基于关联分析的中老年体检数据的挖掘

根据中老年体检报告,运用Apriori算法挖掘各个指标之间的联系,为医生、患者提供诊断参考与建议。通过安徽省某三甲医院的体检数据,筛选出40岁及以上的中老年人群为研究对象,应用数据挖掘中关联规则的Apriori算法对超重、心电图、脂肪肝、血脂、血压、血糖、尿常规、吸烟、饮酒、总胆固醇等体检指标之间的关联关系进行分析研究。研究表明,体检者的个人不良习惯、超重、高龄、高血糖和脂肪肝等都密切相关,互相影响,提出中老年人群应加强对慢性疾病的预防,保持良好的作息习惯等相关建议。     

基于ARM和Zigbee的心血管功能测试诊断平台设计与实现

心血管疾病作为慢性疾病之首,严重威胁全国10.3%人群的生命健康,对其进行有效的防控治疗已成为当下研究热点;而目前国内慢性疾病信息实时采集终端与分析诊断平台匮乏,致使心血管等慢性疾病无法得到跟踪和快速有效的治疗;为此,文章利用物联网技术,结合SQL Server数据库,使用C#语言以及WPF开发技术设计了基于物联网的心血管功能测试及诊断平台;该平台通过终端实现对心血管疾病患者健康信息的采集,采用Zigbee通讯技术上传至云端,平台通过Socket技术接收数据并给出诊断结论,生成体检报告,系统测试中涉及了心率、心输出量CO、心搏出量SV以及脉搏波形特征量K等关键参数,结果验证了该心血管功能测试诊断平台的合理性与有效性,实验结果达到了预期目标,为及早发现和治疗心血管等慢性疾病提供了数据支撑与平台基础。     

智慧医院的健康管理平台建设实践

医院通过建设健康管理平台,有利于建立用户云健康档案,分析健康数据,自动生成评估报告,为用户健康提供建议。方法:基于国网EA架构,以健康管理数据为基础,依托对数据的处理能力和应用理解能力,通过智能用户画像分析、智能管理效果评估等功能构建系统。结论:笔者所在单位使用健康管理平台一年以来,整合了体检报告、膳食运动、健康数据,实现对用户的健康动态化管理,全面提升了医院的健康管理水平。     

医疗体检数据预处理方法研究

原始体检数据存在信息模糊、有噪声、不完整和冗余的问题,无法直接用于疾病的风险评估与预测。由于体检数据在结构和格式等方面的不足,不适合采用传统的数据预处理方法。为了充分挖掘体检数据中有价值的信息,从多角度提出了针对体检数据的预处理方法：通过基于压缩方法的数据归约,降低了体检数据预处理的时间及空间复杂度;通过基于分词和权值的字段匹配算法,完成了体检数据的清洗,解决了体检数据不一致的问题;通过基于线性函数的数据变换,实现了历年体检数据的一致性和连续性。实验结果表明,基于分词和权值的字段匹配算法,相对于传统算法具有更高的准确性。     

基于Word2vec和卷积神经网络特征提取的双高疾病预测

在高血压和高血脂疾病的预测研究中,针对体检数据中文本型数据特征提取问题,提出利用Word2vec和卷积神经网络相结合的方法(WV-CNN)对数据中的文本特征进行特征提取,建立预测模型.利用Doc2 vec方法进行特征提取的对比实验,结果证明该预测方法的特征提取能力在不同输入数据数量级和不同预测方面都有很好的表现,对双高...     

