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Hierarchical forecasting of hospital admissions

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Introduction Visualization 1. Trend 2. Seasonality Trend and seasonality 3. Anomaly Conclusion Introduction The aim of this series of blogs is to do time series forecasting with libraries that conform to tidyverse principles and there are two of these time series meta-packages modeltime which is created to be the time series equivalent of tidymodels fpp3 which is created to do tidy time series and has been nicknamed the tidyverts.

Predicting pneumonia outcomes: Results (using DataRobot API)

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This post is a supplementary material for an assignment. The assignment is part of the Augmented Machine Learning unit for a Specialised Diploma in Data Science for Business. The aim of this project is to classify if patients with Community Acquired Pneumonia (CAP) became better after seeing a doctor or became worse despite seeing a doctor. Previously, EDA (part1, part 2)and feature enginnering was done in R.

Predicting pneumonia outcomes: Modelling via DataRobot API

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This post is a supplementary material for an assignment. The assignment is part of the Augmented Machine Learning unit for a Specialised Diploma in Data Science for Business. The aim of this project is to classify if patients with Community Acquired Pneumonia (CAP) became better after seeing a doctor or became worse despite seeing a doctor. Previously, EDA (part1, part 2)and feature enginnering was done in R.

Predicting pneumonia outcomes: Feature Engineering

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Intro This post is a supplementary material for an assignment. The assignment is part of the Augmented Machine Learning unit for a Specialised Diploma in Data Science for Business. The aim of the assignment is to use DataRobot for predictive modelling. Exploratory data analysis and feature engineering will be done here in R before the data is imported into DataRobot. The aim of this project is to classify if patients with Community Acquired Pneumonia (CAP) became better after seeing a doctor or became worse despite seeing a doctor.

Predicting pneumonia outcomes: EDA part 2

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Intro 9 Lab_ related category Missing Lab values Outlier Lab values High Lab_Hb levels Low Lab_Neu High Lab_Sugar 10 CS_ cultures related category 11 Abx_ antibiotics related category 11i Class of empirical antibiotics given 11ii Antibiotics given Number of antibiotics given 11iii Duration of antibiotics 12 Care_ continuum of care status category replace 99 Admission status Breathing aid 13 V_ vaccine related category Wrap up This post is a supplementary material for an assignment.