Detection of bovine leukemia virus in beef cattle kept in the Central Coast Regions of Vietnam.

Bovine leukemia virus (BLV) is the etiologic agent of enzootic bovine leucosis. Our previous study showed the BLV existence in cattle kept in the Red River Delta Region of Vietnam. However, no positive samples were identified in beef cattle. Besides, information related to the BLV circulation in the remained parts of Vietnam is limited. Therefore, we tested the existence of BLV in 48 beef cattle kept in the Central Coast Regions. Nested PCR targeting the BLV-env-gp51 confirmed the prevalence of 14.6% in investigated regions. Phylogenetic analysis suggested the co-existence of genotypes 1 and 10. The close relationship between strains found in Vietnam, Thailand, Myanmar, and China was revealed suggesting the possibility of BLV transmission through the movement of live cattle.

Understanding the COVID-19 Infodemic: Analyzing User-Generated Online Information During a COVID-19 Outbreak in Vietnam.

OBJECTIVES: Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020. METHODS: We analyzed user-generated online information about five public health interventions during the Da Nang outbreak. We compared the volume, source, sentiment polarity, and engagements of online posts before, during, and after the outbreak using negative binomial and logistic regression, and assessed the content validity of the 500 most influential posts. RESULTS: Most of the 54,528 online posts included were generated during the outbreak (n = 46,035; 84.42%) and by online newspapers (n = 32,034; 58.75%). Among the 500 most influential posts, 316 (63.20%) contained genuine information, 10 (2.00%) contained misinformation, 152 (30.40%) were non-factual opinions, and 22 (4.40%) contained unverifiable information. All misinformation posts were made during the outbreak, mostly on social media, and were predominantly negative. Higher levels of engagement were observed for information that was unverifiable (incidence relative risk [IRR] = 2.83; 95% confidence interval [CI], 1.33-0.62), posted during the outbreak (before: IRR = 0.15; 95% CI, 0.07-0.35; after: IRR = 0.46; 95% CI, 0.34-0.63), and with negative sentiment (IRR = 1.84; 95% CI, 1.23-2.75). Negatively toned posts were more likely to be misinformation (odds ratio [OR] = 9.59; 95% CI, 1.20-76.70) or unverified (OR = 5.03; 95% CI, 1.66-15.24). CONCLUSIONS: Misinformation and unverified information during the outbreak showed clustering, with social media being particularly affected. This indepth assessment demonstrates the value of analyzing online "infodemics" to inform public health responses.

Using 'infodemics' to understand public awareness and perception of SARS-CoV-2: A longitudinal analysis of online information about COVID-19 incidence and mortality during a major outbreak in Vietnam, July-September 2020.

BACKGROUND: Trends in the public perception and awareness of COVID-19 over time are poorly understood. We conducted a longitudinal study to analyze characteristics and trends of online information during a major COVID-19 outbreak in Da Nang province, Vietnam in July-August 2020 to understand public awareness and perceptions during an epidemic. METHODS: We collected online information on COVID-19 incidence and mortality from online platforms in Vietnam between 1 July and 15 September, 2020, and assessed their trends over time against the epidemic curve. We explored the associations between engagement, sentiment polarity, and other characteristics of online information with different outbreak phases using Poisson regression and multinomial logistic regression analysis. We assessed the frequency of keywords over time, and conducted a semantic analysis of keywords using word segmentation. RESULTS: We found a close association between collected online information and the evolution of the COVID-19 situation in Vietnam. Online information generated higher engagements during compared to before the outbreak. There was a close relationship between sentiment polarity and posts' topics: the emotional tendencies about COVID-19 mortality were significantly more negative, and more neutral or positive about COVID-19 incidence. Online newspaper reported significantly more information in negative or positive sentiment than online forums or social media. Most topics of public concern followed closely the progression of the COVID-19 situation during the outbreak: development of the global pandemic and vaccination; the unfolding outbreak in Vietnam; and the subsiding of the outbreak after two months. CONCLUSION: This study shows how online information can reflect a public health threat in real time, and provides important insights about public awareness and perception during different outbreak phases. Our findings can help public health decision makers in Vietnam and other low and middle income countries with high internet penetration rates to design more effective communication strategies during critical phases of an epidemic.