个人健康体检意向的调查分析

据相关部门统计,我国目前十大死亡原因中,与“三高”相关的死亡人占总死亡人数的27％．这些疾病可以通过个人健康体检得到预防。可见,个人体检对身体健康的重要性。利用呼和浩特市居民个人健康体检的调查问卷数据．运用SPSS软件,对问卷中的六个属性变量进行对数线性回归模型的分析．探寻了影响居民个人健康体检的意向及其原因,并得出相应的结论。     

数据挖掘技术在哮喘疾病诊断中的作用研究

在对哮喘疾病分析的基础上,结合数据挖掘技术的粗糙集理论,对哮喘病人生理数据库中的数据进行医学数据挖掘,从中寻找哮喘病人的发病特征及其相关性,为医生对哮喘病人进行诊断、治疗和研究工作提供数据支撑,并且在对哮喘疾病数据发掘的基础上,对哮喘疾病的临床研究、诊断、治疗、病症关系等4大类影响因素进行分析与管理,提高哮喘疾病防治的水平。     

医疗大数据在心脑血管疾病中的应用研究

医疗大数据的广泛应用,给心脑血管疾病的研究带来新的机遇.合理利用数据特征进行分析,对心脑血管疾病在医药作用、疾病关联、风险预警等方面的发展具有推动意义.但心脑血管疾病大数据存在一定的个性特征,特别是关联性强、周期性长、隐私性敏感等方面,给数据的应用造成一定的影响.文章阐述了医疗大数据的发展对心脑血管疾病研究的影响,并主要分析心脑血管疾病大数据所体现的特征及应用.同时结合医疗领域与信息领域,提出了心脑血管疾病大数据所面临的挑战和展望.     

面向健康体检数据的多目标Top-k频繁模式挖掘方法

为解决现有的模式挖掘方法没有充分利用体检数据中检查项的异常程度与特定疾病之间相关性的问题,提出一种面向健康体检数据的多目标Top-k频繁模式挖掘方法.首先,针对体检数据的特点,提出异常度和覆盖率两个指标,在此基础上,将Top-k频繁模式挖掘建模为一个多目标优化问题;其次,针对该问题,提出一种基于偏好的种群初始化策略和一个面向模式和项的双层更新策略,并基于此设计一种高效的进化多目标优化算法进行求解.实验结果表明,所提出方法所获得的Top-k个模式不仅能够有效地反映其与特定疾病之间的关联性,而且能够提供多样化的模式,为健康管理提供重要的参考依据.     

吉林市某银行196名职员不同性别间健康体检分析

目的:了解吉林市某银行职员的健康状况,对疾病的发生做到早发现、早预防、早诊断,为其预防保健工作提供科学依据。方法:选取吉林市某银行196名职员作为研究对象,对其进行健康体检,并对体检结果进行整理,采用SPSS17.0软件进行统计分析。结果:不同性别职员间,除多导心电图异常和血常规异常指标外,其余主要疾病及异常指标具有显著性差异(P0.05)结论:总体上看,吉林市某银行职员健康体检中人员总数196人,共检出异常178人,占总体检人数的90.82%;在本次调查中,主要疾病及异常的检出率男性显著高于女性(P0.05)。     

Pattern analysis in daily physical activity data for personal health management

Purpose: sedentary lifestyles have resulted in an increasing number of people who are at increased risk of various conditions and diseases, including overweight, obesity, and metabolic syndromes. Our objective was to systematically record the daily life journal on a platform to increase the self-awareness and improve the sedentary lifestyle and to assist clinicians in understanding and facilitating patients’ daily physical activity.Method: we developed a portable activity pattern recognition system designed to automatically recognize the daily activity habits of users, and provide visualized life logs on the wellness self-management platform for patients and clinicians. Based on the participants’ and the clinician’s comments, appropriate modifications were made.Results: persuading people to improve their activities during non-working hours can enhance the general physical activity. Since users’ smartphones automatically monitor their energy expenditure, healthcare professionals can use these data to assist their patients in addressing health problems stemming from the obesity or metabolic syndromes, thus empowering users to avert or delay the progression of diabetes, cardiovascular disease and other complications.Discussion and conclusions: the clinical pilot study showed the feasibility of applying this persuasive technology to improve the physical activity of overweight people. The limitation of the study is the need for Wi-Fi and 3G environments and a smartphone.     

