Uncovering ecological state dynamics with hidden Markov models
Ecological systems can often be characterised by changes among a finite set of underlying
states pertaining to individuals, populations, communities or entire ecosystems through time …
states pertaining to individuals, populations, communities or entire ecosystems through time …
Multi-state models for panel data: the msm package for R
C Jackson - Journal of statistical software, 2011 - jstatsoft.org
Panel data are observations of a continuous-time process at arbitrary times, for example,
visits to a hospital to diagnose disease status. Multi-state models for such data are generally …
visits to a hospital to diagnose disease status. Multi-state models for such data are generally …
How patients with multiple sclerosis acquire disability
Patients with multiple sclerosis acquire disability either through relapse-associated
worsening (RAW) or progression independent of relapse activity (PIRA). This study …
worsening (RAW) or progression independent of relapse activity (PIRA). This study …
Doctor ai: Predicting clinical events via recurrent neural networks
E Choi, MT Bahadori, A Schuetz… - Machine learning for …, 2016 - proceedings.mlr.press
Leveraging large historical data in electronic health record (EHR), we developed Doctor AI,
a generic predictive model that covers observed medical conditions and medication uses …
a generic predictive model that covers observed medical conditions and medication uses …
[HTML][HTML] Predicting healthcare trajectories from medical records: A deep learning approach
Personalized predictive medicine necessitates the modeling of patient illness and care
processes, which inherently have long-term temporal dependencies. Healthcare …
processes, which inherently have long-term temporal dependencies. Healthcare …
: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive systems from
electronic medical records. At present, an important missing element is detecting predictive …
electronic medical records. At present, an important missing element is detecting predictive …
Deepcare: A deep dynamic memory model for predictive medicine
Personalized predictive medicine necessitates modeling of patient illness and care
processes, which inherently have long-term temporal dependencies. Healthcare …
processes, which inherently have long-term temporal dependencies. Healthcare …
[图书][B] Hidden Markov models for time series: an introduction using R
W Zucchini, IL MacDonald - 2009 - taylorfrancis.com
Reveals How HMMs Can Be Used as General-Purpose Time Series ModelsImplements all
methods in RHidden Markov Models for Time Series: An Introduction Using R applies …
methods in RHidden Markov Models for Time Series: An Introduction Using R applies …
[HTML][HTML] Predictive modeling of the progression of Alzheimer's disease with recurrent neural networks
The number of service visits of Alzheimer's disease (AD) patients is different from each other
and their visit time intervals are non-uniform. Although the literature has revealed many …
and their visit time intervals are non-uniform. Although the literature has revealed many …
Unsupervised learning of disease progression models
Chronic diseases, such as Alzheimer's Disease, Diabetes, and Chronic Obstructive
Pulmonary Disease, usually progress slowly over a long period of time, causing increasing …
Pulmonary Disease, usually progress slowly over a long period of time, causing increasing …