Informatics

14 pages, 3824 KiB

Open AccessEditor’s ChoiceArticle

A Machine Learning-Based Multiple Imputation Method for the Health and Aging Brain Study–Health Disparities

by Fan Zhang, Melissa Petersen, Leigh Johnson, James Hall, Raymond F. Palmer, Sid E. O’Bryant and on behalf of the Health and Aging Brain Study (HABS–HD) Study Team

Informatics 2023, 10(4), 77; https://doi.org/10.3390/informatics10040077 - 11 Oct 2023

Viewed by 2613

Abstract

The Health and Aging Brain Study–Health Disparities (HABS–HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS–HD is missing data. It is impossible to achieve accurate machine learning (ML) if [...] Read more.

The Health and Aging Brain Study–Health Disparities (HABS–HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS–HD is missing data. It is impossible to achieve accurate machine learning (ML) if data contain missing values. Therefore, developing a new imputation methodology has become an urgent task for HABS–HD. The three missing data assumptions, (1) missing completely at random (MCAR), (2) missing at random (MAR), and (3) missing not at random (MNAR), necessitate distinct imputation approaches for each mechanism of missingness. Several popular imputation methods, including listwise deletion, min, mean, predictive mean matching (PMM), classification and regression trees (CART), and missForest, may result in biased outcomes and reduced statistical power when applied to downstream analyses such as testing hypotheses related to clinical variables or utilizing machine learning to predict AD or MCI. Moreover, these commonly used imputation techniques can produce unreliable estimates of missing values if they do not account for the missingness mechanisms or if there is an inconsistency between the imputation method and the missing data mechanism in HABS–HD. Therefore, we proposed a three-step workflow to handle missing data in HABS–HD: (1) missing data evaluation, (2) imputation, and (3) imputation evaluation. First, we explored the missingness in HABS–HD. Then, we developed a machine learning-based multiple imputation method (MLMI) for imputing missing values. We built four ML-based imputation models (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and lasso and elastic-net regularized generalized linear model (GLMNET)) and adapted the four ML-based models to multiple imputations using the simple averaging method. Lastly, we evaluated and compared MLMI with other common methods. Our results showed that the three-step workflow worked well for handling missing values in HABS–HD and the ML-based multiple imputation method outperformed other common methods in terms of prediction performance and change in distribution and correlation. The choice of missing handling methodology has a significant impact on the accompanying statistical analyses of HABS–HD. The conceptual three-step workflow and the ML-based multiple imputation method perform well for our Alzheimer’s disease models. They can also be applied to other disease data analyses. Full article

(This article belongs to the Special Issue Novel Informatics Algorithms and Applications to Biomedicine and Healthcare)

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15 pages, 2586 KiB

Open AccessEditor’s ChoiceArticle

Exploring How Healthcare Organizations Use Twitter: A Discourse Analysis

by Aditya Singhal and Vijay Mago

Informatics 2023, 10(3), 65; https://doi.org/10.3390/informatics10030065 - 8 Aug 2023

Viewed by 2744

Abstract

The use of Twitter by healthcare organizations is an effective means of disseminating medical information to the public. However, the content of tweets can be influenced by various factors, such as health emergencies and medical breakthroughs. In this study, we conducted a discourse [...] Read more.

The use of Twitter by healthcare organizations is an effective means of disseminating medical information to the public. However, the content of tweets can be influenced by various factors, such as health emergencies and medical breakthroughs. In this study, we conducted a discourse analysis to better understand how public and private healthcare organizations use Twitter and the factors that influence the content of their tweets. Data were collected from the Twitter accounts of five private pharmaceutical companies, two US and two Canadian public health agencies, and the World Health Organization from 1 January 2020, to 31 December 2022. The study applied topic modeling and association rule mining to identify text patterns that influence the content of tweets across different Twitter accounts. The findings revealed that building a reputation on Twitter goes beyond just evaluating the popularity of a tweet in the online sphere. Topic modeling, when applied synchronously with hashtag and tagging analysis can provide an increase in tweet popularity. Additionally, the study showed differences in language use and style across the Twitter accounts’ categories and discussed how the impact of popular association rules could translate to significantly more user engagement. Overall, the results of this study provide insights into natural language processing for health literacy and present a way for organizations to structure their future content to ensure maximum public engagement. Full article

(This article belongs to the Special Issue Novel Informatics Algorithms and Applications to Biomedicine and Healthcare)

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23 pages, 4537 KiB

Open AccessEditor’s ChoiceArticle

A Machine-Learning-Based Motor and Cognitive Assessment Tool Using In-Game Data from the GAME2AWE Platform

by Michail Danousis and Christos Goumopoulos

Informatics 2023, 10(3), 59; https://doi.org/10.3390/informatics10030059 - 9 Jul 2023

Cited by 1 | Viewed by 2465

Abstract

With age, a decline in motor and cognitive functionality is inevitable, and it greatly affects the quality of life of the elderly and their ability to live independently. Early detection of these types of decline can enable timely interventions and support for maintaining [...] Read more.