A novel data mining mechanism considering bio-signal and environmental data with applications on asthma monitoring

Chronic asthmatic sufferers need to be constantly observed to prevent sudden attacks. In order to improve the efficiency and effectiveness of patient monitoring, we proposed in this paper a novel data mining mechanism for predicting attacks of chronic diseases by considering of both bio-signals of patients and environmental factors. We proposed two data mining methods, namely Pattern Based Decision Tree (PBDT) and Pattern Based Class-Association Rule (PBCAR). Both methods integrate the concepts of sequential pattern mining to extract features of asthma attacks, and then build classifiers with the concepts of decision tree mining and rule-based method respectively. Besides the general clinical data of patients, we considered environmental factors, which are related to many chronic diseases. For experimental evaluations, we adopted the children asthma allergic dataset collated from a hospital in Taiwan as well as the environmental factors like weather and air pollutant data. The experimental results show that PBCAR delivers 86.89% of accuracy and 84.12% of recall, and PBDT shows 87.52% accuracy and 85.59 of recall. These results also indicate that our methods can perform high accuracy and recall on predictions of chronic disease attacks. The readable rules of both classifiers can provide patients and healthcare workers with insights on essential illness related information. At the same time, additional environmental factors of input data are also proven to be valuable in predicting attacks.     

肠道微生物与人体健康综述

人体肠道中含有大量的微生物。研究表明，肠道微生物的群落结构在人体的许多生理功能上发挥重要作用，如机体物质代谢、能量吸收、胃肠道功能的完善及免疫功能的调节等。人体的许多慢性疾病，比如肥胖症、与肥胖相关的炎症反应、炎症性肠病、抑郁症等都与胃肠道微生物的群落结构失衡有关。肠道微生物与人体相互作用关系的研究对许多慢性病的预防和治疗，以及保持人体健康具有一定的指导意义。     

Web-based remote human pulse monitoring system with intelligent data analysis for home health care

Many countries have already become aging societies, as evidenced by annually decreasing fertility rates. Elderly individuals often live independently because their families cannot look after them. Therefore, computer-assisted nursing has received increasing attention in modern society, explaining why intelligent systems with physiology signal monitoring for e-health care is an emerging area of development, owing to the urgent needs of homecare for elderly people suffering chronic or sudden diseases at home. Importantly, a physiology signal monitoring system can help medical staff to monitor and analyze physiology signal effectively, such that they can not only monitor the patients’ physiology states immediately, but also reduce medical cost and avoid having to visit doctors in hospital. Therefore, this study adopts system on chip (SOC) techniques to develop an embedded human pulse monitoring system with intelligent data analysis mechanism for disease detection and long-term health care. The proposed system can be applied to monitor and analyze pulse signal in daily life. The proposed system also has a friendly web-based interface for medical staff to observe immediate pulse signals for remote treatment. Hence, the proposed system provides aids long-distance medical treatment, exploring trends of potential chronic diseases, and urgent situations informing for sudden diseases. Moreover, this study also presents an intelligent data analysis scheme based on the modified cosine similarity measure to diagnose abnormal pulses for exploring potential chronic diseases.     

From mobility data to habits and common pathways

Many aspects of our lives are associated with places and the activities we perform on a daily basis. Most of them are recurrent and demand displacement of the individual between regular places like going to work, school or other important personal locations. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics, especially because humans are frequently looking for uniformity to support their decisions and make their actions easier or even automatic. In this work, we propose a method for discovering common pathways across users' habits from human mobility data. By using a density-based clustering algorithm, we identify the most preferable locations the users visit, we apply a Gaussian mixture model over these places to automatically separate among all traces, the trajectories that follow patterns in order to discover the representations of individual's habits. By using the longest common sub-sequence algorithm, we search for the trajectories that are more similar over the set of users' habits trips by considering the distance that pairs of users or habits share on the same path. The proposed method is evaluated over two real-world GPS datasets and the results show that the approach is able to detect the most important places in a user's life, detect the routine activities and identify common routes between users that have similar habits paving the way for research techniques in carpooling, recommendation and prediction systems.     

