Artif Intell Med - From spoken narratives to domain knowledge: mining linguistic data for medical image understanding.

Tópicos

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Resumo

JECTIVES: Extracting useful visual clues from medical images allowing accurate diagnoses requires physicians' domain knowledge acquired through years of systematic study and clinical training. This is especially true in the dermatology domain, a medical specialty that requires physicians to have image inspection experience. Automating or at least aiding such efforts requires understanding physicians' reasoning processes and their use of domain knowledge. Mining physicians' references to medical concepts in narratives during image-based diagnosis of a disease is an interesting research topic that can help reveal experts' reasoning processes. It can also be a useful resource to assist with design of information technologies for image use and for image case-based medical education systems.METHODS AND MATERIALS: We collected data for analyzing physicians' diagnostic reasoning processes by conducting an experiment that recorded their spoken descriptions during inspection of dermatology images. In this paper we focus on the benefit of physicians' spoken descriptions and provide a general workflow for mining medical domain knowledge based on linguistic data from these narratives. The challenge of a medical image case can influence the accuracy of the diagnosis as well as how physicians pursue the diagnostic process. Accordingly, we define two lexical metrics for physicians' narratives--lexical consensus score and top N relatedness score--and evaluate their usefulness by assessing the diagnostic challenge levels of corresponding medical images. We also report on clustering medical images based on anchor concepts obtained from physicians' medical term usage. These analyses are based on physicians' spoken narratives that have been preprocessed by incorporating the Unified Medical Language System for detecting medical concepts.RESULTS: The image rankings based on lexical consensus score and on top 1 relatedness score are well correlated with those based on challenge levels (Spearman correlation>0.5 and Kendall correlation>0.4). Clustering results are largely improved based on our anchor concept method (accuracy>70% and mutual information>80%).CONCLUSIONS: Physicians' spoken narratives are valuable for the purpose of mining the domain knowledge that physicians use in medical image inspections. We also show that the semantic metrics introduced in the paper can be successfully applied to medical image understanding and allow discussion of additional uses of these metrics.

Resumo Limpo

jectiv extract use visual clue medic imag allow accur diagnos requir physician domain knowledg acquir year systemat studi clinic train especi true dermatolog domain medic specialti requir physician imag inspect experi autom least aid effort requir understand physician reason process use domain knowledg mine physician refer medic concept narrat imagebas diagnosi diseas interest research topic can help reveal expert reason process can also use resourc assist design inform technolog imag use imag casebas medic educ systemsmethod materi collect data analyz physician diagnost reason process conduct experi record spoken descript inspect dermatolog imag paper focus benefit physician spoken descript provid general workflow mine medic domain knowledg base linguist data narrat challeng medic imag case can influenc accuraci diagnosi well physician pursu diagnost process accord defin two lexic metric physician narrativeslex consensus score top n related scoreand evalu use assess diagnost challeng level correspond medic imag also report cluster medic imag base anchor concept obtain physician medic term usag analys base physician spoken narrat preprocess incorpor unifi medic languag system detect medic conceptsresult imag rank base lexic consensus score top related score well correl base challeng level spearman correl kendal correl cluster result larg improv base anchor concept method accuraci mutual informationconclus physician spoken narrat valuabl purpos mine domain knowledg physician use medic imag inspect also show semant metric introduc paper can success appli medic imag understand allow discuss addit use metric

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