Field Of Biomedical Informatics Research Articles

Biomedical Informatics: State of the Art, Challenges, and Opportunities

Biomedical informatics can be considered as a multidisciplinary research and educational field situated at the intersection of computational sciences (including computer science, data science, mathematics, and statistics), biology, and medicine. In recent years, there have been advances in the field of biomedical informatics. The current article highlights some interesting state-of-the-art research outcomes in these fields. These include research outcomes in areas like (i) computational biology and medicine, (ii) explainable artificial intelligence (XAI) in biomedical research and clinical practice, (iii) machine learning (including deep learning) methods and application for bioinformatics and healthcare, (iv) imaging informatics, as well as (v) medical statistics and data science. Moreover, the current article also discusses some existing challenges and potential future directions for these research areas to advance the fields of biomedical informatics.

Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis

Background and Objective:Accurate classification of liver disease stages provides crucial insights into patient prognosis, aiding in the prediction of disease outcomes and influencing clinical decision-making. There is an urgent need for non-invasive methods to diagnose various stages of liver dysfunction and uncover hidden pattern based on individual disease characteristics. Method:One popular and effective approach is collecting serum biomarker samples. The study was conducted on collected serum biomaker samples of 81 patients with Inflammatory Bowel Disease (IBD) of Changhua Christian Hospital in China, including 36 with Crohn’s disease (CD) and 45 with Ulcerative Colitis (UC) using Latent Semantic Analysis(LSA) and machine learning (ML) techniques.Machine Learning algorithms Random Forest (RF), Logistic Regression (LR), XGBoost (XGB), and Support Vector Classifier (SVC), were utilized to predict liver risk associated with conditions including Hepatitis, Autoimmune Hepatitis (AIH), Alcoholic Liver Disease (ALD), and Non-Alcoholic Fatty Liver Disease (NAFLD). Models’ accuracy was assessed using K-Fold Cross-Validation (CV).Distinct pattern were identified using Latent Semantic Analysis(LSA). Furthermore, SHAP plots were utilized for enhanced interpretability, highlighting essential features for liver dysfunction levels. Results:The inflammatory profile, mixed disease profile, and healthy profile were the three distinct clusters were identified with LSA. The RF model achieved high accuracy of 0.94±0.06. Serum Glutamate Pyruvate Transaminase (GPT), Age at Diagnosis (AAD), Erythrocyte Sedimentation Rate (ESR), C-reactive protein (CRP) were found the most key important features in liver disease staging increment. Conclusion:The research significantly contributes to the fields of biomedical informatics and clinical decision-making. The developed model offers valuable decision-making tools for clinicians, enabling early and targeted interventions.

Advancing Healthcare Diagnostics: A Study on Gaussian Naive Bayes Classification of Blood Samples

This research paper presents a comprehensive analysis of the Gaussian Naive Bayes (GNB) classifier's application in predicting health conditions from blood samples, underpinned by a handcrafted dataset representative of typical physiological ranges. Through a meticulous 5-fold cross-validation approach, the study assesses the GNB model's performance in terms of accuracy, precision, recall, and F1-score, revealing not only high efficacy but also consistent improvement in predictive capability across successive folds. A detailed confusion matrix provides further insights into the model's classification proficiency. The results affirmatively address the research hypotheses, indicating the GNB classifier's reliability and effectiveness as a diagnostic tool. With the increasing need for rapid and accurate medical diagnostics, the study's findings underscore the potential of even simple machine learning models to augment traditional blood test analyses, thereby offering significant contributions to the field of biomedical informatics. The research lays the groundwork for future explorations into the integration of machine learning in clinical settings, advocating for the verification of these promising results with real-world clinical data and the comparative analysis of various machine learning models. The potential for automated, precise diagnostic processes paves the way for enhanced patient care and resource optimization in healthcare.

Don Lindberg's influence on future generations: The U.S. National Library of Medicine's biomedical informatics research training programs.

Through his visionary leadership as Director of the U.S. National Library of Medicine (NLM), Donald A. B. Lindberg M.D. influenced future generations of informatics professionals and the field of biomedical informatics itself. This chapter describes Dr. Lindberg’s role in sponsoring and shaping the NLM’s Institutional T15 training programs.