Musculoskeletal pain in operating room nurses: Associations with quality of work life,working posture,socio-demographic and job characteristics

This cross-sectional study evaluated the association of socio-demographic and job characteristics as well as quality of work life (QWL) and working posture with the presence of musculoskeletal pain (MSP) among 144 operating room (OR) nurses in Iran. A questionnaire (including the Nordic Musculoskeletal Questionnaire [NMQ] and the QWL questionnaire) and direct observations of working postures using the Rapid Entire Body Assessment (REBA) method were used. A high prevalence of MSP, particularly in the low back (61.9%), knees (60.5%), ankles/feet (55.8%) and neck (44.9%) were found. The overall mean REBA score of 7.7 suggested that most OR nurses (with posture assessed at action levels 3 and 4) needed an immediate investigation and changes in their working habits and workstations to reduce the risk level. Work-related factors including type of operating room, work shifts (rotating), feeling pressure due to work, total QWL and its dimensions (particularly health and safety, knowledge and esteem needs) and working postures were associated with the presence of MSP in different body regions. Socio-demographic factors including gender (being female) and little or no involvement in sport/physical activity were also independently associated with the occurrence of complaints. The findings emphasise the need for multiple component ergonomic interventions involving the socio-demographic and work-related physical and psychosocial factors for prevention of MSP in this occupational group.     

Internet of things smart medical system and nursing intervention of glucocorticoid drug use

The Internet of Things is described as a single Internet connection that allows switching data network physical devices. These devices are not necessarily complex technical progress. However, it simplifies the process and allows healthcare professionals timely complete tasks. Focus on healthcare and technology companies tend to invest a lot of money in the Internet of Things. Today, most high-tech equipments have some form of connection, from wearable devices (such as biological sensors) to have a Wi-Fi or Bluetooth X-ray equipment. IoT-enabled medical devices provide essential data to support the performance of health care professionals. Corticosteroids reduce inflammation in various diseases and immune activation standard therapy. However, like other potent drugs, it is not without side effects. Glucocorticoid-related side effects may be related to most of the major organ systems. All musculoskeletal, gastrointestinal, cardiovascular, endocrine, neurological psychiatry, dermatology, eye diseases and immune system side effects may occur. Glucocorticoid treatment with various side effects, but careful monitoring and appropriate prevention strategies may minimize these side effects. Doctors need to enable patients to understand these data and determine the most appropriate for their prevention and treatment options.     

A mobile system for sedentary behaviors classification based on accelerometer and location data

BackgroundSedentary behaviors are associated to the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Accelerometers and inclinometers have been used to estimate sedentary behaviors, however a major limitation is that these devices do not provide enough contextual information in order to recognize the specific sedentary behavior performed, e.g., sitting or lying watching TV, using the PC, sitting at work, driving, etc.ObjectivePropose and evaluate the precision of a mobile system for objectively measuring six sedentary behaviors using accelerometer and location data.ResultsThe system is implemented as an Android Mobile App, which identifies individual’s sedentary behaviors based on accelerometer data taken from the smartphone or a smartwatch, and symbolic location data obtained from Bluetooth Low Energy (BLE) beacons. The system infers sedentary behaviors by means of a supervised Machine Learning Classifier. The precision of the classification of five of the six studied sedentary behaviors exceeded 95% using accelerometer data from a smartwatch attached to the wrist and 98% using accelerometer data from a smartphone put into the pocket. Statistically significant improvement in the average precision of the classification due to the use of BLE beacons was found by comparing the precision of the classification using accelerometer data only, and BLE beacons localization technology.ConclusionsThe proposed system provides contextual information of specific sedentary behaviors by inferring with very high precision the physical location where the sedentary event occurs. Moreover, it was found that, when accelerometers are put in the user’s pocket, instead of the wrist and, when symbolic location is inferred using BLE beacons; the precision in the classification is improved. In practice, the proposed system has the potential to contribute to the understanding of the context and determinants of sedentary behaviors, necessary for the implementation and monitoring of personalized noncommunicable diseases prevention programs, for instance, sending sedentary behavior alerts, or providing personalized recommendations on physical activity. The system could be used at work to promote active breaks and healthy habits.