bluuebunny/crossref_metadata_2025 huggingface dataset:
Rule-Based Natural Language Processing Pipeline to Detect Medication-Related Named Entities: Insights for Transfer Learning
Authors: Wong Zoie S.Y. ,   Data Artist Team, Waters Neil , Kuo Nicholas I-Hsien , Liu Jiaxing
Published: January 2024

We document the procedure and performance of a rule-based NLP system that, using transfer learning, automatically extracts essential named entities related to drug errors from Japanese free-text incident reports. Subsequently, we used the rule-based annotated data to fine-tune a pre-trained BERT model and examined the performance of medication-related incident report prediction. The rule-based pipeline achieved a macro-F1-score of 0.81 in an internal dataset and the BERT model fine-tuned with rule-annotated data achieved a macro-F1-score of 0.97 and 0.75 for named entity recognition and relation extraction tasks, respectively. The model can be deployed to other, similar problems in medication-related clinical texts.
Extracting Spatio-Temporal Trends in Medical Research Prioritization Through Natural Language Processing of Case Report Abstracts
Authors: Yao Lean Franzl Lim , Liew Kongmeng , Wakamiya Shoko , Aramaki Eiji
Published: January 2024

Medical research prioritization is an important aspect of decision-making by researchers and relevant stakeholders. The ever-increasing availability of technology and data has opened doors to new discoveries and new questions. This makes it difficult for researchers and relevant stakeholders to make well-informed decisions about the research areas they want to support and the nations they should look for collaborations. It is, therefore, useful to look at the spatio-temporal trends of medical research prioritization to gain insight into popular and neglected areas of research as well as the allocation of prioritization of each nation. In this study, we develop a system that collects, classifies, and summarizes case report abstracts according to the location, time, and disease category of the report. The additional classifications allow us to visualize and monitor the trends in medical research prioritization by location, time, and disease category.
Fertility-Related Conversations in the Context of COVID-19 and Vaccinations
Authors: Palmer Christopher , Khademi Sedigh , Javed Muhammad , Dimaguila Gerardo Luis , Buttery Jim
Published: January 2024

We used text analysis to examine trends and self-reporting in Twitter regarding COVID-19 disease and COVID-19 vaccines’ effects on fertility. Trends and self-reporting related to COVID19 vaccines indicates that further research is needed to enable women to feel they have sufficient information to make decisions regarding vaccination, together with social listening and engagement with women’s concerns regarding vaccination effects.
Predicting Medical Event Occurrence Using Medical Insurance Claims Big Data
Authors: Yoshimoto Hiromasa , Mitsutake Naohiro , Goda Kazuo
Published: January 2024

Medical events are often infrequent, thus becomes hard to predict. In this paper, we focus on predictor that forecasts whether a medical event would occur in the next year, and analyzes the impact of event’s frequency and data size via predictor’s performance. In the experiment, we made 1572 predictors for medical events using Medical Insurance Claims (MICs) data from 800,000 participants and 205.8 million claims over 8 years. The result revealed that (a) forecasting error will be increased when predicting low-frequency events, and (b) increasing the number of training dataset reduces errors. This result suggests that increasing data size is a key to solve low frequency problems. However, we still need additional methods to cope with sparse and imbalanced data.
A Hemodialysis Mortality Prediction Model Based on Active Contrastive Learning
Authors: Wang Feng , Chi Shengqiang , Li Xueyao , Zhang Hang , Li Jingsong
Published: January 2024

Hemodialysis (HD) is the main treatment for end-stage renal disease with high mortality and heavy economic burdens. Predicting the mortality risk in patients undergoing maintenance HD and identifying high-risk patients are critical to enable early intervention and improve quality of life. In this study, we proposed a two-stage protocol based on electronic health record (EHR) data to predict mortality risk of maintenance HD patients. First, we developed a multilayer perceptron (MLP) model to predict mortality risk. Second, an Active Contrastive Learning (ACL) method was proposed to select sample pairs and optimize the representation space to improve the prediction performance of the MLP model. Our ACL method outperforms other methods and has an average F1-score of 0.820 and an average area under the receiver operating characteristic curve of 0.853. This work is generalizable to analyses of cross-sectional EHR data, while this two-stage approach can be applied to other diseases as well.
Prognosticating Fetal Growth Restriction and Small for Gestational Age by Medical History
Authors: Sufriyana Herdiantri , Amani Fariska Zata , Al Hajiri Aufar Zimamuz Zaman , Wu Yu-Wei , Su Emily Chia-Yu
Published: January 2024

