An Apple Watch Dashboard for HiGHmed Heart Insufficency Patients

Wearables are commercially available devices allowing continuous monitoring of users' health parameters. Their easy availability, increasing accuracy and functionality render them relevant for medical practice, specifically for longitudinal monitoring. There are clear benefits for the health care system, such as the opportunity of timely interventions by monitoring a patient during his daily life, resulting in a cost reduction in medical care and improved patient well-being. However, some tools are essential to enable the application of wearables in medical daily practice. For example, there is a need for software solutions that allow clinicians to quickly and easily analyze data from devices of their patients. The goal of this study was to develop a dashboard for physicians, which allows rapid data interpretation of longitudinal data from the Apple Watch. The prototype dashboard is an interactive web-based visualization platform utilizing Plotly. The dashboard displays the most important parameters like heart rate, steps per day, activity, exercise collected by the Apple Watch in a user-friendly and accessible way. Clear visualization makes it easy to identify trends or deviations in the data and see how these changes in daily behaviour affect patients' health. Our software is a key component to monitor patients with heart failure who participate in the HiGHmed use case cardiology project.

Current use of effect size or confidence interval analyses in clinical and biomedical research

The isolated use of the statistical hypothesis testing for two group comparison has limitations, and its combination with effect size or confidence interval analysis as complementary statistical tests is recommended. In the present work, we estimate the use of these complementary statistical tests (i.e. effect size or confidence interval) in recently published in research articles in clinical and biomedical areas. Methods: The ProQuest database was used to search published studies in academic journals between 2019 and 2020. The analysis was carried out using terms that represent five areas of clinical and biomedical research: "brain", "liver", "heart", "dental", and "covid-19". A total of 119,558 published articles were retrieved. Results: The relative use of complementary statistical tests in clinical and biomedical publications was low. The highest frequency usage of complementary statistical tests was among articles that also used statistical hypothesis testing for two-sample comparison. Publications with the term "covid-19" showed the lowest usage rate of complementary statistical tests when all article were analyzed but presented the highest rate among articles that used hypothesis testing. Conclusion: The low use of effect size or confidence interval in two-sample comparison suggests that coordinate measures should be taken in order to increase the use of this analysis in clinical and biomedical research. Their use should be emphasized in statistical disciplines for college and graduate students, become a routine procedure in research laboratories, and recommended by reviewers and editors of scientific journals.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11192-021-04150-3.

Impact of the Choice of Cross-Validation Techniques on the Results of Machine Learning-Based Diagnostic Applications

Objective

With advances in data availability and computing capabilities, artificial intelligence and machine learning technologies have evolved rapidly in recent years. Researchers have taken advantage of these developments in healthcare informatics and created reliable tools to predict or classify diseases using machine learning-based algorithms. To correctly quantify the performance of those algorithms, the standard approach is to use cross-validation, where the algorithm is trained on a training set, and its performance is measured on a validation set. Both datasets should be subject-independent to simulate the expected behavior of a clinical study. This study compares two cross-validation strategies, the subject-wise and the record-wise techniques; the subject-wise strategy correctly mimics the process of a clinical study, while the record-wise strategy does not.

Methods

We started by creating a dataset of smartphone audio recordings of subjects diagnosed with and without Parkinson's disease. This dataset was then divided into training and holdout sets using subject-wise and the record-wise divisions. The training set was used to measure the performance of two classifiers (support vector machine and random forest) to compare six cross-validation techniques that simulated either the subject-wise process or the record-wise process. The holdout set was used to calculate the true error of the classifiers.

RESULTS

The record-wise division and the record-wise cross-validation techniques overestimated the performance of the classifiers and underestimated the classification error.

Conclusions

In a diagnostic scenario, the subject-wise technique is the proper way of estimating a model's performance, and record-wise techniques should be avoided.

