Big Data Application in Biomedical Research and Health Care: A Literature Review

Speech-based recognition and estimating severity of PTSD using machine learning

BACKGROUND

Traditional methodologies for diagnosing post-traumatic stress disorder (PTSD) primarily rely on interviews, incurring considerable costs and lacking objective indices. Integrating biomarkers and machine learning techniques into this diagnostic process has the potential to facilitate accurate PTSD assessment by clinicians.

METHODS

We assembled a dataset encompassing recordings from 76 individuals diagnosed with PTSD and 60 healthy controls. Leveraging the openSmile framework, we extracted acoustic features from these recordings and employed a random forest algorithm for feature selection. Subsequently, these selected features were utilized as inputs for six distinct classification models and a regression model.

RESULTS

Classification models employing a feature set of 18 elements yielded robust binary prediction outcomes for PTSD. Notably, the RF model achieved peak accuracy at 0.975 with the highest AUC of 1.0. In terms of the regression model, it exhibited significant predictive capability for PCL-5 scores (MSE = 0.90, MAE = 0.76, R2 = 0.10, p < 0.001). Noteworthy was the correlation coefficient of 0.33 (p < 0.01) between predicted and actual values.

LIMITATIONS

Firstly, the process of feature selection may compromise the stability of models, which leads to potentially overestimating results. Secondly, it is hard to elucidate the nature of biological mechanisms behind between PTSD patients and healthy individuals. Lastly, the regression model has a limited prediction for PTSD.

CONCLUSIONS

Distinct speech patterns differentiate PTSD patients and controls. Classification models accurately discern both groups. Regression model gauges PTSD severity, but further validation on larger datasets is needed.

Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review

BACKGROUND

Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs.

OBJECTIVE

This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs.

METHODS

Using a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs.

RESULTS

A total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms.

CONCLUSIONS

The synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.

TICS-M scores in an oldest-old normative cohort identified by computable phenotype

Objective: To (1) examine the distribution of Telephone Interview for Cognitive Status modified (TICS-m) scores in oldest-old individuals (age 85 and above) identified as cognitively healthy by a previously validated electronic health records-based computable phenotype (CP) and (2) to compare different cutoff scores for cognitive impairment in this population. Method: CP identified 24,024 persons, 470 were contacted and 252 consented and completed the assessment. Associations of TICS-m score with age, sex, and educational categories (<10 years, 11-15 years, and >16 years) were examined. The number of participants perceived as impaired was studied with commonly used cutoff scores (27-31). Results: TICS-m score ranged from 18 to 44 with a mean of 32.6 (SD = 4.7) in older adults aged 85-99 years old. A linear regression model including (range-restricted) age, education, and sex, showed beta estimates comparable to previous findings. Different cutoff scores (27 to 31) generated slightly lower MCI and dementia prevalence rates of participants meeting the criteria for the impairments than studies of younger elderly using traditional recruitment methods. Conclusions: The use of validated computable phenotype to identify a normative cohort generated a normative distribution for the TICS-m consistent with prior findings from more effortful approaches to cohort identification and established expected TICS-m performance in the oldest-old population.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Mapping the Apps: Ethical and Legal Issues with Crowdsourced Smartphone Data using mHealth Applications

More than 5 billion people in the world own a smartphone. More than half of these have been used to collect and process health-related data. As such, the existing volume of potentially exploitable health data is unprecedentedly large and growing rapidly. Mobile health applications (apps) on smartphones are some of the worst offenders and are increasingly being used for gathering and exchanging significant amounts of personal health data from the public. This data is often utilized for health research purposes and for algorithm training. While there are advantages to utilizing this data for expanding health knowledge, there are associated risks for the users of these apps, such as privacy concerns and the protection of their data. Consequently, gaining a deeper comprehension of how apps collect and crowdsource data is crucial. To explore how apps are crowdsourcing data and to identify potential ethical, legal, and social issues (ELSI), we conducted an examination of the Apple App Store and the Google Play Store in North America and Europe to identify apps that could potentially gather health data through crowdsourcing. Subsequently, we analyzed their privacy policies, terms of use, and other related documentation to gain insights into the utilization of users' data and the possibility of repurposing it for research or algorithm training purposes. More specifically, we reviewed privacy policies to identify clauses pertaining to the following key categories: research, data sharing, privacy/confidentiality, commercialization, and return of findings. Based on the results of these app search, we developed an App Atlas that presents apps which crowdsource data for research or algorithm training. We identified 46 apps available in the European and Canadian markets that either openly crowdsource health data for research or algorithm training or retain the legal or technical capability to do so. This app search showed an overall lack of consistency and transparency in privacy policies that poses challenges to user comprehensibility, trust, and informed consent. A significant proportion of applications presented contradictions or exhibited considerable ambiguity. For instance, the vast majority of privacy policies in the App Atlas contain ambiguous or contradictory language regarding the sharing of users' data with third parties. This raises a number of ethico-legal concerns which will require further academic and policy attention to ensure a balance between protecting individual interests and maximizing the scientific utility of crowdsourced data. This article represents a key first step in better understanding these concerns and bringing attention to this important issue.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41649-024-00296-3.

Enhancing selection of alcohol consumption-associated genes by random forest

Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17 873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.

Artificial intelligence in clinical pharmacology: A case study and scoping review of large language models and bioweapon potential

This paper aims to explore the possibility of employing large language models (LLMs) - a type of artificial intelligence (AI) - in clinical pharmacology, with a focus on its possible misuse in bioweapon development. Additionally, ethical considerations, legislation and potential risk reduction measures are analysed. The existing literature is reviewed to investigate the potential misuse of AI and LLMs in bioweapon creation. The search includes articles from PubMed, Scopus and Web of Science Core Collection that were identified using a specific protocol. To explore the regulatory landscape, the OECD.ai platform was used. The review highlights the dual-use vulnerability of AI and LLMs, with a focus on bioweapon development. Subsequently, a case study is used to illustrate the potential of AI manipulation resulting in harmful substance synthesis. Existing regulations inadequately address the ethical concerns tied to AI and LLMs. Mitigation measures are proposed, including technical solutions (explainable AI), establishing ethical guidelines through collaborative efforts, and implementing policy changes to create a comprehensive regulatory framework. The integration of AI and LLMs into clinical pharmacology presents invaluable opportunities, while also introducing significant ethical and safety considerations. Addressing the dual-use nature of AI requires robust regulations, as well as adopting a strategic approach grounded in technical solutions and ethical values following the principles of transparency, accountability and safety. Additionally, AI's potential role in developing countermeasures against novel hazardous substances is underscored. By adopting a proactive approach, the potential benefits of AI and LLMs can be fully harnessed while minimizing the associated risks.