With age, a decline in motor and cognitive functionality is inevitable, and it greatly affects the quality of life of the elderly and their ability to live independently. Early detection of these types of decline can enable timely interventions and support for maintaining functional independence and improving overall well-being. This paper explores the potential of the GAME2AWE platform in assessing the motor and cognitive condition of seniors based on their in-game performance data. The proposed methodology involves developing machine learning models to explore the predictive power of features that are derived from the data collected during gameplay on the GAME2AWE platform. Through a study involving fifteen elderly participants, we demonstrate that utilizing in-game data can achieve a high classification performance when predicting the motor and cognitive states. Various machine learning techniques were used but Random Forest outperformed the other models, achieving a classification accuracy ranging from 93.6% for cognitive screening to 95.6% for motor assessment. These results highlight the potential of using exergames within a technology-rich environment as an effective means of capturing the health status of seniors. This approach opens up new possibilities for objective and non-invasive health assessment, facilitating early detections and interventions to improve the well-being of seniors. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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16 pages, 8768 KiB

Open AccessEditor’s ChoiceArticle

Genealogical Data Mining from Historical Archives: The Case of the Jewish Community in Pisa

by Angelica Lo Duca, Andrea Marchetti, Manuela Moretti, Francesca Diana, Mafalda Toniazzi and Andrea D’Errico

Informatics 2023, 10(2), 42; https://doi.org/10.3390/informatics10020042 - 11 May 2023

Cited by 1 | Viewed by 2147

Abstract

The Jewish community archive in Pisa owns a vast collection of documents and manuscripts that date back centuries. These documents contain valuable genealogical information, including birth, marriage, and death records. This paper aims to describe the preliminary results of the Archivio Storico della [...] Read more.

The Jewish community archive in Pisa owns a vast collection of documents and manuscripts that date back centuries. These documents contain valuable genealogical information, including birth, marriage, and death records. This paper aims to describe the preliminary results of the Archivio Storico della Comunita Ebraica di Pisa (ASCEPI) project, with a focus on the extraction of data from the Nati, Morti e Ballottati (NMB) Registry document in the archive. The NMB Registry contains about 1900 records of births, deaths, and balloted individuals within the Jewish community in Pisa. The study uses a semiautomatic pipeline of digitization, transcription, and Natural Language Processing (NLP) techniques to extract personal data such as names, surnames, birth and death dates, and parental names from each record. The extracted data are then used to build a knowledge base and a genealogical tree for a representative family, Supino. This study demonstrates the potential of using NLP and rule-based techniques to extract valuable information from historical documents and to construct genealogical trees. Full article

(This article belongs to the Special Issue ICT for Genealogical Data)

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16 pages, 8289 KiB

Open AccessEditor’s ChoiceArticle

Development and Internal Validation of an Interpretable Machine Learning Model to Predict Readmissions in a United States Healthcare System

by Amanda L. Luo, Akshay Ravi, Simone Arvisais-Anhalt, Anoop N. Muniyappa, Xinran Liu and Shan Wang

Informatics 2023, 10(2), 33; https://doi.org/10.3390/informatics10020033 - 27 Mar 2023

Viewed by 2943

Abstract

(1) One in four hospital readmissions is potentially preventable. Machine learning (ML) models have been developed to predict hospital readmissions and risk-stratify patients, but thus far they have been limited in clinical applicability, timeliness, and generalizability. (2) Methods: Using deidentified clinical data from [...] Read more.

