编程技术网

关注微信公众号,定时推送前沿、专业、深度的编程技术资料。

 找回密码
 立即注册

QQ登录

只需一步,快速开始

极客时间

在医疗世界中的医疗保健深深学习

Dowymn 机器学习 2022-1-10 23:46 174人围观

腾讯云服务器

This article was published as a part of the Data Science Blogathon

客观的

热烈欢迎所有读者。本文是关于探索医学和医疗保健领域的人工情报援助文艺复兴的可能性。来自传统方法的范式转变是需要一小时来提高患者结果的质量。本文将对人工智能领域工作的人来说是非常有益的,试图进入医疗保健领域以及在医疗和医疗科学领域工作的人,试图进入人工智能领域。

Image Source: Flanders Vaccine.  Flanders. health series of online Pitch&Match sessions – Flanders Vaccine

内容

1.介绍a)什么是深入学习?b)涵盖的医疗条件/程序c)具有人为干预的现实生活。链接到深度学习代码3.用于从传统方法到机器驱动方法的范式转变4.Deep学习方法5.罗尔关于解决这些条件相关的相关病情和并发症的深度学习。经济和申请7.医疗保健世界的深度学习应用程序8.结论9.References

1.介绍

a)什么是深入学习?

深度学习可以用作有效的工具,以确定在我们身体中发展的某些条件的模式,比临床医生更快。深入学习通过数据输入,权重和偏置的组合来模拟人脑的工作机制。聚类数据和具有高精度的预测仍然是深度学习的基本机制。

b)涵盖的医疗条件/程序

1.甲状腺切除术 - 1.切除甲状腺或抑制其功能的抑制是甲状腺切除术。腺体是专门用于分泌不需要代谢所需的材料的细胞的聚集。甲状腺是我们体内的腺体之一,其分泌甲状腺素(T4)和T3)负责代谢的甲状腺素(T3)。2.心血管疾病 - 心血管是指心脏和血管,并且与这些相关的任何病症都被称为心血管疾病。常见的心血管疾病是心绞痛,心律失常,充血性心力衰竭,心肌梗塞,冠状动脉内容,和高血压。3.败血症 - 血液或其他组织中的病原生物体存在。当感染释放血液中的化学物质时,会发生触发阴性炎症反应,导致主要器官衰竭。

c)具有传统方法的现实生活事件

一个52岁的女子被击败了巴尔的摩医院急诊室,抱怨脚。她被诊断出脚趾的干坏疽,被录取为一般病房。在第三天,她显示肺炎的症状和像往常一样,是抗生素的疗程。 3天后,她经历了快速的心动过速(凸起心率)以及更快的呼吸。 12小时内,她的病情随着腐蚀的休克而恶化。她被转移到ICU,但她的主要机构在另一个接一个地失败了,最后,在第22天,她去世了(阿什利,2017年)。根据Johns Hopkins Medical的Armstrong患者安全和质量研究所的医生,脓毒症休克是危及生命的病情,死亡率为50%,未经治疗的败血症一小时增加了8%的死亡率。因此,临床医生必须非常快速地诊断条件。但是,随着临床医生劳动力的短缺,如何实现这一目标?答案是使用“机器”。在即将到来的部分中,我们将讨论使用机器管理每个给定条件的有用性。

2.链接到深度学习代码

为了熟悉绳索,下面给出了以逐步的方式理解深度学习码的链接

Gearing up to dive into Mariana Trench of Deep Learning

Plunging into Deep Learning carrying a red wine

3.从传统方法转向机器驱动方法的原因

医院设置中的患者结果的质量处于破旧状态。如被疼痛所承认的女士的结果所见,为更好的患者结果进行范式转变是很重要的。从传统方法转向机器驱动方法的原因是a)临床医生劳动力不足以迎合增加的人群,具有多样化的条件。b)难以扩大临床医生,但容易扩大机器。c)私立医院的消费主义方法,影响患者结果的质量。d)官僚主义瓶颈,特别是政府控制的医院,导致健康状况不佳。通过以上局限性的所有内容,机器驱动的方法将不受影响,从而具有带来更好的结果。

4.深入学习方法

该概念来自人工神经网络(ANN)。关键是构建几个隐藏的层并生成大量培训数据,使机器能够了解更有用的功能以获得更好的预测和分类。ravi等人提出的不同类型的深度学习架构,相关图像及其特征。(2017)已讨论下文

S. No Architecture type of Deep Learning Features
1 Deep Neural Network 1.Used for classification or regression

2.More than 2 hidden layers involved

3.Complex, the non-linear hypothesis is allowed to be expressed.