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

Open Data governance at the Canadian Open Neuroscience Platform (CONP): From the Walled Garden to the Arboretum

Scientific research communities pursue dual imperatives in implementing strategies to share their data. These communities attempt to maximize the accessibility of biomedical data for downstream research use, in furtherance of open science objectives. Simultaneously, such communities safeguard the interests of research participants through data stewardship measures and the integration of suitable risk disclosures to the informed consent process. The Canadian Open Neuroscience Platform (CONP) convened an Ethics and Governance Committee composed of experts in bioethics, neuroethics, and law to develop holistic policy tools, organizational approaches, and technological supports to align the open governance of data with ethical and legal norms. The CONP has adopted novel platform governance methods that favor full data openness, legitimated through the use of robust deidentification processes and informed consent practices. The experience of the CONP is articulated as a potential template for other open science efforts to further build upon. This experience highlights informed consent guidance, deidentification practices, ethicolegal metadata, platform-level norms, and commercialization and publication policies as the principal pillars of a practicable approach to the governance of open data. The governance approach adopted by the CONP stands as a viable model for the broader neuroscience and open science communities to adopt for sharing data in full open access.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics-Part I: Why Is It Needed?

A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as "big data." Often vaguely defined, big data refers to large and complex datasets consisting of myriad datatypes that can be integrated to address complex questions. Big data offers a wealth of information that can be accessed only by those who pose the right questions and have sufficient technical knowhow and analytical skills. The intersection comprised of the gut-brain axis, the intestinal microbiome and multi-ome, and several other interconnected organ systems poses particular challenges and opportunities for those engaged in gastrointestinal and liver research. Unfortunately, there is currently a shortage of clinicians, scientists, and physician-scientists with the training needed to use and analyze big data at the scale necessary for widespread implementation of precision medicine. Here, we review the importance of training in the use of big data, the perils of insufficient training, and potential solutions that exist or can be developed to address the dearth of individuals in GI and hepatology research with the necessary level of big data expertise.

Connecting Agent-Based Models with High-Dimensional Parameter Spaces to Multidimensional Data Using SMoRe ParS: A Surrogate Modeling Approach

Across a broad range of disciplines, agent-based models (ABMs) are increasingly utilized for replicating, predicting, and understanding complex systems and their emergent behavior. In the biological and biomedical sciences, researchers employ ABMs to elucidate complex cellular and molecular interactions across multiple scales under varying conditions. Data generated at these multiple scales, however, presents a computational challenge for robust analysis with ABMs. Indeed, calibrating ABMs remains an open topic of research due to their own high-dimensional parameter spaces. In response to these challenges, we extend and validate our novel methodology, Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS), arriving at a computationally efficient framework for connecting high dimensional ABM parameter spaces with multidimensional data. Specifically, we modify SMoRe ParS to initially confine high dimensional ABM parameter spaces using unidimensional data, namely, single time-course information of in vitro cancer cell growth assays. Subsequently, we broaden the scope of our approach to encompass more complex ABMs and constrain parameter spaces using multidimensional data. We explore this extension with in vitro cancer cell inhibition assays involving the chemotherapeutic agent oxaliplatin. For each scenario, we validate and evaluate the effectiveness of our approach by comparing how well ABM simulations match the experimental data when using SMoRe ParS-inferred parameters versus parameters inferred by a commonly used direct method. In so doing, we show that our approach of using an explicitly formulated surrogate model as an interlocutor between the ABM and the experimental data effectively calibrates the ABM parameter space to multidimensional data. Our method thus provides a robust and scalable strategy for leveraging multidimensional data to inform multiscale ABMs and explore the uncertainty in their parameters.

Spatial mapping of tumor heterogeneity in whole-body PET-CT: a feasibility study

BACKGROUND

Tumor heterogeneity is recognized as a predictor of treatment response and patient outcome. Quantification of tumor heterogeneity across all scales may therefore provide critical insight that ultimately improves cancer management.

METHODS

An image registration-based framework for the study of tumor heterogeneity in whole-body images was evaluated on a dataset of 490 FDG-PET-CT images of lung cancer, lymphoma, and melanoma patients. Voxel-, lesion- and subject-level features were extracted from the subjects' segmented lesion masks and mapped to female and male template spaces for voxel-wise analysis. Resulting lesion feature maps of the three subsets of cancer patients were studied visually and quantitatively. Lesion volumes and lesion distances in subject spaces were compared with resulting properties in template space. The strength of the association between subject and template space for these properties was evaluated with Pearson's correlation coefficient.

RESULTS

Spatial heterogeneity in terms of lesion frequency distribution in the body, metabolic activity, and lesion volume was seen between the three subsets of cancer patients. Lesion feature maps showed anatomical locations with low versus high mean feature value among lesions sampled in space and also highlighted sites with high variation between lesions in each cancer subset. Spatial properties of the lesion masks in subject space correlated strongly with the same properties measured in template space (lesion volume, R = 0.986, p < 0.001; total metabolic volume, R = 0.988, p < 0.001; maximum within-patient lesion distance, R = 0.997, p < 0.001). Lesion volume and total metabolic volume increased on average from subject to template space (lesion volume, 3.1 ± 52 ml; total metabolic volume, 53.9 ± 229 ml). Pair-wise lesion distance decreased on average by 0.1 ± 1.6 cm and maximum within-patient lesion distance increased on average by 0.5 ± 2.1 cm from subject to template space.

CONCLUSIONS

Spatial tumor heterogeneity between subsets of interest in cancer cohorts can successfully be explored in whole-body PET-CT images within the proposed framework. Whole-body studies are, however, especially prone to suffer from regional variation in lesion frequency, and thus statistical power, due to the non-uniform distribution of lesions across a large field of view.

