Machine learning approaches classify clinical malaria outcomes based on haematological parameters.
Machine learning approaches classify clinical malaria outcomes based on haematological parameters.
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DOI：
10.1186/s12916-020-01823-3
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发表时间：
2020-11-30
期刊：
影响因子：
9.3
通讯作者：
Otto TD
中科院分区：
文献类型：
--
作者：
Morang'a CM;Amenga-Etego L;Bah SY;Appiah V;Amuzu DSY;Amoako N;Abugri J;Oduro AR;Cunnington AJ;Awandare GA;Otto TD
疟疾仍然是一个重大的全球健康负担，91个国家的32亿多人仍然面临着这种疾病的风险。准确区分疟疾与其他疾病，特别是单纯性疟疾（UM）与非疟疾感染（nMI）仍然是一项挑战。此外，快速诊断测试（RDTs）的成功受到Pfhrp 2/3缺失和低寄生虫血症敏感性降低的威胁。血液学指标分析可用于协助查明可能的疟疾病例，以便进一步诊断，特别是从流行地区返回的旅行者。作为精准医学的一个新应用，我们的目标是评估机器学习（ML）方法，这些方法可以使用血液学参数准确地对nMI，UM和严重疟疾（SM）进行分类。我们获得了在加纳收集的2，207名参与者的血液学数据：nMI（n = 978），SM（n = 526）和UM（n = 703）。测试了六种不同的ML方法，以选择最佳方法。一个人工神经网络（ANN）与三个隐藏层用于UM，SM，和uMI的多分类。开发了二进制分类器以进一步识别可以区分UM或SM与nMI的参数。局部可解释模型不可知解释（LIME）被用来解释二进制分类器。多分类模型具有大于85%的训练和测试准确率，以区分临床疟疾和nMI。为了区分UM和nMI，我们的方法将血小板计数、红细胞（RBC）计数、淋巴细胞计数和百分比确定为UM的最佳分类器，测试准确度为0.801（AUC = 0.866，F1评分= 0.747）。为了区分SM和nMI，分类器的测试准确度为0.96（AUC = 0.983，F1评分= 0.944），平均血小板体积和平均细胞体积是SM的唯一分类器。随机森林用于确认分类，结果表明，血小板和RBC计数是UM的主要分类器，无论患者年龄和采样位置等可能的混杂因素如何。该研究提供了将UM和SM从nMI中分类的概念验证方法，表明ML方法是临床决策支持的可行工具。在未来，ML方法可以被纳入临床决策支持算法，用于诊断急性发热性疾病和监测对急性SM治疗的反应，特别是在地方性环境中。
Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters. We obtained haematological data from 2,207 participants collected in Ghana: nMI (n = 978), SM (n = 526), and UM (n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers. The multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location. The study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings.
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影响因子：
9.3
作者：
Hanson J;Phu NH;Hasan MU;Charunwatthana P;Plewes K;Maude RJ;Prapansilp P;Kingston HW;Mishra SK;Mohanty S;Price RN;Faiz MA;Dondorp AM;White NJ;Hien TT;Day NP
通讯作者：
Day NP
影响因子：
3
作者：
Angelo, Kristina M.;Libman, Michael;Esposito, Douglas H.
通讯作者：
Esposito, Douglas H.
影响因子：
6.4
作者：
Lampah, Daniel A.;Yeo, Tsin W.;Price, Ric N.
通讯作者：
Price, Ric N.
影响因子：
2.9
作者：
Cohen JF;Korevaar DA;Altman DG;Bruns DE;Gatsonis CA;Hooft L;Irwig L;Levine D;Reitsma JB;de Vet HC;Bossuyt PM
通讯作者：
Bossuyt PM
影响因子：
3
作者：
Kotepui, Manas;Phunphuech, Bhukdee;Duangmano, Suwit
通讯作者：
Duangmano, Suwit