This study aimed to develop and externally validate a prognostic prediction model for screening fetal growth restriction (FGR)/small for gestational age (SGA) using medical history. From a nationwide health insurance database (n=1,697,452), we retrospectively selected visits of 12-to-55-year-old females to healthcare providers. This study used machine learning (including deep learning) and 54 medical-history predictors. The best model was a deep-insight visible neural network (DI-VNN). It had area under the curve of receiver operating characteristics (AUROC) 0.742 (95% CI 0.734 to 0.750) and a sensitivity of 49.09% (95% CI 47.60% to 50.58% at with 95% specificity). Our model used medical history for screening FGR/SGA with moderate accuracy by DI-VNN. In future work, we will compare this model with those from systematically-reviewed, previous studies and evaluate if this model’s usage impacts patient outcomes.
Hierarchical Label Distribution Learning for Disease Prediction
Authors: Ren Yi , Xia Jing , Yu Ziyi , Zhang Zhenchuan , Zhou Tianshu , Tian Yu , Li Jingsong
Published: January 2024

The prediction of disease can facilitate early intervention, comprehensive diagnosis and treatment, thereby benefiting healthcare and reducing medical costs. While single class and multi-class learning methods have been applied for disease prediction, they are inadequate in distinguishing between primary and secondary diagnoses, which is crucial for treatments. In this paper, label distribution is suggested to describe the diagnosis, which assigns the description degree to quantify the diagnosis. Additionally, a novel hierarchical label distribution learning (HLDL) model is proposed to make fine-grained predictions based on the hierarchical classification of diseases, taking into account the relationship among diseases. The experimental results on real-world datasets demonstrate that the HLDL model outperforms the baselines with statistical significance.
YouTube Videos for Public Health Literacy? A Machine Learning Pipeline to Curate Covid-19 Videos
Authors: Guo Yawen , Liu Xiao , Susarla Anjana , Padman Rema
Published: January 2024

The COVID-19 pandemic has highlighted the dire necessity to improve public health literacy for societal resilience. YouTube provides a vast repository of user-generated health information in a multi-media-rich format which may be easier for the public to understand and use if major concerns about content quality and accuracy are addressed. This study develops an automated solution to identify, retrieve and shortlist medically relevant and understandable YouTube videos that domain experts can subsequently review and recommend for disseminating and educating the public on the COVID-19 pandemic and similar public health outbreaks. Our approach leverages domain knowledge from human experts and machine learning and natural language processing methods to provide a scalable, replicable, and generalizable approach that can also be applied to enhance the management of many health conditions.
Personalized Prediction of Parkinson’s Disease Progression Based on Deep Gaussian Processes
Authors: Pan Changrong , Tian Yu , Zhou Tianshu , Li Jingsong
Published: January 2024

Parkinson’s disease is a chronic progressive neurodegenerative disease with highly heterogeneous symptoms and progression. It is helpful for patient management to establish a personalized model that integrates heterogeneous interpretation methods to predict disease progression. In the study, we propose a novel approach based on a multi-task learning framework to divide Parkinson’s disease progression modeling into an unsupervised clustering task and a disease progression prediction task. On the one hand, the method can cluster patients with different progression trajectories and discover new progression patterns of Parkinson’s disease. On the other hand, the discovery of new progression patterns helps to predict the future progression of Parkinson’s disease markers more accurately through parameter sharing among multiple tasks. We discovered three different Parkinson’s disease progression patterns and achieved better prediction performance (MAE=5.015, RMSE=7.284, r2=0.727) than previously proposed methods on Parkinson’s Progression Markers Initiative datasets, which is a longitudinal cohort study with newly diagnosed Parkinson’s disease.
Data Visualization of CRISPR-Cas9 Guide RNA Design Tools
Authors: Jain Yatish , Izzath Fathima Afra Mohamed , Wilson Laurence O.W. , Bauer Denis C.
Published: January 2024

With the advancement of genomic engineering and genetic modification techniques, the uptake of computational tools to design guide RNA increased drastically. Searching for genomic targets to design guides with maximum on-target activity (efficiency) and minimum off-target activity (specificity) is now an essential part of genome editing experiments. Today, a variety of tools exist that allow the search of genomic targets and let users customize their search parameters to better suit their experiments. Here we present an overview of different ways to visualize these searched CRISPR target sites along with specific downstream information like primer design, restriction enzyme activity and mutational outcome prediction after a double-stranded break. We discuss the importance of a good visualization summary to interpret information along with different ways to represent similar information effectively.
Visualising Healthcare Process Variability
Authors: Chen Hunter Chen , Armas-Cervantes Abel , Irving Vickie , Dorricott Brian , Capurro Daniel
Published: January 2024