Improved multi-classification of breast cancer histopathological images using handcrafted features and deep neural network (dense layer)

Breast cancer (BC) classification has become a point of concern within the field of biomedical informatics in the health care sector in recent years. This is because it is the second-largest cause of cancer-related fatalities among women. The medical field has attracted the attention of researchers in applying machine learning techniques to the detection, and monitoring of life-threatening diseases such as breast cancer (BC). Proper detection and monitoring contribute immensely to the survival of BC patients, which is largely dependent on the analysis of pathological images. Automatic detection of BC based on pathological images and the use of a Computer-Aided Diagnosis (CAD) system allow doctors to make a more reliable decision. Recently, Deep Learning algorithms like Convolution Neural Network have been proven to be reliable in detecting BC targets from pathological images. Several research efforts have been undertaken in the binary classification of histopathological images. However, few approaches have been proposed for the multi-classification of histopathological images. The classification accuracy produced by these approaches are inefficient since they considered only texture-based extracted features and they used some techniques that cannot extract some of the main features from the images. Also, these techniques still suffered from the issue of overfitting. In this work, handcrafted feature extraction techniques (Hu moment, Haralick textures, and color histogram) and Deep Neural Network (DNN) are employed for breast cancer multi-classification using histopathological images on the BreakHis dataset. The features extracted using the handcrafted techniques are used to train the DNN classifiers with four dense layers and Softmax. Further, the data augmentation method was employed to address the issue of overfitting. The results obtained reveal that the use of handcrafted approach as feature extractors and DNN classifiers had a better performance in breast cancer multi-classification than other approaches in the literature. Moreover, it was also noted that augmentation of data plays a key role in further improvement of classification accuracy. The proposed method achieved an accuracy score of 97.87% for 40x, 97.60% for 100x, 96.10% for 200x, and 96.84% for 400x for the magnification-dependent histopathological images classification. The results also showed that the proposed method for using the Handcrafted feature extraction method with DNN classifier had a better performance in multi-classification of breast cancer using histopathological images than most of the related works in the literature.

Don Lindberg's Influence on Future Generations: The U.S. National Library of Medicine's Biomedical Informatics Research Training Programs.

Through his visionary leadership as Director of the U.S. National Library of Medicine (NLM), Donald A.B. Lindberg M.D. influenced future generations of informatics professionals and the field of biomedical informatics itself. This chapter describes Dr. Lindberg's role in sponsoring and shaping the NLM's Institutional T15 training programs.

A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets

BackgroundSemantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue.MethodsFirst, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets.Results and conclusionsWe focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson’s disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.

What it means to be a woman in the field of biomedical informatics: exploring the lived experiences of women managers in the kingdom of Saudi Arabia.

Although women in the field of biomedical informatics (BMI) are part of a golden era, little is known about their lived experiences as informaticians. Guided by feminist standpoint theory, this study aims to understand the impact of social change in the Kingdom of Saudi Arabia- in the form of new policies supporting women and health technological advancements-in the field of BMI and its women informaticians. We conducted semistructured telephone interviews with 7 women managers in the field of BMI, identified through LinkedIn. We analyzed interview transcripts to generate themes about their lived experiences, how they perceived health information technology tools, identified challenges that may hinder the advancement of the field, and explored the future of BMI from their perspectives. During our analysis, we utilized a feminist theoretical approach. Women managers in the field of BMI shared similar experiences and perspectives. Our analysis generated 10 themes: (1) career beginning, (2) opportunities given, (3) career achievements, (4) gender-based experiences, (5) meaning of BMI, (6) meaning of health information technology tools, (7) challenges, (8) overcoming challenges, (9) future and hopes, and (10) meaning of "2030 Saudi vision." Early in their careers, participantsexperienced limited opportunities and misperceptions in understanding what the field of informatics represents. Participants did not feel that gender was an issue, despite what feminist theory would have predicted. Recognizing the lived experiences of women in the field of BMI contributes to our collective understanding of how these experiences may enhance our knowledge of the field.

Faster R-CNN approach for detection and quantification of DNA damage in comet assay images

Background and Objective:DNA damage analysis can provide valuable information in several areas ranging from the diagnosis/treatment of a disease to the monitoring of the effects of genetic and environmental influences. The evaluation of the damage is determined by comet scoring, which can be performed by a skilled operator with a manual procedure. However, this approach becomes very time-consuming and the operator dependency results in the subjectivity of the damage quantification and thus in a high inter/intra-operator variability. Methods:In this paper, we aim to overcome this issue by introducing a Deep Learning methodology based on Faster R-CNN to completely automatize the overall approach while discovering unseen discriminative patterns in comets. Results:The experimental results performed on two real use-case datasets reveal the higher performance (up to mean absolute precision of 0.74) of the proposed methodology against other state-of-the-art approaches. Additionally, the validation procedure performed by expert biologists highlights how the proposed approach is able to unveil true comets, often unseen from the human eye and standard computer vision methodology. Conclusions:This work contributes to the biomedical informatics field by the introduction of a novel approach based on established object detection Deep Learning technique for evaluating the DNA damage. The main contribution is the application of Faster R-CNN for the detection and quantification of DNA damage in comet assay images, by fully automatizing the detection/classification DNA damage task. The experimental results extracted in two real use-case datasets demonstrated (i) the higher robustness of the proposed methodology against other state-of-the-art Deep Learning competitors, (ii) the speeding up of the comet analysis procedure and (iii) the minimization of the intra/inter-operator variability.

