Daniel S Barron, Stephen Heisig, Carla Agurto, Raquel Norel, Brittany Quagan, Albert Powers, Michael L Birnbaum, Todd Constable, Guillermo Cecchi, John H Krystal
{"title":"Feasibility Analysis of Phenotype Quantification from Unstructured Clinical Interactions.","authors":"Daniel S Barron, Stephen Heisig, Carla Agurto, Raquel Norel, Brittany Quagan, Albert Powers, Michael L Birnbaum, Todd Constable, Guillermo Cecchi, John H Krystal","doi":"10.5334/cpsy.78","DOIUrl":null,"url":null,"abstract":"<p><p>We conducted a feasibility analysis to determine the quality of data that could be collected ambiently during routine clinical conversations. We used inexpensive, consumer-grade hardware to record unstructured dialogue and open-source software tools to quantify and model face, voice (acoustic and language) and movement features. We used an external validation set to perform proof-of-concept predictive analyses and show that clinically relevant measures can be produced without a restrictive protocol.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":" ","pages":"1-7"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11104416/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational psychiatry (Cambridge, Mass.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5334/cpsy.78","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We conducted a feasibility analysis to determine the quality of data that could be collected ambiently during routine clinical conversations. We used inexpensive, consumer-grade hardware to record unstructured dialogue and open-source software tools to quantify and model face, voice (acoustic and language) and movement features. We used an external validation set to perform proof-of-concept predictive analyses and show that clinically relevant measures can be produced without a restrictive protocol.