Two similar patients undergoing the same procedure might follow different pathways inside a hospital. Some of this variation is expected, but too much variation is associated with increased adverse events. Currently, there are no standard methods to establish when variability for any given clinical process becomes excessive. In this study we use process mining techniques to describe clinical processes and calculate and visualise clinical variability. We selected a sample of patients undergoing elective coronary bypass surgery from the MIMIC dataset, represented their clinical processes in the form of traces, and calculated variability metrics for each process execution and for the complete set of processes. We then analysed the subset of processes with the highest and lowest relative variability and compared their clinical outcomes. We established that processes with the greatest variability were associated with longer length of stay (LOS) with a dose-response relationship: the higher the variability, the longer the LOS. This study provides an effective way to estimate and visualise clinical variability and to understand its impact on patient relevant outcomes.
An Approach for Generating Realistic Australian Synthetic Healthcare Data
Authors: Diouf Ibrahima , Grimes John , O’Brien Mitchell J. , Hassanzadeh Hamed , Truran Donna , Ngo Hoa , Raniga Parnesh , Lawley Michael , Bauer Denis C. , Hansen David , Khanna Sankalp , Reguant Roc
Published: January 2024

Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient’s anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.
Extracting Dynamic Information of Temporal Clinical Data to Predict the Outcome in Critically Ill Patients
Authors: Xia Jing , Ren Yi , Zhang Zhenchuan , Wang Feng , Tian Yu , Zhou Tianshu , Li Jingsong
Published: January 2024

Outcome prediction is essential for the administration and treatment of critically ill patients. For those patients, clinical measurements are continuously monitored and the time-varying data contains rich information for assessing the patients’ status. However, it is unclear how to capture the dynamic information effectively. In this work, multiple feature extraction methods, i.e. statistical feature classification methods and temporal modeling methods, such as recurrent neural network (RNN), were analyzed on a critical illness dataset with 18415 cases. The experimental results show when the dimension increases from 10 to 50, the RNN algorithm is gradually superior to the statistical feature classification methods with simple logic. The RNN model achieves the largest AUC value of 0.8463. Therefore, the temporal modeling methods are promising to capture temporal features which are predictive of the patients’ outcome and can be extended in more clinical applications.
Quality of Person-Generated Healthy Walking Data: An Explorative Analysis
Authors: Kim Hyeoneui , Im Eun-young , Ahn Ga-in
Published: January 2024

Despite the potential benefits of Person Generated Health Data (PGHD), data quality issues impede its use. This study examined the effect of different methods for filtering armband data on determining the amount of healthy walking and the consistency between healthy walking captured using armbands and health diaries. Four weeks of armband and health diary data were acquired from 103 college students. Armband data filtering was performed using heart rate measures and minimum daily step counts as a proxy for adequate daily wear time. No substantial differences in the filtered armband datasets were observed by filtering methods. Significant gaps were observed between healthy walking amounts determined from armband data and through the health diary. Future studies need to explore more diverse data filtering methods and their impact on health outcome assessments.
Instance Selection Algorithms for Predictive Modelling in Telehealth Applications
Authors: Wiesmüller Fabian , Hayn Dieter , Hoffmann Florian , Hanke Sten , Kastner Peter , Falgenhauer Markus , Schreier Günter
Published: January 2024

Telehealth services are becoming more and more popular, leading to an increasing amount of data to be monitored by health professionals. Machine learning can support them in managing these data. Therefore, the right machine learning algorithms need to be applied to the right data. We have implemented and validated different algorithms for selecting optimal time instances from time series data derived from a diabetes telehealth service. Intrinsic, supervised, and unsupervised instance selection algorithms were analysed. Instance selection had a huge impact on the accuracy of our random forest model for dropout prediction. The best results were achieved with a One Class Support Vector Machine, which improved the area under the receiver operating curve of the original algorithm from 69.91 to 75.88 %. We conclude that, although hardly mentioned in telehealth literature so far, instance selection has the potential to significantly improve the accuracy of machine learning algorithms.
Equitable Machine Learning for Hypoglycaemia Risk Management
Authors: Rodriguez Jhordany , Padilla Daniel , Bruce Lenert , Thow Ben , Pradhan Malcolm
Published: January 2024

We developed a machine learning (ML) model for the detection of patients with high risk of hypoglycaemic events during their hospital stay to improve the detection and management of hypoglycaemia. Our model was trained on data from a regional local health care district in Australia. The model was found to have good predictive performance in the general case (AUC 0.837). We conducted subgroup analysis to ensure that the model performed in a way that did not disadvantage population subgroups, in this case based on gender or indigenous status. We found that our specific problem domain assisted us in reducing unwanted bias within the model, because it did not rely on practice patterns or subjective judgements for the outcome measure. With careful analysis for equity there is great potential for ML models to automate the detection of high-risk cohorts and automate mitigation strategies to reduce preventable errors.
MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggregation for Lesion Detection in Multi-Phase CT Images
Authors: Kitrungrotsakul Titinunt , Xu Yingying , Chen Qingqing , Liu Jing , Li Yinhao , Lin Lanfen , Hu Hongjie , Tong Ruofeng , Li Jingsong , Chen Yen-Wei
Published: January 2024

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%.
Vision-Based Assistance for Vocal Fold Identification in Laryngoscopy with Knowledge Distillation
Authors: Dao Thao Thi Phuong , Pham Minh-Khoi , Tran Mai-Khiem , Ha Chanh Cong , Van Boi Ngoc , Tran Bich Anh , Tran Minh-Triet
Published: January 2024

