo, Maria Rossi, Emanuela Valsecchi, Maria Grazia Galimberti, Stefania Bonfanti, Paolo Clinic of Infectious Diseases Fondazione IRCCS San Gerardo dei Tintori Biostatistics and Clinical Epidemiology Fondazione IRCCS San Gerardo dei Tintori Bicocca Bioinformatics Biostatistics and Bioimaging Centre (B4) School of Medicine and Surgery University of Milano-Bicocca School of Medicine University of Milano-Bicocca Department of Medicine and Technological Innovation University of Insubria Unit of Infectious Diseases ASST Papa Giovanni XXIII Clinic of Infectious Diseases ASST Santi Paolo e Carlo Clinic of Infectious Diseases University of Brescia Unit of Infectious Diseases ASST Cremona Department of Health Sciences University of Milan Clinic of Infectious Diseases ASST Grande Ospedale Metropolitano Niguarda Unit of Infectious Diseases ASST Mantova Unit of Infectious Diseases ASST Ovest Milano Clinic of Infectious Diseases University of Pavia Fondazione IRCCS Policlinico San Matteo Università Vita-Salute San Raffaele IRCCS Humanitas Research Hospital Unit of Infectious Diseases ASST Nord Milano Unit of Infectious Diseases IRCCS Ospedale San Raffaele University of Milano Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Department of Pathophysiology and Transplantation Università degli Studi di Milano Unit of Infectious Diseases

Objective: Monoclonal antibodies (mAbs) against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) reduced Coronavirus Disease 2019 (COVID-19) hospitalizations in people at risk of clinical worsening. Real-world descriptions are limited. Methods: CONDIVIDIAMO, a two-year multicenter observational study, consecutively enrolled SARS-CoV-2 outpatients with ≥1 risk factor for COVID-19 progression receiving mAbs. Demographic data, underlying medical condition, type of mAbs combination received, duration of symptoms before mAbs administration, COVID-19 vaccination history, were collected upon enrolment and centrally recorded. Data on outcomes (hospitalizations, reasons of hospitalization, deaths) were prospectively collected. The primary endpoint was the rate of hospitalization or death in a 28-day follow-up, whichever occurred first;subjects were censored at the day of last follow-up or up to 28 days. The Kaplan-Meier method was used to estimate the incidence rate curve in time. The Cox regression model was used to assess potential risk factors for unfavorable outcome. Results were shown as hazard ratio (HR) along with the corresponding 95 % Confidence Interval (95%CI). Results: Among 1534 subjects (median [interquartile range, IQR] age 66.5 [52.4–74.9] years, 693 [45.2 %] women), 632 (41.2 %) received bamlanivimab ± etesevimab, 209 (13.6 %) casirivimab/imdevimab, 586 (38.2 %) sotrovimab, 107 (7.0 %) tixagevimab/cilgavimab. After 28-day follow-up, 87/1534 (5.6 %, 95%CI: 4.4%–6.8 %) met the primary outcome (85 hospitalizations, 2 deaths). Hospitalizations for COVID-19 (52, 3.4 %) occurred earlier than for other reasons (33, 2.1 %), after a median (IQR) of 3.5 (1–7) versus 8 (3–15) days (p = 0.006) from mAbs administration. In a multivariable Cox regression model, factors independently associated with increased hospitalization risk were age (hazard ratio [HR] 1.02, 95%CI 1.00–1.03, p = 0.021), immunodeficiency (HR 1.78, 95%CI 1.11–2.85, p = 0.017), pre-Omicron ca

关键词： COVID-19 Hospitalization Immunodeficiency Monoclonal antibodies Omicron variant SARS-CoV-2

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