bluuebunny/crossref_metadata_2025 huggingface dataset:
AHD2FHIR: A Tool for Mapping of Natural Language Annotations to Fast Healthcare Interoperability Resources – A Technical Case Report
Authors: Scheible Raphael , Caliskan Deniz , Fischer Patrick , Thomczyk Fabian , Zabka Susanne , Schneider Henning , Boeker Martin , Schulz Stefan , Prokosch Hans-Ulrich , Gulden Christian
Published: June 2022

A significant portion of data in Electronic Health Records is only available as unstructured text, such as surgical or finding reports, clinical notes and discharge summaries. To use this data for secondary purposes, natural language processing (NLP) tools are required to extract structured information. Furthermore, for interoperable use, harmonization of the data is necessary. HL7 Fast Healthcare Interoperability Resources (FHIR), an emerging standard for exchanging healthcare data, defines such a structured format. For German-language medical NLP, the tool Averbis Health Discovery (AHD) represents a comprehensive solution. AHD offers a proprietary REST interface for text analysis pipelines. To build a bridge between FHIR and this interface, we created a service that translates the communication around AHD from and to FHIR. The application is available under an open source license.
An Ontology-Based Semantic Model for Sharing and Reusability of Clinical Pathways Across Context (ShaRE-CP)
Authors: Bediang Georges , Falquet Gilles , Geissbuhler Antoine
Published: June 2022

Clinical pathways (CP) enable a standardized and an efficient management of patients with common pathologies. As operational tools, they take into account knowledge from guidelines and from the context (e.g. availability of resources) in which different interventions are to be carried out. Mastering the coherence of interactions between all these knowledge domains is a major challenge for the implementation of CP. This scientific work led to the development of an ontology called Shareable and Reusable Clinical Pathway Ontology (ShaRE-CP) which integrates four knowledge domains (CP, guidelines, health resources and context) and to the establishment of existing semantic links between them. The consistency of this semantic model has been validated by using reasoners. This ontology can serve as a basis for the development of a decision support system for planning and managing patient care.
Identifying and Reconciling Patients’ Allergy Information Within the Electronic Health Record
Authors: Vallamkonda Sachin , Ortega Carlos A. , Lo Ying Chih , Blackley Suzanne V. , Wang Liqin , Seger Diane L. , Blumenthal Kimberly G. , Plasek Joseph M. , Goss Foster , Zhou Li
Published: June 2022

Allergy information is often documented in diverse sections of the electronic health record (EHR). Systematically reconciling allergy information across the EHR is critical to improve the accuracy and completeness of patients’ allergy lists and ensure patient safety. In this retrospective cohort study, we examined the prevalence of incompleteness, inaccuracy, and redundancy of allergy information for patients with a clinical encounter at any Mass General Brigham facility between January 1, 2018 and December 31, 2018. We identified 4 key places in the EHR containing reconcilable allergy information: 1) allergy modules (including free text comments and duplicate allergen entries), 2) medication laboratory tests results, 3) oral medication allergy challenge tests, and 4) medication orders that have been discontinued due to adverse drug reactions (ADRs). Within our cohort, 718,315 (45.2% of the total 1,588,979) patients had an active allergy entry; of which, 266,275 (37.1%) patient’s records indicated a need for reconciliation.
Design of an Integrated Clinical Research Informatics System for a Multi-Centre and Multi-Visit Prospective Birth Cohort Study
Authors: Sinitkul Ratchaneewan , Maude Richard J. , Nithirochananont Ussanai
Published: June 2022

To conduct a multi-center prospective study over more than one year requires an efficient system that can synchronize collection of data from several sources in real-time and facilitate remote data management. This paper describes the design and use of an in-house data collection and sample information management system that was used in a prospective birth cohort study in Thailand. Participants were enrolled from three hospitals and were required to visit their respective hospital and complete self-administered questionnaires (SAQ) at every visit. The in-house informatics system required integration of the data collection streams that can handle three different types of data (SAQ, clinical record, and laboratory sample tracking). The system has been implemented in the pilot phase of a birth cohort study and has demonstrated its usability for further application to an expanded study.
Exploring Determinants of Longevity of Biomedical Databases
Authors: Finkelstein Joseph , Guarino Jennifer , Huo Xingyue , Borziak Kirill , Parvanova Irena
Published: June 2022

The maintenance of biomedical databases requires ongoing and systematic efforts in keeping them up-to-date which may affect long-term sustainability. Since research has become more reliant on publicly available biomedical data collections, it is important to understand factors affecting their longevity. The aim of this article was to explore potential determinants of biomedical database longevity. To build an analytical dataset, we used Database journal that have been created as an open access platform for presenting biological databases. A stratified analysis of all the original databases published in Database journal between 2009 and 2016 was conducted depending on their accessibility status. Overall, 35% of 518 analyzed databases were not accessible in 2020. We showed that databases with higher citation counts from institutions with higher scientific output were significantly more likely to be currently accessible. Databases from researchers with higher h-index were more likely to be accessible. Further investigation is warranted to identify factors affecting longevity of high impact databases.
Mobile Application Model for Supporting in Wound Treatment
Authors: Saggioro Mateus Fontes , Eler Gabrielle Jacklin , Figueiredo Fernanda Coelho , Shishido Henrique Yoshikazu
Published: June 2022