Laryngoscopy images play a vital role in merging computer vision and otorhinolaryngology research. However, limited studies offer laryngeal datasets for comparative evaluation. Hence, this study introduces a novel dataset focusing on vocal fold images. Additionally, we propose a lightweight network utilizing knowledge distillation, with our student model achieving around 98.4% accuracy-comparable to the original EfficientNetB1 while reducing model weights by up to 88%. We also present an AI-assisted smartphone solution, enabling a portable and intelligent laryngoscopy system that aids laryngoscopists in efficiently targeting vocal fold areas for observation and diagnosis. To sum up, our contribution includes a laryngeal image dataset and a compressed version of the efficient model, suitable for handheld laryngoscopy devices.
Identifying Determinants of Survival Disparities in Multiple Myeloma Patients Using Electronic Health Record Data
Authors: Cui Wanting , Finkelstein Joseph
Published: January 2024

Multiple myeloma (MM) is one of the most common hematological malignancies. The goal of this study was to analyze the sociodemographic, economic, and genetic characteristics of long-term and short-term survival of multiple myeloma patients using EHR data from an academic medical center in New York City. The de-identified analytical dataset comprised 2,111 patients with MM who were stratified based on the length of survival into two groups. Demographic variables, cancer stage, income level, and genetic mutations were analyzed using descriptive statistics and logistic regression. Age, race, and cancer stage were all significant factors that affected the length of survival of multiple myeloma patients. In contrast, gender and income level were not significant factors based on the multivariate adjusted analysis. Older adults, African American patients, and patients who were diagnosed with stage III of multiple myeloma were the people most likely to exhibit short-term survival after the MM diagnosis.
Facebook Post Credibility as a Predictor of Vaccine Hesitancy in the US
Authors: Aleksandric Ana , Dangal Anisha , Nilizadeh Shirin , Mustata Wilson Gabriela
Published: January 2024

Vaccine hesitancy represents a barrier to public health efforts aiming to mitigate the pandemic by performing global interventions. One of the reasons behind vaccine hesitancy is mistrust towards the health system that partially originated due to the misinformation shared over the internet. This study examined the association between the credibility of the sources regarding the COVID-19 vaccine posted on social media and the vaccination rate at the state level in the United States. Study findings suggest that sharing more Facebook posts with links to low-credibility sources about vaccination is associated with a lower number of new vaccinations at the state level in the US. This indicates an urgent need for social media-leveraged interventions through which public health officials can share reliable information to educate populations about vaccine benefits and reduce vaccine hesitancy.
Strategies to Improve Statin Medication Adherence Among Patients at Risk of Cardiovascular Disease Identified Through Electronic Health Records: A Literature Review
Authors: Rakhshanda Shagoofa , Liaw Siaw-Teng , Rhee Joel , Rye Kerry-Anne , Jonnagaddala Jitendra
Published: January 2024

Statin is a group of lipid/cholesterol-lowering medications that is commonly used for primary and secondary prevention of cardiovascular diseases (CVD). In Australia, this is the first line of pharmacological therapy for CVD risk management. High-risk patients who do not adhere to lipid-modifying medicines have an increased risk of CVD mortality, hospitalization, and revascularization. However, studies show that 67% of patients are non-adherent to statins. As such, improving statin adherence through various strategies is very important. This literature review delves into the studies from the past 10 years to identify the various strategies used and their effectiveness to improve statin adherence. The initial search results on PubMed showed 157 articles and based on the inclusion and exclusion criteria, 7 articles were finally used for this review. The patients in the studies were identified through electronic health records. The findings suggest that education, counselling and motivation through face-to-face interaction, phone calls or text messages, reminder messages and frequent follow-up visits are good strategies to improve statin adherence. Alongside these, simplifying regimens, switching combinations of medicines, or using alternate dosing have also been shown to improve statin adherence. In summary, counselling and face-to-face interaction are effective methods for improving statin adherence. The use of electronic health record (EHR) systems combined with targeted interventions delivered to patients identified to be non-adherent to statin may further improve statin adherence.
Uncertainty in Breast Cancer Risk Prediction: A Conformal Prediction Study of Race Stratification
Authors: Millar Alexander S. , Arnn John , Himes Sam , Facelli Julio C.
Published: January 2024

The use of Artificial Intelligence (AI) in medicine has attracted a great deal of attention in the medical literature, but less is known about how to assess the uncertainty of individual predictions in clinical applications. This paper demonstrates the use of Conformal Prediction (CP) to provide insight on racial stratification of uncertainty quantification for breast cancer risk prediction. The results presented here show that CP methods provide important information about the diminished quality of predictions for individuals of minority racial backgrounds.
Development of Machine Learning Prediction Models for Self-Extubation After Delirium Using Emergency Department Data
Authors: Matsumoto Koutarou , Nohara Yasunobu , Sakaguchi Mikako , Takayama Yohei , Yamashita Takanori , Soejima Hidehisa , Nakashima Naoki
Published: January 2024

