中西医结合治疗动眼神经麻痹疗效观察

目的了解中西医结合法治疗动眼神经麻痹的临床疗效。方法对我院2012-03—2014-03收治的动眼神经麻痹患者进行抽样,择取74例患者随机分成2组,对照组予以基础性西医疗法,实验组在对照组治疗基础上予以中医疗法(包括针刺及服用中药正容汤等),观察2组患者的临床治疗效果。结果实验组总有效率(94.60%)明显高于对照组(64.86%),差异具有统计学意义(P0.05)。结论中西医结合法治疗动眼神经麻痹临床疗效确切,安全系数高,值得临床大力推广使用。     

Efficacy of percutaneous and transcutaneous tibial nerve stimulation in women with idiopathic overactive bladder: A prospective randomised controlled trial

BackgroundDifferent studies have reported the efficacy of percutaneous tibial nerve stimulation (PTNS) and transcutaneous tibial nerve stimulation (TTNS) in treating idiopathic overactive bladder (OAB). However, no study has compared the effectiveness of PTNS and TTNS added to bladder training (BT) in idiopathic OAB.ObjectiveTo compare the efficacy of PTNS and TTNS added to BT in women with idiopathic OAB.MethodsWe randomised 60 women with idiopathic OAB into 3 groups. Group 1 (n = 19) received BT, Group 2 (n = 19) received PTNS in addition to BT, and Group 3 (n = 20) received TTNS in addition to BT. PTNS and TTNS were performed 2 days a week, for 30 min a day, for a total of 12 sessions for 6 weeks. Patients were evaluated by incontinence severity (pad test), a 3-day voiding diary (frequency of voiding, incontinence episodes, nocturia and number of pads used), symptom severity, quality of life, treatment success (positive response rate), treatment satisfaction (Likert scale), discomfort level and preparation time for stimulation (sec).ResultsAt the end of treatment; severity of incontinence, frequency of voiding, incontinence episodes, nocturia, number of pads used, symptom severity and quality of life were significantly improved in Groups 2 and 3 versus Group 1 (P < 0.0167). Treatment success and treatment satisfaction were higher in Groups 2 and 3 than Group 1 (P < 0.001 and P < 0.0167, respectively). Level of discomfort was lower, treatment satisfaction was higher and preparation time for stimulation was shorter in Group 3 than Group 2 (P < 0.05).ConclusionBoth the PTNS plus BT and TTNS plus BT were more effective than BT alone in women with idiopathic OAB. These 2 tibial nerve stimulation methods had similar clinical efficacy but with slight differences: TTNS had shorter preparation time, less discomfort level and higher patient satisfaction than PTNS.     

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.     

“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。