4.The learning process is very slow

5.Training is not trivial

Image Source: Ravi et al. (2017) https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7801947 

S. No Architecture type of Deep Learning Features
2 Deep Auto Encoder 1.For feature extraction or dimensionality reduction

2.Same number of input and output nodes

3.Unsupervised learning method.

4.Labelled data for training is not required

5. Requires a pre-training stage

Image Source: Ravi et al. (2017) https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7801947 

S. No Architecture type of Deep Learning Features
3 Deep Belief Network 1.Allows supervised as well as unsupervised training of the network

2.Has undirected connection just at the top 2 layers

3.Layer by layer greedy learning strategy to start the network

4.Likelihood maximization

5.Computationally expensive training process

Image Source: Ravi et al. (2017) https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7801947

S. No Architecture type of Deep Learning Features
4 Deep Boltzmann Machine 1.Connections existing among all layers of the network are not properly directed2.Stochastic maximum likelihood algorithm is applied to maximize the lower bound of the likelihood.3.Involves top-down feedback

4.Time complexity for the inference is higher

5.For large datasets optimization is not pragmatic

Image Source: Ravi et al. (2017) https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7801947

S. No Architecture type of Deep Learning Features
5 Recurrent Neural Network 1.Used where the output depends on the previous computations.

2.Ability to memorize sequential events

3.Capacity to model time dependencies

4.Suitable for NLP (Natural language processing) applications

5.Frequent learning issues

Image Source: Ravi et al. (2017) https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7801947

S. No Architecture type of Deep Learning Features
6 Convolutional Neural Network 1.Conducive for 2D data like images

2.Each of the convolutional filters transforms its input to a 3D output volume of neuron activations

3.Limited neuron connections required

4.Requirements for many layers to find the complete hierarchy of the visual features

5.Large dataset comprising labelled images is required

Image Source: Ravi et al. (2017)  https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7801947    

5.在解决这些条件下产生的相关病情和并发症方面的深度学习

让我们涵盖一开始就提到的前2个医疗程序/条件,并了解这些条件的深度学习如何帮助。

1.甲状腺切除术 -

During thyroidectomy, the identification of recurrent laryngeal nerve (RLN) is of paramount importance. The recurrent laryngeal nerve pertains to the larynx (voice box) and is a part of the vagus nerve which supplies all the internal muscles. During thyroidectomy, distinguishing RLN from small-caliber blood vessels is very important. The rate of postoperative complications arising out of inexperienced surgeon’s work is quite high. Can computer vision tools enable surgeons to identify RLN in a better manner? Let’s see The methods/models of Deep Learning, their procedures, and results applied in the identification of RLN as put forth by Gong et al. (2021) have been discussed below

Dataset – 277 images from 130 patients obtained by using a digital SLR (Nikon 3000) and smartphone(apple)
S. No Methods/Models Procedures Results Remarks
1 Segmentation Images tagged with far away or close-up picture distance->Manual annotations of nerve segmentation for each image by head and neck endocrine surgeons->RLN segmentations evaluated quantitively using DSC (Dice similarity coefficient) against ground truth surgeon annotation applying k-fold cross-validation(k=5) DSC = 2TP/(2TP+FP+FN) where TP = True Positive; FP = False Positive, and FN = False Negative DSC is the primary metric for segmentation
2 Cropping model Images cropped to focus on surgical anatomy of interest->cropped images segmented by the nerve segmentation model->AP (Average Precision) metric was reported which ranges from 0 to 1 with AP>0.5 is considered to be a good model AP (across all 5 folds) = 0.756