Developing Clinical Phenotype Data Collection Standards for Research in Africa

Modern biomedical research is characterised by its high-throughput and interdisciplinary nature. Multiproject and consortium-based collaborations requiring meaningful analysis of multiple heterogeneous phenotypic datasets have become the norm; however, such analysis remains a challenge in many regions across the world. An increasing number of data harmonisation efforts are being undertaken by multistudy collaborations through either prospective standardised phenotype data collection or retrospective phenotype harmonisation. In this regard, the Phenotype Harmonisation Working Group (PHWG) of the Human Heredity and Health in Africa (H3Africa) consortium aimed to facilitate phenotype standardisation by both promoting the use of existing data collection standards (hosted by PhenX), adapting existing data collection standards for appropriate use in low- and middle-income regions such as Africa, and developing novel data collection standards where relevant gaps were identified. Ultimately, the PHWG produced 11 data collection kits, consisting of 82 protocols, 38 of which were existing protocols, 17 were adapted, and 27 were novel protocols. The data collection kits will facilitate phenotype standardisation and harmonisation not only in Africa but also across the larger research community. In addition, the PHWG aims to feed back adapted and novel protocols to existing reference platforms such as PhenX.

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

Lossy Image Compression in a Preclinical Multimodal Imaging Study

The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compression in pre-clinical contexts as a potential alleviation of this problem. In this work, the authors study the application of lossy image coding to compress high-resolution volumetric biomedical data. The impact of compression on the metrics and interpretation of volumetric data was quantified for a correlated multimodal imaging study to characterize murine tumor vasculature, using volumetric high-resolution episcopic microscopy (HREM), micro-computed tomography (µCT), and micro-magnetic resonance imaging (µMRI). The effects of compression were assessed by measuring task-specific performances of several biomedical experts who interpreted and labeled multiple data volumes compressed at different degrees. We defined trade-offs between data volume reduction and preservation of visual information, which ensured the preservation of relevant vasculature morphology at maximum compression efficiency across scales. Using the Jaccard Index (JI) and the average Hausdorff Distance (HD) after vasculature segmentation, we could demonstrate that, in this study, compression that yields to a 256-fold reduction of the data size allowed to keep the error induced by compression below the inter-observer variability, with minimal impact on the assessment of the tumor vasculature across scales.

Evaluation of a Computable Phenotype for Successful Cognitive Aging

Objective

To establish, apply, and evaluate a computable phenotype for the recruitment of individuals with successful cognitive aging.

Participants and Methods

Interviews with 10 aging experts identified electronic health record (EHR)-available variables representing successful aging among individuals aged 85 years and older. On the basis of the identified variables, we developed a rule-based computable phenotype algorithm composed of 17 eligibility criteria. Starting September 1, 2019, we applied the computable phenotype algorithm to all living persons aged 85 years and older at the University of Florida Health, which identified 24,024 individuals. This sample was comprised of 13,841 (58%) women, 13,906 (58%) Whites, and 16,557 (69%) non-Hispanics. A priori permission to be contacted for research had been obtained for 11,898 individuals, of whom 470 responded to study announcements and 333 consented to evaluation. Then, we contacted those who consented to evaluate whether their cognitive and functional status clinically met out successful cognitive aging criteria of a modified Telephone Interview for Cognitive Status score of more than 27 and Geriatric Depression Scale of less than 6. The study was completed on December 31, 2022.

Results

Of the 45% of living persons aged 85 years and older included in the University of Florida Health EHR database identified by the computable phenotype as successfully aged, approximately 4% of these responded to study announcements and 333 consented, of which 218 (65%) met successful cognitive aging criteria through direct evaluation.

Conclusion

The study evaluated a computable phenotype algorithm for the recruitment of individuals for a successful aging study using large-scale EHRs. Our study provides proof of concept of using big data and informatics as aids for the recruitment of individuals for prospective cohort studies.

Drugst.One - A plug-and-play solution for online systems medicine and network-based drug repurposing

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis

OBJECTIVE

Diagnosis of lupus nephritis (LN) is a complex process, which usually requires renal biopsy. We aim to establish a machine learning pipeline to help diagnosis of LN.

METHODS

A cohort of 681 systemic lupus erythematosus (SLE) patients without LN and 786 SLE patients with LN was established, and a total of 95 clinical, laboratory data and 17 meteorological indicators were collected. After tenfold cross-validation, the patients were divided into training set and test set. The features selected by collective feature selection method of mutual information (MI) and multisurf were used to construct the models of logistic regression, decision tree, random forest, naive Bayes, support vector machine (SVM), light gradient boosting (LGB), extreme gradient boosting (XGB), and artificial neural network (ANN), the models were compared and verified in post-analysis.

RESULTS

Collective feature selection method screens out antistreptolysin (ASO), retinol binding protein (RBP), lupus anticoagulant 1 (LA1), LA2, proteinuria and other features, and the hyperparameter optimized XGB (ROC: AUC = 0.995; PRC: AUC = 1.000, APS = 1.000; balance accuracy: 0.990) has the best performance, followed by LGB (ROC: AUC = 0.992; PRC: AUC = 0.997, APS = 0.977; balance accuracy: 0.957). The worst performance is naive Bayes model (ROC: AUC = 0.799; PRC: AUC = 0.822, APS = 0.823; balance accuracy: 0.693). In the composite feature importance bar plots, ASO, RF, Up/Ucr, and other features play important roles in LN.

CONCLUSION

We developed and validated a new and simple machine learning pathway for diagnosis of LN, especially the XGB model based on ASO, LA1, LA2, proteinuria, and other features screened out by collective feature selection.

Big data and variceal rebleeding prediction in cirrhosis patients

Big data has convincing merits in developing risk stratification strategies for diseases. The 6 “V”s of big data, namely, volume, velocity, variety, veracity, value, and variability, have shown promise for real-world scenarios. Big data can be applied to analyze health data and advance research in preclinical biology, medicine, and especially disease initiation, development, and control. A study design comprises data selection, inclusion and exclusion criteria, standard confirmation and cohort establishment, follow-up strategy, and events of interest. The development and efficiency verification of a prognosis model consists of deciding the data source, taking previous models as references while selecting candidate predictors, assessing model performance, choosing appropriate statistical methods, and model optimization. The model should be able to inform disease development and outcomes, such as predicting variceal rebleeding in patients with cirrhosis. Our work has merits beyond those of other colleagues with respect to cirrhosis patient screening and data source regarding variceal bleeding.