Digitizing Data Management for Intraoperative Neuromonitoring

Intraoperative neurophysiological monitoring (IOM) enables a function-preserving surgical strategy for surgeries of brain or spinal cord pathologies by neurophysiological measurements. However, the IOM data management at neurosurgical institutions are often either not digitized or inefficient in terms of collecting, storing and processing of IOM data. Here, we describe the development of a web application, called IOM-Manager, as a first step towards the complete digitization of the IOM workflow. The web application is used for structured protocoling based on standardized protocol entry catalog, data archiving, and data analysis. These functionalities are based on the results of the requirement engineering of a process analysis, a survey with potential users and a market analysis. A usability test with one IOM team indicated the IOM-Manager and its other components can in fact solve many problems of existing solutions.

Optode Based Chemical Imaging-Possibilities, Challenges, and New Avenues in Multidimensional Optical Sensing

Seeing is believing, as the saying goes, and optical sensors (so-called optodes) are tools that can make chemistry visible. Optodes react reversibly and quickly (seconds to minutes) to changing analyte concentrations, enabling the spatial and temporal visualization of an analyte in complex environments. By being available as planar sensor foils or in the form of nano- or microparticles, optodes are flexible tools suitable for a wide array of applications. The steadily grown applications of in particular oxygen (O₂) and pH optodes in fields as diverse as medical, environmental, or material sciences is proof for the large demand of optode based chemical imaging. Nevertheless, the full potential of this technology is not exhausted yet, challenges have to be overcome, and new avenues wait to be taken. Within this Perspective, we look at where the field currently stands, highlight several successful examples of optode based chemical imaging and ask what it will take to advance current state-of-the-art technology. It is our intention to point toward some potential blind spots and to inspire further developments.

FhirExtinguisher: A FHIR Resource Flattening Tool Using FHIRPath

Data analysis with popular statistical toolchains like R usually needs to be performed on "flat tables" (so-called dataframes). However, data exchange is often done with FHIR, a format that is based on a hierarchical data model. In this paper, we want to present our tool FhirExtinguisher, which tackles the problems of loading FHIR data into statistical tools by extending the FHIRSearch API with an additional projection layer using FHIRPath. This projection layer can be used to select the data elements of interest and create a CSV file, which can be easily read as dataframe by almost any statistical toolchain.

Artificial Intelligence and Machine Learning to Predict Student Performance during the COVID-19

Artificial intelligence is based on algorithms that enable machines to make decisions instead of humans. This technology improves user experiences in a variety of areas. In this paper we discuss an intelligent solution to predict the performance of Moroccan students in the region of Guelmim Oued Noun through a recommendation system using artificial intelligence techniques during the COVID-19.

First Steps to Evaluate an NLP Tool's Medication Extraction Accuracy from Discharge Letters

INTRODUCTION

The aim of this study is to evaluate the use of a natural language processing (NLP) software to extract medication statements from unstructured medical discharge letters.

METHODS

Ten randomly selected discharge letters were extracted from the data warehouse of the University Hospital Erlangen (UHE) and manually annotated to create a gold standard. The AHD NLP tool, provided by MIRACUM's industry partner was used to annotate these discharge letters. Annotations by the NLP tool where then compared to the gold standard on two levels: phrase precision (whether or not the whole medication statement has been identified correctly) and token precision (whether or not the medication name has been identified correctly within correctly discovered medication phrases).

RESULTS

The NLP tool detected medication related phrases with an overall F-measure of 0.852. The medication name has been identified correctly with an overall F-measure of 0.936.

DISCUSSION

This proof-of-concept study is a first step towards an automated scalable evaluation system for MIRACUM's industry partner's NLP tool by using a gold standard. Medication phrases and names have been correctly identified in most cases by the NLP system. Future effort needs to be put into extending and validating the gold standard.

The Uptake and Factors Associated with Mastectomy among Chinese Women with Breast Cancer: A Retrospective Observational Study

OBJECTIVE

There are limited data concerning the use of mastectomy and associated factors in China in recent years. This study aimed to investigate the uptake of mastectomy and determine the associations between patients' characteristics and mastectomy among Chinese women with breast cancer.

