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

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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.     

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.     

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.     

Multidimensional Ultrasound Doppler Signal Analysis for Fetal Activity Monitoring

Fetal activity parameters such as movements, heart rate and the related parameters are essential indicators of fetal wellbeing, and no device provides simultaneous access to and sufficient estimation of all of these parameters to evaluate fetal health. This work was aimed at collecting these parameters to automatically separate healthy from compromised fetuses. To achieve this goal, we first developed a multi-sensor–multi-gate Doppler system. Then we recorded multidimensional Doppler signals and estimated the fetal activity parameters via dedicated signal processing techniques. Finally, we combined these parameters into four sets of parameters (or four hyper-parameters) to determine the set of parameters that is able to separate healthy from other fetuses. To validate our system, a data set consisting of two groups of fetal signals (normal and compromised) was established and provided by physicians. From the estimated parameters, an instantaneous Manning-like score, referred to as the ultrasonic score, was calculated and was used together with movements, heart rate and the associated parameters in a classification process employing the support vector machine method. We investigated the influence of the sets of parameters and evaluated the performance of the support vector machine using the computation of sensibility, specificity, percentage of support vectors and total classification error. The sensitivity of the four sets ranged from 79% to 100%. Specificity was 100% for all sets. The total classification error ranged from 0% to 20%. The percentage of support vectors ranged from 33% to 49%. Overall, the best results were obtained with the set of parameters consisting of fetal movement, short-term variability, long-term variability, deceleration and ultrasound score. The sensitivity, specificity, percentage of support vectors and total classification error of this set were respectively 100%, 100%, 35% and 0%. This indicated our ability to separate the data into two sets (normal fetuses and pathologic fetuses), and the results highlight the excellent match with the clinical classification performed by the physicians. This work indicates the feasibility of detecting compromised fetuses and also represents an interesting method of close fetal monitoring during the entire pregnancy.     

Endovascular deep brain stimulation: Investigating the relationship between vascular structures and deep brain stimulation targets

BackgroundEndovascular delivery of current using ‘stentrodes’ – electrode bearing stents – constitutes a potential alternative to conventional deep brain stimulation (DBS). The precise neuroanatomical relationships between DBS targets and the vascular system, however, are poorly characterized to date.ObjectiveTo establish the relationships between cerebrovascular system and DBS targets and investigate the feasibility of endovascular stimulation as an alternative to DBS.MethodsNeuroanatomical targets as employed during deep brain stimulation (anterior limb of the internal capsule, dentatorubrothalamic tract, fornix, globus pallidus pars interna, medial forebrain bundle, nucleus accumbens, pedunculopontine nucleus, subcallosal cingulate cortex, subthalamic nucleus, and ventral intermediate nucleus) were superimposed onto probabilistic vascular atlases obtained from 42 healthy individuals. Euclidian distances between targets and associated vessels were measured. To determine the electrical currents necessary to encapsulate the predefined neurosurgical targets and identify potentially side-effect inducing substrates, a preliminary volume of tissue activated (VTA) analysis was performed.ResultsSix out of ten DBS targets were deemed suitable for endovascular stimulation: medial forebrain bundle (vascular site: P1 segment of posterior cerebral artery), nucleus accumbens (vascular site: A1 segment of anterior cerebral artery), dentatorubrothalamic tract (vascular site: s2 segment of superior cerebellar artery), fornix (vascular site: internal cerebral vein), pedunculopontine nucleus (vascular site: lateral mesencephalic vein), and subcallosal cingulate cortex (vascular site: A2 segment of anterior cerebral artery). While VTAs effectively encapsulated mfb and NA at current thresholds of 3.5 V and 4.5 V respectively, incremental amplitude increases were required to effectively cover fornix, PPN and SCC target (mean voltage: 8.2 ± 4.8 V, range: 3.0–17.0 V). The side-effect profile associated with endovascular stimulation seems to be comparable to conventional lead implantation. Tailoring of targets towards vascular sites, however, may allow to reduce adverse effects, while maintaining the efficacy of neural entrainment within the target tissue.ConclusionsWhile several challenges remain at present, endovascular stimulation of select DBS targets seems feasible offering novel and exciting opportunities in the neuromodulation armamentarium.     

NIKKISO DBB-27型血液透析机液路系统工作原理及故障报警分析

分析了NIKKISO DBB-27型血液透析机液路系统的系统结构及各组成部分的工作原理,并对相关故障报警的诊断和排除进行了详细论述,为及时、准确地诊断和排除血液透析机的故障提供了理论基础。     

一种西门子新型X线机的综合性能与安装

本文叙述西门子型号为Polydoros 80/100的新型X线主机,该主机采用微电脑控制技术、光纤维传输技术及中频技术,X射线剂量精度高,影像质量优良,故障率低,是一种技术较为成熟的X线主机,也是当今西门子公司产品的主流机种。该机的安装调试具有一定难度,本文同时针对这一问题进行讨论,并总结归纳出一套安装调试全过程。