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JAMIA at 30: looking back and forward

Journal of the American Medical informatics Association 2024年第1期31卷 1-9页

作者： Stead, William W. Miller, Randolph A. Ohno-Machado, Lucila Bakken, Suzanne Department of Biomedical Informatics Department of Medicine Vanderbilt University Medical Center Nashville United States Department of Biomedical Informatics Vanderbilt University School of Medicine Alexandria United States Section of Biomedical Informatics & Data Science Yale University School of Medicine New Haven CT 06520 United States School of Nursing Department of Biomedical Informatics Data Science Institute Columbia University NY 10032 NY United States

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data Quality in Medical Real-World data - An Oncological Use Case

Data Quality in Medical Real-World Data - An Oncological Use...

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34th Medical informatics Europe Conference, MIE 2024

作者： Gehrmann, Julia Beyan, Oya Institute for Biomedical Informatics Cologne Germany Department of Data Science and Artificial Intelligence Fraunhofer FIT Sankt Augustin Germany

ISBN: (纸本)9781643685335

data quality deficiencies significantly limit the applicability of real-world data in data-driven medical research. In this study, using an oncological use case, we report and discuss common quality deficiencies in real-world medical datasets, such as missing data, class imbalances, and timeliness issues. We compiled a multidepartmental real-world dataset comprising 13861 cancer cases diagnosed at University Hospital Cologne and examined data quality throughout the data integration process. © 2024 The Authors.

关键词： data Quality Germany Medical data Oncology Real-World data

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Characterization of a new fluorescence lifetime imaging ophthalmoscope

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ACTA OPHTHALMOLOGICA 2024年第S279期102卷

作者： Nycz, Julia Link, Dietmar Klemm, Matthias Klee, Sascha Haueisen, Jens Institute of Biomedical Engineering and Informatics Technische Universität Ilmenau Ilmenau Germany Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems Austria

Aims/Purpose: Fluorescence lifetime imaging ophthalmoscopy (FLIO) allows in vivo measurement of autofluorescence intensity decays of endogenous fluorophores in the ocular fundus. So far, only devices from Heidelberg Engineering based on the Spectralis system have been used in FLIO research. Here, we present and characterize a new FLIO device based on the RETImap system from Roland Consult. Methods: The device is based on a confocal scanning laser ophthalmoscope (35° field, 512 × 512 px). A ps diode laser (BDL-SMN 473 nm, Becker & Hickl GmbH, Berlin, Germany) excites autofluorescence. The fluorescence photons are split into a short (498–560 nm, SSC) and a long (560–720 nm, LSC) spectral channel (one HPM-100-40 detector [Becker & Hickl GmbH] each) and are detected by time-correlated single photon counting (SPC-160, Becker & Hickl GmbH). We determined the maximum laser power (ILT2400, International Light Technologies, Inc. Peabody, MA, USA) and analysed the instrument's behaviour at three different laser power levels (150 μW, 200 μW and max.) in terms of laser spectrum (CAS140CT, Instrument Systems GmbH, Munich, Germany) and instrument response function (IRF). The IRF was determined using a 25 μM Eosin Y solution, mixed with a 5 M solution of potassium iodide, placed in a flat cuvette (110-OS, Hellma GmbH & Co. KG, Müllheim, Germany) in front of the objective lens of the FLIO device. Fluorescence measurements of approximately 1-min duration were performed three times for all three laser powers. The IRF and the full width at half maximum (FWHM) were calculated using FLIMX software ( *** ). Results: The max. laser power was 280 μW. The peak wavelengths of the laser spectra were 467.6 (150 μW), 467.9 (200 μW) and 468.0 nm (280 μW). IRF FWHM in SSC were 298.6 ± 1.1 ps (150 μW), 341.0 ± 2.5 ps (200 μW) and 347.5 ± 6.0 ps (280 μW). In LSC, the IRF FWHM were 290.4 ± 3.8 ps (150 μW), 344.0 ± 3.4 ps (200 μW) and 358.8 ± 1.3 ps (280 μW). Results are mean ± standard devi

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Adjustable Cuffless Smartphone Attachment (ACSA+) for Estimation of Blood Pressure Trends: A Pilot Study

Adjustable Cuffless Smartphone Attachment (ACSA+) for Estima...