Transvenous retrograde embolization of ruptured brain arteriovenous malformations: A case report and review of the literature.

Hemorrhagic stroke due to ruptured brain arteriovenous malformations (AVMs) is a common cause in young stroke patients. When the ruptured AVMs are in deep location, the choice of endovascular intervention with the arterial approach to AVM embolization is routine but in many cases, it is not feasible due to the inability to access because of the small and tortuous arterial branch, however, the intravenous approach also results in high complete obliteration rates but also carries a higher risk of stroke than the intra-arterial route. We describe a 36-year-old female patient diagnosed with intracranial and intraventricular hemorrhage who underwent complete transvenous embolization of the ruptured AVMs, and achieved near-complete clinical recovery after 1 month with the modified Rankin scale 1.

Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records.

Aim: To use available electronic administrative records to identify data reliability, predict discharge destination, and identify risk factors associated with specific outcomes following hospital admission with stroke, compared to stroke specific clinical factors, using machine learning techniques. Method: The study included 2,531 patients having at least one admission with a confirmed diagnosis of stroke, collected from a regional hospital in Australia within 2009-2013. Using machine learning (penalized regression with Lasso) techniques, patients having their index admission between June 2009 and July 2012 were used to derive predictive models, and patients having their index admission between July 2012 and June 2013 were used for validation. Three different stroke types [intracerebral hemorrhage (ICH), ischemic stroke, transient ischemic attack (TIA)] were considered and five different comparison outcome settings were considered. Our electronic administrative record based predictive model was compared with a predictive model composed of "baseline" clinical features, more specific for stroke, such as age, gender, smoking habits, co-morbidities (high cholesterol, hypertension, atrial fibrillation, and ischemic heart disease), types of imaging done (CT scan, MRI, etc.), and occurrence of in-hospital pneumonia. Risk factors associated with likelihood of negative outcomes were identified. Results: The data was highly reliable at predicting discharge to rehabilitation and all other outcomes vs. death for ICH (AUC 0.85 and 0.825, respectively), all discharge outcomes except home vs. rehabilitation for ischemic stroke, and discharge home vs. others and home vs. rehabilitation for TIA (AUC 0.948 and 0.873, respectively). Electronic health record data appeared to provide improved prediction of outcomes over stroke specific clinical factors from the machine learning models. Common risk factors associated with a negative impact on expected outcomes appeared clinically intuitive, and included older age groups, prior ventilatory support, urinary incontinence, need for imaging, and need for allied health input. Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.

The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology.

BACKGROUND: ASD and ADHD are prevalent neurodevelopmental disorders that frequently co-occur and have strong evidence for a degree of shared genetic aetiology. Behavioural and neurocognitive heterogeneity in ASD and ADHD has hampered attempts to map the underlying genetics and neurobiology, predict intervention response, and improve diagnostic accuracy. Moving away from categorical conceptualisations of psychopathology to a dimensional approach is anticipated to facilitate discovery of data-driven clusters and enhance our understanding of the neurobiological and genetic aetiology of these conditions. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project is one of the first large-scale, family-based studies to take a truly transdiagnostic approach to ASD and ADHD. Using a comprehensive phenotyping protocol capturing dimensional traits central to ASD and ADHD, the MAGNET project aims to identify data-driven clusters across ADHD-ASD spectra using deep phenotyping of symptoms and behaviours; investigate the degree of familiality for different dimensional ASD-ADHD phenotypes and clusters; and map the neurocognitive, brain imaging, and genetic correlates of these data-driven symptom-based clusters. METHODS: The MAGNET project will recruit 1,200 families with children who are either typically developing, or who display elevated ASD, ADHD, or ASD-ADHD traits, in addition to affected and unaffected biological siblings of probands, and parents. All children will be comprehensively phenotyped for behavioural symptoms, comorbidities, neurocognitive and neuroimaging traits and genetics. CONCLUSION: The MAGNET project will be the first large-scale family study to take a transdiagnostic approach to ASD-ADHD, utilising deep phenotyping across behavioural, neurocognitive, brain imaging and genetic measures.