METHODS

A retrospective analysis of female breast cancer cases from 1st January 2015 to 31st December 2019 from a tertiary hospital was conducted. Socio-demographic data, clinical data, and surgery types were collected by reviewing the medical record system. Chi-squared test, Fisher's exact test and multivariate logistic regression analysis were used to determine any correlations of patients' characteristics with mastectomy.

RESULTS

A total of 1,171 women with breast cancer were identified, and 76.60% of them underwent a mastectomy. The mastectomy rates showed an increase from 70.62% in 2015 to 86.87% in 2017 and then dropped to 71.91% in 2019. Women undergoing mastectomy were older and were more likely to be married and have at least one child. They had an advanced cancer stage, larger tumour size, and more lymph node invasion and were positive for HER-2 overexpression. Older age, larger tumour size (2-5 cm), higher cancer stages (stage 2- stage 3) and being positive for HER-2 were the four independent variables that significantly predicted the uptake of mastectomy.

CONCLUSIONS

Our results showed a wide application of mastectomy in China and uncovered the factors associated with mastectomy uptake from a single-centre experience. Findings suggested the potential overuse of mastectomy among women with early-stage breast cancer, and highlighted the significance of promoting cancer screening in China. Findings could be also used to develop relevant provisions and interventions to facilitate breast cancer treatment decision-making and screening planning.
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Brain tissues have single-voxel signatures in multi-spectral MRI

Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q-space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.

Opportunities and challenges of text mining in aterials research

Research publications are the major repository of scientific knowledge. However, their unstructured and highly heterogenous format creates a significant obstacle to large-scale analysis of the information contained within. Recent progress in natural language processing (NLP) has provided a variety of tools for high-quality information extraction from unstructured text. These tools are primarily trained on non-technical text and struggle to produce accurate results when applied to scientific text, involving specific technical terminology. During the last years, significant efforts in information retrieval have been made for biomedical and biochemical publications. For materials science, text mining (TM) methodology is still at the dawn of its development. In this review, we survey the recent progress in creating and applying TM and NLP approaches to materials science field. This review is directed at the broad class of researchers aiming to learn the fundamentals of TM as applied to the materials science publications.

Reporting Survey Based Studies - a Primer for Authors

The coronavirus disease 2019 (COVID-19) pandemic has led to a massive rise in survey-based research. The paucity of perspicuous guidelines for conducting surveys may pose a challenge to the conduct of ethical, valid and meticulous research. The aim of this paper is to guide authors aiming to publish in scholarly journals regarding the methods and means to carry out surveys for valid outcomes. The paper outlines the various aspects, from planning, execution and dissemination of surveys followed by the data analysis and choosing target journals. While providing a comprehensive understanding of the scenarios most conducive to carrying out a survey, the role of ethical approval, survey validation and pilot testing, this brief delves deeper into the survey designs, methods of dissemination, the ways to secure and maintain data anonymity, the various analytical approaches, the reporting techniques and the process of choosing the appropriate journal. Further, the authors analyze retracted survey-based studies and the reasons for the same. This review article intends to guide authors to improve the quality of survey-based research by describing the essential tools and means to do the same with the hope to improve the utility of such studies.

Timing Uncertainty in Collective Risk Dilemmas Encourages Group Reciprocation and Polarization

Social dilemmas are often shaped by actions involving uncertain returns only achievable in the future, such as climate action or voluntary vaccination. In this context, uncertainty may produce non-trivial effects. Here, we assess experimentally — through a collective risk dilemma — the effect of timing uncertainty, i.e. how uncertainty about when a target needs to be reached affects the participants' behaviors. We show that timing uncertainty prompts not only early generosity but also polarized outcomes, where participants' total contributions are distributed unevenly. Furthermore, analyzing participants' behavior under timing uncertainty reveals an increase in reciprocal strategies. A data-driven game-theoretical model captures the self-organizing dynamics underpinning these behavioral patterns. Timing uncertainty thus casts a shadow on the future that leads participants to respond early, whereas reciprocal strategies appear to be important for group success. Yet, the same uncertainty also leads to inequity and polarization, requiring the inclusion of new incentives handling these societal issues.