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19th World Congress on Medical and Health informatics, MedInfo 2023

作者： Sholokhova, Kseniia Jian, Wen-Shan Yang, Hsuan-Chia Li, Yu-Chuan Jack Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Graduate Institute of Data Science Taipei Medical University Taipei Taiwan

ISBN: (纸本)9781643684567

Among the elderly, hypertension remains one of the prevalent health conditions, which requires monitoring and intervention strategies. Nevertheless, regular reporting of blood pressure (BP) from these individuals still poses multiple challenges. However, most people own cell phone and are engaged in phone conversations daily. Here, we propose an adjustable cuffless smartphone attachment (ACSA+) equipped with a PPG sensor for the estimation of BP during phone conversations. ACSA+ can be easily attached to the back of any modern cell phone. ACSA+ will help to continuously collect BP data and store it as a trend line. © 2024 International Medical informatics Association (IMIA) and IOS Press.

关键词： Machine learning

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Enhancing Diabetic Retinopathy Screening with Sequential Deep Learning Models 7

Enhancing Diabetic Retinopathy Screening with Sequential Dee...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Chennai India Yale University Section For Biomedical Informatics And Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350371406

Diabetic retinopathy is a serious medical disorder that, if left untreated, can result in visual impairment or blindness. The precise and timely categorization of its severity is critical for appropriate medical management. This research describes an automated method for determining the severity of diabetic retinopathy using deep learning algorithms. The suggested methodology adopts a two-step process for identifying diabetic retinopathy and then categorizing its severity. In this technique, two pre-trained deep learning models, InceptionResNetV2 and DenseNet121, are used. For training and assessment purposes, the 'APTOS 2019 Blindness Detection' dataset which consists of five severity classes is used as the foundation. To overcome class imbalance, data augmentation approaches are used to improve the model's performance. The results of the experiment show that the strategy is effective, with an accuracy of 83.10 % and an F1 score of 82.77 %. This study contributes to the development of automated systems for assessing diabetic retinopathy, perhaps enhancing early detection and treatment. © 2023 IEEE.

关键词： Medical imaging

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A comprehensive evaluation of large language models in mining gene relations and pathway knowledge

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Quantitative Biology 2024年第4期12卷 360-374页

作者： Muhammad Azam Yibo Chen Micheal Olaolu Arowolo Haowang Liu Mihail Popescu Dong Xu Department of Electrical Engineering and Computer Science University of MissouriColumbiaMissouriUSA Bond Life Sciences Center University of MissouriColumbiaMissouriUSA Institute for Data Science and Informatics University of MissouriColumbiaMissouriUSA Department of Biomedical Informatics Biostatistics and Medical EpidemiologyUniversity of MissouriColumbiaMissouriUSA

Understanding complex biological pathways,including gene–gene interactions and gene regulatory networks,is critical for exploring disease mechanisms and drug *** literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the ***-scale language models(LLMs)trained on extensive text corpora contain rich biological information,and they can be mined as a biological knowledge *** study assesses 21 LLMs,including both application programming interface(API)-based models and open-source models in their capacities of retrieving biological *** evaluation focuses on predicting gene regulatory relations(activation,inhibition,and phosphorylation)and the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway *** indicated a significant disparity in model ***-based models GPT-4 and Claude-Pro showed superior performance,with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction,and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction,***-source models lagged behind their API-based counterparts,whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations,*** KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for *** study suggests that LLMs are informative in gene network analysis and pathway mapping,but their effectiveness varies,necessitating careful model *** work also provides a case study and insight into using LLMs das knowledge *** code is publicly available at the website of GitHub(Muh-aza).