Predicting the Recurrence of Common Bile Duct Stones After ERCP Treatment with Automated Machine Learning Algorithms

BACKGROUND

Recurrence of common bile duct stones (CBDs) commonly happens after endoscopic retrograde cholangiopancreatography (ERCP). The clinical prediction models for the recurrence of CBDs after ERCP are lacking.

AIMS

We aim to develop high-performance prediction models for the recurrence of CBDS after ERCP treatment using automated machine learning (AutoML) and to assess the AutoML models versus the traditional regression models.

METHODS

473 patients with CBDs undergoing ERCP were recruited in the single-center retrospective cohort study. Samples were divided into Training Set (65%) and Validation Set (35%) randomly. Three modeling approaches, including fully automated machine learning (Fully automated), semi-automated machine learning (Semi-automated), and traditional regression were applied to fit prediction models. Models' discrimination, calibration, and clinical benefits were examined. The Shapley additive explanations (SHAP), partial dependence plot (PDP), and SHAP local explanation (SHAPLE) were proposed for the interpretation of the best model.

RESULTS

The area under roc curve (AUROC) of semi-automated gradient boost machine (GBM) model was 0.749 in Validation Set, better than the other fully/semi-automated models and the traditional regression models (highest AUROC = 0.736). The calibration and clinical application of AutoML models were adequate. Through the SHAP-PDP-SHAPLE pipeline, the roles of key variables of the semi-automated GBM model were visualized. Lastly, the best model was deployed online for clinical practitioners.

CONCLUSION

The GBM model based on semi-AutoML is an optimal model to predict the recurrence of CBDs after ERCP treatment. In comparison with traditional regressions, AutoML algorithms present significant strengths in modeling, which show promise in future clinical practices.

Exploring data reduction strategies in the analysis of continuous pressure imaging technology

BACKGROUND

Science is becoming increasingly data intensive as digital innovations bring new capacity for continuous data generation and storage. This progress also brings challenges, as many scientific initiatives are challenged by the shear volumes of data produced. Here we present a case study of a data intensive randomized clinical trial assessing the utility of continuous pressure imaging (CPI) for reducing pressure injuries.

OBJECTIVE

To explore an approach to reducing the amount of CPI data required for analyses to a manageable size without loss of critical information using a nested subset of pressure data.

METHODS

Data from four enrolled study participants excluded from the analytical phase of the study were used to develop an approach to data reduction. A two-step data strategy was used. First, raw data were sampled at different frequencies (5, 30, 60, 120, and 240 s) to identify optimal measurement frequency. Second, similarity between adjacent frames was evaluated using correlation coefficients to identify position changes of enrolled study participants. Data strategy performance was evaluated through visual inspection using heat maps and time series plots.

RESULTS

A sampling frequency of every 60 s provided reasonable representation of changes in interface pressure over time. This approach translated to using only 1.7% of the collected data in analyses. In the second step it was found that 160 frames within 24 h represented the pressure states of study participants. In total, only 480 frames from the 72 h of collected data would be needed for analyses without loss of information. Only ~ 0.2% of the raw data collected would be required for assessment of the primary trial outcome.

CONCLUSIONS

Data reduction is an important component of big data analytics. Our two-step strategy markedly reduced the amount of data required for analyses without loss of information. This data reduction strategy, if validated, could be used in other CPI and other settings where large amounts of both temporal and spatial data must be analysed.

Evaluation of a decided sample size in machine learning applications

BACKGROUND

An appropriate sample size is essential for obtaining a precise and reliable outcome of a study. In machine learning (ML), studies with inadequate samples suffer from overfitting of data and have a lower probability of producing true effects, while the increment in sample size increases the accuracy of prediction but may not cause a significant change after a certain sample size. Existing statistical approaches using standardized mean difference, effect size, and statistical power for determining sample size are potentially biased due to miscalculations or lack of experimental details. This study aims to design criteria for evaluating sample size in ML studies. We examined the average and grand effect sizes and the performance of five ML methods using simulated datasets and three real datasets to derive the criteria for sample size. We systematically increase the sample size, starting from 16, by randomly sampling and examine the impact of sample size on classifiers' performance and both effect sizes. Tenfold cross-validation was used to quantify the accuracy.

RESULTS

The results demonstrate that the effect sizes and the classification accuracies increase while the variances in effect sizes shrink with the increment of samples when the datasets have a good discriminative power between two classes. By contrast, indeterminate datasets had poor effect sizes and classification accuracies, which did not improve by increasing sample size in both simulated and real datasets. A good dataset exhibited a significant difference in average and grand effect sizes. We derived two criteria based on the above findings to assess a decided sample size by combining the effect size and the ML accuracy. The sample size is considered suitable when it has appropriate effect sizes (≥ 0.5) and ML accuracy (≥ 80%). After an appropriate sample size, the increment in samples will not benefit as it will not significantly change the effect size and accuracy, thereby resulting in a good cost-benefit ratio.

CONCLUSION

We believe that these practical criteria can be used as a reference for both the authors and editors to evaluate whether the selected sample size is adequate for a study.

Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathologic Predictive Power of RhoB in Rectal Cancer Biopsy

RhoB protein belongs to the Rho GTPase family, which plays an important role in governing cell signaling and tissue morphology. RhoB expression is known to have implications in pathologic processes of diseases. Investigation in the regulation and communication of this protein, detected by immunohistochemical staining on the microscope, is worth exploring to gain insightful information that may lead to identifying optimal disease treatment options. In particular, the role of RhoB in rectal cancer is not well discovered. Here, we report that methods of deep learning-based image analysis and the decomposition of multiway arrays discover the predictive factor of RhoB in two cohorts of patients with rectal cancer having survival rates of <5 and >5 years. The analysis results show distinctions between the tensor decomposition factors of the two cohorts.

Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning

Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.

