A systematic literature review of automatic Alzheimer's disease detection from speech and language.

OBJECTIVE: In recent years numerous studies have achieved promising results in Alzheimer's Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research. MATERIALS AND METHODS: We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved? RESULTS AND DISCUSSION: We identified 33 eligible studies and 5 main findings: participants' demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups. CONCLUSION: The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics

The first step in the development of Text Mining technology for Cancer Risk Assessment: identifying and organizing scientific evidence in risk assessment literature.

BACKGROUND: One of the most neglected areas of biomedical Text Mining (TM) is the development of systems based on carefully assessed user needs. We have recently investigated the user needs of an important task yet to be tackled by TM -- Cancer Risk Assessment (CRA). Here we take the first step towards the development of TM technology for the task: identifying and organizing the scientific evidence required for CRA in a taxonomy which is capable of supporting extensive data gathering from biomedical literature. RESULTS: The taxonomy is based on expert annotation of 1297 abstracts downloaded from relevant PubMed journals. It classifies 1742 unique keywords found in the corpus to 48 classes which specify core evidence required for CRA. We report promising results with inter-annotator agreement tests and automatic classification of PubMed abstracts to taxonomy classes. A simple user test is also reported in a near real-world CRA scenario which demonstrates along with other evaluation that the resources we have built are well-defined, accurate, and applicable in practice. CONCLUSION: We present our annotation guidelines and a tool which we have designed for expert annotation of PubMed abstracts. A corpus annotated for keywords and document relevance is also presented, along with the taxonomy which organizes the keywords into classes defining core evidence for CRA. As demonstrated by the evaluation, the materials we have constructed provide a good basis for classification of CRA literature along multiple dimensions. They can support current manual CRA as well as facilitate the development of an approach based on TM. We discuss extending the taxonomy further via manual and machine learning approaches and the subsequent steps required to develop TM technology for the needs of CRA.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

3015 u-verbiä

Kirja-arvioRäisänen, Alpo: Suomen kielen u-johtimiset verbitKielenainekset-u- (kieli: suomi, sivulla: 261-)-utu- (kieli: suomi, sivulla: 264)-Vntu- (kieli: suomi, sivulla: 264)astua (kieli: suomi, sivulla: 263)asua (kieli: suomi, sivulla: 263

Etymologiaa Etelä-Euroopasta

Kirja-arvioMalkiel, Yakov: Yakov Malkiel Etymolog

Lehmuksen (ja vähän erään kotieläimenkin) nimestä

Kielenaineksetlehmeri (kieli: suomi, sivulla: 335)lehmottaa (kieli: suomi, sivulla: 335)lehmus (kieli: suomi, sivulla: 334-336)lehmuskoivu (kieli: suomi, sivulla: 335)lehmuskuusi (kieli: suomi, sivulla: 334)lehmuskuusi (kieli: suomi, sivulla: 335)lehmusmänty (kieli: suomi, sivulla: 335)lehmä (kieli: suomi, sivulla: 336)lehmältäinen (kieli: suomi, sivulla: 335)lehnakka (kieli: suomi, sivulla: 335)lesmus (kieli: suomi, sivulla: 335)lõhme (kieli: viro, sivulla: 334)lõhmus (kieli: viro, sivulla: 334)lumi (kieli: suomi, sivulla: 335)löhmys (kieli: suomi, sivulla: 335)löhmäkkä, löhnäkkä (kieli: suomi, sivulla: 335)nauta (kieli: suomi, sivulla: 336)niini (kieli: suomi, sivulla: 334-336)niinipuu (kieli: suomi, sivulla: 334)niinipuu (kieli: suomi, sivulla: 336)pärnä (kieli: suomi, sivulla: 334)vennä (kieli: suomi, sivulla: 336

Tuore näkemys unkarin taivutusmorfologiasta

Kirja-arvioAbondolo, Daniel Mario: Hungarian inflectional morphologyKielenaineksetetuprosodinen (kieli: suomi, sivulla: 254)juuri (kieli: suomi, sivulla: 254)koodi (kieli: suomi, sivulla: 254)subjektikonjugaatio (kieli: suomi, sivulla: 255)takaprosodinen (kieli: suomi, sivulla: 254

Ovatko reuna ja leuka sittenkin balttilaisia lainasanoja?

Are the Finnish words reuna 'edge' and leuka 'chin' of Baltic origin after all? (englanti)Kielenaineksetkeula, kepla (kieli: suomi, sivulla: 84)kuka (kieli: suomi, sivulla: 84-)lìwuga (kieli: viro, sivulla: 84)lõõga (kieli: viro, sivulla: 84)lõug (kieli: viro, sivulla: 84)neula, niekla (kieli: suomi, sivulla: 84)peikalo, peukalo (kieli: suomi, sivulla: 84)peukalo (kieli: suomi, sivulla: 84)peura (kieli: suomi, sivulla: 84)reuna (kieli: suomi, sivulla: 84-)rõun (kieli: viro, sivulla: 84)seula, siekla (kieli: suomi, sivulla: 84)seura (kieli: suomi, sivulla: 84)sõber (kieli: viro, sivulla: 84)teuras (kieli: suomi, sivulla: 84)tõbras (kieli: viro, sivulla: 84

Refleksiiviverbien semantiikkaa

Kirja-arvioKoivisto, Vesa: Suomen verbikantaisten UtU-verbijohdosten semantiikka