(1) One in four hospital readmissions is potentially preventable. Machine learning (ML) models have been developed to predict hospital readmissions and risk-stratify patients, but thus far they have been limited in clinical applicability, timeliness, and generalizability. (2) Methods: Using deidentified clinical data from the University of California, San Francisco (UCSF) between January 2016 and November 2021, we developed and compared four supervised ML models (logistic regression, random forest, gradient boosting, and XGBoost) to predict 30-day readmissions for adults admitted to a UCSF hospital. (3) Results: Of 147,358 inpatient encounters, 20,747 (13.9%) patients were readmitted within 30 days of discharge. The final model selected was XGBoost, which had an area under the receiver operating characteristic curve of 0.783 and an area under the precision-recall curve of 0.434. The most important features by Shapley Additive Explanations were days since last admission, discharge department, and inpatient length of stay. (4) Conclusions: We developed and internally validated a supervised ML model to predict 30-day readmissions in a US-based healthcare system. This model has several advantages including state-of-the-art performance metrics, the use of clinical data, the use of features available within 24 h of discharge, and generalizability to multiple disease states. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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24 pages, 1423 KiB

Open AccessEditor’s ChoiceArticle

Affective Design Analysis of Explainable Artificial Intelligence (XAI): A User-Centric Perspective

by Ezekiel Bernardo and Rosemary Seva

Informatics 2023, 10(1), 32; https://doi.org/10.3390/informatics10010032 - 16 Mar 2023

Cited by 5 | Viewed by 6271

Abstract

Explainable Artificial Intelligence (XAI) has successfully solved the black box paradox of Artificial Intelligence (AI). By providing human-level insights on AI, it allowed users to understand its inner workings even with limited knowledge of the machine learning algorithms it uses. As a result, [...] Read more.

Explainable Artificial Intelligence (XAI) has successfully solved the black box paradox of Artificial Intelligence (AI). By providing human-level insights on AI, it allowed users to understand its inner workings even with limited knowledge of the machine learning algorithms it uses. As a result, the field grew, and development flourished. However, concerns have been expressed that the techniques are limited in terms of to whom they are applicable and how their effect can be leveraged. Currently, most XAI techniques have been designed by developers. Though needed and valuable, XAI is more critical for an end-user, considering transparency cleaves on trust and adoption. This study aims to understand and conceptualize an end-user-centric XAI to fill in the lack of end-user understanding. Considering recent findings of related studies, this study focuses on design conceptualization and affective analysis. Data from 202 participants were collected from an online survey to identify the vital XAI design components and testbed experimentation to explore the affective and trust change per design configuration. The results show that affective is a viable trust calibration route for XAI. In terms of design, explanation form, communication style, and presence of supplementary information are the components users look for in an effective XAI. Lastly, anxiety about AI, incidental emotion, perceived AI reliability, and experience using the system are significant moderators of the trust calibration process for an end-user. Full article

(This article belongs to the Special Issue Feature Papers in Human-Computer Interaction)

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24 pages, 16713 KiB

Open AccessEditor’s ChoiceArticle

The Influence of Light and Color in Digital Paintings of Environmental Issues on Emotions and Cognitions

by Witthaya Hosap, Chaowanan Khundam, Patibut Preeyawongsakul, Varunyu Vorachart and Frédéric Noël

Informatics 2023, 10(1), 26; https://doi.org/10.3390/informatics10010026 - 3 Mar 2023

Cited by 1 | Viewed by 4696

Abstract

This study aimed to examine the use of light and color in digital paintings and their effect on audiences’ perceptions of environmental issues. Five digital paintings depicting environmental issues have been designed. Digital painting techniques created black-and-white, monochrome, and color images. Each image [...] Read more.

This study aimed to examine the use of light and color in digital paintings and their effect on audiences’ perceptions of environmental issues. Five digital paintings depicting environmental issues have been designed. Digital painting techniques created black-and-white, monochrome, and color images. Each image used utopian and dystopian visualization concepts to communicate hope and despair. In the experiment, 225 volunteers representing students in colleges were separated into three independent groups: the first group was offered black-and-white images, the second group was offered monochromatic images, and the third group was offered color images. After viewing each image, participants were asked to complete questionnaires about their emotions and cognitions regarding environmental issues, including identifying hope and despair and the artist’s perspective at the end. The analysis showed no differences in emotions and cognitions among participants. However, monochromatic images were the most emotionally expressive. The results indicated that the surrounding atmosphere of the images created despair, whereas objects inspired hope. Artists should emphasize the composition of the atmosphere and the objects in the image to convey the concepts of utopia and dystopia to raise awareness of environmental issues. Full article

(This article belongs to the Section Social Informatics and Digital Humanities)