Objective: This article presents a mobile application model for the treatment tracking of in-hospital wounds. Methods: A survey of the literature on mobile apps for wound monitoring was carried out. Health professionals were interviewed and wound forms were analyzed to synthesize the application’s fields and features. We designed the application model using a prototyping tool. Results: The prototype features interfaces for patient evaluation, different functionalities according to the role of the health professional, a dashboard for monitoring the open admissions, selection of wound locations using a body model, treatment prescribing, tracking the wounds using photos, and generating wound reports. Conclusion: The adoption of this application could optimize wounds’ treatment, increase patient safety, reduce material expenditures and time for professionals with rework in the dressing procedure.
Standardization of Personal Health Records in the Portable Health Clinic System
Authors: Islam Rafiqul , Yokota Fumihiko , Kikuchi Kimiyo , Nishikitani Mariko , Izukura Rieko , Sato Yoko , Rahman Mahmudur , Sultana Nazneen , Nessa Meherun , Ahmed Ashir , Nakashima Naoki
Published: June 2022

A personal health record (PHR) is not only a collection of personal health data but also a personal healthcare and disease management tool for individual patients. Recently, PHRs have been considered indispensable tools for patient engagement in the area of noncommunicable diseases (NCDs) and have gained a special importance. Unfortunately, similar to several other developing countries, Bangladesh remains far behind in establishing a standard PHR system for the country despite the fact that the growth of NCDs is extremely high and accounts for approximately 70% of the total diseases experienced in the country. The Portable Health Clinic system, which has a PHR feature, was established in Bangladesh in 2010. This PHR system requires standardization for each country. The objective of this research is to standardize this PHR system with reference to the PHR system proposed by the Japanese Clinical Societies, which is a pioneer of work in this field in Asia.
How the Content of Progress Notes Affects Readers’ Perceptions of Their Usefulness and the Burden Placed on Writers: Improving Interprofessional Communication
Authors: Kagawa Rina , Tsurushima Hideo
Published: June 2022

Electronic health records should efficiently store the information required for clinical decision-making and contain progress notes that reference this information. However, beyond the inclusion of subjective data, objective data, assessment, and plan framework, the content required to make progress notes useful for readers with diverse specialties has not been clarified. Moreover, the documentation burden that including additional content places on medical doctors (MDs) has not been determined. We conducted a questionnaire with 74 MDs, nurses, and other clinical professionals to determine whether they found progress notes with varying specific contents useful. In addition, the degree of the burden of writing progress notes that contain specific content was measured when 25 MDs were instructed to add specific content. Our results reveal that progress notes are more useful for clinical reasoning for readers other than MDs when more specific information is included; this can be achieved without increasing the documentation burden.
The Implication of Latent Information Quality to the Reproducibility of Secondary Use of Electronic Health Records
Authors: Fu Sunyang , Wen Andrew , Pagali Sandeep , Zong Nansu , St Sauver Jennifer , Sohn Sunghwan , Fan Jungwei , Liu Hongfang
Published: June 2022

Reproducibility is an important quality criterion for the secondary use of electronic health records (EHRs). However, multiple barriers to reproducibility are embedded in the heterogeneous EHR environment. These barriers include complex processes for collecting and organizing EHR data and dynamic multi-level interactions occurring during information use (e.g., inter-personal, inter-system, and cross-institutional). To ensure reproducible use of EHRs, we investigated four information quality dimensions and examine the implications for reproducibility based on a real-world EHR study. Four types of IQ measurements suggested that barriers to reproducibility occurred for all stages of secondary use of EHR data. We discussed our recommendations and emphasized the importance of promoting transparent, high-throughput, and accessible data infrastructures and implementation best practices (e.g., data quality assessment, reporting standard).
End-User Need Assessment for Developing Electronic Integrated Antenatal Care (e-iANC)
Authors: Markam Hosizah , Tamzil Fachmi , Wiharto Mulyo
Published: June 2022

Electronic Integrated Antenatal Care is a web-based application for antenatal care (ANC) data and information management for independent practice midwives. This study aimed to assess the end-user needs for e-iANC development. Their needs for data and information were represented by a use case diagram. Five key stakeholders were identified related to the development of an e-iANC. Interviewing representatives of each informant provided the functions, and information content and flows for each function. The Extended e-iANC needs and promoted insight among all of the actors’ perspectives. The use-case scenario for extended e-iANC includes all of the elements of midwifery care include antenatal care, childbirth, puerperium, immunization, and family planning. The deployment of integrated information of extended e-iANC with the primary healthcare applications and the district health department is necessary for comprehensive information on maternal and child care innovation.
Extending PICO with Observation Normalization for Evidence Computing
Authors: Turfah Ali , Liu Hao , Stewart Latoya A. , Kang Tian , Weng Chunhua
Published: June 2022