Delirium is common in the emergency department, and once it develops, there is a risk of self-extubation of drains and tubes, so it is critical to predict delirium before it occurs. Machine learning was used to create two prediction models in this study: one for predicting the occurrence of delirium and one for predicting self-extubation after delirium. Each model showed high discriminative performance, indicating the possibility of selecting high-risk cases. Visualization of predictors using Shapley additive explanation (SHAP), a machine learning interpretability method, showed that the predictors of delirium were different from those of self-extubation after delirium. Data-driven decisions, rather than empirical decisions, on whether or not to use physical restraints or other actions that cause patient suffering will result in improved value in medical care.
A Step Towards Building Health Digital Twins: Patient Phenotype Representation for Health Outcome Prediction
Authors: Hassanzadeh Hamed , Boyle Justin , Khanna Sankalp
Published: January 2024

Precision medicine aims to provide more effective interventions and preventive options to patients by considering their individual risk factors and by employing available evidence. This proof of concept study presents an approach towards generating holistic virtual representations of patients, a.k.a. health digital twins. The developed virtual representations were applied in two health outcome prediction case studies for readmission and in-hospital mortality predictions. The results demonstrated the effectiveness of the virtual representations to facilitate predictive analysis in practicing precision medicine.
Medication Adherence Support Applications for Chronic Arthritis Patients: Healthcare Providers’ Perspective in Saudi Arabia
Authors: Altuwayrib Saleh , Win Khin Than , Freeman Mark
Published: January 2024

Mobile health applications provide chronic disease patients with different capabilities and resources to support medication adherence. The study aims to understand the health care providers’ (HCP) perceptions and recommendations about the design features and content of medication adherence support apps for individuals with chronic arthritis conditions in Saudi Arabia. Individual interviews were conducted with ten participants, such as rheumatologists, health educators, pharmacists, informaticians, and representatives from the Saudi Arthritis Association. The thematic analysis is utilised to code data and develop themes that help researchers in the design process. Four themes are identified: informational content, utilitarian, motivational, and socialisation features. The app content should improve arthritis patient awareness about medication management and adherence. Provide features that enable patients to set medication management and self-monitoring goals. The application design should be trustworthy, usable, enjoyable, and accessible for a diverse group of patients and respect patient privacy.
Development of Ward Visitor Management System in the COVID-19 Epidemic
Authors: Bi Yongdong , Liu Siru , Liu Jialin
Published: January 2024

The purpose of this study was to design, develop, and deploy a visitor management system (VMS) to effectively manage visitors during COVID-19. The VMS was designed using the User-Centered Design (UCD) methodology. The iterative process of UCD includes 3 interviews and 5 usability tests and cognitive walkthrough cycles. This system comprised six parts: the WEB server provides visit scheduling service; the database server stores visit data and provides visit data services; the mobile application server provides security checks and scanning services; the electronic medical record (EMR) server provides ward data service; the Internet application gateway provides health code data service and exchanges health code data with the Sichuan Tianfu Health Code platform, and the service bus enables the centralized exchange and integration of visit data. The visit management system optimizes the workflow of ward visitors, improves staff productivity, and reduces the risk of infection transmission.
Matching Patients to Accelerate Clinical Trials (MPACT): Enabling Technology for Oncology Clinical Trial Workflow
Authors: Do Nhan V. , Elbers Danne C. , Fillmore Nathanael R. , Ajjarapu Samuel , Bergstrom Steven J. , Bihn John , Corrigan June K. , Dhond Rupali , Dipietro Svitlana , Dolgin Arkadiy , Feldman Theodore C. , Goryachev Sergey D. , Huhmann Linden B. , La Jennifer , Marcantonio Paul A. , McGrath Kyle M. , Miller Stephen J. , Nguyen Vinh Q. , Schneeloch George R. , Sung Feng-Chi , Swinnerton Kaitlin N. , Tarren Amelia H. , Tosi Hannah M. , Valley Danielle , Vo Austin D. , Yildirim Cenk , Zheng Chunlei , Zwolinski Robert , Sarosy Gisele A. , Loose David , Shannon Colleen , Brophy Mary T.
Published: January 2024

