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Development and validation of manually modified and supervised machine learning (ML) clinical assessment algorithms for malaria in Nigerian children

Provisionally accepted
The final version of the article will be published here soon pending final quality checks
 Megan MCLAUGHLIN1*,  Karell G. Pellé2*, Samuel V. Scarpino3*, Aisha Giwa4,  Ezra Mount-Finette1, Nada Haidar4, Fatima Adamu4, Temitope Adeyoju4, Nirmal Ravi4,  Adam Thompson4, Barry Health1, 5, Sabine Dittrich2 and  Barry Finette1, 5
  • 1THINKMD, Inc., United States
  • 2Foundation for Innovative New Diagnostics, Switzerland
  • 3Network Science Institute, Northeastern University, United States
  • 4Independent researcher, Nigeria
  • 5University of Vermont, United States

In 2018, children under-five represented 67% of malaria deaths globally (WHO, 2020). Digital health technology represents an opportunity for improved access and quality healthcare in Low-to-Middle Income Countries (LMICs). Several electronic point-of-care (POC) clinical-decision support algorithms (CDSAs) have been developed based on the WHO’s Integrated Management of Childhood Illnesses (IMCI) protocol to guide frontline health workers (FHWs) through the clinical assessment, triage, and treatment of children under-5 years of age. THINKMD developed a progressive web-based, integrated POC clinical assessment, triage, treatment, and recommendation platform that incorporates IMCI protocols and other evidenced-based medicine. Here we present field-based feasibility and utilization studies in Kano, Nigeria focused on using the THINKMD mHealth platform with an embedded integrated malaria rapid diagnostic test (mRDT) to manually and via the development of machine learning (ML) algorithms improve the POC screening of febrile children in Nigeria. Using paired results with clinical health data and malaria risk assessment data from over 555 patients seen at five different health clinics, we trained a ML algorithm to identify malaria cases using symptom and location data. Among 480 children identified as at risk of malaria following IMCI guidelines, 66.7% had positive mRDT results and 33.3% had negative mRDT results. The overall sensitivity and specificity of THINKMD mHealth platform (field-test) was 43% and 64%, respectively, and the malaria PPV was 71% compared with 67%. Confirmatory mRDT results and symptomology data then served as the basis for generating new algorithms both manually and through a supervised ML random forest approach, the later using 80% of our field-based data as the ML training set and 20% to test our new ML logic. New ML-based malaria clinical algorithms showed an increased sensitivity and specificity of 60% and 79%, respectively, with a PPV and NPV of 76% and 65%. Results confirm that incorporation of mRDT results into digital mHealth POC platforms can improve assessment of children with fever and can be used in the development of more accurate machine-learning-based frontline clinical assessment algorithms to improve identification of malaria/non malaria attributable febrile illnesses.

Keywords: Digital Health, IMCI, Rapid diagnostic test, machine learning, Febrile illness

Received: 20 Apr 2020; Accepted: 11 Aug 2021.

Copyright: © 2021 MCLAUGHLIN, Pellé, Scarpino, Giwa, Mount-Finette, Haidar, Adamu, Adeyoju, Ravi, Thompson, Health, Dittrich and Finette. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Mx. Megan MCLAUGHLIN, THINKMD, Inc., Burlington, United States
Mx. Karell G. Pellé, Foundation for Innovative New Diagnostics, Geneva, Geneva, Switzerland
Mx. Samuel V. Scarpino, Network Science Institute, Northeastern University, Boston, United States