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Acad Emerg Med:大數(shù)據(jù)模型可改善對住院患者的預(yù)后預(yù)測
發(fā)布時(shí)間:2016/02/23 信息來源:查看

Acad Emerg Med:大數(shù)據(jù)模型可改善對住院患者的預(yù)后預(yù)測

2016年2月22日 訊 /生物谷BIOON/ --在美國有超過一半的院內(nèi)死亡都與嚴(yán)重感染或敗血癥直接相關(guān),近日來自耶魯大學(xué)等機(jī)構(gòu)的研究者開發(fā)了一種預(yù)測模型,其可以利用當(dāng)?shù)夭∪说拇髷?shù)據(jù),并且利用機(jī)器學(xué)習(xí)方法來幫助鑒別那些有疾病風(fēng)險(xiǎn)的患者,這種新型方法比當(dāng)前的臨床實(shí)踐方法要好,該研究發(fā)表在國際雜志Academic Emergency Medicine上。

當(dāng)前急診醫(yī)生可以利用簡單的計(jì)算器或評分系統(tǒng)來作為臨床的決策準(zhǔn)則幫助確定哪些患者更易因院內(nèi)敗血癥而死亡,然而這些方法常常并不能成功鑒別出高風(fēng)險(xiǎn)的病人,因?yàn)檫@僅僅是基于有限的信息,而這并不能夠計(jì)算出數(shù)據(jù)的復(fù)雜性,其僅僅是利用不同病人群體而開發(fā)出的。

本文中,科學(xué)家開發(fā)的這種新型模型就利用了來自當(dāng)?shù)夭∪说碾娮咏】涤涗浀拇罅繑?shù)據(jù),這種名為“隨機(jī)森林模型”的方法就可以對來自病人的數(shù)據(jù)進(jìn)行處理分析并且做出一定的預(yù)測;這種新型的大數(shù)據(jù)分析方法遠(yuǎn)勝于當(dāng)前的模型,而且可以潛在地在每5000份嚴(yán)重的敗血癥患者中進(jìn)行分類出200-300名患者。

R. Andrew Taylor博士指出,利用機(jī)器學(xué)習(xí)技術(shù)并且結(jié)合大量的突變(超過500個(gè)突變),我們就可以開發(fā)出一種新型模型來潛在幫助更好地預(yù)測院內(nèi)患者的敗血癥死亡率。研究人員希望后期在促進(jìn)這種大數(shù)據(jù)分析模型使用的時(shí)候,同時(shí)還可以幫助對患者進(jìn)行實(shí)時(shí)地監(jiān)測,研究者的目的就是獲取患者的數(shù)據(jù),并且開發(fā)出新型的學(xué)習(xí)健康系統(tǒng),即開發(fā)出預(yù)測性的模型來應(yīng)用于改善患者的健康之中。(生物谷Bioon.com)

doi:10.1111/acem.12876
PMC:
PMID:

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach

R. Andrew Taylor MD, MHS*, Joseph R. Pare MD, Arjun K. Venkatesh MD, MBA, MHS, Hani Mowafi MD, MPH, Edward R. Melnick MD, MHS, William Fleischman MD andM. Kennedy Hall MD, MHS?

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Objectives Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data–driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. Methods This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics. Results There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p ≤ 0.003 for all comparisons). Conclusions In this proof-of-concept study, a local big data–driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions.


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