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As Rwanda approaches the UNAIDS Fast Track goals which recommend that 95% of HIV-infected individuals know their status, of whom 95% should receive treatment and 95% of those on treatment achieve viral suppression, the country currently relies on an inefficient paper, and disjointed electronic, systems for case-based surveillance (CBS). Rwanda has established an ecosystem of interoperable systems based on open standards to support HIV CBS. Data were successfully exchanged between an EMR, a client registry, laboratory information system and DHIS-2 Tracker, and subsequently, a complete analytic dataset was ingested into MS-Power Business Intelligence (MS-PowerBI) for analytics and visualization of the CBS data. Existing challenges included inadequate workforce capacity to support mapping of data elements to HL7 FHIR resources. Interoperability optimization to support CBS is work in progress and rigorous evaluations on the effect on health information exchange on monitoring patient outcomes are needed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Collaboration across disciplinary boundaries is vital to address the complex challenges and opportunities in Digital Health. We present findings and experiences of applying the principles of Team Science to a digital health research project called 'The Wearable Clinic'. Challenges faced were a lack of shared understanding of key terminology and concepts, and differences in publication cultures between disciplines. We also encountered more profound discrepancies, relating to definitions of "success" in a research project. We recommend that collaborative digital health research projects select a formal Team Science methodology from the outset. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) is widely applied in contemporary clinic. Due to the radiation risks carried by X-rays, the imaging and post-processing methods of low-dose CT (LDCT) become popular topics in academia and industrial community. Generally, LDCT presents strong noise and artifacts, while existing algorithms cannot completely overcome the blurring effects and meantime reduce the noise. The proposed method enables CT extend to independent frequency channels by wavelet transformation, then two separate networks are established for low-frequency denoising and high-frequency reconstruction. The clean signals from high-frequency channel are reconstructed through channel translation, which is essentially effective in preserving detailed structures. The public dataset from Mayo Clinic was used for model training and testing. The experiments showed that the proposed method achieves a better quantitative result (PSNR: 37.42dB, SSIM: 0.8990) and details recovery visually, which demonstrates our framework can better restore the detailed features while significantly suppressing the noise. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, including phenotypic (age, sex, BMI and smoking status), medical imaging (carotid artery thickness) and genotypic. We use a machine learning models and the UK Biobank cohort to measure the prediction capacity of these 3 variable categories, both in combination and isolation. We demonstrate that phenotypic variables from the Framingham risk score have the best prediction capacity, although a combination of phenotypic, medical imaging and genotypic variables deliver the most specific models. Furthermore, we demonstrate that Variant Spark, a random forest based GWAS platform, performs effective feature selection for SNP-based genotype variables, identifying 115 significantly associated SNPs to the CAD phenotype. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is a highly malignant cancer of the digestive tract and is rapidly progressing and spreading clinically. Automatic and accurate pancreatic tissue segmentation in abdominal CT images is essential for the early diagnosis of pancreatic-related diseases. It is challenging that the pancreas is small in size and complex in morphology. To address this problem, we propose a dual-attention model fusing CNN and Transformer to effectively activate pancreas-related features expression. The CNN structure weights the importance of pancreasrelated features at the channel level and weakens the background information. Transformer feature aggregation module constructs spatial correlations among long-distance pixels from a global perspective. This study is validated on the NIHTCIA dataset and achieved a mean Dice Similarity Coefficient of 85.82%, which is outperforming than the state-of-the-art methods. The visualization of surface distance also demonstrates the effective segmentation of pancreas boundary details by the proposed model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Cascade-HF protocol is a Continuous Remote Patient Monitoring (CRPM) study at a major health system in the United States to reduce Heart Failure (HF)-related hospitalizations and readmissions using wearable biosensors to collect physiological data over a 30-day period to determine decompensation risk among HF patients. The alerts produced, coupled with electronic patient-reported outcomes, are utilized daily by the home health team, and escalated to the heart failure team as needed, for proactive actions. Limited research has examined anticipating the implementation and workflow challenges of such complex CRPM studies such as resource planning and staffing decisions that leverage the recorded data to drive clinical preparedness and operational efficiency. This preliminary analysis applies discrete event simulation modeling to the Cascade-HF protocol using pilot data from a soft launch to assess workload of the clinical team, evaluate escalation patterns and provide decision support recommendations to enable scale-up for all post-discharge patients. [ABSTRACT FROM AUTHOR]