关键词： biomedical text mining gene-gene interaction KEGG pathway large language model

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Stroke Prediction Using Machine Learning 5

Stroke Prediction Using Machine Learning

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5th IEEE International Conference on Advances in Electronics, Computers and Communications, ICAECC 2023

作者： Abinandhini, D.M. Kumar, Aman Teja, Gudi Vishnu Sukesh, Mandava Divya, S. Chauhan, Naman Oviya, I.R. Raja, Kalpana Chennai India School of Medicine Yale University New Haven Section for Biomedical Informatics and Data Science CT United States

ISBN: (纸本)9798350305449

A stroke is a serious medical emergency that happens when bleeding or blood clots cut off the blood flow to a part of the brain. With a mortality rate of 5.5 million per year, it ranks as the second leading cause of death globally. Over 15 million individuals experience a stroke each year, and one person dies from one every four minutes. According to the World Health Organization, stroke is the main cause of death and disability worldwide (WHO). Identifying the many stroke warning signs helps lessen the severity of the stroke. A stroke can be avoided in up to 80% of instances because it is typically the result of a poor lifestyle. As a result, stroke prediction becomes important and should be employed to stop it from causing long-term harm. The current study uses a variety of machine learning models, including Gaussian Naive Bayes, Logistic Regression, Support Vector Machine (SVM), KNN and Random Forest to predict stroke. The paper presents the comparison among all machine learning algorithms. Analysis of results revealed that KNN had the least accuracy of 76.32% and Random Forest had the highest accuracy of 94.81%. © 2023 IEEE.

关键词： Logistic regression

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ML Based Phenotype Analysis Using Differential Gene Expression data in Schizophrenia 4

ML Based Phenotype Analysis Using Differential Gene Expressi...

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4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Aadhithyan, D. Shabarirajan, K.J. Maniappan, Varun Shivanesh, B. Divya, S. Praghaadeesh, R. Raja, Kalpana Oviya, I.R. Chennai India School Of Medicine Yale University Section For Biomedical Informatics And Data Science New HavenCT United States

ISBN: (纸本)9798350384628

In this paper, differential gene expression (DGE) data in schizophrenia is analysed using machine learning approaches with the objective of predicting cell phenotypes related to the condition. The dataset contains information on cis eQTNs, gene orientation, probe information, gene expression profiles, and SNP information. When first investigating well-known machine learning methods, a hybrid model combining Random Forests and XGBoost is suggested, however it only offers a little increase. Increases in predicted accuracy of more than 99.12% are made possible by the H2O AutoML package. The results demonstrate the capability of ML-based analysis to unravel the underlying biological mechanisms of schizophrenia and contribute to the design of individualised therapeutic approaches. The paper represents the capability of machine learning to comprehend the relationship between gene expression patterns and complicated illnesses like schizophrenia. © 2023 IEEE.

关键词： Machine learning

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Evaluating and Improvi g the Performance and Racial Fairness of Algori ims for GFR Estimation 1

Evaluating and Improvi g the Performance and Racial Fairness...

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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Zhang, Linying Kim, Tevin Richter, Lauren R. Hripcsak, George Department of Biomedical Informatics Columbia University New YorkNY United States Institutefor Informatics Data Science and Biostatistics Washington University in St. Louis St. LouisMO United States

ISBN: (纸本)9798350371987

data-driven clinical prediction algorithms are used widely by clinicians. Understanding what factors can impact the performance and fairness of data-driven algorithms is an important step towards achieving equitable healthcare. To investigate the impact of modeling choices on the algorithmic performance and fairness, we make use of a case study to build a prediction algorithm for estimating glomerular filtration rate (GFR) based on the patient's electronic health record (EHR). We compare three distinct approaches for estimating GFR: CKD-EPI equations, epidemiological models, and EHR-based models. For epidemiological models and EHR-based models, four machine learning models of varying computational complexity (i.e., linear regression, support vector machine, random forest regression, and neural network) were compared. Performance metrics included root mean squared error (RMSE), median difference, and the proportion of GFR estimates within 30% of the measured GFR value (P30). Differential performance between non-African American and African American group was used to assess algorithmic fairness with respect to race. Our study showed that the variable race had a negligible effect on error, accuracy, and differential performance. Furthermore, including more relevant clinical features (e.g., common comorbidities of chronic kidney disease) and using more complex machine learning models, namely random forest regression, significantly lowered the estimation error of GFR. However, the difference in performance between African American and non-African American patients did not decrease, where the estimation error for African American patients remained consistently higher than non-African American patients, indicating that more objective patient characteristics should be discovered and included to improve algorithm performance. ©2024 IEEE.

关键词： Mean square error

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification 3

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Yale University Section for Biomedical Informatics and Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350372120

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing. © 2024 IEEE.

关键词： Ophthalmology

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