Data harnessing to nurture the human mind for a tailored approach to the child

Big data in pediatrics is an ocean of structured and unstructured data. Big data analysis helps to dive into the ocean of data to filter out information that can guide pediatricians in their decision making, precision diagnosis, and targeted therapy. In addition, big data and its analysis have helped in the surveillance, prevention, and performance of the health system. There has been a considerable amount of work in pediatrics that we have tried to highlight in this review and some of it has been already incorporated into the health system. Work in specialties of pediatrics is still forthcoming with the creation of a common data model and amalgamation of the huge "omics" database. The physicians entrusted with the care of children must be aware of the outcome so that they can play a role to ensure that big data algorithms have a clinically relevant effect in improving the health of their patients. They will apply the outcome of big data and its analysis in patient care through clinical algorithms or with the help of embedded clinical support alerts from the electronic medical records. IMPACT: Big data in pediatrics include structured, unstructured data, waveform data, biological, and social data. Big data analytics has unraveled significant information from these databases. This is changing how pediatricians will look at the body of available evidence and translate it into their clinical practice. Data harnessed so far is implemented in certain fields while in others it is in the process of development to become a clinical adjunct to the physician. Common databases are being prepared for future work. Diagnostic and prediction models when incorporated into the health system will guide the pediatrician to a targeted approach to diagnosis and therapy.

Improving child health through Big Data and data science

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

Data Analytics in Healthcare: A Tertiary Study

The field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analytics applications in different healthcare sectors, including diagnosis and disease profiling, diabetes, Alzheimer's disease, and sepsis. Machine learning and data mining were the most widely used data analytics techniques in healthcare applications, with a rising trend in popularity. Healthcare data analytics studies often utilize four popular databases in their primary study search, typically select 25-100 primary studies, and the use of research guidelines such as PRISMA is growing. The results may help both data analytics and healthcare researchers towards relevant and timely literature reviews and systematic mappings, and consequently, towards respective empirical studies. In addition, the meta-analysis presents a high-level perspective on prominent data analytics applications in healthcare, indicating the most popular topics in the intersection of data analytics and healthcare, and provides a big picture on a topic that has seen dozens of secondary studies in the last 2 decades.

Prediction of COVID-19 diagnosis based on openEHR artefacts

Nowadays, we are facing the worldwide pandemic caused by COVID-19. The complexity and momentum of monitoring patients infected with this virus calls for the usage of agile and scalable data structure methodologies. OpenEHR is a healthcare standard that is attracting a lot of attention in recent years due to its comprehensive and robust architecture. The importance of an open, standardized and adaptable approach to clinical data lies in extracting value to generate useful knowledge that really can help healthcare professionals make an assertive decision. This importance is even more accentuated when facing a pandemic context. Thus, in this study, a system for tracking symptoms and health conditions of suspected or confirmed SARS-CoV-2 patients from a Portuguese hospital was developed using openEHR. All data on the evolutionary status of patients in home care as well as the results of their COVID-19 test were used to train different ML algorithms, with the aim of developing a predictive model capable of identifying COVID-19 infections according to the severity of symptoms identified by patients. The CRISP-DM methodology was used to conduct this research. The results obtained were promising, with the best model achieving an accuracy of 96.25%, a precision of 99.91%, a sensitivity of 92.58%, a specificity of 99.92%, and an AUC of 0.963, using the Decision Tree algorithm and the Split Validation method. Hence, in the future, after further testing, the predictive model could be implemented in clinical decision support systems.

Compressed gastric image generation based on soft-label dataset distillation for medical data sharing

BACKGROUND AND OBJECTIVE

  Sharing of medical data is required to enable the cross-agency flow of healthcare information and construct high-accuracy computer-aided diagnosis systems. However, the large sizes of medical datasets, the massive amount of memory of saved deep convolutional neural network (DCNN) models, and patients^' privacy protection are problems that can lead to inefficient medical data sharing. Therefore, this study proposes a novel soft-label dataset distillation method for medical data sharing.

METHODS

The proposed method distills valid information of medical image data and generates several compressed images with different data distributions for anonymous medical data sharing. Furthermore, our method can extract essential weights of DCNN models to reduce the memory required to save trained models for efficient medical data sharing.

RESULTS

The proposed method can compress tens of thousands of images into several soft-label images and reduce the size of a trained model to a few hundredths of its original size. The compressed images obtained after distillation have been visually anonymized; therefore, they do not contain the private information of the patients. Furthermore, we can realize high-detection performance with a small number of compressed images.

CONCLUSIONS

The experimental results show that the proposed method can improve the efficiency and security of medical data sharing.

Multiple sclerosis and computational biology (Review)

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.

Correlates of wanting to seek help for mental health and substance use concerns by sexual and gender minority young adults during the COVID-19 pandemic: A machine learning analysis

The COVID-19 pandemic has worsened the mental health and substance use challenges among many people who are Two Spirit, lesbian, gay, bisexual, transgender, queer, questioning, and intersex (2SLGBTQI+). We aimed to identify the important correlates and their effects on the predicted likelihood of wanting to seek help among 2SLGBTQI+ young adults for mental health or substance use concerns during the pandemic. A cross-sectional survey was conducted in 2020-2021 among 2SLGBTQI+ young adults aged 16-29 living in two Canadian provinces (Ontario and Quebec). Among 1414 participants, 77% (n = 1089) wanted to seek help for their mental health or substance use concerns during the pandemic, out of these, 69.8% (n = 760) reported delay in accessing care. We built a random forest (RF) model to predict the status of wanting to seek help, which achieved moderately high performance with an area under the receiver operating characteristic curve (AUC) of 0.85. The top 10 correlates of wanting to seek help were worsening mental health, age, stigma and discrimination, and adverse childhood experiences. The interactions of adequate housing with certain sexual orientations, gender identities and mental health challenges were found to increase the likelihood of wanting to seek help. We built another RF model for predicting risk of delay in accessing care among participants who wanted to seek help (n = 1089). The model identified a similar set of top 10 correlates of delay in accessing care but lacked adequate performance (AUC 0.61). These findings can direct future research and targeted prevention measures to reduce health disparities for 2SLGBTQI+ young adults.

Computer-aided food engineering

Computer-aided food engineering (CAFE) can reduce resource use in product, process and equipment development, improve time-to-market performance, and drive high-level innovation in food safety and quality. Yet, CAFE is challenged by the complexity and variability of food composition and structure, by the transformations food undergoes during processing and the limited availability of comprehensive mechanistic frameworks describing those transformations. Here we introduce frameworks to model food processes and predict physiochemical properties that will accelerate CAFE. We review how investments in open access, such as code sharing, and capacity-building through specialized courses could facilitate the use of CAFE in the transformation already underway in digital food systems.