The My Cancer Genome clinical trial data model and trial curation workflow.

ObjectiveAs clinical trials evolve in complexity, clinical trial data models that can capture relevant trial data in meaningful, structured annotations and computable forms are needed to support accrual.Material and MethodsWe have developed a clinical trial information model, curation information system, and a standard operating procedure for consistent and accurate annotation of cancer clinical trials. Clinical trial documents are pulled into the curation system from publicly available sources. Using a web-based interface, a curator creates structured assertions related to disease-biomarker eligibility criteria, therapeutic context, and treatment cohorts by leveraging our data model features. These structured assertions are published on the My Cancer Genome (MCG) website.ResultsTo date, over 5000 oncology trials have been manually curated. All trial assertion data are available for public view on the MCG website. Querying our structured knowledge base, we performed a landscape analysis to assess the top diseases, biomarker alterations, and drugs featured across all cancer trials.DiscussionBeyond curating commonly captured elements, such as disease and biomarker eligibility criteria, we have expanded our model to support the curation of trial interventions and therapeutic context (ie, neoadjuvant, metastatic, etc.), and the respective biomarker-disease treatment cohorts. To the best of our knowledge, this is the first effort to capture these fields in a structured format.ConclusionThis paper makes a significant contribution to the field of biomedical informatics and knowledge dissemination for precision oncology via the MCG website.Key wordsknowledge representation, My Cancer Genome, precision oncology, knowledge curation, cancer informatics, clinical trial data model

Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images-a Comparative Insight.

Automatic multi-classification of breast cancer histopathological images has remained one of the top-priority research areas in the field of biomedical informatics, due to the great clinical significance of multi-classification in providing diagnosis and prognosis of breast cancer. In this work, two machine learning approaches are thoroughly explored and compared for the task of automatic magnification-dependent multi-classification on a balanced BreakHis dataset for the detection of breast cancer. The first approach is based on handcrafted features which are extracted using Hu moment, color histogram, and Haralick textures. The extracted features are then utilized to train the conventional classifiers, while the second approach is based on transfer learning where the pre-existing networks (VGG16, VGG19, and ResNet50) are utilized as feature extractor and as a baseline model. The results reveal that the use of pre-trained networks as feature extractor exhibited superior performance in contrast to baseline approach and handcrafted approach for all the magnifications. Moreover, it has been observed that the augmentation plays a pivotal role in further enhancing the classification accuracy. In this context, the VGG16 network with linear SVM provides the highest accuracy that is computed in two forms, (a) patch-based accuracies (93.97% for 40×, 92.92% for 100×, 91.23% for 200×, and 91.79% for 400×); (b) patient-based accuracies (93.25% for 40×, 91.87% for 100×, 91.5% for 200×, and 92.31% for 400×) for the classification of magnification-dependent histopathological images. Additionally, "Fibro-adenoma" (benign) and "Mucous Carcinoma" (malignant) classes have been found to be the most complex classes for the entire magnification factors.

Big data and biomedical informatics: Preparing for the modernization of clinical neuropsychology

Objective: Neuropsychology is poised for a fundamental shift as we modernize the ways in which behavior is measured. The amount and complexity of data generated by these new methods will be several orders of magnitude greater than what is currently created by analog measures and will quickly adopt characteristics of “Big Data.” Adequate preparation for managing the influx of data will be critical for technology integration and modernization to be successful. Drawing from information technology, mathematics, statistics, computer science, and engineering, as well as, biology, genetics, and medicine, the field of biomedical informatics has rapidly evolved from its early days in computational biology to a burgeoning independent discipline that has much to offer neuropsychology.Method: Following a critical review of the relevant literature, the present article (1) provides an introductory overview of biomedical informatics and how these concepts are relevant to neuropsychology; (2) describes how biomedical informatics applications can be utilized to leverage existing data sources more effectively; and (3) discusses ideas for future developments designed to facilitate integration of new data derived from novel, technologically driven measurement tools. Within this context, applications intended for use by both the individual neuropsychologist to increase clinical efficiencies, as well as, larger field-wide initiatives intended to generate new information and derive new knowledge are discussed.Conclusions: By no means a comprehensive review of biomedical informatics, the present paper highlights that our approach to data needs to become a multidisciplinary endeavor in order to develop applications capable of effectively utilizing modern data sources.

