Medical Informatics and Data Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 55864

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Special Issue Editor

Medical Informatics and Data Analysis Research Group, University of Oulu, P.O. Box 5000, FI-90014 Oulu, Finland
Interests: medical statistics; data informatics; statistics in medical journals; statistical computing; statistical modelling; data presentation; bibliometrics; information retrieval
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue aims to bring together information on established statistical methods and new data analysis methodologies in biostatistics, epidemiology health sciences, dentistry and clinical medicine. Such resource would provide support for researchers at all levels in these fields, as well as students, publishing articles on a wide array of methods, how those methods should be applied, plus examples of application in practice.

All health care professionals and medical researchers face the challenge of keeping abreast of a body of knowledge that is expanding at an astonishing rate. The current views on the causes, mechanisms, and treatment methods of diseases are advancing too rapidly for any physician or researcher to achieve personal experience with all of the new findings. This has led to a growing reliance on the published literature to learn about new discoveries that can ultimately influence diagnostic evaluations, therapeutic decisions and public health guidelines. Statistical methods play an important role in medical publications. This is reflected in the high proportion of articles that are essentially statistical in character. Most papers published in medical journals contain some element of statistical methods, analysis and interpretation. In addition, mathematical statisticians and data science researchers introduce new data analysis methods marked by a rapid expansion in computing capability.

In summary, this Special Issue is an opportunity for the scientific community to present research on the application and complexity of data analytical methods and to give insight into new challenges in medical informatics and data analysis. Both original research and review articles are open to submission in this issue.

Prof. Dr. Pentti Nieminen
Guest Editor

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • Data analysis
  • Statistical methods
  • Statistical reporting
  • Epidemiology
  • Public health
  • Clinical research
  • Methodology
  • Publications
  • Medical informatics

Published Papers (14 papers)

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Editorial

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5 pages, 184 KiB  
Editorial
Applications of Medical Informatics and Data Analysis Methods
by Pentti Nieminen
Appl. Sci. 2020, 10(20), 7359; https://doi.org/10.3390/app10207359 - 21 Oct 2020
Cited by 5 | Viewed by 3271
Abstract
The science of statistics contributes to the development and application of tools for the design, analysis, and interpretation of empirical medical studies [...] Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)