Association of public health interventions and COVID-19 incidence in Vietnam, January to December 2020.

BACKGROUND: Vietnam implemented various public health interventions such as contact tracing and testing, mandatory quarantine, and lockdowns in response to coronavirus disease 2019 (COVID-19). However, the effects of these measures on the epidemic remain unclear. METHODS: This article describes the public health interventions in relation to COVID-19 incidence. Maximum likelihood estimations were used to assess containment delays (time between symptom onset and start of isolation) and multivariable regression was employed to identify associated factors between interventions and COVID-19 incidence. The effective reproductive numbers (Rt) were calculated based on transmission pairs. RESULTS: Interventions were introduced periodically in response to the epidemic. Overall, 817 (55.4%) among 1474 COVID-19 cases were imported. Based on a serial interval of 8.72 ± 5.65 days, it was estimated that Rt decreased to below 1 (lowest at 0.02, 95% CI 0-0.12) during periods of strict border control and contact tracing, and increased ahead of new clusters. The main method to detect cases shifted over time from passive notification to active case-finding at immigration or in lockdown areas, with containment delays showing significant differences between modes of case detection. CONCLUSIONS: A combination of early, strict, and consistently implemented interventions is crucial to control COVID-19. Low-middle income countries with limited capacity can contain COVID-19 successfully using non-pharmaceutical interventions.

Timeliness of contact tracing among flight passengers during the COVID-19 epidemic in Vietnam.

BACKGROUND: International air travel plays an important role in the global spread of SARS-CoV-2, and tracing of close contacts is an integral part of the public health response to COVID-19. We aimed to assess the timeliness of contact tracing among airline passengers arriving in Vietnam on flights containing COVID-19 cases and investigated factors associated with timeliness of contact tracing. METHODS: We included data from 2228 passengers on 22 incoming flights between 2 and 19 March 2020. Contact tracing duration was assessed separately for the time between the date of index case confirmation and date of contact tracing initiation (interval I), and the date of contact tracing initiation and completion (interval II). We used log-rank tests and multivariable Poisson regression models to identify factors associated with timeliness. RESULTS: The median duration of interval I and interval II was one (IQR: 1-2) and 3 days (IQR: 2-5), respectively. The contact tracing duration was shorter for passengers from flights where the index case was identified through mandatory testing directly upon arrival (median = 4; IQR: 3-5) compared to flights with index case detection through self-presentation at health facilities after arrival (median = 7; IQR: 5-8) (p-value = 0.018). Cumulative hazards for successful tracing were higher for Vietnamese nationals compared to non-Vietnamese nationals (p < 0.001). CONCLUSIONS: Contact tracing among flight passengers in the early stage of the COVID-19 epidemic in Vietnam was timely though delays occurred on high workload days. Mandatory SARS-CoV-2 testing at arrival may reduce contact tracing duration and should be considered as an integrated screening tool for flight passengers from high-risk areas when entering low-transmission settings with limited contact tracing capacity. We recommend a standardized risk-based contact tracing approach for flight passengers during the ongoing COVID-19 epidemic.

Re-positive testing, clinical evolution and clearance of infection: results from COVID-19 cases in isolation in Viet Nam.

OBJECTIVE: Asymptomatic infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and test re-positivity after a negative test have raised concerns about the ability to effectively control the coronavirus disease 2019 (COVID-19) pandemic. We aimed to investigate the prevalence of COVID-19 asymptomatic and pre-symptomatic infections during the second wave of COVID-19 in Viet Nam, and to better understand the duration of SARS-CoV-2 infection and the dynamics between the evolution of clinical symptoms and SARS-CoV-2 test positivity among confirmed COVID-19 cases. METHODS: We conducted a cohort analysis on the first 50 confirmed cases during the second COVID-19 wave in Viet Nam using clinical, laboratory and epidemiological data collected from 9 March to 30 April 2020. Kaplan-Meier estimates were used to assess time to clearance of SARS-CoV-2 infection, and log-rank tests were used to explore factors related to time to SARS-CoV-2 infection clearance. RESULTS: Most cases (58%) had no typical signs or symptoms of COVID-19 at the time of diagnosis. Ten cases (20%) were re-positive for SARS-CoV-2 during infection. Eight cases (16%) experienced COVID-19 symptoms after testing negative for SARS-CoV-2. The median duration from symptom onset until clearance of infection was 14 days (range: 6-31); it was longer in re-positive and older patients and those with pre-existing conditions. CONCLUSION: Asymptomatic and pre-symptomatic infections were common during the second wave of COVID-19 in Viet Nam. Re-positivity was frequent during hospitalization and led to a long duration of SARS-CoV-2 infection.