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Timing uncertainty influences experimental observations in the collective risk game

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It induces subjects to contribute earlier and in a polarized manner

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Successful players adopt reciprocal strategies, responding in kind to past actions

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Coordination gets more difficult under high timing uncertainty

The trend of prevalence of pain in Korea from 2005 to 2016

BACKGROUND

Korean society is afflicted with rapid aging. Aging is a risk factor for pain, and pain can reduce patients' quality of life. Thus, adequate management and monitoring of changing trends accompanying the demographic shift are highly valuable. However, this study was conducted because no studies have investigated the recent changes in the prevalence of pain.

METHODS

The extent of the prevalence of pain was determined by questions related to quality of life based on the data derived from the Korea National Health and Nutrition Survey (KNHNS) from 2005 to 2016. The annual frequencies of the pain group and severe pain group were calculated using the survey questionnaire. Multiple logistic regression analysis was performed to determine possible differences in prevalence by year.

RESULTS

The prevalence of pain in all populations was 30.6% in 2005 and 18.9% in 2016. The average prevalence from 2005 to 2016 was 21.9%. A declining trend occurred over time with an odds ratio of 0.929 per year (95% CI: 0.921-0.938). The prevalence of severe pain was 2.35% in 2005 and 1.88% in 2016. Likewise, a decrease was observed over time, with an odds ratio of 0.920 per year at 95% CI 0.901-0.939. The decline in age-/sex-stratified analysis also showed a statistically significant trend in all groups.

CONCLUSIONS

The prevalence of pain in Korean society, based on the KNHNS, has declined since 2005. Such a trend was observed in all ages and sexs, and was most significant in the elderly.

[Letter to the Editor about the Article "Is Medical Education Changing? Five Challenges for the Near Future"]

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Has the COVID-19 pandemic affected the susceptibility to cyberbullying in India?

Owing to the COVID-19 induced lockdown in India, most people’s internet activity surged, leading to an expected increase in the rate of cybercrimes. This research focuses on analyzing whether the factors significant in cyberbullying susceptibility changed with the lockdown. The study was conducted by surveying 256 students before the pandemic, in October 2019, and 118 students during the lockdown, in June 2020. This included questions about the respondents’ demographics, online presence, experience with offline bullying, perception of other’s opinions, and the instances of cyberbullying that apply to them. The results showed factors important in both timespans, namely (i) experience with offline bullying; (ii) individuals’ perceptiveness to others’ opinions; (iii) frequency of social media posts. Additionally, in the period before lockdown, factors namely (i) tendency to interact with strangers online; (ii) whether they’ve started a relationship online (iii) hours spent on social media; were found significant. Conversely, during the lockdown, additional distinct factors namely (i) being opinionated on public platforms; (ii) preference of Instagram; (iii) preferred gaming platform; (iv) number of games played; (v) sexual orientation; (vi) age were significant. With the change in variables in the two timespans, we can conclude that the pandemic has affected our susceptibility to cyberbullying.

Sphetcher: Spherical Thresholding Improves Sketching of Single-Cell Transcriptomic Heterogeneity

The massive size of single-cell RNA sequencing datasets often exceeds the capability of current computational analysis methods to solve routine tasks such as detection of cell types. Recently, geometric sketching was introduced as an alternative to uniform subsampling. It selects a subset of cells (the sketch) that evenly cover the transcriptomic space occupied by the original dataset, to accelerate downstream analyses and highlight rare cell types. Here, we propose algorithm Sphetcher that makes use of the thresholding technique to efficiently pick representative cells within spheres (as opposed to the typically used equal-sized boxes) that cover the entire transcriptomic space. We show that the spherical sketch computed by Sphetcher constitutes a more accurate representation of the original transcriptomic landscape. Our optimization scheme allows to include fairness aspects that can encode prior biological or experimental knowledge. We show how a fair sampling can inform the inference of the trajectory of human skeletal muscle myoblast differentiation.

