利用微信平台延续性护理对老年腰椎间盘突出症术后患者康复及生活质量的影响

目的探讨微信平台延续性护理对老年腰椎间盘突出症术后患者康复及生活质量的影响。方法选取2015年6月—2018年5月在郑州市骨科医院接受手术治疗的老年腰椎间盘突出症患者96例进行研究。对照组行常规延续性护理,观察组行微信平台延续性护理。对比两组康复效果、治疗依从性及生活质量。计数资料采用卡方检验分析,计量资料采用t检验分析。结果观察组有效率为95.83%,明显高于对照组的81.25%。观察组总依从率为91.67%,明显高于对照组的77.08%。干预前两组生活质量量表得分相当,治疗后两组生活质量均有明显改善,但观察组改善程度更为明显。结论微信平台延续性护理相比传统延续性护理可明显提高老年腰椎间盘突出症患者康复效果、提高治疗依从性和改善生活质量。     

后外侧结构重建在新鲜股骨颈骨折人工股骨头置换术中的应用

目的探讨后外侧结构重建对后外侧入路人工股骨头置换术术后早期关节脱位的影响。方法选取2016年9月至2017年8月于我院行后外侧入路初次人工股骨头置换术的股骨颈骨折患者60例,根据术中是否修补关节囊及外旋肌群分为重建组(33例:舌形切开关节囊,术中将关节囊及外旋肌群原位缝合在大转子后方及臀中肌肌腱附着处)和对照组(27例:切除关节囊后,术中未进行外旋肌群修复重建)。比较两组的手术情况及术后近期关节功能情况。结果重建组的手术时间为(45.0±15.3) min,长于对照组的(35.0±12.4) min (P <0.05)。重建组术腔引流量为(200.0±80.0) m L,少于对照组的(420.0±120.6) m L (P <0.05)。重建组的早期脱位率为0.000%(0例),与对照组的7.407%(2例)比较无统计学差异(P>0.05)。重建组术后Harris评分为(92.0±3.4)分,高于对照组的(88.2±5.0)分(P <0.05)。结论在后外侧入路人工股骨头置换过程中行后外侧结构重建能够有效减少术腔引流量,提高髋关节Harris评分,对维持髋关节软组织平衡具有重要意义。     

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.     

Registries,Databases and Repositories for Developing Artificial Intelligence in Cancer Care

Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches.     

不同剂量骨水泥PKP联合雌激素对绝经后女性骨质疏松性腰椎体压缩性骨折患者的影响

目的探讨不同剂量骨水泥PKP联合雌激素对绝经后女性骨质疏松性腰椎体压缩性骨折患者的影响。方法选取2015年1月至2018年6月我院收治的120例老年女性骨质疏松性腰椎体压缩性骨折患者为研究对象,按行PKP手术治疗时骨水泥注入的剂量不同分为三组(A组2-4 mL、B组4-5 mL、C组5-6 mL),所有患者均随访1年,比较术后三组患者VAS评分、ODI评分、伤椎椎体前缘高度、椎体中部高度及并发症的发生率。结果三组患者相比,治疗前VAS评分、ODI评分组间比较差异无显著性(P0.05);术后3天及1年后随访时,三组患者各项指标均较治疗前显著改善,组内比较差异具有显著性(P0.05),组间比较差异无显著性(P0.05);治疗前伤椎椎体前缘高度及椎体中部高度组间比较差异无显著性(P0.05);术后3天及1年后随访时,三组患者治疗后椎体前缘、中间高度均较术前明显增加(P0.05),组间比较椎体前缘高度:C组B组A组,椎体中间高度:C组B组A组,且组间比较有统计学差异(P0.05); A组患者并发症的发生率(12.5%)明显低于B组(30.0%)、C组(41.0%),有统计学差异(P0.05)。结论 PKP术中注入小剂量骨水泥(2-4 mL)对于治疗老年女性OVCFs具有良好的治疗效果及较高的安全性。     

“人工智能+X”背景下医学信息管理专业研究生培养模式

阐述当前“人工智能+X”背景下市场对医学信息管理专业人才能力的需求，分析医学信息管理专业人才培养现状，提出从重塑学科人才培养目标、优化课程内容与课程设置、建设“双师型”导师队伍、搭建多方协同共建共享在线平台及设立“政用产学研”联合培养基地等方面探索医学信息管理专业研究生培养模式，以期培养适应人工智能时代发展，具备学科优势特色的高层次、高水平、高质量的复合型、应用型、创新型人才。