Using Genetic Algorithms to Optimise Current and Future Health Planning

From a publication of 28/10/2010, of the authors Saoshi Sasaki, Alexis J Comber, Hiroshi Suzuki and Chris Brunsdon:

Abstract

Background: Ambulance response time is a crucial factor in patient survival. The number of emergency cases (EMS cases) requiring an ambulance is increasing due to changes in population demographics. This is decreasing ambulance response times to the emergency scene. This paper predicts EMS cases for 5-year intervals from 2020, to 2050 by correlating current EMS cases with demographic factors at the level of the census area and predicted population changes. It then applies a modified grouping genetic algorithm to compare current and future optimal locations and numbers of ambulances. Sets of potential locations were evaluated in terms of the (current and predicted) EMS case distances to those locations.

Results: Future EMS demands were predicted to increase by 2030 using the model (R2 = 0.71). The optimal locations of ambulances based on future EMS cases were compared with current locations and with optimal locations modelled on current EMS case data. Optimising the location of ambulance stations locations reduced the average response times by 57 seconds. Current and predicted future EMS demand at modelled locations were calculated and compared.

Conclusions: The reallocation of ambulances to optimal locations improved response times and could contribute to higher survival rates from life-threatening medical events. Modelling EMS case ‘demand’ over census areas allows the data to be correlated to population characteristics and optimal ‘supply’ locations to be identified. Comparing current and future optimal scenarios allows more nuanced planning decisions to be made. This is a generic methodology that could be used to provide evidence in support of public health planning and decision making.
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සුබස ඉඟිය

සුබස ඉඟිය යනු ෆයර්ෆොක්ස් (Firefox) වෙබ් බ්‍රව්සරය හෝ තන්ඩබර්ඩ් (Thunderbird) මෘදුකාංගය සඳහා ඈඳිය හැකි, සිංහල ඔබට බොහෝ ප්‍රයෝජනවත් මෘදුකාංගයකි. එමගින් මූලිකව ම සිදු කරනුයේ ඔබ කියවන ඉංග්‍රීසි වෙබ් පිටුවල ඇති නොතේරෙන වදන්හි සිංහල තේරුම අවම ආයාසයකින් බලා ගැනීමට සැලැස්වීමයි. මෙම ඈඳුම ෆයර්ෆොක්ස් බ්‍රව්සරයේ හෝ තන්ඩබර්ඩ්හි ස්ථාපිත කිරීමෙන් පසු, ඔබ කළ යුත්තේ ඔබට නොතේරෙන ඉංග්‍රීසි වදන මත මූසික දර්ශකය (mouse pointer) රැඳවීම පමණි. ක්‍ෂණිකව ඉංග්‍රීසි වදනට අදාළ සිංහල තේරුම එම වදනට පහළින් දිස්වෙනු ඇත. ඉංග්‍රීසි වදනින් මූසික දර්ශකය ඉවත් කර ගත් වහා ම තේරුම අදෘශ්‍යමාන වේ. එනම් යම් වෙබ් පිටුවක අන්තර්ගතය කියවීමට කිසිම බාධාවක් නොමැති ව නොතේරන ඉංග්‍රීසි වදන්වල සිංහල තේරුම් බලා ගැනීමට මෙම මෘදුකාංගය ඉඩ ප්‍රස්ථාව සලස්වා දෙයි. ඈඳුම් මෘදුකාංගය ස්ථාපනය කර ගත් දා පටන් අන්තර්ජාලයේ ඉංග්‍රීසි බසින් ඇති වෙබ් පිටු කියවීමේ දී ඉංග්‍රීසි-සිංහල ශබ්දකෝෂයක් භාවිත කිරීමට අවශ්‍ය නොවේ.

වැඩි විස්තර සඳහා සුබස ඉඟිය‍ට හෝ ෆයර්ෆොක්ස් වල ස්ථාපනය කිරීම සඳහා Add-on එකට යන්න

Picking a cricket 11 via genetic algorithm

‘Cricket team selection using Genetic Algorithm’. This is the title of a paper accepted by the International Scientific Committee of the International Congress on Sports Dynamics held in Melbourne on September 1. The paper was presented by S N Omkar, the yoga guru of the Indian cricket team.

Omkar believes his procedure can help pick a team of 15 or so from the hundreds of aspirants. The theory, formulated along with R Verma of Regional Engineering College, Warangal, takes into account performance and fitness data of players and gives an optimised and balanced team with right number of batsmen, bowlers and wicket-keepers without any human interference.

The other paper submitted by Omkar dwells on ‘Yogic Sun salutation for better cricketing’. The Bangalore-based IISc scientist explains his work in an exclusive interview to The Times of India . Excerpts:

Could you explain in simple terms the process of team selection through genetic algorithm?