AP (far away image) = 0.677

AP (close up image) = 0.872

3 Nerve segmentation model Similar to the cropping model DSCRange = 0.343(+/- 0.077, n=78, 95% CI) to 0.707(+/- 0.075, n=40, 95% CI) The lowest DSC was obtained under far away, bright lightning and the highest DSC was obtained under close up, medium lighting.
4 Nerve width estimation Similar to above model but images are illustrated with army-navy retractors, and maximum width estimates are derived from ground-truth (gt) and predicted nerve segmentations (pr) Estimations using different auto-cropped input images and predicted segmentations: i) gt=1.434 mm, pr=1.009mmii) gt=3.994 mm, pr=3.317mm iii) gt=3.184 mm, pr=3.634 mm iv) gt=3.485 mm, pr=3.203 mm

可以推断出从两者衍生的估计是相似的。可以在下面的图像中看到涉及结束到结束训练和推理管道的整个过程的图示

                                                              Image Source: Gong et al. (2021) www.nature.com/scientificreports

有希望的结果表明,深度学习具有能够在进行甲状腺作业时对外科医生提供洞察力的能力。

2.心血管疾病 -

适当的心脏病诊断是阻止不良结果的关键。深度学习有可能有效地诊断关键因素。让我们看看它是怎么做的。心脏超声成像的深层学习管道具有三个零件。采集和预处理数据,网络选择和培训和评估(Cao等,2019)。下面可以在过程的流程图

1. Medical images collected in DICOM format

2. Images converted to a JPG/PNG format

3. Preprocessing by removing poor quality, denoising, and tagging

4. Selection of network preferably 152 layers of ResNet from Keras or SGD and Dropout optimization techniques

5. Adjusting parameters according to the network to optimize performance

6. Test the network

NB: DICOM = Digital imaging and communications in medicine SGD = Stochastic gradient descent

Cao等人。(2019)使用深度学习提出了心脏图像分割和心脏图像分类的一些研究内容。这些已在下列表中总结

Author Year Dataset Segmentation content Method Dice
Wenjia Bai et al 2017 UK Biobank study Short Axis Heart Semi-supervised Learning 0.92
Ozan Oktay et al 2017 UK Digital Heart Project short-axis cardiac ACNN 0.939
Yakun Chang et a 2018 ACDC short-axis cardiac FCN 0.90
Author Year Dataset Classification content Method Accuracy
Lasya PriyaKotu et al 2015 Author self-made data the risk of arrhythmias k-NN 0.94
Houman Ghaemmaghami et al 2017 Author self-made data heart-sound TDNN 0.95
Ali Madani et al 2018 Author self-made data view of echocardiograms CNN 0.978

深度学习已经发展成为预测心血管疾病隐藏模式的重要工具,并有助于早期诊断临界条件。

6.经济化的制品及其应用

败血症是一种致命的医疗状况,尽管临床医生的阿森纳血清抗生素仍然难以治疗。早期诊断是关键,甚至一个小时延迟增加了8%的死亡率,从研究人员所观察到的死亡率,从我们在一开始就看到的示例中明显。早期攻击性治疗对于改善死亡率结果是重要的,但是通过可用的临床工具,难以预测谁会发展败血症及其相关表现形式。

已经证明了(有针对性的实时预警评分)是准确且快速诊断败血症的答案。Henry,Hager,Pronovost,&Suria(2015)通过将方法应用于MIMIC-II临床数据库来开发了一种模型。根据SSC(Surviving Sepsis Campaign)指导方针,器官功能障碍或更具体的SIR(全身炎症反应综合征)被定义为4个中的任何2个标准的存在

i)Systolic blood pressure 2.0 mmol/L; urine output < 0.5 mL/kg for more than 2 hours despite adequate hydration. ii)Serum creatinine > 2.0 mg/dL in absence of renal insufficiency iii)Bilirubin > 2 mg/dL in absence of any liver disease iv)Acute lung injury with PaO2/FiO2 < 200 in the presence of pneumonia or with PaO2/FiO2 < 250 in the absence of pneumonia

根据Henry,Hager,Pronovost和Suria(2015)制定模型的步骤如下

1)数据集的分离

随机采样完成以将数据分成开发和验证集。开发套件= 13,181名患者(1836例阳性,11,178个阴性治疗后右审查的患者)验证= 3053名患者(455例阳性,2556例,42例患者治疗后右审查)患者称为消极案件那些没有开发化脓性休克的人。