Local data commons: the sleeping beauty in the community of data commons

Background

Public Data Commons (PDC) have been highlighted in the scientific literature for their capacity to collect and harmonize big data. On the other hand, local data commons (LDC), located within an institution or organization, have been underrepresented in the scientific literature, even though they are a critical part of research infrastructure. Being closest to the sources of data, LDCs provide the ability to collect and maintain the most up-to-date, high-quality data within an organization, closest to the sources of the data. As a data provider, LDCs have many challenges in both collecting and standardizing data, moreover, as a consumer of PDC, they face problems of data harmonization stemming from the monolithic harmonization pipeline designs commonly adapted by many PDCs. Unfortunately, existing guidelines and resources for building and maintaining data commons exclusively focus on PDC and provide very little information on LDC.

Results

This article focuses on four important observations. First, there are three different types of LDC service models that are defined based on their roles and requirements. These can be used as guidelines for building new LDC or enhancing the services of existing LDC. Second, the seven core services of LDC are discussed, including cohort identification and facilitation of genomic sequencing, the management of molecular reports and associated infrastructure, quality control, data harmonization, data integration, data sharing, and data access control. Third, instead of commonly developed monolithic systems, we propose a new data sharing method for data harmonization that combines both divide-and-conquer and bottom-up approaches. Finally, an end-to-end LDC implementation is introduced with real-world examples.

Conclusions

Although LDCs are an optimal place to identify and address data quality issues, they have traditionally been relegated to the role of passive data provider for much larger PDC. Indeed, many LDCs limit their functions to only conducting routine data storage and transmission tasks due to a lack of information on how to design, develop, and improve their services using limited resources. We hope that this work will be the first small step in raising awareness among the LDCs of their expanded utility and to publicize to a wider audience the importance of LDC.

A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction

The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpretability and medical expertise limits its clinical application. This paper combines knowledge representation learning and deep learning methods, and a disease prediction model is constructed. The model initially constructs the relationship graph between the physical indicator and the test value based on the normal range of human physical examination index. And the human physical examination index for testing value by knowledge representation learning model is encoded. Then, the patient physical examination data is represented as a vector and input into a deep learning model built with self-attention mechanism and convolutional neural network to implement disease prediction. The experimental results show that the model which is used in diabetes prediction yields an accuracy of 97.18% and the recall of 87.55%, which outperforms other machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost). Compared with the best performing random forest method, the recall is increased by 5.34%, respectively. Therefore, it can be concluded that the application of medical knowledge into deep learning through knowledge representation learning can be used in diabetes prediction for the purpose of early detection and assisting diagnosis.

Interactive exploration of a global clinical network from a large breast cancer cohort

Despite unprecedented amount of information now available in medical records, health data remain underexploited due to their heterogeneity and complexity. Simple charts and hypothesis-driven statistics can no longer apprehend the content of information-rich clinical data. There is, therefore, a clear need for powerful interactive visualization tools enabling medical practitioners to perceive the patterns and insights gained by state-of-the-art machine learning algorithms. Here, we report an interactive graphical interface for use as the front end of a machine learning causal inference server (MIIC), to facilitate the visualization and comprehension by clinicians of relationships between clinically relevant variables. The widespread use of such tools, facilitating the interactive exploration of datasets, is crucial both for data visualization and for the generation of research hypotheses. We demonstrate the utility of the MIIC interactive interface, by exploring the clinical network of a large cohort of breast cancer patients treated with neoadjuvant chemotherapy (NAC). This example highlights, in particular, the direct and indirect links between post-NAC clinical responses and patient survival. The MIIC interactive graphical interface has the potential to help clinicians identify actionable nodes and edges in clinical networks, thereby ultimately improving the patient care pathway.

Parametric and non-parametric estimation of reference intervals for routine laboratory tests: an analysis of health check-up data for 260 889 young men in the South Korean military

OBJECTIVES

Determination of reference intervals (RIs) using big data faces several obstacles due to heterogeneity in analysers, period and ethnicity. The present study aimed to establish the RIs for routine common blood count (CBC) and biochemistry laboratory tests in homogeneous, healthy, male Korean soldiers in their 20s using a large health check-up data set, comparing parametric and non-parametric estimation.

DESIGN

A multicentre, cross-sectional study.

SETTING

Seven armed forces hospitals in South Korea.

PARTICIPANTS

A total of 609 649 men underwent health examination when promoted to corporal between January 2015 and September 2021. 260 889 eligible individuals aged 20-25 were included in the analysis.

MAIN OUTCOMES AND MEASURES

The RIs were established by parametric and non-parametric methods. In the parametric approach, maximum likelihood estimation was applied to measure the Box-Cox transformation parameter and the values at the 2.5th and 97.5th percentiles were recalculated. The non-parametric approach adopted the Tukey's exclusion test and the values at the 2.5th and 97.5th percentiles were obtained. Classification by body mass index was also performed.

RESULTS

The obtained RIs for haematology parameters were comparable between devices. If the values followed a Gaussian distribution, parametric and non-parametric methods were well matched for haematology and biochemical markers. When the values were right-skewed, the upper limits were higher with parametric than with non-parametric methods. Participants with obesity showed higher RIs for CBC, some liver function tests and some lipid profiles than participants without obesity.

CONCLUSIONS

Using data from healthy, male Korean soldiers in their 20s, we proposed the RIs for CBC and biochemical parameters, comparing parametric and non-parametric estimation. As such approaches based on large data sets become more prevalent, further studies are needed to discriminate eligible individuals and determine RIs in an extrapolated sample.