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28 pages, 6916 KiB

Open AccessEditor’s ChoiceArticle

OA-Pain-Sense: Machine Learning Prediction of Hip and Knee Osteoarthritis Pain from IMU Data

by Wafaa Salem Almuhammadi, Emmanuel Agu, Jean King and Patricia Franklin

Informatics 2022, 9(4), 97; https://doi.org/10.3390/informatics9040097 - 6 Dec 2022

Cited by 4 | Viewed by 5584

Abstract

Joint pain is a prominent symptom of Hip and Knee Osteoarthritis (OA), impairing patients’ movements and affecting the joint mechanics of walking. Self-report questionnaires are currently the gold standard for Hip OA and Knee OA pain assessment, presenting several problems, including the fact [...] Read more.

Joint pain is a prominent symptom of Hip and Knee Osteoarthritis (OA), impairing patients’ movements and affecting the joint mechanics of walking. Self-report questionnaires are currently the gold standard for Hip OA and Knee OA pain assessment, presenting several problems, including the fact that older individuals often fail to provide accurate self-pain reports. Passive methods to assess pain are desirable. This study aims to explore the feasibility of OA-Pain-Sense, a passive, automatic Machine Learning-based approach that predicts patients’ self-reported pain levels using SpatioTemporal Gait features extracted from the accelerometer signal gathered from an anterior-posterior wearable sensor. To mitigate inter-subject variability, we investigated two types of data rescaling: subject-level and dataset-level. We explored six different binary machine learning classification models for discriminating pain in patients with Hip OA or Knee OA from healthy controls. In rigorous evaluation, OA-Pain-Sense achieved an average accuracy of 86.79% using the Decision Tree and 83.57% using Support Vector Machine classifiers for distinguishing Hip OA and Knee OA patients from healthy subjects, respectively. Our results demonstrate that OA-Pain-Sense is feasible, paving the way for the development of a pain assessment algorithm that can support clinical decision-making and be used on any wearable device, such as smartphones. Full article

(This article belongs to the Section Health Informatics)

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28 pages, 6383 KiB

Open AccessEditor’s ChoiceArticle

Breast Cancer Tumor Classification Using a Bag of Deep Multi-Resolution Convolutional Features

by David Clement, Emmanuel Agu, John Obayemi, Steve Adeshina and Wole Soboyejo

Informatics 2022, 9(4), 91; https://doi.org/10.3390/informatics9040091 - 28 Oct 2022

Cited by 9 | Viewed by 3459

Abstract

Breast cancer accounts for 30% of all female cancers. Accurately distinguishing dangerous malignant tumors from benign harmless ones is key to ensuring patients receive lifesaving treatments on time. However, as doctors currently do not identify 10% to 30% of breast cancers during regular [...] Read more.

Breast cancer accounts for 30% of all female cancers. Accurately distinguishing dangerous malignant tumors from benign harmless ones is key to ensuring patients receive lifesaving treatments on time. However, as doctors currently do not identify 10% to 30% of breast cancers during regular assessment, automated methods to detect malignant tumors are desirable. Although several computerized methods for breast cancer classification have been proposed, convolutional neural networks (CNNs) have demonstrably outperformed other approaches. In this paper, we propose an automated method for the binary classification of breast cancer tumors as either malignant or benign that utilizes a bag of deep multi-resolution convolutional features (BoDMCF) extracted from histopathological images at four resolutions (40×, 100×, 200× and 400×) by three pre-trained state-of-the-art deep CNN models: ResNet-50, EfficientNetb0, and Inception-v3. The BoDMCF extracted by the pre-trained CNNs were pooled using global average pooling and classified using the support vector machine (SVM) classifier. While some prior work has utilized CNNs for breast cancer classification, they did not explore using CNNs to extract and pool a bag of deep multi-resolution features. Other prior work utilized CNNs for deep multi-resolution feature extraction from chest X-ray radiographs to detect other conditions such as pneumoconiosis but not for breast cancer detection from histopathological images. In rigorous evaluation experiments, our deep BoDMCF feature approach with global pooling achieved an average accuracy of 99.92%, sensitivity of 0.9987, specificity (or recall) of 0.9797, positive prediction value (PPV) or precision of 0.99870, F1-Score of 0.9987, MCC of 0.9980, Kappa of 0.8368, and AUC of 0.9990 on the publicly available BreaKHis breast cancer image dataset. The proposed approach outperforms the prior state of the art for histopathological breast cancer classification as well as a comprehensive set of CNN baselines, including ResNet18, InceptionV3, DenseNet201, EfficientNetb0, SqueezeNet, and ShuffleNet, when classifying images at any individual resolutions (40×, 100×, 200× or 400×) or when SVM is used to classify a BoDMCF extracted using any single pre-trained CNN model. We also demonstrate through a carefully constructed set of experiments that each component of our approach contributes non-trivially to its superior performance including transfer learning (pre-training and fine-tuning), deep feature extraction at multiple resolutions, global pooling of deep multiresolution features into a powerful BoDMCF representation, and classification using SVM. Full article

