Tag Archives: ggplot2

Genetic algorithms: a simple R example

Genetic algorithm is a search heuristic. GAs can generate a vast number of possible model solutions and use these to evolve towards an approximation of the best solution of the model. Hereby it mimics evolution in nature.

GA generates a population, the individuals in this population (often called chromosomes) have a given state. Once the population is generated, the state of these individuals is evaluated and graded on their value. The best individuals are then taken and crossed-over – in order to hopefully generate 'better' offspring – to form the new population. In some cases the best individuals in the population are preserved in order to guarantee 'good individuals' in the new generation (this is called elitism).

The GA site by Marek Obitko has a great tutorial for people with no previous knowledge on the subject.

To explain the example I will use my version of the Knapsack problem.

You are going to spend a month in the wilderness. You’re taking a backpack with you, however, the maximum weight it can carry is 20 kilograms. You have a number of survival items available, each with its own number of “survival points”. You’re objective is to maximize the number of survival points.

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August 1, 2012Permalink 22 Comments

Evolution average number of beds per hospital

The OECD collects (among a lot of other statistics) information on the number of hospitals and hospital beds per country. These two parameters combined and its evolution over the years could give an indication on whether or not the country’s hospital landscape is evolving towards large medical centers, small scale hospital settings or whether there is no trend to detect.
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July 27, 2012Permalink 3 Comments

Estimating required hospital bed capacity

Estimating required hospital bed capacity requires a thorough analysis. There are a lot of ways of approaching a capacity requirement problem, but I think we can agree that a simple spreadsheet analysis just won't cut it.

The approach described in this post makes use of discrete-event simulation and, just to clarify, makes abstraction from a lot of variables which should be taken into consideration in a real-life analysis.

To explain the approach, the following case will be used:

An emergency department of a small regional hospital receives complains about its emergency admission capacity. After investigation of its admission data it becomes clear that their service level is not up to par with that of other hospitals. Therefore, plans for the redesign of the emergency department and an investment in its emergency bed capacity are presented. The plan proposes a new bed capacity of 12 beds (coming from a previous of 10 beds). The Chief of Medicine wants to know what the effect of this investment will be on their emergency admission service level.

The emergency department has recorded data on the interarrival times of patients that are admitted (or should be admitted) to an emergency bed. The following graph shows the interarrival distribution (triangular: mode=5, min=.1, max=12):

plot of chunk unnamed-chunk-1

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July 23, 2012Permalink Leave a comment

Using discrete-event simulation to simulate hospital processes

Discrete-event simulation is a very useful tool when it comes to simulating alternative scenario’s for current of future business operations. Let’s take the following case;

Patients of an outpatient diabetes clinic are complaining about long waiting times, this seems to have an adverse effect on patient satisfaction and patient retention. Today, one doctor runs the outpatient clinic. Hospital management has decided that in order to increase patient retention rates, a nurse will be hired to assist and take over some of the doctor’s tasks. This should result in decreased waiting times and, hopefully, increased patient retention rates.

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July 12, 2012Permalink Leave a comment