Research

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17 pages, 2285 KiB  
Article
Medical Assistant Mobile Application for Diabetes Control by Simulating a Compartmental Model
by Martín Hernández-Ordoñez, Marco Aurelio Nuño-Maganda, Carlos Adrián Calles-Arriaga, Abelardo Rodríguez-León, Guillermo Efren Ovando-Chacon, Rolando Salazar-Hernández, Omar Montaño-Rivas and José Margarito Canseco-Cortinas
Appl. Sci. 2020, 10(19), 6846; https://doi.org/10.3390/app10196846 - 29 Sep 2020
Cited by 2 | Viewed by 1985
Abstract
This paper presents an educational mobile assistant application for type 1 diabetes patients. The proposed application is based on four mathematical models that describe the glucose-insulin-glucagon dynamics using a compartmental model, with additional equations to reproduce aerobic exercise, gastric glucose absorption by the [...] Read more.
This paper presents an educational mobile assistant application for type 1 diabetes patients. The proposed application is based on four mathematical models that describe the glucose-insulin-glucagon dynamics using a compartmental model, with additional equations to reproduce aerobic exercise, gastric glucose absorption by the gut, and subcutaneous insulin absorption. The medical assistant was implemented in Java and deployed and validated on several smartphones with Android OS. Multiple daily doses can be simulated to perform intensive insulin therapy. As a result, the proposed application shows the influence of exercise periods, food intakes, and insulin treatments on the glucose concentrations. Four parameter variations are studied, and their corresponding glucose concentration plots are obtained, which show agreement with simulators of the state of the art. The developed application is focused on type-1 diabetes, but this can be extended to consider type-2 diabetes by modifying the current mathematical models. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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20 pages, 701 KiB  
Article
Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis
by Afnan M. Alhassan and Wan Mohd Nazmee Wan Zainon
Appl. Sci. 2020, 10(18), 6626; https://doi.org/10.3390/app10186626 - 22 Sep 2020
Cited by 17 | Viewed by 2624
Abstract
Contemporary medicine depends on a huge amount of information contained in medical databases. Thus, the extraction of valuable knowledge, and making scientific decisions for the treatment of disease, has progressively become necessary to attain effective diagnosis. The obtainability of a large amount of [...] Read more.
Contemporary medicine depends on a huge amount of information contained in medical databases. Thus, the extraction of valuable knowledge, and making scientific decisions for the treatment of disease, has progressively become necessary to attain effective diagnosis. The obtainability of a large amount of medical data leads to the requirement of effective data analysis tools for extracting constructive knowledge. This paper proposes a novel method for heart disease diagnosis. Here, the pre-processing of medical data is done using log-transformation that converts the data to its uniform value range. Then, the feature selection process is performed using sparse fuzzy-c-means (FCM) for selecting significant features to classify medical data. Incorporating sparse FCM for the feature selection process provides more benefits for interpreting the models, as this sparse technique provides important features for detection, and can be utilized for handling high dimensional data. Then, the selected features are given to the deep belief network (DBN), which is trained using the proposed Taylor-based bird swarm algorithm (Taylor-BSA) for detection. Here, the proposed Taylor-BSA is designed by combining the Taylor series and bird swarm algorithm (BSA). The proposed Taylor-BSA–DBN outperformed other methods, with maximal accuracy of 93.4%, maximal sensitivity of 95%, and maximal specificity of 90.3%, respectively. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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14 pages, 2772 KiB  
Article
Handling Skewed Data: A Comparison of Two Popular Methods
by Hanan M. Hammouri, Roy T. Sabo, Rasha Alsaadawi and Khalid A. Kheirallah
Appl. Sci. 2020, 10(18), 6247; https://doi.org/10.3390/app10186247 - 09 Sep 2020
Cited by 21 | Viewed by 10265
Abstract
Scientists in biomedical and psychosocial research need to deal with skewed data all the time. In the case of comparing means from two groups, the log transformation is commonly used as a traditional technique to normalize skewed data before utilizing the two-group t [...] Read more.
Scientists in biomedical and psychosocial research need to deal with skewed data all the time. In the case of comparing means from two groups, the log transformation is commonly used as a traditional technique to normalize skewed data before utilizing the two-group t-test. An alternative method that does not assume normality is the generalized linear model (GLM) combined with an appropriate link function. In this work, the two techniques are compared using Monte Carlo simulations; each consists of many iterations that simulate two groups of skewed data for three different sampling distributions: gamma, exponential, and beta. Afterward, both methods are compared regarding Type I error rates, power rates and the estimates of the mean differences. We conclude that the t-test with log transformation had superior performance over the GLM method for any data that are not normal and follow beta or gamma distributions. Alternatively, for exponentially distributed data, the GLM method had superior performance over the t-test with log transformation. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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22 pages, 2924 KiB  
Article
A Prostate MRI Segmentation Tool Based on Active Contour Models Using a Gradient Vector Flow
by Joaquín Rodríguez, Gilberto Ochoa-Ruiz and Christian Mata
Appl. Sci. 2020, 10(18), 6163; https://doi.org/10.3390/app10186163 - 04 Sep 2020
Cited by 5 | Viewed by 2989
Abstract
Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of [...] Read more.
Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of a Regions of Interest (ROI) containing a tumor tissue. In this paper, a new GUI tool based on a semi-automatic prostate segmentation is presented. The main rationale behind this tool and the focus of this article is facilitate the time consuming segmentation process used for annotating images in the clinical practice, enabling the radiologists to use novel and easy to use semi-automatic segmentation techniques instead of manual segmentation. In this work, a detailed specification of the proposed segmentation algorithm using an Active Contour Models (ACM) aided with a Gradient Vector Flow (GVF) component is defined. The purpose is to help the manual segmentation process of the main ROIs of prostate gland zones. Finally, an experimental case of use and a discussion part of the results are presented. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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26 pages, 5196 KiB  
Article
Applying Machine Learning for Healthcare: A Case Study on Cervical Pain Assessment with Motion Capture
by Juan de la Torre, Javier Marin, Sergio Ilarri and Jose J. Marin
Appl. Sci. 2020, 10(17), 5942; https://doi.org/10.3390/app10175942 - 27 Aug 2020
Cited by 15 | Viewed by 3657
Abstract
Given the exponential availability of data in health centers and the massive sensorization that is expected, there is an increasing need to manage and analyze these data in an effective way. For this purpose, data mining (DM) and machine learning (ML) techniques would [...] Read more.