Clinical trial enrollment is impeded by the significant time burden placed on research coordinators screening eligible patients. With 50,000 new cancer cases every year, the Veterans Health Administration (VHA) has made increased access for Veterans to high-quality clinical trials a priority. To aid in this effort, we worked with research coordinators to build the MPACT (Matching Patients to Accelerate Clinical Trials) platform with a goal of improving efficiency in the screening process. MPACT supports both a trial prescreening workflow and a screening workflow, employing Natural Language Processing and Data Science methods to produce reliable phenotypes of trial eligibility criteria. MPACT also has a functionality to track a patient’s eligibility status over time. Qualitative feedback has been promising with users reporting a reduction in time spent on identifying eligible patients.
Developing eHealth for Home Dialysis: Clinicians’ Needs for a Digital Patient Engagement Platform
Authors: Hölsä Sini , Viitanen Johanna , Valkonen Paula , Lääveri Tinja , Rauta Virpi
Published: January 2024

eHealth solutions such as digital patient engagement platforms (DPEPs) aim at enhancing communication and collaboration between patients and clinicians. From the clinicians’ viewpoint, concerns exist about new information systems (IS) leading to increased workload and interoperability problems. This article aims to support the development and implementation of DPEPs from the end-users’ perspective. We studied clinicians’ needs for a new DPEP developed to support home dialysis (HD) care. Eight clinicians participated in remote semi-structured interviews. Clinicians had positive expectations for the new DPEP as it could provide an overall picture of patients’ status, support patients’ self-care, and save time during patient visits. However, they had concerns about successful implementation, changes to workflows, and integration issues. To conclude, it is important to design and agree on changes in work practices, patient care, and complex IS environments when implementing new DPEP solutions in clinics.
Speech Emotion Recognition Applied to Real-World Medical Consultation
Authors: Huang Ching-Tzu , Huang Chih-Wei , Yang Hsuan-Chia , Li Yu-Chuan (Jack)
Published: January 2024

Since 2020, the COVID-19 epidemic has changed our lives in healthcare behaviors. Forced to wear masks influenced doctor-patient interaction perceptions truly, thus, to build a satisfying relationship is not just empathize with facial expressions. The voice becomes more important for the sake of conquering the burden of masks. Hence, verbal and non-verbal communication will be crucial criteria for doctor-patient interaction during medical consultations and other conversations. In these years, speech emotion recognition has been a popular research domain. In spite of abundant work conducted, nonverbal emotion recognition in medical scenarios is still required to reveal. In this study, we investigate YAMNet transfer learning on Chinese Mandarin speech corpus NTHU-NTUA Chinese Interactive Emotion Corpus (NNIME) and use real-world dermatology clinic recording to test the generalization capability. The results showed that the accuracy validated on NNIME data was 0.59 for activation prediction and 0.57 for valence. Furthermore, the validation accuracy on the doctor-patient dataset was 0.24 for activation and 0.58 for valence, respectively.
Analyzing Patient-Sharing Network Using an Administrative Claim Database in Japan
Authors: Ishikawa Tomoki , Kako Akihito , Yoshimoto Hiromasa , Hattori Junko , Goda Kazuo , Mitsutake Naohiro
Published: January 2024

We analyzed the behavior of patient with a focus on patient-sharing based on the methodology of network analysis. We used an administrative healthcare claims database from September of the years 2008-2020 to identify shared patients with hypertension. The patients’ behavior of visiting multiple medical facilities was extracted as graphical data, and we calculated density and centrality as indicators to evaluate the structure of the patient sharing network. Our findings indicate that density, reciprocity, and transitivity increased over time, and that centrality and PageRank were correlated.
Implementation of Artificial Intelligence Applications in Australian Healthcare Organisations: Environmental Scan Findings
Authors: Janssen Anna B. , Kavisha Shah , Johnson Alison , Marinic Anna , Teede Helena , Shaw Tim
Published: January 2024

Artificial Intelligence (AI) has great potential to improve healthcare, but implementation into routine practice remains a challenge. This study scoped the extent to which AI and Natural Language Processing (NLP) is being implemented into routine practice in Australian healthcare organisations. An environmental scan of publicly available data was undertaken to identify AI applications. Publicly available data consisted of news posts from Australian public healthcare organisations and conference proceedings from key research organisations. Two researchers reviewed and analysed posts related to AI applications to create a list of potential implementation case studies. The final list of AI applications was reviewed by a governance committee in order to identify any missing applications. One application was identified by the governance committee and subsequently added. The environmental scan identified eighteen AI applications, of which eleven met all eligibility criteria. Only one application included NLP. Twelve applications were included when the application identified by the governance committee was added to the list. Implementation of AI applications is spread across four broad categories of use: 1) Decision Support, 2) Monitoring Treatment Effectiveness, 3) Personalised Care and 4) Risk Prediction.
Interdisciplinary Learning Health System Response to Public Vaccine Concerns
Authors: Dimaguila Gerardo Luis , Javed Muhammad , Clothier Hazel J. , Hickman Jo , Petrovic Dan , Machingaifa Francesca , Kaufman Jessica , Habibabadi Sedigh Khademi , Palmer Christopher , Buttery Jim
Published: January 2024

