Literature DB >> 15694621

Automated spoken dialogue system for hypertensive patient home management.

Toni Giorgino1, Ivano Azzini, Carla Rognoni, Silvana Quaglini, Mario Stefanelli, Roberto Gretter, Daniele Falavigna.   

Abstract

Recent advances in automatic speech recognition and related technologies allow computers to carry on conversations by telephone. We developed an intelligent dialogue system that interacts with hypertensive patients to collect data about their health status. Patients thus avoid the inconvenience of traveling for frequent face to face visits to monitor the clinical variables they can easily measure at home; the physician is facilitated in acquiring patient information and cardiovascular risk, which is evaluated from the data according to noted guidelines. Controlled trials to assess the clinical efficacy are under way.

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Year:  2005        PMID: 15694621     DOI: 10.1016/j.ijmedinf.2004.04.026

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  6 in total

1.  Development of a health management support system for patients with diabetes mellitus at home.

Authors:  Shoko Tani; Terutaka Marukami; Atsuko Matsuda; Akiko Shindo; Keiko Takemoto; Hiroshi Inada
Journal:  J Med Syst       Date:  2010-06       Impact factor: 4.460

2.  Telemedicine and diabetes management: current challenges and future research directions.

Authors:  Riccardo Bellazzi
Journal:  J Diabetes Sci Technol       Date:  2008-01

Review 3.  The Personalization of Conversational Agents in Health Care: Systematic Review.

Authors:  Ahmet Baki Kocaballi; Shlomo Berkovsky; Juan C Quiroz; Liliana Laranjo; Huong Ly Tong; Dana Rezazadegan; Agustina Briatore; Enrico Coiera
Journal:  J Med Internet Res       Date:  2019-11-07       Impact factor: 5.428

4.  Conversational agents in healthcare: a systematic review.

Authors:  Liliana Laranjo; Adam G Dunn; Huong Ly Tong; Ahmet Baki Kocaballi; Jessica Chen; Rabia Bashir; Didi Surian; Blanca Gallego; Farah Magrabi; Annie Y S Lau; Enrico Coiera
Journal:  J Am Med Inform Assoc       Date:  2018-09-01       Impact factor: 4.497

5.  Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks.

Authors:  Kazuyuki Matsumoto; Manabu Sasayama; Taiga Kirihara
Journal:  Sensors (Basel)       Date:  2022-01-17       Impact factor: 3.576

6.  Automated conversational agents for post-intervention follow-up: a systematic review.

Authors:  L Geoghegan; A Scarborough; J C R Wormald; C J Harrison; D Collins; M Gardiner; J Bruce; J N Rodrigues
Journal:  BJS Open       Date:  2021-07-06
  6 in total

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