Given the exponential availability of data in health centers and the massive sensorization that is expected, there is an increasing need to manage and analyze these data in an effective way. For this purpose, data mining (DM) and machine learning (ML) techniques would be helpful. However, due to the specific characteristics of the field of healthcare, a suitable DM and ML methodology adapted to these particularities is required. The applied methodology must structure the different stages needed for data-driven healthcare, from the acquisition of raw data to decision-making by clinicians, considering the specific requirements of this field. In this paper, we focus on a case study of cervical assessment, where the goal is to predict the potential presence of cervical pain in patients affected with whiplash diseases, which is important for example in insurance-related investigations. By analyzing in detail this case study in a real scenario, we show how taking care of those particularities enables the generation of reliable predictive models in the field of healthcare. Using a database of 302 samples, we have generated several predictive models, including logistic regression, support vector machines, k-nearest neighbors, gradient boosting, decision trees, random forest, and neural network algorithms. The results show that it is possible to reliably predict the presence of cervical pain (accuracy, precision, and recall above 90%). We expect that the procedure proposed to apply ML techniques in the field of healthcare will help technologists, researchers, and clinicians to create more objective systems that provide support to objectify the diagnosis, improve test treatment efficacy, and save resources. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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20 pages, 1071 KiB  
Article
A Comparison of Deep Learning Methods for ICD Coding of Clinical Records
by Elias Moons, Aditya Khanna, Abbas Akkasi and Marie-Francine Moens
Appl. Sci. 2020, 10(15), 5262; https://doi.org/10.3390/app10155262 - 30 Jul 2020
Cited by 32 | Viewed by 5258
Abstract
In this survey, we discuss the task of automatically classifying medical documents into the taxonomy of the International Classification of Diseases (ICD), by the use of deep neural networks. The literature in this domain covers different techniques. We will assess and compare the [...] Read more.
In this survey, we discuss the task of automatically classifying medical documents into the taxonomy of the International Classification of Diseases (ICD), by the use of deep neural networks. The literature in this domain covers different techniques. We will assess and compare the performance of those techniques in various settings and investigate which combination leverages the best results. Furthermore, we introduce an hierarchical component that exploits the knowledge of the ICD taxonomy. All methods and their combinations are evaluated on two publicly available datasets that represent ICD-9 and ICD-10 coding, respectively. The evaluation leads to a discussion of the advantages and disadvantages of the models. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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11 pages, 4336 KiB  
Article
Castration-Resistant Prostate Cancer Outcome Prediction Using Phased Long Short-Term Memory with Irregularly Sampled Serial Data
by Jihwan Park, Mi Jung Rho, Hyong Woo Moon and Ji Youl Lee
Appl. Sci. 2020, 10(6), 2000; https://doi.org/10.3390/app10062000 - 15 Mar 2020
Cited by 3 | Viewed by 2020
Abstract
It is particularly desirable to predict castration-resistant prostate cancer (CRPC) in prostate cancer (PCa) patients, and this study aims to predict patients’ likely outcomes to support physicians’ decision-making. Serial data is collected from 1592 PCa patients, and a phased long short-term memory (phased-LSTM) [...] Read more.
It is particularly desirable to predict castration-resistant prostate cancer (CRPC) in prostate cancer (PCa) patients, and this study aims to predict patients’ likely outcomes to support physicians’ decision-making. Serial data is collected from 1592 PCa patients, and a phased long short-term memory (phased-LSTM) model with a special module called a “time-gate” is used to process the irregularly sampled data sets. A synthetic minority oversampling technique is used to overcome the data imbalance between two patient groups: those with and without CRPC treatment. The phased-LSTM model is able to predict the CRPC outcome with an accuracy of 88.6% (precision-recall: 91.6%) using 120 days of data or 94.8% (precision-recall: 96.9%) using 360 days of data. The validation loss converged slowly with 120 days of data and quickly with 360 days of data. In both cases, the prediction model takes four epochs to build. The overall CPRC outcome prediction model using irregularly sampled serial medical data is accurate and can be used to support physicians’ decision-making, which saves time compared to cumbersome serial data reviews. This study can be extended to realize clinically meaningful prediction models. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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21 pages, 2219 KiB  
Article
A Decision Support System for Elective Surgery Scheduling under Uncertain Durations
by Daniel Clavel, Cristian Mahulea, Jorge Albareda and Manuel Silva
Appl. Sci. 2020, 10(6), 1937; https://doi.org/10.3390/app10061937 - 12 Mar 2020
Cited by 6 | Viewed by 3290
Abstract
The operation room (OR) is one of the most expensive material resources in hospitals. Additionally, the demand for surgical service is increasing due to the aging population, while the number of surgical interventions performed is stagnated because of budget reasons. In this context, [...] Read more.
The operation room (OR) is one of the most expensive material resources in hospitals. Additionally, the demand for surgical service is increasing due to the aging population, while the number of surgical interventions performed is stagnated because of budget reasons. In this context, the importance of improving the efficiency of the surgical service is accentuated. The main objective of this work is to propose and to evaluate a Decision Support System (DSS) for helping medical staff in the automatic scheduling of elective patients, improving the efficiency of medical teams’ work. First, the scheduling criteria are fixed and then the scheduling problem of elective patients is approached by a mathematical programming model. A heuristic algorithm is proposed and included in the DSS. Moreover, other different features are implemented in a software tool with a friendly user interface, called CIPLAN. Considering realistic data, a simulation comparison of the scheduling obtained using the approach presented in this paper and other similar approaches in the bibliography is shown and analyzed. On the other hand, a case study considering real data provided by the Orthopedic Surgical Department (OSD) of the “Lozano Blesa” hospital in Zaragoza (HCU) is proposed. The simulation results show that the approach presented here obtains similar occupation rates and similar confidence levels of not exceeding the available time than approaches in the bibliography. However, from the point of view of respecting the order of the patients in the waiting list, the approach in this paper obtains scheduling much more ordered. In the case of the Orthopedic Surgical Department of the “Lozano Blesa” hospital in Zaragoza, the occupation rate may be increased by 2.83%, which represents a saving of 110,000 euros per year. Moreover, medical doctors (who use this tool) consider CIPLAN as an intuitive, rapid and efficient software solution that can make easier the corresponding task. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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15 pages, 329 KiB  