While the PICO framework is widely used by clinicians for clinical question formulation when querying the medical literature, it does not have the expressiveness to explicitly capture medical findings based on any standard. In addition, findings extracted from the literature are represented as free-text, which is not amenable to computation. This research extends the PICO framework with Observation elements, which capture the observed effect that an Intervention has on an Outcome, forming Intervention-Observation-Outcome triplets. In addition, we present a framework to normalize Observation elements with respect to their significance and the direction of the effect, as well as a rule-based approach to perform the normalization of these attributes. Our method achieves macro-averaged F1 scores of 0.82 and 0.73 for identifying the significance and direction attributes, respectively.
MedSentinel – A Smart Sentinel for Biomedical Online Search Demonstrated by a COVID-19 Search
Authors: Schölly Reto , Yazijy Suhail , Kellmeyer Philipp
Published: June 2022

We present a work-in-progress software project which aims to assist cross-database medical research and knowledge acquisition from heterogeneous sources. Using a Natural Language Processing (NLP) model based on deep learning algorithms, topical similarities are detected, going beyond measures of connectivity via citation or database suggestion algorithms. A network is generated based on the NLP-similarities between them, and then presented within an explorable 3D environment. Our software will then generate a list of publications and datasets which pertain to a certain topic of interest, based on their level of similarity in terms of knowledge representation.
Hybrid Methods of Bibliographic Coupling and Text Similarity Measurement for Biomedical Paper Recommendation
Authors: Guo Hongmei , Shen Zhesi , Zeng Jianxun , Hong Na
Published: June 2022

The amount of available scientific literature is increasing, and studies have proposed various methods for evaluating document-document similarity in order to cluster or classify documents for science mapping and knowledge discovery. In this paper, we propose hybrid methods for bibliographic coupling (BC) and linear evaluation of text or content similarity: We combined BC with BM25, Cosine, and PMRA to compare their performances with single methods in paper recommendation tasks using TREC Genomics Track 2005datasets. For paper recommendation, BC and text-based methods complement each other, and hybrid methods were better than single methods. The combinations of BC with BM25 and BC with Cosine performed better than BC with PMRA. The performances were best when the weights of BM25, Cosine, and PMRA were 0.025, 0.2, and 0.2, respectively, in hybrid methods. For paper recommendation, the combinations of BC with text-based methods were better than BC or text-based methods used alone. The choice of method should depend on the actual data and research needs. In the future, the underlying reasons for the differences in performance and the specific part or type of information they complement in text clustering or recommendation need to be examined.
Clinical Usefulness of Drug-Disease Interaction Alerts from a Clinical Decision Support System, MedGuard, for Patient Safety: A Single Center Study
Authors: Poly Tahmina Nasrin , Islam Md. Mohaimenul , Li Yu-Chuan (Jack)
Published: June 2022

Clinical decision support systems have been widely used in healthcare, yet few studies have concurrently measured the clinical effectiveness of CDSSs, and the appropriateness of alerts with physicians’ response to alerts. We conducted a retrospective analysis of prescriptions caused disease-medication related alerts. Medication orders for outpatients’ prescriptions, all aged group were included in this study. All the prescriptions were reviewed, and medication orders compared with a widely used medication reference (UpToDate) and other standard guidelines. We reviewed 1,409 CDS alerts (2.67% alert rate) on 52,654 prescriptions ordered during the study period. 545 (38.70%) of alerts were overridden. Override appropriateness was 2.20% overall. However, the rate of alert acceptance was higher, ranging from 11.11 to 92.86%. The MedGuard system had a lower overridden rate than other systems reported in previous studies. The acceptance rate of alerts by physicians was high. Moreover, false-positive rate was low. The MedGuard system has the potential to reduce alert fatigue and to minimize the risk of patient harm.
Detecting Drug-Drug Interactions in COVID-19 Patients
Authors: Jeong Eugene , Person Anna K. , Stollings Joanna L. , Su Yu , Li Lang , Chen You
Published: June 2022

COVID-19 patients with multiple comorbid illnesses are more likely to be using polypharmacy to treat their COVID-19 disease and comorbid conditions. Previous literature identified several DDIs in COVID-19 patients; however, various DDIs are unrecognized. This study aims to discover novel DDIs by conducting comprehensive research on the FDA Adverse Event Reporting System (FAERS) data from January 2020 to March 2021. We applied seven algorithms to discover DDIs. In addition, the Liverpool database containing DDI confirmed by clinical trials was used as a gold standard to determine novel DDIs in COVID-19 patients. The seven models detected 2,516 drug-drug pairs having adverse events (AEs), 49 out of which were confirmed by the Liverpool database. The remaining 2,467 drug pairs tested to be significant by the seven models can be candidate DDIs for clinical trial hypotheses. Thus, the FAERS database, along with informatics approaches, provides a novel way to select candidate drug-drug pairs to be examined in COVID-19 patients.
Development, Implementation and Initial Results of CDSS Recommendations for Patients at Risk of Hereditary Breast Cancer
Authors: Rosa Juan , Tajerian Matias , Zin Yael , Brunner Mauricio , Lopez Natalia , Gaiera Alejandro , Butti Matias , Menazzi Sebastián , Chanfreau Hernan , Luna Daniel , Rubin Luciana , Benitez Sonia
Published: June 2022

