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 off-site. 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-semi-supervised" feature reduction strategy in order to increase performance accuracy.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningClassificationGeneral Classificationtext-classificationText ClassificationSimilar Papers 제목 키워드 기반
Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia
Lower-and-middle income countries are faced with challenges arising from a lack of data on cause of death (COD), which can limit decisions on population health and disease management. A verbal autopsy(VA) can provide inf…
BIG-bench Machine LearningManagementLAVA: Language Model Assisted Verbal Autopsy for Cause-of-Death Determination
Verbal autopsy (VA) is a critical tool for estimating causes of death in resource-limited settings where medical certification is unavailable. This study presents LA-VA, a proof-of-concept pipeline that combines Large La…
Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?
We present two models for combining word and character embeddings for cause-of-death classification of verbal autopsy reports using the text of the narratives. We find that for smaller datasets (500 to 1000 records), add…
ClassificationGeneral ClassificationMulti-task learning for interpretable cause of death classification using key phrase prediction
We introduce a multi-task learning model for cause-of-death classification of verbal autopsy narratives that jointly learns to output interpretable key phrases. Adding these key phrases outperforms the baseline model and…
General ClassificationMulti-Task LearningText ClassificationLeveraging Language Models and Machine Learning in Verbal Autopsy Analysis
In countries without civil registration and vital statistics, verbal autopsy (VA) is a critical tool for estimating cause of death (COD) and inform policy priorities. In VA, interviewers ask proximal informants for detai…