Marknadens största urval
Snabb leverans

Computational Estimation and Prediction in Public Health

Om Computational Estimation and Prediction in Public Health

Using computational approaches utilizing large datasets to investigate public health information is an important mechanism for institutions seeking to identify strategies for improving public health. The art in computational approaches, for example in health research, is managing the trade-offs between the two perspectives: first, inference and s econd, p rediction. Many techniques from statistical methods (SM) and machine learning (ML) may, in principle, be used for both perspectives. However, SM has a well established focus on inference by building probabilistic models which allows us to determine a quantitative measure of confidence about the magnitude of the effect. Simulation-based validation approaches can be used in conjunction with SM to explicitly verify assumptions and redefine t he specified model, if n ecessary. On the other hand, ML uses general-purpose algorithms to find p atterns t hat b est p redict t he o utcome and makes minimal assumptions about the data-generating process; and may be more effective in a number of situations. My work employs both SM- and ML- based computational approaches to investigate particular public health problems. Chapter One provides philosophical background and compares the application of the two approaches in public health. Chapter Two describes and implements penalized Cox proportional hazard models for time-varying covariates time-to-event data. Chapter Three applies traditional survival models and machine learning algorithms to predict survival times of cancer patients, while incorporating the information about the time-varying covariates. Chapter Four discusses and implements various approaches for computing predictions and effects for generalized linear (mixed) models. Finally, Chapter Five implements and compares various statistical models for handling univariate and multivariate binary outcomes for water, sanitation and hygiene (WaSH) data.

Visa mer
  • Språk:
  • Engelska
  • ISBN:
  • 9798869082787
  • Format:
  • Häftad
  • Sidor:
  • 136
  • Utgiven:
  • 18 December 2023
  • Mått:
  • 152x8x229 mm.
  • Vikt:
  • 209 g.
  Fri leverans
Leveranstid: 2-4 veckor
Förväntad leverans: 11 Juni 2024

Beskrivning av Computational Estimation and Prediction in Public Health

Using computational approaches utilizing large datasets to investigate public health
information is an important mechanism for institutions seeking to identify strategies
for improving public health. The art in computational approaches, for example
in health research, is managing the trade-offs between the two perspectives:
first, inference and s econd, p rediction. Many techniques from statistical methods
(SM) and machine learning (ML) may, in principle, be used for both perspectives.
However, SM has a well established focus on inference by building probabilistic
models which allows us to determine a quantitative measure of confidence about
the magnitude of the effect. Simulation-based validation approaches can be used
in conjunction with SM to explicitly verify assumptions and redefine t he specified
model, if n ecessary. On the other hand, ML uses general-purpose algorithms
to find p atterns t hat b est p redict t he o utcome and makes minimal assumptions
about the data-generating process; and may be more effective in a number of situations.
My work employs both SM- and ML- based computational approaches to
investigate particular public health problems. Chapter One provides philosophical
background and compares the application of the two approaches in public health.
Chapter Two describes and implements penalized Cox proportional hazard models
for time-varying covariates time-to-event data. Chapter Three applies traditional
survival models and machine learning algorithms to predict survival times of cancer
patients, while incorporating the information about the time-varying covariates.
Chapter Four discusses and implements various approaches for computing predictions
and effects for generalized linear (mixed) models. Finally, Chapter Five
implements and compares various statistical models for handling univariate and
multivariate binary outcomes for water, sanitation and hygiene (WaSH) data.

Användarnas betyg av Computational Estimation and Prediction in Public Health



Hitta liknande böcker
Boken Computational Estimation and Prediction in Public Health finns i följande kategorier:

Gör som tusentals andra bokälskare

Prenumerera på vårt nyhetsbrev för att få fantastiska erbjudanden och inspiration för din nästa läsning.