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Sphetcher distils large-scale scRNA-seq data down to a small selection of cells

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Spheres of small radius around selected cells cover the original transcriptomic space

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Selection enhances and accelerates downstream analysis such as trajectory inference

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Sphetcher can leverage existing annotation of known cell types

Generation of Fine Grained Demographic Information for Epidemiological Analysis

Cancer risks may be influenced by local exposures such as working conditions or nuclear waste repositories. To find influences, local accumulations of cancer rates are used, for which finely granulated data should be utilized. In particular, high-resolution demographic data for a reference population are important, but often not available for data protection reasons. Therefore, estimation methods are necessary to approximate small-scale demographic data as accurately as possible. This paper presents an approach to project existing epidemiological and public data to a common granularity with respect to attribute characteristics such as place of residence, age or smoking status to allow for analyses such as local accumulations and consistently falls below an average relative error of 5%.

Is Medical Education Changing? Five Challenges for the Near Future

N/a.

Application of the 2017 KDIGO Guideline for the Evaluation and Care of Living Kidney Donors to Clinical Practice

The Kidney Disease: Improving Global Outcomes (KDIGO) 2017 "Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors" was developed to assist medical professionals who evaluate living kidney donor candidates and provide care before, during, and after donation. This guideline Work Group concluded that a comprehensive approach to donor candidate risk assessment should replace eligibility decisions on the basis of assessments of single risk factors in isolation. To address all issues important to living donors in a pragmatic and comprehensive guideline, many of the guideline recommendations were on the basis of expert consensus opinion even when no direct evidence was available. To advance available evidence, original data analyses were also undertaken to produce a "proof-of-concept" risk projection model for kidney failure. This was done to illustrate how the community can advance a new quantitative framework of risk that considers each candidate's profile of demographic and health characteristics. A public review by stakeholders and subject matter experts as well as industry and professional organizations informed the final formulation of the guideline. This review highlights the guideline framework, key concepts, and recommendations, and uses five patient scenarios and 12 guideline statements to illustrate how the guideline can be applied to support living donor evaluation and care in clinical practice.

Thyroid Hormone Use in the United States, 1997-2016

BACKGROUND

Thyroid disorders are among the most commonly treated conditions by the United States health care system. The number of patients reporting thyroid hormone use has increased in recent years, but it is unknown if there have been differential increases in the number of treated individuals within different demographic groups. Previous research has also not evaluated how expenditures for different thyroid hormone medications have changed in recent years.

METHODS

Using data from the 1997 through 2016 Medical Expenditure Panel Survey, we calculated the proportion of adults reporting thyroid hormone prescriptions by 3 demographic variables (age, sex, and race) and determined expenditures from thyroid hormone prescriptions by medication type (overall, generic, Synthroid or Cytomel, and other brand).

RESULTS

Between 1997 and 2016, the proportion of adults who reported thyroid hormone use increased from 4.1% (95% CI, 3.7-4.4) to 8.0% (95% CI, 7.5-8.5). Most of the growth in thyroid hormone use occurred among adults aged >65, and use was also more common among females and non-Hispanic whites. Expenditures from thyroid hormones increased from $1.1 billion (95% CI, 0.9-1.3) in 1997 to $3.2 billion dollars (95% CI, 2.9-3.6) in 2016. Generic thyroid hormone prescriptions comprised 18.1% of all thyroid hormone prescriptions in 2004 (95% CI, 15.8-20.4) and 80.8% of all thyroid hormone prescriptions (95% CI, 78.4-83.2) in 2016.