(This article belongs to the Section Health Informatics)

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30 pages, 4221 KiB

Open AccessEditor’s ChoiceArticle

Development of a Chatbot for Pregnant Women on a Posyandu Application in Indonesia: From Qualitative Approach to Decision Tree Method

by Indriana Widya Puspitasari, Fedri Ruluwedrata Rinawan, Wanda Gusdya Purnama, Hadi Susiarno and Ari Indra Susanti

Informatics 2022, 9(4), 88; https://doi.org/10.3390/informatics9040088 - 27 Oct 2022

Cited by 8 | Viewed by 7153

Abstract

With the widespread application of digital healthcare, mobile health (mHealth) services are also developing in maternal and child health, primarily through community-based services, such as Posyandu in Indonesia. Patients need media for consultation and decision-making, while health workers are constrained in responding quickly. [...] Read more.

With the widespread application of digital healthcare, mobile health (mHealth) services are also developing in maternal and child health, primarily through community-based services, such as Posyandu in Indonesia. Patients need media for consultation and decision-making, while health workers are constrained in responding quickly. This study aimed to obtain information from pregnant women and midwives in developing a decision tree model as material for building a semi-automated chatbot. Using an exploratory qualitative approach, semi-structured interviews were conducted through focus group discussions (FGD) with pregnant women (n = 10) and midwives (n = 12) in March 2022. The results showed 38 codes, 15 categories, and 7 subthemes that generated 3 major themes: maternal health education, information on maternal health services, and health monitoring. The decision tree method was applied from these themes based on the needs of users, evidence, and expert sources to ensure quality. In summary, the need to use a semi-automated chatbot can be applied to education about maternal health and monitoring, where severe cases should be provided with non-automated communication with midwives. Applying the decision tree method ensured quality content, supported a clinical decision, and assisted in early detection. Furthermore, future research needs to measure user evaluation. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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18 pages, 2920 KiB

Open AccessEditor’s ChoiceArticle

Classification of Malaria Using Object Detection Models

by Padmini Krishnadas, Krishnaraj Chadaga, Niranjana Sampathila, Santhosha Rao, Swathi K. S. and Srikanth Prabhu

Informatics 2022, 9(4), 76; https://doi.org/10.3390/informatics9040076 - 27 Sep 2022

Cited by 36 | Viewed by 12023

Abstract

Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often [...] Read more.

Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often time consuming and subject to error. Thus, the automated detection and classification of the malaria type and stage of progression can provide a quicker and more accurate diagnosis for patients. In this research, we used two object detection models, YOLOv5 and scaled YOLOv4, to classify the stage of progression and type of malaria parasite. We also used two different datasets for the classification of stage and parasite type while assessing the viability of the dataset for the task. The dataset used is comprised of microscopic images of red blood cells that were either parasitized or uninfected. The infected cells were classified based on two broad categories: the type of malarial parasite causing the infection and the stage of progression of the disease. The dataset was manually annotated using the LabelImg tool. The images were then augmented to enhance model training. Both models YOLOv5 and scaled YOLOv4 proved effective in classifying the type of parasite. Scaled YOLOv4 was in the lead with an accuracy of 83% followed by YOLOv5 with an accuracy of 78.5%. The proposed models may be useful for the medical professionals in the accurate diagnosis of malaria and its stage prediction. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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22 pages, 5434 KiB

Open AccessEditor’s ChoiceArticle

A Visual Data Storytelling Framework

by Yangjinbo Zhang, Mark Reynolds, Artur Lugmayr, Katarina Damjanov and Ghulam Mubashar Hassan

Informatics 2022, 9(4), 73; https://doi.org/10.3390/informatics9040073 - 23 Sep 2022

Cited by 12 | Viewed by 8540

Abstract

While the consumption of visual information becomes a daily commodity integrated into our lives, data visualisation is dominated by dashboards and charts. The main contribution of this article is an advanced way to visualise data as a data story. We converged paradigms from [...] Read more.

