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NLP Based Automated Screening Tools for Alzheimer’s Disease
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science.
2022 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Severely life-impairing and often lethal dementia illnesses such as Alzheimer’s disease are of the greatest medical interest. And while a cure might yet be years in the future, there are immense benefits to gain from detecting disease debut as early as possible, from both an individual and a societal perspective. In this study we explore new approaches to Alzheimer’s screening, utilizing the recent technology leaps within natural language processing and automated speech recognition. We propose a digital, mobile application based platform for psychometric data collection that can be used by patients and research participants in a non-clinical environment. In particular, we implement automated versions of two well-recognized psychometric tests for Alzheimer’s screening: the Verbal Learning Test and the Story Recall Task. We perform a qualitative evaluation of results from 46 sessions of these tests, as well as a semi-structured interview with a clinician, and find automated psychometric tools promising for future endeavors within Alzheimer’s screening, but that the method has inherent difficulties that needs to be counteracted. We also discuss the potential economic upsides with automating parts of the screening and diagnosis processes for dementia related diseases, and conclude that there are massive savings to make – up to 600 million SEK yearly in Sweden alone.

Abstract [sv]

Kraftigt livshämmande och ofta dödliga demenssjukdomar som Alzheimers är av stort medicinskt intresse. Och medan ett botemedel ännu kan vara långt borta finns det stora fördelar att dra från tidigare upptäckande av sjukdomens debut, ur både ett individuellt och ett samhälleligt perspektiv. I den här studien utforskar vi nya tillvägagångssätt för screening av Alzheimers och drar nytta av nya framsteg inom natural language processing och automated speech recognition. Vi föreslår en digital, mobilapplikations-baserad plattform för psykometrisk datainsamling, som kan användas av patienter och forskningsdeltagare i en icke-klinisk miljö. Rent konkret implementerar vi automatiserade versioner av två vedertagna psykometriska tester för Alzheimers-screening: Verbal Learning Test och Story Recall Task. Vi utför en kvalitativ evaluering av resultaten från 46 sessioner av dessa tester samt en semistrukturerad intervju med en kliniker, och finner att automatiserade psykometriska verktyg är lovande för framtida ansträngningar inom Alzheimers-screening, men att metoden har inneboende svårigheter som måste motarbetas. Vi diskuterar även de potentiella ekonomiska fördelarna med att automatisera delar av screening- och diagnosticeringsprocesserna för demensrelaterade sjukdomar, och kommer fram till att det finns massiva besparingar att göra – uppåt 600 miljoner kronor årligen bara i Sverige.

Place, publisher, year, edition, pages
2022. , p. 11
Series
TRITA-EECS-EX ; 2022:346
Keywords [en]
alzheimer’s, dementia, digital, screening, nlp
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-319362OAI: oai:DiVA.org:kth-319362DiVA, id: diva2:1699777
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Examiners
Available from: 2022-10-03 Created: 2022-09-28 Last updated: 2022-10-03Bibliographically approved

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CiteExportLink to record
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