CONCLUSIONS

Thyroid hormone use nearly doubled over the last 20 years, and increased use was associated with being older, female, and non-Hispanic white. During the same time period, thyroid hormone expenditures almost tripled.

Stacking Ensemble Technique for Classifying Breast Cancer

Objectives

Breast cancer is the second most common cancer among Korean women. Because breast cancer is strongly associated with negative emotional and physical changes, early detection and treatment of breast cancer are very important. As a supporting tool for classifying breast cancer, we tried to identify the best meta-learner model in a stacking ensemble when the same machine learning models for the base learner and meta-learner are used.

Methods

We used machine learning models, such as the gradient boosted model, distributed random forest, generalized linear model, and deep neural network in a stacking ensemble. These models were used to construct a base learner, and each of them was used as a meta-learner again. Then, we compared the performance of machine learning models in the meta-learner to determine the best meta-learner model in the stacking ensemble.

Results

Experimental results showed that using the GBM as a meta-learner led to higher accuracy than that achieved with any other model for breast cancer data and using the GLM as a meta learner led to low root-mean-squared error for both sets of breast cancer data.

Conclusions

We compared the performance of every meta-learner model in a stacking ensemble as a supporting tool for classifying breast cancer. The study showed that using specific models as a metalearner resulted in better performance than single classifiers, and using GBM and GLM as a meta-learner is appropriate as a supporting tool for classifying breast cancer data.

Non-compartmental data analysis using SimBiology and MATLAB

MATLAB® is widely used for numerical analysis, modeling, and simulation. One of MATLAB's tools, SimBiology®, is often used for pharmacokinetic, pharmacodynamic model and dynamic systems; however, SimBiology seems to be rarely used for non-compartmental analysis (NCA), and the published official documentation provides a poor description of the analysis algorithm for NCA. Therefore, we conducted NCAs with a hypothetical dataset and some scenarios and compared the results. According to the results of this study, SimBiology estimates parameters using the unweighted linear regression for the terminal slope and linear interpolation method. Moreover, although the documentation describing the actual analysis algorithm used to process non-numeric data is not easily accessible to users, users may introduce numeric data at time zero to perform NCA properly. Using the command window, users can perform analyses more quickly and effectively. If the NCA official documentation were improved, SimBiology might be more widely adopted to perform NCA in clinical pharmacology.

Genomic Common Data Model for Biomedical Data in Clinical Practice

A common data model for clinical NGS panel data that is used in a distributed research network to achieve large scale to make evidence for improving patient care should be developed. This study developed OMOP-CDM extension for NGS panel data and confirmed the feasibility of the model by finding the differences between a database generated by research-purpose and clinical practice. We believe this data model can be used in distributed research model and will facilitate the usage of the clinical NGS data in patient care.

MedEx - Data Analytics for Medical Domain Experts in Real-Time

Translational research in the medical sector is dependent on clear communication between all participants. Visualization helps to represent data from different sources in a comprehensible way across disciplines. Existing tools for clinical data management are usually monolithic and technically challenging to set up, others require a transformation into specific data models while providing mostly non-interactive visualizations or being specialized to very particular use cases. Statistical programming languages (R, Julia) on the other hand offer great flexibility in data analytics, but are harder to access for clinicians with little to no programming expertise. Our software, the Medical Data Explorer (MedEx), aims to fill this gap as light-weight, intuitive, web-based solution with simple data import routes. We couple a modern dynamic web interface with an in-memory database solution for near real-time responsiveness. MedEx provides multiple visualization options (Scatterplot, correlation heatmap, bar chart, grouped boxplot, grouped histogram, coplot) to get an easy overview on the loaded data as well as to perform pattern discovery and elementary statistics. We demonstrate the utility of MedEx, by example, on data from the cardiology research warehouse of Heidelberg University Hospital. In summary, our tool empowers clinicians to conduct their own interactive exploratory data analysis.