2)模型开发

首先,进行患者特异性测量流的处理以计算分类为重要,临床和实验室组的特征。然后通过应用监督学习算法的目标预警评分中使用的系数估计。通过学习算法自发地选择预测化脓性休克的特征,并且包括预测特征列表及其系数的模型是输出。2.1)估计模型系数使用Cox比例危险模型的监控信号使用时间拟合,直到腐蚀休克发作。作为下面给出的等式计算冲击风险

                                   λ(t |X) =  λ0t*exp{XβT}

X= Features, t= time,  λ0= Time-varying baseline hazard function, β = regression coefficient A limitation occurred with this model in the form of unknown or censored event times. So, a multiple imputations–based approach was used to address model parameter estimation for the Cox proportional hazards model as it was easy to implement. From each of the N copies of the development data set, a separate model was trained. The regularization parameter was found to be 0.01 using 10-fold cross-validation on the first sampled data set and was fixed to this value for training the subsequent models. Finally, Rubin’s equations were applied to combine resulting predictions to compute the final risk value as the average of risk values from the output of each of the N models.

3)模型评估

从开发集获得的模型系数是固定的并应用于验证中的患者,如前所述观察到它们。随着新数据的可用性,在验证集中的每位患者的每个患者重新计算了TREWSCORE,导致每个人的脓毒休克的时间风险。对于固定的风险阈值,如果他或她的风险轨迹在诱导症休克开始前的检测阈值高于检测阈值的情况下升高,则识别出脓毒休克的高风险,并且计算得敏感性和特异性。

4)结果

通过该算法选择了最重要的特征的特征的子集,并为它们学习了一组重量。每个时间点的特征在开始时标记,才能缩短,直到化脓性休克(图1)发出,并用于随着时间的推移产生TREWScore风险预测(图2)。

           Fig 1 . Image Source: Henry, Hager, Pronovost, & Suria (2015) https://stm.sciencemag.org

     Fig 2. Image Source: Henry, Hager, Pronovost, & Suria (2015) https://stm.sciencemag.org

通过树丛24小时24小时的中位数,给予临床医生在败血症转变为脓毒症之前进行干预的时间。

医疗保健世界深度学习的挑战

1.有时的数据不足导致低精度2.具有深度学习模型的解释性问题使其成为一个黑匣子的形状。罕见疾病的疾病特异性数据的限制4.许多时间不能直接使用原始数据由于DNN 5的输入。通过向输入样本添加小的更改可以轻松欺骗DNN,从而导致错误分类

结论

医疗保健部门正在将基于纸张的记录转变为电子健康记录(EHR)。这将打开在该领域应用深度学习的机会的洪水,并大修医疗保健系统。这将改善患者临床医生的互动,从而导致医院设置中的健康结果改善。深度学习将在未来担任临床医生成功的关键。

参考

1. Ashley, S. (2017). Using Artificial Intelligence to spot Hospitals’ Silent Killer. NOVA. Retrieved from https://www.pbs.org/wgbh/nova/article/ai-sepsis-detection/

2. Cao, Y et al. (2019). Deep Learning Methods for Cardiovascular Diseases. Journal of Artificial Intelligence and Systems. Retrieved from https://iecscience.org/journals/AIS

3. Gong, J et al. (2021). Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy. Scientific Reports. Retrieved from www.nature.com/scientificreports

4. Henry, K, E., Hager, D, N., Pronovost, P, J., & Suria, S. (2015). A targeted real-time early warning score (TREWScore) for septic shock. Science Translational Medicine. Retrieved from https://stm.sciencemag.org

5. Ravi, D et al. (2017). Deep Learning for Health Informatics. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7801947

6. Richards, S. (2019). Early Warning Algorithm Targeting Sepsis at John Hopkins. John Hopkins Medicine. Retrieved from https://www.hopkinsmedicine.org/news/articles/early-warning-algorithm-targeting-sepsis-deployed-at-johns-hopkins

7. https://artemia.com/blog_post/ai-trends-in-modern-healthcare/

本文中显示的媒体不受分析vidhya所拥有的,并在提交人的自由裁量权使用。

有关的

腾讯云服务器 阿里云服务器
关注微信
^