While the consumption of visual information becomes a daily commodity integrated into our lives, data visualisation is dominated by dashboards and charts. The main contribution of this article is an advanced way to visualise data as a data story. We converged paradigms from digital storytelling, serious games, and data visualisation to turn data into useful insights. The creation, management, and analysis of data have been increasingly given more attention in industry and professional practices. However, the potential of packaging data and analytic results into easily digestible and visually explorable content intended for non-professional audiences has not yet been investigated to its full extent. We contributed towards overcoming the gap between data analytics and data presentation. By integrating a story-like environment and entertainment into data visualisation, we explore the possibilities of efficiently communicating data and insights to general audiences in a casual context. We present this modular approach to customising messages for visual data storytelling from an information and communication perspective, including a test prototype developed to illustrate our data storytelling framework. Full article

(This article belongs to the Section Social Informatics and Digital Humanities)

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13 pages, 832 KiB

Open AccessEditor’s ChoiceArticle

Evaluating and Revising the Digital Citizenship Scale

by Randy Connolly and Janet Miller

Informatics 2022, 9(3), 61; https://doi.org/10.3390/informatics9030061 - 19 Aug 2022

Cited by 9 | Viewed by 4059

Abstract

Measuring citizen activities in online environments is an important enterprise in fields as diverse as political science, informatics, and education. Over the past decade, a variety of scholars have proposed survey instruments for measuring digital citizenship. This study investigates the psychometric properties of [...] Read more.

Measuring citizen activities in online environments is an important enterprise in fields as diverse as political science, informatics, and education. Over the past decade, a variety of scholars have proposed survey instruments for measuring digital citizenship. This study investigates the psychometric properties of one such measure, the Digital Citizenship Scale (DCS). While previous investigations of the DCS drew participants exclusively from single educational environments (college students, teachers), this study is the first with a survey population (n = 1820) that includes both students and the general public from multiple countries. Four research questions were addressed, two of which were focused on the validity of the DCS for this wider population. Our results suggest refining the 26-item five-factor DCS tool into an abbreviated 19-item four-factor instrument. The other two research questions investigated how gender, generation, and nationality affect DCS scores and the relationship between the different DCS factors. While gender was found to have a minimal effect on scores, nationality and age did have a medium effect on the online political activism factor. Technical skills by themselves appear to play little role in predicting online political engagement; the largest predictor of online political engagement was critical perspective and a willingness to use the Internet in active ways beyond simply consuming content. Full article

(This article belongs to the Section Social Informatics and Digital Humanities)

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28 pages, 3344 KiB

Open AccessEditor’s ChoiceArticle

Visual Analytics for Predicting Disease Outcomes Using Laboratory Test Results

by Neda Rostamzadeh, Sheikh S. Abdullah, Kamran Sedig, Amit X. Garg and Eric McArthur

Informatics 2022, 9(1), 17; https://doi.org/10.3390/informatics9010017 - 25 Feb 2022

Viewed by 3731

Abstract

Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a disease outcome. SUNRISE integrates [...] Read more.

Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a disease outcome. SUNRISE integrates frequent itemset mining (i.e., Eclat algorithm) with extreme gradient boosting (XGBoost) to develop more specialized and accurate prediction models. It also includes interactive visualizations to allow the user to interact with the model and track the decision process. SUNRISE helps the user probe the prediction model by generating input examples and observing how the model responds. Furthermore, it improves the user’s confidence in the generated predictions and provides them the means to validate the model’s response by illustrating the underlying working mechanism of the prediction models through visualization representations. SUNRISE offers a balanced distribution of processing load through the seamless integration of analytical methods with interactive visual representations to support the user’s cognitive tasks. We demonstrate the usefulness of SUNRISE through a usage scenario of exploring the association between laboratory test results and acute kidney injury, using large provincial healthcare databases from Ontario, Canada. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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18 pages, 378 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Human-Computer Interaction in Digital Mental Health

by Luke Balcombe and Diego De Leo

Informatics 2022, 9(1), 14; https://doi.org/10.3390/informatics9010014 - 22 Feb 2022

Cited by 35 | Viewed by 32786

Abstract

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have [...] Read more.

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance. Full article

(This article belongs to the Special Issue Feature Papers in Human-Computer Interaction)

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