In Victoria, Australia, jurisdictional vaccine safety service is conducted by SAEFVIC (Surveillance of Adverse Events Following Vaccination in the Community). SAEFVIC developed a public Vaccine Safety Report (saefvic.online/vaccinesafety) to present key surveillance information. This study applies an interdisciplinary learning health system approach to evaluate the report, taking into consideration public expressions of concern on social media.
A Skills Assessment Pathways-Based Program Assessment Approach in Multidisciplinary Graduate Health Informatics
Authors: Purkayastha Saptarshi , Patri Akshita Venkat , Yerrabandi Vedasree
Published: January 2024

Multidisciplinary graduate education programs are hard to assess because of interdependent competencies. Students in these programs come with diverse disciplinary undergraduate degrees, and it is critical to identify knowledge gaps among these diverse learner groups to provide support to fill these gaps. Health Informatics (HI) is a multidisciplinary field in which health, technology, and social science knowledge are foundational to building HI competencies. In 2017, the American Medical Informatics Association identified ten functional domains in which HI competencies are divided. Using pre/post-semester knowledge assessment surveys of graduate students (n=60) between August 2021 to May 2022 in one of the largest graduate HI programs in the United States, we identified courses (n=9) across the curriculum that help build HI-specific competencies. Using statistical analysis, we identified three skills pathways by correlating knowledge gained with course learning objectives and used this to modify the curriculum over four semesters. These skills pathways are connected through one or two courses, where students can choose electives or, in some instances, course modules or assignments that link the skills pathways. Moreover, there is a statistically significant difference in how students gain these skills depending on their prior training, even though they take the same set of courses. Gender and other demographics did not show statistical differences in skills gained. Additionally, we found that research assistantships and internships/practicums provide additional skills not covered in our HI curriculum. Our program assessment methodology and resulting curricular changes might be relevant to HI and other multidisciplinary graduate training programs.
Building a More Diverse Public Health Informatics Workforce: Preliminary Results
Authors: Fenton Susan H. , Mustata Wilson Gabriela
Published: January 2024

The US public health infrastructure has been historically underfunded, a condition that was exacerbated by the COVID-19 pandemic. This was especially noted in the area of public health informatics. It was also acknowledged that the lack of a diverse public health workforce made it more difficult to address biases and disparities effectively. In 2021 the Office of the National Coordinator awarded $73 million to 10 awardees to develop public health informatics and technology (PHIT) workforce training. The Gaining Equity in Training for Public Health Informatics and Technology (GET PHIT) award utilizes various methods to train and engage minority and underserved populations in the field of public health informatics. Evaluations of the bootcamps and internships to date have shown generally positive results, both in terms of skills acquired and overall experiences. These results indicate that integrating the fields of public health and data science in non-degree, short-term experiences can have positive outcomes.
Health Professionals’ Use of Online Communities for Interprofessional Peer Education
Authors: Forsyth Rowena , Amon Krestina L. , Ridout Brad , Campbell Andrew J.
Published: January 2024

Online communities in health care are well established as technologies for facilitating interactions between patients with common conditions and communication between patients and health professionals. Less well known is how health professionals use these platforms to connect with their peers, particularly for interprofessional education or support. We present early results from our exploratory international mixed methods (survey and interview) study of health professionals’ experience of engaging with online communities for interprofessional peer education. Our results show that health professionals had relatively high levels of confidence engaging with multiple platforms. They used the platforms to formally and informally share evidence-based content, engage in collegial debate and connect with international colleagues. Health professionals are keen to engage with online communities for education and are aware of how these sites’ functionality can promote engagement with interdisciplinary peers.
Open Source Software in Healthcare: International Case Series from the IMIA Open Source Working Group
Authors: Paton Chris , Amarakoon Pamod , Braa Jørn , Kobayashi Shinji , Marcelo Alvin , Kane Tom , Fraser Hamish , Hannan Terry
Published: January 2024

In this case series, we demonstrate how open-source software has been widely adopted as the primary health information system in many low- and middle-income countries, and for government-developed applications in high-income settings. We discuss the concept of Digital Global Goods and how the general approach of releasing software developed through public funding under open-source licences could improve the delivery of healthcare in all settings through increased transparency and collaboration as well as financial efficiency.
Herding Cats in Pandemic Times – Towards Technological and Organizational Convergence of Heterogeneous Solutions for Investigating and Mastering the Pandemic in University Medical Centers
Authors: Krefting Dagmar , Mutters Nico T. , Pryss Rüdiger , Sedlmayr Martin , Boeker Martin , Dieterich Christoph , Koll Carolin , Mueller Martina , Slagman Anna , Waltemath Dagmar , Wulf Antje , Zenker Sven
Published: January 2024

To understand and handle the COVID-19 pandemic, digital tools and infrastructures were built in very short timeframes, resulting in stand-alone and non-interoperable solutions. To shape an interoperable, sustainable, and extensible ecosystem to advance biomedical research and healthcare during the pandemic and beyond, a short-term project called “Collaborative Data Exchange and Usage” (CODEX+) was initiated to integrate and connect multiple COVID-19 projects into a common organizational and technical framework. In this paper, we present the conceptual design, provide an overview of the results, and discuss the impact of such a project for the trade-off between innovation and sustainable infrastructures.
Piloting a Big Data Epidemiology Approach to Support Frail, Homebound, and Bedridden People
Authors: Pinero De Plaza Maria Alejandra , Beleigoli Alline , Kitson Alison , McMillan Penelope , Barrera Causil Carlos Javier
Published: January 2024