Growth and NO2-Sensing Properties of Biaxial p-SnO/n-ZnO Heterostructured Nanowires.

Biaxial p-SnO/n-ZnO heterostructured nanowires (average length of 10 µm) were grown onto a glass substrate by thermal evaporation in vacuum. These nanowires had spherical ball tips, and the size of the SnO part increased gradually from the top to the bottom of the nanowire, but the corresponding size of ZnO varied slightly. The Sn-Zn alloy formed in the tips resulted in determined as the catalyst of the growth of the ZnO nanowires. The growth process of the p-SnO/n-ZnO biaxial nanowires is discussed based on vapor-liquid-solid (VLS) based on the subsequent growth process: the VLS catalytic growth of the ZnO nanowire and subsequent epitaxial SnO growth on the sidewall of the pregrown ZnO nanowire. An epitaxial relationship, (001)SnO//(110)ZnO and [110]SnO//[002]ZnO, was observed in the biaxial p-SnO/n-ZnO heterostructured nanowires. The gas-sensing properties of the as-synthesized p-SnO/n-ZnO nanowires were investigated. The results show that the device exhibit a good performance to the ppb-level NO2 at room temperature (25 °C) without light illumination. The detection limit of the p-SnO/n-ZnO sensor to NO2 is 50 ppb. Moreover, the NO2-sensing properties of the p-SnO/n-ZnO device were investigated under various relative humidity. Finally, the NO2-sensing mechanism of the p-SnO/n-ZnO nanowires was proposed and discussed.

Monitoring fine root growth to identify optimal fertilization timing in a forest plantation: A case study in Northeast Vietnam.

Fertilizer is applied widely to improve the productivity of plantations. Traditionally, fertilization is conducted in spring and/or in the early rainy season, and it is believed to support the growth of planted trees in the growing season. Little attention to date has been paid on identification of the optimal timing of fertilization and fertilizer dose. In this study, application of the fine root monitoring technique in identifying optimal fertilization timing for an Acacia plantation in Vietnam is described. The study used two fertilizer doses (100 and 200 g NPK/tree) and three fertilization timings (in spring; in the early rainy season; and based on the fine root monitoring technique to identify when the fine roots reach their growth peak). As expected fertilization timings significantly affected growth and above-ground biomass (AGB) of the plantation. Fertilization based on the fine root monitoring technique resulted in the highest growths and AGB, followed by fertilization in the early rainy season and then in spring. Applying fertilizer at 200 g NPK/tree based on the fine root monitoring technique increased diameter at breast height (DBH) by 16%, stem height by 8%, crown diameter (Dc) by 16%, and AGB by 40% as compared to early rainy season fertilization. Increases of 32% DBH, 23% stem height, 44% Dc, and 87% AGB were found in fertilization based on fine root monitoring technique compared to spring fertilization. This study concluded that forest growers should use the fine root monitoring technique to identify optimal fertilization timing for higher productivity.

Usefulness of Wearable Cameras as a Tool to Enhance Chronic Disease Self-Management: Scoping Review.