Breast cancer represents 23% of all cancers diagnosed among women each year. BRCA1 and BRCA2 are tumor suppressor genes related to the most frequent form of hereditary breast and ovarian cancer, as well as other types of cancer. The aim of this work is to describe the development of Clinical Decision Support Systems (CDSS) for referral to genetic counseling in patients at increased risk of pathogenic variants in BRCA1 and BRCA2, and to describe results during the pilot study implementation (from January 5, 2021 to March 5, 2021). To achieve integration and system interoperability, we used FHIR and CDS-Hooks within the CDSS development. A total of 142 alerts were triggered by the system for 72 physicians in 98 patients. Results showed an acceptance rate for the recommendation of 2.1%, which could improve using intrusive alerts in all of the hooks.
Annotation Guidelines for Medication Errors in Incident Reports: Validation Through a Mixed Methods Approach
Authors: Wong Zoie S.Y. , Qiao Yuchen , Sasano Ryohei , Zhang Hongkuan , Taneda Kenichiro , Ushiro Shin
Published: June 2022

At present no adequate annotation guidelines exists for incident report learning. This study aims at utilizing multiple quantitative and qualitative evidence to validate annotation guidelines for incident reporting of medication errors. Through multiple approaches via annotator training, annotation performance evaluation, exit surveys, and user and expert interviews, a mixed methods explanatory sequential design was utilized to collect 2-stage evidence for validation. We recruited two patient safety experts to participate in piloting, three annotators to receive annotation training and provide user feedback, and two incident report system designers to offer expert comments. Regarding the annotation performance evaluation, the overall accuracy reached 97% and 90% for named entity identification and attribute identification respectively. Participants provided invaluable comments and opinions towards improving the annotation methods. The mixed methods approach created a significant evidential basis for the use of annotation guidelines for incident report of medication errors. Further expansion of the guidelines and external validity present options for future research.
Perioperative Care Structures and Non-Routine Events: Network Analysis
Authors: Chen You , Alrifai Mhd Wael , Gong Yang , Evan Rhodes , Slagle Jason , Malin Bradley , France Daniel
Published: June 2022

Non-routine events (NREs) are any aspect of care perceived by clinicians as a deviation from optimal care. The reporting of NREs to peers (or care teams) may help healthcare organizations improve patient safety in high-risk work environments (e.g., surgery). While various factors, including care structure and organizational factors may influence a clinician’s NRE reporting behavior, their role has not been systematically studied. We conducted a retrospective study relying on NREs and electronic health records to determine if perioperative interaction structures among clinicians are associated with the frequency of NRE reporting in a large academic medical center. The data covers November 1, 2016, to January 31, 2019 and includes 295 perioperative clinicians, 225 neonatal surgical cases, and 543 NREs. Using network analysis, we measured a clinician’s status in interaction structures according to the sociometric factors of degree, betweenness, and eigenvector centrality. We applied a proportional odds model to measure the relationship between each sociometric factor and NRE reporting frequency. Our findings indicate that the centrality of clinicians is directly associated with the quantity of NREs per surgical case.
Patient Safety Informatics: Meeting the Challenges of Emerging Digital Health
Authors: McInerney Ciarán , Benn Jonathan , Dowding Dawn , Habli Ibrahim , Jenkins David A. , McCrorie Carolyn , Peek Niels , Randell Rebecca , Williams Richard , Johnson Owen A.
Published: June 2022

The fourth industrial revolution is based on cyber-physical systems and the connectivity of devices. It is currently unclear what the consequences are for patient safety as existing digital health technologies become ubiquitous with increasing pace and interact in unforeseen ways. In this paper, we describe the output from a workshop focused on identifying the patient safety challenges associated with emerging digital health technologies. We discuss six challenges identified in the workshop and present recommendations to address the patient safety concerns posed by them. A key implication of considering the challenges and opportunities for Patient Safety Informatics is the interdisciplinary contribution required to study digital health technologies within their embedded context. The principles underlying our recommendations are those of proactive and systems approaches that relate the social, technical and regulatory facets underpinning patient safety informatics theory and practice.
Using Publicly Available Recall and Safety Alert Reports for Learning from Technology-Induced Error
Authors: Borycki Elizabeth M. , Kushniruk Andre W.
Published: June 2022