Frail, homebound, and bedridden people (FHBP) are people living at home whose daily life is physically limited to the boundary of their houses because of their ongoing health, energy, and psychosocial or socio-functional impairments. This definition needs a scientific, systematic, and data-driven view of the distribution (frequency, pattern) and determinants (causes, risk factors) of health-related states and adverse events experienced by FHBP. Thus, we piloted a big data epidemiology approach (Multiple Correspondence Analysis and data visualization) from 300 survey responses about FHBP experiences and identified a positive correlation between perceived health status and reported impairments.
Discovering Social Determinant of Health Risk Factors for Perinatal Morbidity Through Real World Data
Authors: Gao Cheng , Chen You
Published: January 2024

Gestational diabetes and preterm birth are perinatal morbidities that significantly impact women and infants’ health. While clinical factors like cesarean delivery, multiple gestation, preeclampsia, and hypertensive disorder are associated with these conditions, it is increasingly recognized that social determinants of health play a crucial role. This study aims to measure the associations between the social vulnerability index (SVI) and these perinatal morbidities using multivariate logistic regression models. The results indicate that factors across all four themes in SVI are significantly associated with these conditions. These findings suggest that interventions targeting these areas are needed to achieve better reproductive health.
Utilizing Electronic Dental Records to Predict Neuro-Degenerative Diseases in a Dental Setting: A Pilot Study
Authors: Patel Jay , Wu Huanmei
Published: January 2024

Limited research demonstrates the possible correlations between dental diseases and neurodegenerative diseases like Alzheimer’s disease (AD) and Parkinson’s disease (PD). Nevertheless, dental diseases are often overlooked while assessing the risk of AD and PD in clinical settings. It is unknown whether AD/PD risk can be predicted using electronic dental record (EDR) data collected in a routine dental setting. This pilot study determined the feasibility of predicting AD/PD using 84 features routinely captured in the EDR. We utilized the Temple University School of Dentistry clinic data of 27,138 patients. Using a natural language processing (NLP) approach (accuracy=97%), we identified patients with AD/PD and their matched controls (matched by age and gender). XGBoost machine learning model with 10-fold cross-validation was applied for prediction. With 77% accuracy, we found 53 features significantly associated with AD/PD that could be utilized to predict the risk of AD/PD. Further studies are warned to confirm these findings.
A Blockchain-Based Approach for Patient Data Alignment Across Institutions
Authors: Zhou Tianshu , Lu Yao , Zhang Yin , Wang Jiaqi , Tian Yu , Li Jingsong
Published: January 2024

Clinical studies need multi-center, long-term patient data, which are difficult to align. We present a blockchain-based approach that uses cryptographic matching and attribute-based encryption for secure data alignment, aggregation, and access. It improves efficiency, lowers data synchronization, and facilitates cross-institutional patient data association and visualization.
The Role of HL7 FHIR in the European Project GATEKEEPER
Authors: Gazzarata Roberta , Chronaki Catherine , Gallego Alba , Gaeta Eugenio , Fico Giuseppe , Zampognaro Paolo , Mercalli Franco , Giuliani Francesco , Allocca Carlo , Cangioli Giorgio
Published: January 2024

The European Project GATEKEEPER aims to develop a platform and marketplace to ensure a healthier independent life for the aging population. In this platform the role of HL7 FHIR is to provide a shared logical data model to collect data in heterogeneous living, which can be used by AI Service and the Gatekeeper HL7 FHIR Implementation Guide was created for this purpose. Independent pilots used this IG and illustrate the impact of the approach, benefit, value, and scalability.
Designing Medication-Related Profiles for Japanese ePrescriptions with HL7 FHIR
Authors: Kobayashi Shinji , Kimura Masahiko , Kodama Yoshinori , Takada Atsushi , Ohe Kazuhiko
Published: January 2024

HL7 FHIR is the standard for healthcare information exchange. In November 2022, our medication subgroup developed 8 profiles and 23 extensions for medication procedures in Japan, as part of the JP Core Implementation Guide 1.1. Our work demonstrates the ability of HL7 FHIR to describe Japanese prescription procedures while also addressing the requirements of other countries
Development of Clinical Decision Support System for Patient Blood Management in Hospital Information System
Authors: Bae Ye Seul , Kim Kyung Hwan
Published: January 2024

A data pipeline was developed to send and receive patient blood management (PBM) data from all medical institutions in Korea. By incorporating the collected data with national big data, the system will be able to generate key performance index for each medical institution. The central PBM system also provides feedback to each individual medical institution.