BACKGROUND: Self-management is a critical component of chronic disease management and can include a host of activities, such as adhering to prescribed medications, undertaking daily care activities, managing dietary intake and body weight, and proactively contacting medical practitioners. The rise of technologies (mobile phones, wearable cameras) for health care use offers potential support for people to better manage their disease in collaboration with their treating health professionals. Wearable cameras can be used to provide rich contextual data and insight into everyday activities and aid in recall. This information can then be used to prompt memory recall or guide the development of interventions to support self-management. Application of wearable cameras to better understand and augment self-management by people with chronic disease has yet to be investigated. OBJECTIVE: The objective of our review was to ascertain the scope of the literature on the use of wearable cameras for self-management by people with chronic disease and to determine the potential of wearable cameras to assist people to better manage their disease. METHODS: We conducted a scoping review, which involved a comprehensive electronic literature search of 9 databases in July 2017. The search strategy focused on studies that used wearable cameras to capture one or more modifiable lifestyle risk factors associated with chronic disease or to capture typical self-management behaviors, or studies that involved a chronic disease population. We then categorized and described included studies according to their characteristics (eg, behaviors measured, study design or type, characteristics of the sample). RESULTS: We identified 31 studies: 25 studies involved primary or secondary data analysis, and 6 were review, discussion, or descriptive articles. Wearable cameras were predominantly used to capture dietary intake, physical activity, activities of daily living, and sedentary behavior. Populations studied were predominantly healthy volunteers, school students, and sports people, with only 1 study examining an intervention using wearable cameras for people with an acquired brain injury. Most studies highlighted technical or ethical issues associated with using wearable cameras, many of which were overcome. CONCLUSIONS: This scoping review highlighted the potential of wearable cameras to capture health-related behaviors and risk factors of chronic disease, such as diet, exercise, and sedentary behaviors. Data collected from wearable cameras can be used as an adjunct to traditional data collection methods such as self-reported diaries in addition to providing valuable contextual information. While most studies to date have focused on healthy populations, wearable cameras offer promise to better understand self-management of chronic disease and its context.

Potential to mitigate ammonia emission from slurry by increasing dietary fermentable fiber through inclusion of tropical byproducts in practical diets for growing pigs.

OBJECTIVE: Research was conducted to test the effect of including fiber-rich feedstuffs in practical pig diets on nutrient digestibility, nitrogen balance and ammonia emissions from slurry. METHODS: Three Vietnamese fiber sources were screened, namely cassava leaf meal (CL), cassava root residue (CR), and tofu by-product (TF). Accordingly, a control diet (Con) with 10% of dietary non-starch polysaccharides (NSP) and three test diets including one of the three fiber-rich feedstuffs to reach 15% of NSP were formulated. All formulated diets had the same level of crude protein (CP), in vitro ileal protein digestible and metabolisable energy, whereas the in vitro hindgut volatile fatty acid (VFA) production of the test diets was 12% to 20% higher than the control diet. Forty growing barrows with initial body weight at 28.6±1.93 kg (mean±standard deviation) were allocated to the four treatments. When pigs reached about 50 kg of body weight, four pigs from each treatment were used for a nitrogen balance trial and ammonia emission assessment, the remaining six pigs continued the second period of the feeding trial. RESULTS: The TF treatment increased fecal VFA by 33% as compared with the control treatment (p = 0.07), suggesting stimulation of the hindgut fermentation. However, urinary N was not significantly reduced or shifted to fecal N, nor was slurry pH decreased. Accordingly, ammonia emissions were not mitigated. CR and CL treatments failed to enhance in vivo hindgut fermentation, as assessed by fecal VFA and purine bases. On the contrary, the reduction of CP digestibility in the CL treatment enhanced ammonia emissions from slurry. CONCLUSION: Dietary inclusion of cassava and tofu byproducts through an increase of dietary NSP from 10% to 15% might stimulate fecal VFA excretion but this does not guarantee a reduction in ammonia emissions from slurry, while its interaction with protein digestibility even might enhance enhanced ammonia emission.

Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding.

Evidence-based medicine often involves the identification of patients with similar conditions, which are often captured in ICD (International Classification of Diseases (World Health Organization 2013)) code sequences. With no satisfying prior solutions for matching ICD-10 code sequences, this paper presents a method which effectively captures the clinical similarity among routine patients who have multiple comorbidities and complex care needs. Our method leverages the recent progress in representation learning of individual ICD-10 codes, and it explicitly uses the sequential order of codes for matching. Empirical evaluation on a state-wide cancer data collection shows that our proposed method achieves significantly higher matching performance compared with state-of-the-art methods ignoring the sequential order. Our method better identifies similar patients in a number of clinical outcomes including readmission and mortality outlook. Although this paper focuses on ICD-10 diagnosis code sequences, our method can be adapted to work with other codified sequence data.

