IntroductionHeart Failure (HF) is a major health and economic issue worldwide. HF-related expense... more IntroductionHeart Failure (HF) is a major health and economic issue worldwide. HF-related expenses are largely driven by hospital admissions and re-admissions, many of which are potentially preventable. Current self-management programs, however, have failed to reduce hospital admissions. This may be explained by their low predictive power for decompensation and high adherence requirements. Slight alterations in the voice profile may allow to detect decompensation in HF patients at an earlier stage and reduce hospitalizations. This pilot study investigates the potential of voice as a digital biomarker to predict health status deterioration in HF patients.Methods and analysisIn a two-month longitudinal observational study, we collect voice samples and HF-related quality-of-life questionnaires from 35 stable HF patients. Patients use our developed study application installed on a tablet at home during the study period. From the collected data, we use signal processing to extract voice ...
ABSTRACTA long-standing effort in biology is to precisely define and group phenotypes that charac... more ABSTRACTA long-standing effort in biology is to precisely define and group phenotypes that characterize a biological process, and the genes that underpin them. In Saccharomyces cerevisiae and other organisms, functional screens have generated rich lists of phenotypes associated with individual genes. However, it is often challenging to identify sets of phenotypes and genes that are most closely associated with a given biological process. Here, we focused on the 166 phenotypes arising from loss-of-function and the 86 phenotypes from gain-of-function mutations in 571 genes currently assigned to cell cycle-related ontologies in S. cerevisiae. To reduce this complexity, we applied unbiased, computational approaches of correspondence analysis to identify a minimum set of phenotypic variables that accounts for as much of the variability in the data as possible. Loss-of-function phenotypes can be reduced to 20 dimensions, while gain-of-function ones to 14 dimensions. We also pinpoint the c...
IntroductionHeart Failure (HF) is a major health and economic issue worldwide. HF-related expense... more IntroductionHeart Failure (HF) is a major health and economic issue worldwide. HF-related expenses are largely driven by hospital admissions and re-admissions, many of which are potentially preventable. Current self-management programs, however, have failed to reduce hospital admissions. This may be explained by their low predictive power for decompensation and high adherence requirements. Slight alterations in the voice profile may allow to detect decompensation in HF patients at an earlier stage and reduce hospitalizations. This pilot study investigates the potential of voice as a digital biomarker to predict health status deterioration in HF patients.Methods and analysisIn a two-month longitudinal observational study, we collect voice samples and HF-related quality-of-life questionnaires from 35 stable HF patients. Patients use our developed study application installed on a tablet at home during the study period. From the collected data, we use signal processing to extract voice ...
ABSTRACTA long-standing effort in biology is to precisely define and group phenotypes that charac... more ABSTRACTA long-standing effort in biology is to precisely define and group phenotypes that characterize a biological process, and the genes that underpin them. In Saccharomyces cerevisiae and other organisms, functional screens have generated rich lists of phenotypes associated with individual genes. However, it is often challenging to identify sets of phenotypes and genes that are most closely associated with a given biological process. Here, we focused on the 166 phenotypes arising from loss-of-function and the 86 phenotypes from gain-of-function mutations in 571 genes currently assigned to cell cycle-related ontologies in S. cerevisiae. To reduce this complexity, we applied unbiased, computational approaches of correspondence analysis to identify a minimum set of phenotypic variables that accounts for as much of the variability in the data as possible. Loss-of-function phenotypes can be reduced to 20 dimensions, while gain-of-function ones to 14 dimensions. We also pinpoint the c...
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Papers by I Fan Wu