Article
Classification Maps in Studies on the Retirement Threshold
by Agnieszka Bielińska, Dorota Bielińska-Wa̧ż and Piotr Wa̧ż
Appl. Sci. 2020, 10(4), 1282; https://doi.org/10.3390/app10041282 - 14 Feb 2020
Cited by 5 | Viewed by 1544
Abstract
The aim of this work is to present new classification maps in health informatics and to show that they are useful in data analysis. A statistical method, correspondence analysis, has been applied for obtaining these maps. This approach has been applied to studies [...] Read more.
The aim of this work is to present new classification maps in health informatics and to show that they are useful in data analysis. A statistical method, correspondence analysis, has been applied for obtaining these maps. This approach has been applied to studies on expectations and worries related to the retirement threshold. For this purpose two questionnaires formulated by ourselves have been constructed. Groups of individuals and their answers to particular questions are represented by points in the classification maps. The distribution of these points reflects psychological attitudes of the considered population. In particular, we compared structures of the maps searching for factors such as gender, marital status, kind of work, economic situation, and intellectual activity related to the attendance the University of the Third Age, which are essential at the retirement threshold. Generally, in Polish society, retirement is evaluated as a positive experience and the majority of retirees do not want to return to their professional work. This result is independent of the kind of work and of the gender. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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15 pages, 641 KiB  
Article
Investigation of Vocal Fatigue Using a Dose-Based Vocal Loading Task
by Zhengdong Lei, Laura Fasanella, Lisa Martignetti, Nicole Yee-Key Li-Jessen and Luc Mongeau
Appl. Sci. 2020, 10(3), 1192; https://doi.org/10.3390/app10031192 - 10 Feb 2020
Cited by 13 | Viewed by 3556
Abstract
Vocal loading tasks are often used to investigate the relationship between voice use and vocal fatigue in laboratory settings. The present study investigated the concept of a novel quantitative dose-based vocal loading task for vocal fatigue evaluation. Ten female subjects participated in the [...] Read more.
Vocal loading tasks are often used to investigate the relationship between voice use and vocal fatigue in laboratory settings. The present study investigated the concept of a novel quantitative dose-based vocal loading task for vocal fatigue evaluation. Ten female subjects participated in the study. Voice use was monitored and quantified using an online vocal distance dose calculator during six consecutive 30-min long sessions. Voice quality was evaluated subjectively using the CAPE-V and SAVRa before, between, and after each vocal loading task session. Fatigue-indicative symptoms, such as cough, swallowing, and voice clearance, were recorded. Statistical analysis of the results showed that the overall severity, the roughness, and the strain ratings obtained from CAPE-V obeyed similar trends as the three ratings from the SAVRa. These metrics increased over the first two thirds of the sessions to reach a maximum, and then decreased slightly near the session end. Quantitative metrics obtained from surface neck accelerometer signals were found to obey similar trends. The results consistently showed that an initial adjustment of voice quality was followed by vocal saturation, supporting the effectiveness of the proposed loading task. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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17 pages, 2986 KiB  
Article
Using the Importance–Satisfaction Model and Service Quality Performance Matrix to Improve Long-Term Care Service Quality in Taiwan
by Shun-Hsing Chen, Fan-Yun Pai and Tsu-Ming Yeh
Appl. Sci. 2020, 10(1), 85; https://doi.org/10.3390/app10010085 - 20 Dec 2019
Cited by 16 | Viewed by 5626
Abstract
The present study integrates the importance–satisfaction (I-S) model and service quality performance matrix (SQPM) to examine long-term care (LTC) service demands and satisfaction improvement. Many scholars have used a single model to explore project improvement. Each model has advantages, but we think they [...] Read more.
The present study integrates the importance–satisfaction (I-S) model and service quality performance matrix (SQPM) to examine long-term care (LTC) service demands and satisfaction improvement. Many scholars have used a single model to explore project improvement. Each model has advantages, but we think they are too subjective and suggest that it is best to integrate models to determine what should be improved. We established quality attributes of service demands based on more than two sessions of discussions and expert consultations with LTC service users (older adults). The final questionnaire was divided into three parts: a demand survey, satisfaction survey, and demographics survey, and 292 valid questionnaires were collected. The questionnaire items were summarized with means and standard deviations. In this study, if only the I-S model was used to examine LTC in Taiwan, then seven service elements of the system would need to be improved. However, if only the SQPM method was used, then 16 service elements would need to be improved. Only seven service elements were identified by both methods. When time and resources are limited, it is not feasible to take comprehensiveness into account. When many projects must be improved and it is impossible to implement them at the same time, improvement priorities need to be developed. Taiwan lacks sufficient LTC resources, so it is impossible to provide enough resources for all those who need care. To use resources efficiently, the I-S model and SQPM were integrated in this study to identify areas for improvement. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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10 pages, 383 KiB  
Article
Stretched Exponential Survival Analysis for South Korean Females
by Byung Mook Weon
Appl. Sci. 2019, 9(20), 4230; https://doi.org/10.3390/app9204230 - 10 Oct 2019
Cited by 2 | Viewed by 2639
Abstract
South Korea has recently exhibited a remarkable rapid increase in female lifespan. Here, a mathematical analysis is suggested for a clear interpretation of current trends in female lifespan in South Korea. To mathematically analyze life tables, a modified stretched exponential function is employed [...] Read more.
South Korea has recently exhibited a remarkable rapid increase in female lifespan. Here, a mathematical analysis is suggested for a clear interpretation of current trends in female lifespan in South Korea. To mathematically analyze life tables, a modified stretched exponential function is employed and demonstrated to estimate current trends of female lifespan in South Korea based on reliable life tables from 1987 to 2016 taken from the Korean Statistical Information Service. This methodology enables us to perform quantitative and comparative analyses of female lifespan in South Korea with representative industrialized countries such as Japan, France, Australia, Switzerland, UK, Sweden, and USA. This analysis provides quantitative and comparative evidence that South Korea has the highest increase rate of female lifespan over the past three decades. Further application would be feasible for a better estimation of human aging statistics. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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Other