Comparing Worldwide, National, and Independent Notifications about Adverse Drug Reactions Due to COVID-19 Vaccines

The rapid development of effective vaccines against COVID-19 is an extraordinary achievement. However, no medical product can ever be considered risk-free. Several countries have a pharmacovigilance system that detects, assesses, understands, and prevents possible adverse effects of a drug. To benefit from such huge data sources, specialists and researchers need advanced big data analysis tools able to extract value and find valuable insights. This paper defines a general framework for a pharmaceutical data analysis application that provides a predefined (but extensible) set of functions for each data processing step (i.e., data collection, filtering, enriching, analysis, and visualization). As a case study, we present here an analysis of the potential side effects observed following the administration of the COVID-19 vaccines. The experimental evaluation shows that: (i) most adverse events can be classified as non-serious and concern muscle/joint pain, chills and nausea, headache, and fatigue; (ii) the notification rate is higher in the age group 20–39 years and decreases in older age groups and in very young people.

Artifact3-D: New software for accurate, objective and efficient 3D analysis and documentation of archaeological artifacts

The study of artifacts is fundamental to archaeological research. The features of individual artifacts are recorded, analyzed, and compared within and between contextual assemblages. Here we present and make available for academic-use Artifact3-D, a new software package comprised of a suite of analysis and documentation procedures for archaeological artifacts. We introduce it here, alongside real archaeological case studies to demonstrate its utility. Artifact3-D equips its users with a range of computational functions for accurate measurements, including orthogonal distances, surface area, volume, CoM, edge angles, asymmetry, and scar attributes. Metrics and figures for each of these measurements are easily exported for the purposes of further analysis and illustration. We test these functions on a range of real archaeological case studies pertaining to tool functionality, technological organization, manufacturing traditions, knapping techniques, and knapper skill. Here we focus on lithic artifacts, but the Artifact3-D software can be used on any artifact type to address the needs of modern archaeology. Computational methods are increasingly becoming entwined in the excavation, documentation, analysis, database creation, and publication of archaeological research. Artifact3-D offers functions to address every stage of this workflow. It equips the user with the requisite toolkit for archaeological research that is accurate, objective, repeatable and efficient. This program will help archaeological research deal with the abundant material found during excavations and will open new horizons in research trajectories.

Data protection-compliant broad consent for secondary use of health care data and human biosamples for (bio)medical research: Towards a new German national standard

BACKGROUND

The secondary use of deidentified but not anonymized patient data is a promising approach for enabling precision medicine and learning health care systems. In most national jurisdictions (e.g., in Europe), this type of secondary use requires patient consent. While various ethical, legal, and technical analyses have stressed the opportunities and challenges for different types of consent over the past decade, no country has yet established a national consent standard accepted by the relevant authorities.

METHODS

A working group of the national Medical Informatics Initiative in Germany conducted a requirements analysis and developed a GDPR-compliant broad consent standard. The development included consensus procedures within the Medical Informatics Initiative, a documented consultation process with all relevant stakeholder groups and authorities, and the ultimate submission for approval via the national data protection authorities.

RESULTS

This paper presents the broad consent text together with a guidance document on mandatory safeguards for broad consent implementation. The mandatory safeguards comprise i) independent review of individual research projects, ii) organizational measures to protect patients from involuntary disclosure of protected information, and iii) comprehensive information for patients and public transparency. This paper further describes the key issues discussed with the relevant authorities, especially the position on additional or alternative consent approaches such as dynamic consent.

DISCUSSION

Both the resulting broad consent text and the national consensus process are relevant for similar activities internationally. A key challenge of aligning consent documents with the various stakeholders was explaining and justifying the decision to use broad consent and the decision against using alternative models such as dynamic consent. Public transparency for all secondary use projects and their results emerged as a key factor in this justification. While currently largely limited to academic medicine in Germany, the first steps for extending this broad consent approach to wider areas of application, including smaller institutions and medical practices, are currently under consideration.

An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images

The lungs are COVID-19's most important focus, as it induces inflammatory changes in the lungs that can lead to respiratory insufficiency. Reducing the supply of oxygen to human cells negatively impacts humans, and multiorgan failure with a high mortality rate may, in certain circumstances, occur. Radiological pulmonary evaluation is a vital part of patient therapy for the critically ill patient with COVID-19. The evaluation of radiological imagery is a specialized activity that requires a radiologist. Artificial intelligence to display radiological images is one of the essential topics. Using a deep machine learning technique to identify morphological differences in the lungs of COVID-19-infected patients could yield promising results on digital images of chest X-rays. Minor differences in digital images that are not detectable or apparent to the human eye may be detected using computer vision algorithms. This paper uses machine learning methods to diagnose COVID-19 on chest X-rays, and the findings have been very promising. The dataset includes COVID-19-enhanced X-ray images for disease detection using chest X-ray images. The data were gathered from two publicly accessible datasets. The feature extractions are done using the gray level co-occurrence matrix methods. K-nearest neighbor, support vector machine, linear discrimination analysis, naïve Bayes, and convolutional neural network methods are used for the classification of patients. According to the findings, convolutional neural networks' efficiency linked to imaging modalities with fewer human involvements outperforms other traditional machine learning approaches.

Influence of ambient air pollution on successful pregnancy with frozen embryo transfer: A machine learning prediction model

Numerous air pollutants have been reported to influence the outcomes of in vitro fertilization (IVF). However, whether air pollution affects implantation in frozen embryo transfer (FET) process is under debate. We aimed to find the association between ambient air pollution and implantation potential of FET and test the value of adding air pollution data to a random forest model (RFM) predicting intrauterine pregnancy. Using a retrospective study of a 4-year single-center design，we analyzed 3698 cycles of women living in Shanghai who underwent FET between 2015 and 2018. To estimate patients' individual exposure to air pollution, we computed averages of daily concentrations of six air pollutants including PM_2.5, PM₁₀, SO₂, CO, NO₂, and O₃ measured at 9 monitoring stations in Shanghai for the exposure period (one month before FET). Moreover, A predictive model of 15 variables was established using RFM. Air pollutants levels of patients with or without intrauterine pregnancy were compared. Our results indicated that for exposure periods before FET, NO₂ were negatively associated with intrauterine pregnancy (OR: 0.906, CI: 0.816-0.989). AUROC increased from 0.712 to 0.771 as air pollutants features were added. Overall, our findings demonstrate that exposure to NO₂ before transfer has an adverse effect on clinical pregnancy. The performance to predict intrauterine pregnancy will improve with the use of air pollution data in RFM.