Genetic algorithms are inspired by Darwin’s theory of evolution. Simply said, problems are solved by an evolutionary process resulting in a best (fittest) solution (survivor). In other words, the solution is evolved.

The fitness of an organism is measured by its success in its life (survival). Algorithm begins with a set of solutions — represented by chromosomes — called population. In the context of the current problem, each population would consist of team cricketers randomly picked from the probables list. Several such teams, say 2000, are formed. A fitness function is evaluated for each team. The fitness function is based on the performance, health condition, experience, etc., of every member in the team. Constraints like number of spinners or fast bowlers required, scope for new players etc., can also be included.

Based on the fitness of each team, new teams with better fitness values are formed by using some operations like crossover and mutation. The new generation of teams thus formed are most likely to be better than the previous ones. This process of forming new generation of teams is continued until a best fitness value is obtained.

How is this procedure different from an evaluation of batting, bowling fielding performances with the help of statistics? What makes it foolproof?

Genetic algorithms belong to the class of stochastic search methods. It optimises the fitness function chosen, thus differing from statistics. Genetic algorithms operate on a population of solutions and in many practical problems they seem to yield good results.

Take for instance a batsman A (somebody like Srikkanth), who is flashy and wants to strike every ball, and musters up an average of 40. And another batsman B (somebody like Shiv Sundar Das perhaps) who is cautious and judicious, but has a batting average of 35. Let’s assume other factors are equal. In this case, how does the method work? Who would gain entry into the team?

This can be taken care while defining the fitness or objective function. For example, objective function can include a parameter (strike rate) and more weightage can be given in case one is selecting the team for one-dayers.

Apart from this one can also include factors like performance of any individual against a particular team (within the country /abroad) in the past. Also, an individual’s performance on a certain pitch can also be included in the fitness function.

Does this method take into consideration rival strengths too? This is vital in a ‘human’ selection procedure. For example, if Australia has five left-handers, would we go in for two off-spinners? Again, what kind of spinners __ finger spinners or tweakers?

It is possible. All these depend on how one defines the fitness or objective function.

Can this method evaluate who’s the better bat — Sachin Tendulkar and Brian Lara?

This method is used for optimization.

ගූගල් ට්‍රාන්ස්ලිටරේෂන්

ඔබ මීට පෙර  ගූගල්  විසින් සපයන  ට්‍රාන්ස්ලිටරේෂන් භාවිතා  කර  තිබේද? මෙය සිංහලෙන්  ටයිප් කිරීම  සඳහා  ඉතා හොඳ මෙවලමුකි. මෙම  ලිපිය ටයිප් කළේද මෙම  මෙවලුම භාවිත කිරීමෙනි. ඔබ මේ සඳහා  කල යුත්තේ  සිංහලෙන්  ටයිප් කිරීමට  අවශ්‍යය  වචනය, එහි  ශබ්දය සෑදෙන ආකාරයට ඉංග්‍රීසි අකුරුවලින් ටයිප් කිරීමෙනි.  උදාහරණයක්  ලෙස ‘විවෘත’ වචනය ගනිමු. මෙම  වචනය ශබ්ද වෙන ආකාරයට ගූගල්ට්‍රාන්ස්ලිටරේෂන් වලදී ඉංග්‍රීසි අකුරුවලින් ටයිප් කරන්නේ ‘wiwrutha’  ලෙසය. සමහරක් අමාරු වචන මුලදී ටයිප් කිරීමට අපහසු වුවද පුහුණුවක් ලැබූ පසු මෙය ඉතා පහසු වේ.

ගූගල් ට්‍රාන්ස්ලිටරේෂන් භාවිත කිරීමට ක්‍රම දෙකක් තිබේ.

    1. http://www.google.com/transliterate/Sinhalese වෙබ් ලිපිනයට ගොස් එහි ඇති notepad එක භාවිත කර ඔබට අවශ්‍ය ලිපිය ටයිප් කර අවශ්‍ය තැනට පිටපත් කරගැනීම. මේ සඳහා ඉන්ටර්නෙට් පහසුකම ඔබගේ පරිගණකයේ තිබිය යුතුය.
    1. http://www.google.com/transliterate/Sinhalese වෙබ් පිටුවේ ඉහළ දකුණුකෙළවරේ ඇති ‘Google Transliteration IME’ බාගත කොට ඔබගේ පරිගණකයට ස්ථාපනය කිරීම. මෙම මෘදුකාංගය ස්ථාපනය කළවිට  ඔබට  අවශ්‍ය වැඩසටහනට ගොස්  (උදා MS Word)  ඔබගේ පරිගණකයේ  keyboard setting එක Google Transliteration වලට මාරු කිරීම මඟින් එම වැඩසටහනේදී  සිංහලෙන් ටයිප්  කල හැකිය.

මේ පිළිබඳ වැඩි විස්තර Google IME හා Installing Google IME to type in Sinhala  මඟින් ලබා  ගත හැකිය