Accelerating the Computation of Entropy Measures by Exploiting Vectors with Dissimilarity

In the diagnosis of neurological diseases and assessment of brain function, entropy measures for quantifying electroencephalogram (EEG) signals are attracting ever-increasing attention worldwide. However, some entropy measures, such as approximate entropy (ApEn), sample entropy (SpEn), multiscale entropy and so on, imply high computational costs because their computations are based on hundreds of data points. In this paper, we propose an effective and practical method to accelerate the computation of these entropy measures by exploiting vectors with dissimilarity (VDS). By means of the VDS decision, distance calculations of most dissimilar vectors can be avoided during computation. The experimental results show that, compared with the conventional method, the proposed VDS method enables a reduction of the average computation time of SpEn in random signals and EEG signals by 78.5% and 78.9%, respectively. The computation times are consistently reduced by about 80.1~82.8% for five kinds of EEG signals of different lengths. The experiments further demonstrate the use of the VDS method not only to accelerate the computation of SpEn in electromyography and electrocardiogram signals but also to accelerate the computations of time-shift multiscale entropy and ApEn in EEG signals. All results indicate that the VDS method is a powerful strategy for accelerating the computation of entropy measures and has promising application potential in the field of biomedical informatics.

System Diagnosis of Coronary Heart Disease using A Combination of Dimensional Reduction and Data Mining Techniques: A Review

Coronary heart disease is a disease with the highest mortality rates in the world. This makes the development of the diagnostic system as a very interesting topic in the field of biomedical informatics, aiming to detect whether a heart is normal or not. In the literature there are diagnostic system models by combining dimension reduction and data mining techniques. Unfortunately, there are no review papers that discuss and analyze the themes to date. This study reviews articles within the period 2009-2016, with a focus on dimension reduction methods and data mining techniques, validated using a dataset of UCI repository. Methods of dimension reduction use feature selection and feature extraction techniques, while data mining techniques include classification, prediction, clustering, and association rules.

Findings from the 2017 Yearbook Section on Health Information Management

Summary Objectives: To summarize the recent literature and research and present a selection of the best papers published in 2016 in the field of Health Information Management (HIM). Methods: A systematic review of the literature for the IMIA Yearbook HIM section was performed by the two section editors with the help of a medical librarian. We searched bibliographic databases for HIM-related papers were searched using both MeSH headings and key words in titles and abstracts. A shortlist of candidate best papers was first selected by section editors before being peer-reviewed by independent external reviewers. Results: The five papers selected as ‘Best Papers’ illustrate a variety of themes, include authors from a variety of countries, and were published in some of the best journals in the field. The themes of the five best papers include health information exchange, personal health records, patient engagement, data quality, and e-quality measures. Conclusions: The discipline of Health Information Management is increasingly becoming allied with the field of Biomedical Informatics in that both disciplines have interests in common. Traditional HIM areas of expertise (in the pre-electronic health record world), such as coding and privacy and security of health information, are necessary for the electronic exchange and secondary use of health information. With the changes in healthcare delivery brought by the use of electronic health records, addressing issues of information governance is essential. This synopsis discusses these key issues at the intersection of HIM and informatics, examines the potential challenges, and points the way for best practices, future research, and public policy considerations and directions.

An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model version 5.0.

Cohort identification for clinical studies tends to be laborious, time-consuming, and expensive. Developing automated or semi-automated methods for cohort identification is one of the "holy grails" in the field of biomedical informatics. We propose a high-throughput similarity-based cohort identification algorithm by applying numerical abstractions on Electronic Health Records (EHR) data. We implement this algorithm using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which enables sites using this standardized EHR data representation to avail this algorithm with minimum effort for local implementation. We validate its performance for a retrospective cohort identification task on six clinical trials conducted at the Columbia University Medical Center. Our algorithm achieves an average Area Under the Curve (AUC) of 0.966 and an average Precision at 5 of 0.983. This interoperable method promises to achieve efficient cohort identification in EHR databases. We discuss suitable applications of our method and its limitations and propose warranted future work.

Findings from the 2017 Yearbook Section on Health Information Management.