Development of Elderly Life Quality Database in Thailand with a Correlation Feature Analysis

Understanding the context of the elderly is very important for determining guidelines that improve their quality of life. One problem in Thailand, in this context, is that each organization involved in caring for the elderly has its own separate data collection, resulting in mismatches that negatively affect government agencies in their monitoring. This study proposes the development of a central database for elderly care and includes a study of factors affecting their quality of life. The proposed system can be used to collect data, manage data, perform data analysis with multiple linear regression, and display results via a web application in visualizations of many forms, such as graphs, charts, and spatial data. In addition, our system would replace paper forms and increase efficiency in work, as well as in storage and processing. In an observational case study, we include 240 elderly in village areas 5, 6, 7, and 8, in the Makham Tia subdistrict, Muang district, Surat Thani province, Thailand. Data were analyzed with multiple linear regression to predict the level of quality of life by using other indicators in the data gathered. This model uses only 14 factors of the available 39. Moreover, this model has an accuracy of 86.55%, R-squared = 69.11%, p-Value < 2.2×10−16, and Kappa = 0.7994 at 95% confidence. These results can make subsequent data collection more comfortable and faster as the number of questions is reduced, while revealing with good confidence the level of quality of life of the elderly. In addition, the system has a central database that is useful for elderly care organizations in the community, in support of planning and policy setting for elderly care.

A Web Application for Biomedical Text Mining of Scientific Literature Associated with Coronavirus-Related Syndromes: Coronavirus Finder

In this study, a web application was developed that comprises scientific literature associated with the Coronaviridae family, specifically for those viruses that are members of the Genus Betacoronavirus, responsible for emerging diseases with a great impact on human health: Middle East Respiratory Syndrome-Related Coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome-Related Coronavirus (SARS-CoV, SARS-CoV-2). The information compiled on this webserver aims to understand the basics of these viruses' infection, and the nature of their pathogenesis, enabling the identification of molecular and cellular components that may function as potential targets on the design and development of successful treatments for the diseases associated with the Coronaviridae family. Some of the web application's primary functions are searching for keywords within the scientific literature, natural language processing for the extraction of genes and words, the generation and visualization of gene networks associated with viral diseases derived from the analysis of latent semantic space, and cosine similarity measures. Interestingly, our gene association analysis reveals drug targets in understudies, and new targets suggested in the scientific literature to treat coronavirus.

System Architecture of a European Platform for Health Policy Decision Making: MIDAS

Background

Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner.

Methods

The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources.

Results

A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics.

Conclusions

The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions.

Medical-Blocks: A Platform for Exploration, Management, Analysis, and Sharing of Data in Biomedical Research

BACKGROUND

Biomedical research requires healthcare institutions to provide sensitive clinical data to leverage data science and artificial intelligence technologies. However, providing healthcare data to researchers simple and secure, proves to be challenging for healthcare institutions.

OBJECTIVE

We describe and introduce Medical-Blocks, a platform for data exploration, data management, data analysis, and data sharing in biomedical research.

METHODS

The specification requirements for Medical-Blocks included: i) Connection to data sources of healthcare institutions with an interface for data exploration, ii) management of data in an internal file storage system, iii) data analysis through visualization and classification of data, and iv) data sharing via a file hosting service for collaboration. Medical-Blocks should be simple to use via a web-based user interface and extensible with new functionalities by a modular design via microservices ("blocks"). The scalability of the platform should be ensured by containerization. Security and legal regulations were considered during the development.

RESULTS

Medical-Blocks is a web application that runs in the cloud or as a local instance at a healthcare institution. Local instances of Medical-Blocks access data sources such as electronic health records and picture archiving and communications system (PACS) at healthcare institutions. Researchers and clinicians can explore, manage, and analyze the available data through Medical-Blocks. The data analysis involves classification of data for metadata extraction and the formation of cohorts. In collaborations, metadata (e.g., number of patients per cohort) and/or the data itself can be shared through Medical-Blocks locally or via a cloud instance to other researchers and clinicians.

CONCLUSIONS

Medical-Blocks facilitates biomedical research by providing a centralized platform to interact with medical data in collaborative research projects. The access to and management of medical data is simplified. Data can be swiftly analyzed to form cohorts for research and be shared among researchers. The modularity of Medical-Blocks makes the platform feasible for biomedical research where heterogenous medical data is needed.

CLINICALTRIAL

Improving Diabetes-Related Biomedical Literature Exploration in the Clinical Decision-making Process via Interactive Classification and Topic Discovery: Methodology Development Study

BACKGROUND

The amount of available textual health data such as scientific and biomedical literature is constantly growing and becoming more and more challenging for health professionals to properly summarize those data and practice evidence-based clinical decision making. Moreover, the exploration of unstructured health text data is challenging for professionals without computer science knowledge due to limited time, resources, and skills. Current tools to explore text data lack ease of use, require high computational efforts, and incorporate domain knowledge and focus on topics of interest with difficulty.

OBJECTIVE

We developed a methodology able to explore and target topics of interest via an interactive user interface for health professionals with limited computer science knowledge. We aim to reach near state-of-the-art performance while reducing memory consumption, increasing scalability, and minimizing user interaction effort to improve the clinical decision-making process. The performance was evaluated on diabetes-related abstracts from PubMed.

METHODS

The methodology consists of 4 parts: (1) a novel interpretable hierarchical clustering of documents where each node is defined by headwords (words that best represent the documents in the node), (2) an efficient classification system to target topics, (3) minimized user interaction effort through active learning, and (4) a visual user interface. We evaluated our approach on 50,911 diabetes-related abstracts providing a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against 3 other strategies: random selection of training instances, uncertainty sampling that chooses instances about which the model is most uncertain, and an expected gradient length strategy based on convolutional neural networks (CNNs).

RESULTS

For the hierarchical clustering performance, we achieved an F1 score of 0.73 compared to 0.76 achieved by scikit-learn. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1 score of all MeSH codes resulted in a satisfying 0.62 F1 score using our approach, 0.61 using the uncertainty strategy, 0.63 using the CNN, and 0.45 using the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents.

CONCLUSIONS

We proposed an easy-to-use tool for health professionals with limited computer science knowledge who combine their domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore, our approach is memory efficient and highly parallelizable, making it interesting for large Big Data sets. This approach can be used by health professionals to gain deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.