A comparative study of machine learning methods for verbal autopsy text classification

A Verbal Autopsy is the record of an interview about the circumstances of an uncertified death. In developing countries, if a death occurs away from health facilities, a field-worker interviews a relative of the deceased about the circumstances of the death; this Verbal Autopsy can be reviewed offsite. We report on a comparative study of the processes involved in Text Classification applied to classifying Cause of Death: feature value representation; machine learning classification algorithms; and feature reduction strategies in order to identify the suitable approaches applicable to the classification of Verbal Autopsy text. We demonstrate that normalised term frequency and the standard TFiDF achieve comparable performance across a number of classifiers. The results also show Support Vector Machine is superior to other classification algorithms employed in this research. Finally, we demonstrate the effectiveness of employing a ’locally-semisupervised’ feature reduction strategy in order to increase performance accuracy

Automated classification of primary care patient safety incident report content and severity using supervised Machine Learning (ML) approaches

Learning from patient safety incident reports is a vital part of improving healthcare. However, the volume of reports and their largely free-text nature poses a major analytic challenge. The objective of this study was to test the capability of autonomous classifying of free text within patient safety incident reports to determine incident type and the severity of harm outcome. Primary care patient safety incident reports (n=31333) previously expert-categorised by clinicians (training data) were processed using J48, SVM and Naïve Bayes. The SVM classifier was the highest scoring classifier for incident type (AUROC, 0.891) and severity of harm (AUROC, 0.708). Incident reports containing deaths were most easily classified, correctly identifying 72.82% of reports. In conclusion, supervised ML can be used to classify patient safety incident report categories. The severity classifier, whilst not accurate enough to replace manual processing, could provide a valuable screening tool for this critical aspect of patient safety

Dataset analysed for this study.

IntroductionThe Coronavirus disease 2019 (COVID-19) burden, coupled with unprecedented control measures including physical distancing, travel bans, and lockdowns of cities, implemented to stop the spread of the virus, have undoubtedly far-reaching aftereffects on other diseases. In low and middle-income countries (LMICs), a particular worry is the potential impact on Human Immunodeficiency Virus (HIV) and Tuberculosis (TB), as a consequence of possible disruption to health services and limiting access to needed life-saving health care. In Ghana, there is a paucity of information regarding the impact of COVID-19 on disease control, particularly TB and HIV control. This study sought to contribute to bridging this knowledge gap.MethodThe study involved the analysis of secondary data obtained from the District Health Information Management System-2 (DHIMS-2) database of Ghana Health Service, from 2016 to 2020. Data were analysed using an interrupted time-series regression approach to estimate the impact of COVID-19 on TB case notification, HIV testing, and Antiretroviral Therapy (ART) initiations, using March 2020 as the event period.ResultsThe study showed that during the COVID-19 pandemic period, there was an abrupt decline of 20.5% (955CI: 16.0%, 24.5%) in TB case notifications in April and 32.7% (95%CI: 28.8%, 39.1%) in May 2020, with a median monthly decline of 21.4% from April-December 2020. A cumulative loss of 2,128 (20%; 95%CI: 13.3%, 26.7%) TB cases was observed nationwide as of December 2020. There was also a 40.3% decrease in people presenting for HIV tests in the first month of COVID-19 (April 2020) and a cumulative loss of 262620 (26.5%) HIV tests as of December 2020 attributable to the COVID-19 pandemic. ART initiations increased by 39.2% in the first month and thereafter decreased by an average of 10% per month from May to September 2020. Cumulatively, 443 (1.9%) more of the people living with HIV initiated ART during the pandemic period, however, this was not statistically significant.ConclusionThis study demonstrated that the COVID-19 pandemic negatively impacted TB case notifications and HIV testing and counselling services, However, ART initiation was generally not impacted during the first year of the pandemic. Proactive approaches aimed at actively finding the thousands of individuals with TB who were missed in 2020 and increasing HIV testing and counselling and subsequent treatment initiations should be prioritised.</div

Trend of observed and counterfactual number of ART initiation from January 2016 to December 2020 in Ghana.

Trend of observed and counterfactual number of ART initiation from January 2016 to December 2020 in Ghana.</p

Trends of observed and counterfactual number of TB case notifications, HIV testing and ART initiations, 2016–2020.

Trends of observed and counterfactual number of TB case notifications, HIV testing and ART initiations, 2016–2020.</p

Modelled effect of the COVID-19 pandemic on HIV testing and counselling (HTC) in Ghana, from April to December 2020.

Modelled effect of the COVID-19 pandemic on HIV testing and counselling (HTC) in Ghana, from April to December 2020.</p

Trends of observed and counterfactuals number of HIV testing in Ghana from January 2016 to December 2020.

Trends of observed and counterfactuals number of HIV testing in Ghana from January 2016 to December 2020.</p