Objectives: To summarize the recent literature and research and present a selection of the best papers published in 2016 in the field of Health Information Management (HIM). Methods: A systematic review of the literature for the IMIA Yearbook HIM section was performed by the two section editors with the help of a medical librarian. We searched bibliographic databases for HIM-related papers were searched using both MeSH headings and key words in titles and abstracts. A shortlist of candidate best papers was first selected by section editors before being peer-reviewed by independent external reviewers. Results: The five papers selected as 'Best Papers' illustrate a variety of themes, include authors from a variety of countries, and were published in some of the best journals in the field. The themes of the five best papers include health information exchange, personal health records, patient engagement, data quality, and e-quality measures. Conclusions: The discipline of Health Information Management is increasingly becoming allied with the field of Biomedical Informatics in that both disciplines have interests in common. Traditional HIM areas of expertise (in the pre-electronic health record world), such as coding and privacy and security of health information, are necessary for the electronic exchange and secondary use of health information. With the changes in healthcare delivery brought by the use of electronic health records, addressing issues of information governance is essential. This synopsis discusses these key issues at the intersection of HIM and informatics, examines the potential challenges, and points the way for best practices, future research, and public policy considerations and directions.

Can Big Data Fulfill Its Promise?

Big data analytics are widely touted as a key innovation to improve health care. Although the term big data is variably defined, it generally implies the application of advanced statistical analyses, with names such as machine learning, artificial intelligence, or cognitive computing, to data sources that greatly exceed the size and complexity of databases traditionally used for healthcare analyses.1 Somewhat counterintuitively, data volume alone does not qualify an analysis as big data analytics. The term more appropriately refers to analytic and computational techniques, combined with information technology innovations, that were specifically developed to yield insights from the large quantities of data that are increasingly common in the digital economy. Big data analytics offer the promise of turning large amounts of data into superior predictive models that can be used to improve healthcare quality and outcomes. Since its inception, Circulation: Cardiovascular Quality and Outcomes has focused on using scientifically rigorous data analyses to improve health care,2 thus the journal is a natural home for scientific investigations using big data analytic techniques. The current Special Issue highlights several high-quality studies that reveal the depth and breadth of these new methods and points toward a future where big data analytics are incorporated into the daily practice of all clinicians, much as big data analytics routinely impact the everyday online experiences of millions of users of Google, Amazon, and other internet-based companies. Although the term big data is a relatively new invention,1 many of the techniques of big data analytics have been in existence for decades, and the field of biomedical informatics has been critical to their development.3 Several recent phenomena have converged to move big data analytics to the forefront of health care, including the widespread adoption of electronic medical records and subsequent digitization of large volumes of healthcare …

Biomedical informatics advancing the national health agenda: the AMIA 2015 year-in-review in clinical and consumer informatics.

The field of biomedical informatics experienced a productive 2015 in terms of research. In order to highlight the accomplishments of that research, elicit trends, and identify shortcomings at a macro level, a 19-person team conducted an extensive review of the literature in clinical and consumer informatics. The result of this process included a year-in-review presentation at the American Medical Informatics Association Annual Symposium and a written report (see supplemental data). Key findings are detailed in the report and summarized here. This article organizes the clinical and consumer health informatics research from 2015 under 3 themes: the electronic health record (EHR), the learning health system (LHS), and consumer engagement. Key findings include the following: (1) There are significant advances in establishing policies for EHR feature implementation, but increased interoperability is necessary for these to gain traction. (2) Decision support systems improve practice behaviors, but evidence of their impact on clinical outcomes is still lacking. (3) Progress in natural language processing (NLP) suggests that we are approaching but have not yet achieved truly interactive NLP systems. (4) Prediction models are becoming more robust but remain hampered by the lack of interoperable clinical data records. (5) Consumers can and will use mobile applications for improved engagement, yet EHR integration remains elusive.

Silver Anniversary: 25 Editions of the IMIA Yearbook.

To provide an editorial introduction into the special 25th anniversary edition of the IMIA Yearbook of Medical Informatics with discussion of the significance of the Yearbook, past and current editorial teams, and a look into the future. A brief overview of the 2016 anniversary edition of the Yearbook allows for a discussion of the significance and value of the Yearbook to the Biomedical Informatics community as well as a review of changes in Yearbook team and format over time. The IMIA Yearbook celebrates its 25th edition bearing witness to the quality of the IMIA brand, the Yearbook content, as well as to the dedication of and the inordinate amount of labor from the authors and editors of the Yearbook. Editorial teams are to be applauded for their hard work and for their foresight in steering the Yearbook from a paperback to an open access online publication. The special edition provides reviews of past editorials with the knowledge of today. The IMIA Yearbook celebrates a remarkable milestone providing a testament to the maturity of the Biomedical Informatics field. Informaticians across the world are encouraged to thank past editorial teams and celebrate with IMIA.