Estimation of the prevalence of adverse drug reactions from social media.

This work aims to estimate the degree of adverse drug reactions (ADR) for psychiatric medications from social media, including Twitter, Reddit, and LiveJournal. Advances in lightning-fast cluster computing was employed to process large scale data, consisting of 6.4 terabytes of data containing 3.8 billion records from all the media. Rates of ADR were quantified using the SIDER database of drugs and side-effects, and an estimated ADR rate was based on the prevalence of discussion in the social media corpora. Agreement between these measures for a sample of ten popular psychiatric drugs was evaluated using the Pearson correlation coefficient, r, with values between 0.08 and 0.50. Word2vec, a novel neural learning framework, was utilized to improve the coverage of variants of ADR terms in the unstructured text by identifying syntactically or semantically similar terms. Improved correlation coefficients, between 0.29 and 0.59, demonstrates the capability of advanced techniques in machine learning to aid in the discovery of meaningful patterns from medical data, and social media data, at scale.

Predicting healthcare trajectories from medical records: A deep learning approach.

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden - diabetes and mental health - the results show improved prediction accuracy.

A Framework for Mixed-Type Multioutcome Prediction With Applications in Healthcare.

Health analysis often involves prediction of multiple outcomes of mixed type. The existing work is restrictive to either a limited number or specific outcome types. We propose a framework for mixed-type multioutcome prediction. Our proposed framework proposes a cumulative loss function composed of a specific loss function for each outcome type-as an example, least square (continuous outcome), hinge (binary outcome), Poisson (count outcome), and exponential (nonnegative outcome). To model these outcomes jointly, we impose a commonality across the prediction parameters through a common matrix normal prior. The framework is formulated as iterative optimization problems and solved using an efficient block-coordinate descent method. We empirically demonstrate both scalability and convergence. We apply the proposed model to a synthetic dataset and then on two real-world cohorts: a cancer cohort and an acute myocardial infarction cohort collected over a two-year period. We predict multiple emergency-related outcomes-as example, future emergency presentations (binary), emergency admissions (count), emergency length of stay days (nonnegative), and emergency time to next admission day (nonnegative). We show that the predictive performance of the proposed model is better than several state-of-the-art baselines.

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

BACKGROUND: As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. OBJECTIVE: To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. METHODS: A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. RESULTS: The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. CONCLUSIONS: A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.

Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?

BACKGROUND: Preterm birth is a clinical event significant but difficult to predict. Biomarkers such as fetal fibronectin and cervical length are effective, but the often are used only for women with clinically suspected preterm risk. It is unknown whether routinely collected data can be used in early pregnancy to stratify preterm birth risk by identifying asymptomatic women. This paper tries to determine the value of the Victorian Perinatal Data Collection (VPDC) dataset in predicting preterm birth and screening for invasive tests. METHODS: De-identified VPDC report data from 2009 to 2013 were extracted for patients from Barwon Health in Victoria. Logistic regression models with elastic-net regularization were fitted to predict 37-week preterm, with the VPDC antenatal variables as predictors. The models were also extended with two additional variables not routinely noted in the VPDC: previous preterm birth and partner smoking status, testing the hypothesis that these two factors add prediction accuracy. Prediction performance was evaluated using a number of metrics, including Brier scores, Nagelkerke's R(2), c statistic. RESULTS: Although the predictive model utilising VPDC data had a low overall prediction performance, it had a reasonable discrimination (c statistic 0.646 [95% CI: 0.596-0.697] for 37-week preterm) and good calibration (goodness-of-fit p = 0.61). On a decision threshold of 0.2, a Positive Predictive Value (PPV) of 0.333 and a negative predictive value (NPV) of 0.941 were achieved. Data on previous preterm and partner smoking did not significantly improve prediction. CONCLUSIONS: For multiparous women, the routine data contains information comparable to some purposely-collected data for predicting preterm risk. But for nulliparous women, the routine data contains insufficient data related to antenatal complications.

Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data.

OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. METHODS: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. RESULTS: Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. CONCLUSIONS: In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments.