Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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Maîtrise des risques liés à la production des fluides de dialyse dans une unité de télédialyse

Objective

The “Centre Hospitalier Francois Dunan” is located on an isolated island and ensures patients care in hemodialysis thanks to telemedicine support. Many research studies have demonstrated the importance of hemodialysis fluids composition to reduce morbidity in patients on chronic hemodialysis. The aim of this study was to identify the risks inherent in the production of dialysis fluids in a particular context, in order to set up an improvement action plan to improve risk control on the production of dialysis fluids.

A Perioperative Care Display for Understanding High Acuity Patients

Background  The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives  In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods  We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results  Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion  This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.     

园艺互动干预应用于维持性血液透析病人中的效果评价

目的:探讨园艺互动干预应用于维持性血液透析病人中的效果。方法:选择本院收治的行维持性血液透析病人157例,按照入院时间先后顺序分为对照组77例,观察组80例。对照组实施常规干预,观察组实施园艺互动干预。对比两组干预前后心理状态、应对方式、生活质量及总体幸福感变化。结果:观察组干预12周后症状自评量表(SCL-90)各维度评分低于对照组(P<0.05);观察组干预12周后积极应对评分高于对照组,消极应对评分低于对照组(P<0.05);观察组生活质量各维度评分高于对照组(P<0.05);观察组总体幸福感评分高于对照组(P<0.05)。结论:园艺互动干预应用于维持性血液透析病人可改善心理状态及应对方式,提升生活质量与总体幸福感。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

细节护理对尿毒症血液透析患者的效果研究

目的针对尿毒症血液透析患者护理工作中应用护理细节对其生存质量和并发症的作用予以分析和研究。方法 2017年6月-2018年12月选择本院收治的90例尿毒症透析患者作为研究对象,按照硬币法将所有人随机分为两组,其中常规组45例,细节组45例。采用常规护理对常规组予以护理,在常规护理的基础上,采用细节护理干预措施对细节组予以护理。对两组患者的护理效果进行分析和比较。结果在SF-36评分方面,两组患者护理前比较差异无统计学意义(P>0.05),护理后,细节组明显优于常规组(P<0.05)。在并发症发生率方面,细节组明显低于常规组(P<0.05)。结论采用细节护理干预措施对尿毒症血液透析患者进行护理效果很好,患者的生存质量经过护理后具有很大改善,且并发症也明显减少。     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.     

高通量血液透析和低通量血液透析不同模式对老年维持性血液透析患者血压的影响

目的 观察高通量血液透析(HFHD)与低通量血液透析(LFHD)的不同模式运用到老年患者维持性血液透析(MHD)中对其血压的影响。方法 选择2015年2月—2017年10月本院治疗的MHD老年患者74例,先为其施LFHD 3个月,再为其辅以3个月的HFHD,3次/周,4 h/次,对比不同透析模式对患者血压的影响。结果 实施HFHD后患者的平均动脉压(MAP)有所降低,在第3 h与第4 h透析后,血压把控优于LFHD(P<0.05)。结论 对于MHD老年患者来说,实施HFHD是优良的透析方法。