Jump to: Editorial, Research

17 pages, 657 KiB  
Commentary
Ten Points for High-Quality Statistical Reporting and Data Presentation
by Pentti Nieminen
Appl. Sci. 2020, 10(11), 3885; https://doi.org/10.3390/app10113885 - 03 Jun 2020
Cited by 4 | Viewed by 5893
Abstract
Background: Data analysis methods have become an essential part of empirical research papers, especially in health sciences and medical research. It has previously been reported that a noteworthy percentage of articles have flaws in their statistical reporting. Reporting problems have been a long-term [...] Read more.
Background: Data analysis methods have become an essential part of empirical research papers, especially in health sciences and medical research. It has previously been reported that a noteworthy percentage of articles have flaws in their statistical reporting. Reporting problems have been a long-term issue, and despite continued efforts to improve the situation, improvements have been far from satisfactory. One explanation is an inadequate assessment of statistical reporting during peer review. This communication proposes a short instrument to assess the quality of data analysis reporting in manuscripts and published papers. Method: A checklist-type instrument was developed by selecting and refining items from previous reports about the quality of statistical reporting in medical journals and from published guidelines for reporting and data presentation. Items were pretested and modified during pilot studies. A total of 160 original medical research articles that were published in 4 journals were evaluated to test the instrument. Interrater and intrarater agreements were examined by comparing quality scores assigned to 40 articles published in a psychiatric journal. Results: The data analysis reporting test consists of nine questions that assess the quality of health research from a reader’s perspective. The composed scale has a total score ranging from 0 to 10 and discriminated between journals and study designs. A high score suggested that an article had a good presentation of findings in tables and figures and that the description of analysis methods was helpful to readers. Interrater and intrarater agreements were high. Conclusion: An applicable checklist for quickly testing the statistical reporting quality of manuscripts and published research papers was developed. This instrument aims to improve the quality of empirical research in scientific fields where statistical methods play an important role. Full article
(This article belongs to the Special Issue Medical Informatics and Data Analysis)
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