Genetic data is limited and generating new datasets is often an expensive, time-consuming process, involving countless moving parts to genotype and phenotype individuals. While sharing data is beneficial for quality control and software development, privacy and security are of utmost importance. Generating synthetic data is a practical solution to mitigate the cost, time and sensitivities that hamper developers and researchers in producing and validating novel biotechnological solutions to data intensive problems. Existing methods focus on mutation frequencies at specific loci while ignoring epistatic interactions. Alternatively, programs that do consider epistasis are limited to two-way interactions or apply genomic constraints that make synthetic data generation arduous or computationally intensive. To solve this, we developed Polygenic Epistatic Phenotype Simulator (PEPS). Our tool is a probabilistic model that can generate synthetic phenotypes with a controllable level of complexity. [ABSTRACT FROM AUTHOR]

Automatic extraction of relations between drugs/chemicals and proteins from ever-growing biomedical literature is required to build up-to-date knowledge bases in biomedicine. To promote the development of automated methods, BioCreative-VII organized a shared task - the DrugProt track, to recognize drugprotein entity relations from PubMed abstracts. We participated in the shared task and leveraged deep learning-based transformer models pre-trained on biomedical data to build ensemble approaches to automatically extract drug-protein relation from biomedical literature. On the main corpora of 10,750 abstracts, our best system obtained an F1-score of 77.60% (ranked 4th among 30 participating teams), and on the large-scale corpus of 2.4M documents, our system achieved micro-averaged F1-score of 77.32% (ranked 2nd among 9 system submissions). This demonstrates the effectiveness of domain-specific transformer models and ensemble approaches for automatic relation extraction from biomedical literature. [ABSTRACT FROM AUTHOR]

Pain is a common reason for accessing healthcare resources and is a growing area of research, especially in its overlap with mental health. Mental health electronic health records are a good data source to study this overlap. However, much information on pain is held in the free text of these records, where mentions of pain present a unique natural language processing problem due to its ambiguous nature. This project uses data from an anonymised mental health electronic health records database. A machine learning based classification algorithm is trained to classify sentences as discussing patient pain or not. This will facilitate the extraction of relevant pain information from large databases. 1,985 documents were manually triple-annotated for creation of gold standard training data, which was used to train four classification algorithms. The best performing model achieved an F1-score of 0.98 (95% CI 0.98-0.99). [ABSTRACT FROM AUTHOR]

Clinical narratives recording behaviours and emotions of patients are available from EHRs in a forensic psychiatric centre located in Tasmania. This rich data has not been used in risk prediction. Prior work demonstrates natural language processing can be used to identify patient symptoms in these free-text records and can then be used to predict risk. Four dictionaries containing descriptive words of harm were created using the Diagnostic and Statistical Manual of Mental Disorders, the Unified Medical Language System repository, English negative and positive sentiment words, and high-frequency words from the Corpus of Contemporary American English. However, a model based only on these keywords is limited in predictive power. In this study, we introduce an improved NLP approach with a social interaction component to extract additional information about the behavioural and emotional state of patients. These social interactions are subsequently used in a machine-learning model to enhance risk prediction performance. [ABSTRACT FROM AUTHOR]

In the United States, more than 12% of the population will experience thyroid dysfunction. Patient symptoms often reported with thyroid dysfunction include fatigue and weight change. However, little is understood about the relationship between these symptoms documented in the outpatient setting and ordering patterns for thyroid testing among various patient groups by age and sex. We developed a natural language processing and deep learning pipeline to identify patientreported outcomes of weight change and fatigue among patients with a thyroid stimulating hormone test. We built upon prior works by comparing 5 open-source, Bidirectional Encoder Representations from Transformers (BERT) to determine which models could accurately identify these symptoms from clinical texts. For both fatigue (f) and weight change (wc), Bio_ClinicalBERT achieved the highest F1-score (f: 0.900; wc: 0.906) compared BERT (f: 0.899; wc: 0.890), DistilBERT (f: 0.852; wc: 0.912), Biomedical RoBERTa (f: 0.864; wc: 0.904), and PubMedBERT (f: 0.882; wc: 0.892). [ABSTRACT FROM AUTHOR]

Every year there are approximately three million new specialist clinic appointments at local hospital networks in Victoria. CSIRO, in collaboration with Austin Health, have developed two algorithms to assist with waitlist management in their outpatient specialist clinics. This study describes the implementation of these algorithms in software tools developed to support their use and trial in the clinical setting at Austin Health. We discuss the system design and development of both these software tools. We also review the implemented workflow of the tools and discuss how these tools seek to improve current systems. [ABSTRACT FROM AUTHOR]