Information technologies have the potential to increase the safety of healthcare and advance safety science. However, it is now well known that health information systems may also inadvertently introduce new forms of error known as technology-induced error. Such errors may be difficult to detect as they may only appear under conditions of system use in real healthcare settings. In this paper, the authors explore the use and assessment of recall and safety alerts for both identifying and learning from technology-induced error. Publically available safety and recall reports from Canada were analyzed to identify opportunities to improve organizational learning from technology-induced errors. Although a range of error types were identified, it was found that none of the reports provided detailed information about the underlying technical circumstances that led to the need for a recall. Implications for future reporting systems to support learning from technology-induced error are discussed.
Bibliometric Analysis of Chatbots in Health-Trend Shifts and Advancements in Artificial Intelligence for Personalized Conversational Agents
Authors: Pears Matthew , Konstantinidis Stathis
Published: June 2022

Bibliometric analysis provides a summary for research reported in scientific literature. This can highlight pattens and trends in academic research areas, and assist in research directions. Recent growing requirements for efficient communications and increased user learning needs in the health domain, has instigated mass exploitation of chatbots. 2148 documents were analysed to show a shift in research focus around the year 2016. The rate of documents produced in the last few years is more than the collective 20 year period, and future outputs may soar. The emergence of machine and deep learning technology with chatbot usage suggested research opportunity to be exploited in techniques which embed advanced AI abilities. Key authors still spearhead the research direction but a new wave of outputs will further disperse topics into advanced techniques such as personalised disease detections and sophisticated interface that significantly mask any artificiality to their composition.
Use of Robots to Support Those Living with Dementia and Their Caregivers
Authors: Wagner Evangeline , Borycki Elizabeth M. , Kushniruk Andre W.
Published: June 2022

Dementia and other related diseases causing symptoms of mild cognitive impairment are being increasingly diagnosed. These diseases are placing a significant strain on the healthcare system. Robotic technology research has also been increasing, specifically in the field of healthcare and assisted living. This scoping review explores the research at the intersection of dementia and robotic devices. More specifically, this paper looks at how robots can be used in dementia care to gain a deeper understanding of the potential benefits this technology may have on patient and caregiver lives. This research was conducted using PRISMA guidelines. Data were extracted from 13 articles. The researchers found that there is a lack of evidence regarding how robotics can assist patients living with dementia; however, robotic devices can be used by patients to perform some daily tasks in the home.
Using a Wearable Device and Patient Reported Outcome to Evaluate the Influence of Sleep on Quality of Life Among Breast and Prostate Cancer Patients
Authors: Dhar Eshita , Barsasella Diana , Srikanth Sruthi , Panja Ajith Kumar , Malwade Shwetambara , Syed-Abdul Shabbir
Published: June 2022

Few studies have used the combination of subjective and objective measures to investigate sleep problems as a primary outcome of concern in cancer patients. This study highlights the influence of sleep quality and duration on quality of life among breast and prostate cancer patients. Thirty-one participants were included in this study. Sleep quality and duration was assessed using a wearable actigraphy device and Pittsburgh Sleep Quality Index questionnaire. Quality of life was measured by European-Organization-For-Research-And-Treatment-Of-Cancer Quality of Life Questionnaire. t-test statistics further investigates the influence of sleep quality and duration with quality of life in participants. Results implied that those breast cancer patients who were poor sleepers and short sleepers showed greater deficits in areas of quality of life. In contrast, those prostate cancer patients who were long sleepers and good sleepers displayed poorer quality of life using actigraphy.
Data Veracity of Patients and Health Consumers Reported Adverse Drug Reactions on Twitter: Key Linguistic Features, Twitter Variables, and Association Rules
Authors: Lyu Tianchu , Eidson Andrew , Jun Jungmi , Zhou Xiajie , Cui Xiang , Liang Chen
Published: June 2022

As Twitter emerged as an important data source for pharmacovigilance, heterogeneous data veracity becomes a major concern for extracted adverse drug reactions (ADRs). Our objective is to categorize different levels of data veracity and explore linguistic features of tweets and Twitter variables as they may be used for automatic screening high-veracity tweets that contain ADR-related information. We annotated a published Twitter corpus with linguistic features from existing studies and clinical experts. Multinomial logistic regression models found that first-person pronouns, expressing negative sentiment, ADR and drug name being in the same sentence were significantly associated with higher levels of data veracity (p<0.05), using medical terminology and fewer indications were associated with good data veracity (p<0.05), less drug numbers were marginally associated with good data veracity (p=0.053). These findings suggest opportunities for developing machine learning models for automatic screening of ADR-related tweets using key linguistic features, Twitter variables, and association rules.
“Book Music” Representation for Temporal Data, as a Part of the Feature Extraction Process: A Novel Approach to Improve the Handling of Time-Dependent Data in Secondary Use of Healthcare Structured Data
Authors: Chazard Emmanuel , Balaye Pierre , Balcaen Thibaut , Genin Michaël , Cuggia Marc , Bouzille Guillaume , Lamer Antoine
Published: June 2022

Book music is extensively used in street organs. It consists of thick cardboard, containing perforated holes specifying the musical notes. We propose to represent clinical time-dependent data in a tabular form inspired from this principle. The sheet represents a statistical individual, each row represents a binary time-dependent variable, and each hole denotes the “true” value. Data from electronic health records or nationwide medical-administrative databases can then be represented: demographics, patient flow, drugs, laboratory results, diagnoses, and procedures. This data representation is suitable for survival analysis (e.g., Cox model with repeated outcomes and changing covariates) and different types of temporal association rules. Quantitative continuous variables can be discretized, as in clinical studies. The “book music” approach could become an intermediary step in feature extraction from structured data. It would enable to better account for time in analyses, notably for historical cohort analyses based on healthcare data reuse.
Using Interactive Visual Analytics to Optimize Blood Products Inventory at a Blood Bank
Authors: Rad Jaber , Quinn Jason G. , Cheng Calvino , Liwski Robert , Abidi Samina , Abidi Syed Sibte Raza
Published: June 2022

Blood products and their derivatives are perishable commodities that require an efficient inventory management to ensure both a low wastage rate and a high product availability rate. To optimize blood product inventory, Blood Transfusion Services (BTS) need to reduce wastage by avoiding outdates and improving availability of different blood products. We took a blood product lifecycle approach and used advanced visualization techniques to design and develop a highly interactive web-based dashboard to audit retrospective data and consequently, to identify and learn from procedural inefficiencies based on analysis of transactional data. We present pertinent scenarios to show how the blood transfusion staff can use the dashboard to investigate blood product lifecycles so as to probe transition sequence patterns that led to wastage as a means to discover causes of procedural inefficiencies in the BTS.
The Use of Convolutional Neural Networks in the Prediction of Invasive Ductal Carcinoma in Histological Images of Breast Cancer
Authors: de Assis Érika G. , do Patrocínio Zenilton K. G. , Nobre Cristiane Neri
Published: June 2022

This paper presents a deep learning approach for automatic detection and visual analysis of Invasive Ductal Carcinoma (IDC) tissue regions. The method proposed in this work is a convolutional neural network (CNN) for visual semantic analysis of tumor regions for diagnostic support. Detection of IDC is a time-consuming and challenging task, mainly because a pathologist needs to examine large tissue regions to identify areas of malignancy. Deep Learning approaches are particularly suitable for dealing with this type of problem, especially when many samples are available for training, ensuring high quality of the learned features by the classifier and, consequently, its generalization capacity. A 3-hidden-layer CNN with data balancing reached both accuracy and F1-Score of 0.85 and outperforming other approaches from the literature. Thus, the proposed method in this article can serve as a support tool for the identification of invasive breast cancer.
Representation and Normalization of Complex Interventions for Evidence Computing
Authors: Chen Zhehuan , Liu Hao , Liao Stan , Bernard Marguerite , Kang Tian , Stewart Latoya A. , Weng Chunhua
Published: June 2022

Complex interventions are ubiquitous in healthcare. A lack of computational representations and information extraction solutions for complex interventions hinders accurate and efficient evidence synthesis. In this study, we manually annotated and analyzed 3,447 intervention snippets from 261 randomized clinical trial (RCT) abstracts and developed a compositional representation for complex interventions, which captures the spatial, temporal and Boolean relations between intervention components, along with an intervention normalization pipeline that automates three tasks: (i) treatment entity extraction; (ii) intervention component relation extraction; and (iii) attribute extraction and association. 361 intervention snippets from 29 unseen abstracts were included to report on the performance of the evaluation. The average F-measure was 0.74 for treatment entity extraction on an exact match and 0.82 for attribute extraction. The F-measure for relation extraction of multi-component complex interventions was 0.90. 93% of extracted attributes were correctly attributed to corresponding treatment entities.
Understanding Public Perceptions of Measles from Twitter Using Multi-Task Convolutional Neural Networks
Authors: Wang Samuel , Du Jingcheng , Tang Lu , Tao Cui
Published: June 2022

Measles is a highly contagious cause of febrile illness typically seen in young children. Recent years have witnessed the resurgence of measles cases in the United States. Prompt understanding of public perceptions of measles will allow public health agencies to respond appropriately promptly. We proposed a multi-task Convolutional Neural Network (MT-CNN) model to classify measles-related tweets in terms of three characteristics: Type of Message (6 subclasses), Emotion Expressed (6 subclasses), and Attitude towards Vaccination (3 subclasses). A gold standard corpus that contains 2,997 tweets with annotation in these dimensions was manually curated. A variety of conventional machine learning and deep learning models were evaluated as baseline models. The MT-CNN model performed better than other baseline conventional machine learning and the signal-task CNN models, and was then applied to predict unlabeled measles-related Twitter discussions that were crawled from 2007 to 2019, and the trends of public perceptions were analyzed along three dimensions.
A Sample Size Extractor for RCT Reports
Authors: Lin Fengyang , Liu Hao , Moon Paul , Weng Chunhua
Published: June 2022

Sample size is an important indicator of the power of randomized controlled trials (RCTs). In this paper, we designed a total sample size extractor using a combination of syntactic and machine learning methods, and evaluated it on 300 Covid-19 abstracts (Covid-Set) and 100 generic RCT abstracts (General-Set). To improve the performance, we applied transfer learning from a large public corpus of annotated abstracts. We achieved an average F1 score of 0.73 on the Covid-Set testing set, and 0.60 on the General-Set using exact matches. The F1 scores for loose matches on both datasets were over 0.74. Compared with the state-of-the-art tool, our extractor reports total sample sizes directly and improved F1 scores by at least 4% without transfer learning. We demonstrated that transfer learning improved the sample size extraction accuracy and minimized human labor on annotations.
Towards Automated Screening of Literature on Artificial Intelligence in Nursing
Authors: Moen Hans , Alhuwail Dari , Björne Jari , Block Lori , Celin Sven , Jeon Eunjoo , Kreiner Karl , Mitchell James , Ožegović Gabriela , Ronquillo Charlene Esteban , Sequeira Lydia , Tayaben Jude , Topaz Maxim , Veeranki Sai Pavan Kumar , Peltonen Laura-Maria
Published: June 2022

We evaluate the performance of multiple text classification methods used to automate the screening of article abstracts in terms of their relevance to a topic of interest. The aim is to develop a system that can be first trained on a set of manually screened article abstracts before using it to identify additional articles on the same topic. Here the focus is on articles related to the topic “artificial intelligence in nursing”. Eight text classification methods are tested, as well as two simple ensemble systems. The results indicate that it is feasible to use text classification technology to support the manual screening process of article abstracts when conducting a literature review. The best results are achieved by an ensemble system, which achieves a F1-score of 0.41, with a sensitivity of 0.54 and a specificity of 0.96. Future work directions are discussed.
A Qualitative Method for Learning Medical Expert Reasoning
Authors: Sedki Karima , Lamy Jean-Baptiste , Tsopra Rosy
Published: June 2022

The aim of this paper is to propose a qualitative method for learning a model that represents the closest possible experts reasoning and strategies to provide recommendations of antibiotics. The learned model contains an integrity constraint and a preference formula. The former indicates the features that an antibiotic should have to be recommended. The later indicates the rank of recommendation of an antibiotic.
A Multi-Class Approach for the Automatic Detection of Congestive Heart Failure in Windowed ECG
Authors: Rosa Giovanni , Russodivito Marco , Laudato Gennaro , Scalabrino Simone , Colavita Angela Rita , Oliveto Rocco
Published: June 2022

Congestive heart failure (CHF) is a chronic heart disease that causes debilitating symptoms and leads to higher mortality and morbidity. In this paper, we present HARPER, a novel automatic detector of CHF episodes able to distinguish between Normal Sinus Rhythm (NSR), CHF, and no-CHF. The main advantages of HARPER are its reliability and its capability of providing an early diagnosis. Indeed, the method is based on evaluating real-time features and observing a brief segment of ECG signal. HARPER is an independent tool meaning that it does not need any ECG annotation or segmentation algorithms to provide detection. The approach was submitted to complete experimentation by involving both the intra- and inter-patient validation schemes. The results are comparable to the state-of-art methods, highlighting the suitability of HARPER to be used in modern IoMT systems as a multi-class, fast, and highly accurate detector of CHF. We also provide guidelines for configuring a temporal window to be used in the automatic detection of CHF episodes.
Exploiting Social Media for Active Pharmacovigilance: The PVClinical Social Media Workspace
Authors: Kakalou Christine , Dimitsaki Stella , Dimitriadis Vlasios K. , Natsiavas Pantelis
Published: June 2022

The value of social media data for Adverse Drug Reaction (ADR) monitoring is actively investigated. While social media provide a vast amount of data, these data are hard to analyse due to their unstructured nature and lack of credibility. Despite these challenges, social media have been identified as a potentially useful data source, potentially able to “strengthen” the evidence for new ADRs. To this end, PVClinical project aims to build a platform facilitating the investigation of multiple heterogeneous data sources, including social media, to support pharmacovigilance (PV) processes, both in the clinical environment and beyond. In this study, we present the PVClinical Twitter workspace, also highlighting the rationale behind the main design choices, while also discussing the respective challenges.
Expanding Access to CNS-TAP: Design, Development, and Initial Use of a Complex Precision Health Specialty Web App for Neuro-Oncology
Authors: Ravi Karthik , Meng George , Roberts Holly J. , Schepers Allison , Franson Andrea , Koschmann Carl , Marini Bernard L. , Flynn Allen
Published: June 2022

This paper offers a case study to demonstrate how a complex scoring model tool called CNS-TAP, originally created by a neuro-oncology team at one institution, was upgraded and made accessible to a wider audience. In the Results and Discussion, many issues of web app design, development, and sustainability are covered. Overall, we chart a path to expand access to many unique software tools created and needed by today’s medical specialists.
Predicting Objective Performance Using Perceived Cognitive Workload Data in Healthcare Professionals: A Machine Learning Study
Authors: Adapa Karthik , Pillai Malvika , Das Shiva , Mosaly Prithima , Mazur Lukasz
Published: June 2022

Cognitive Workload (CWL) is a fundamental concept in predicting healthcare professionals’ (HCPs) objective performance. The study aims to compare the accuracy of the classical model (utilizes all six dimensions of the National Aeronautics and Space Administration Task Load Index (NASA-TLX)) and novel models (utilize four or five dimensions of NASA-TLX) in predicting HCPs’ objective performance. We use a dataset from our previous human factors research studies and apply a broad selection of supervised machine learning classification techniques to develop data-driven computational models and predict objective performance. The study findings confirm that classical models are better predictors of objective performance than novel models. This has practical implications for research in health informatics, human factors and ergonomics, and human-computer interaction in healthcare. Findings, although promising, cannot be generalized as they are based on a small dataset. Future studies may investigate additional subjective and physiological measures of CWL to predict HCPs’ objective performance.
Mobile Health Applications for Postpartum Depression Management: A Theory-Informed Analysis of Change-Use-Engagement (CUE) Criteria in the Digital Environment
Authors: Zingg Alexandra , Carter Laura , Rogith Deevakar , Selvaraj Sudhakar , Franklin Amy , Myneni Sahiti
Published: June 2022

Postpartum Depression (PPD) is the most common childbirth complication, with approximately 15% of postpartum women experiencing depression symptoms. Mobile applications have potential to expand delivery of mental health interventions. However, our understanding of how these tools engage women with PPD and facilitate positive behavioral changes is limited. In our paper, we analyze 15 commercial PPD applications to understand their role as facilitators of change, engagement, and sustained use. Applications reviewed contained an average of four theory-based behavioral change techniques, and highest patient engagement level reached was to empower patients through patient-generated data. Heuristic violations were identified in areas including user control and freedom, aesthetic and minimalist design, and help and documentation. An inverse correlation was found between the number of theory-based behavior change features and patient engagement. Findings suggest underserved populations may suffer further limitations accessing relevant health resources in the current application market.
Telemedicine Application and Assessment During the COVID-19 Pandemic
Authors: Bezerra Gustavo Marques Fernandes , de Lucena Feitosa Enzo Studart , Vale Catunda João Gabriel , Nogueira Sales Graça Caio , Lucena de Aquino Pedro , Bezerra Neto Antônio George , Bezerra da Silva Junior Geraldo
Published: June 2022

Telemedicine had a significant increase during the novel coronavirus (COVID-19) pandemic, which is still on course. In different parts of the world, healthcare units had to adapt their practice, and several assistance services were remotely delivered. The present study is a systematic review of studies that had applied telemedicine during the COVID-19 pandemic and had assessed its effects on the delivered care. We have covered a 1-year period in order to assess the initiatives developed during the pandemic time and that had already evaluated the effects of the telemedicine program that had been implemented. All the analyzed studies evidenced a positive effect of telemedicine in the treatment of different conditions, including chronic diseases, mental disorders and oncologic diseases. The real impact of the COVID-19 pandemic in the healthcare sector is still to be fully understood, and telemedicine implementation in different areas is one of its positive legacies.
Hidden in Plain Sight: Overlooked Results and Other Errors in Evaluating Online Laboratory Results
Authors: Monkman Helen , MacDonald Leah , Nøhr Christian , Tanaka James W. , Lesselroth Blake J.
Published: June 2022

People are increasingly accessing their own laboratory (lab) results online. However, Canadians may be expected to use different systems to access their results, depending upon where they are tested (e.g., community lab vs. hospital), and these results may be displayed differently. This study examined the extent to which participants without medical expertise (N = 25) made errors identifying lab results (i.e., missing or mis-identifying abnormal results) in a mock report. Six participants overlooked each of the flagged values, 20 participants missed an abnormal result that was not flagged, and 2 participants mis-identified a normal value as out of range. We describe potential causes of these errors and the implications for the design of consumer-facing lab results.
Digitally Mediated Schizophrenia Care – A Swedish Case of Translating, Designing and Expert Evaluation of Dialog+
Authors: Lindenfalk Bertil , Gremyr Andreas , Lundmark Marcus , Jacobsson Torbjörn
Published: June 2022

This work presents initial results of translation, design, development and expert testing of a digitally mediated dialogue based tool for schizophrenia care in Sweden. Dialog+ is a conversational methodology that has been shown to empower patients, promote co-produced and more cost-effective care. Dialog+ was translated and the digital application was redesigned to fit with the local context using human-centered design principles. Initial results of expert user testing, using heuristic evaluations and cognitive walkthroughs, shows promise for the method to work well also in Swedish settings. Initial testing with patients shows promising results. This work exemplifies how care patterns can be improved by considering the information layer of the interaction and creating shared and collaborative working spaces during treatment sessions can help to both empower patients and facilitate a more co-produced treatment plan.