Swarm Intelligence for Medical Treatment Optimisation Prof. John McCall Agenda • • • • • Concepts of swarm intelligence Particle Swarm Optimisation for cancer chemotherapy design Medical Data Modelling and metaheuristic approaches Ant Colony Optimisation for medical data modelling Open Questions Natural Swarms • • • Large numbers of living organisms acting together as a group – Individual actions, social communication Many and varied natural examples: – Flocking and migrating birds – Colonies of bees, wasps, hornets, ants and termites – Swarming insects such as bees and locusts – Schooling fish – Herd animals (migration, protection and stampedes) – Football crowds (Mexican wave), peace marchers, riots Powerful Emergent behaviour – Swarm has capabilities far beyond those of the individual – In particular, can exhibit mass problem-solving intelligence The Naked Jungle (1954) Them (1954) Piranha (1978) The Birds (1963) (1981) Phase IV (1974) Swarm intelligence metaheuristics Sample search space Swarm Activity Social interaction Agenda • • • • • Concepts of swarm intelligence Particle Swarm Optimisation for cancer chemotherapy design Medical Data Modelling and metaheuristic approaches Ant Colony Optimisation for medical data modelling Open Questions Particle Swarm Optimisation (PSO) • • • • • swarm of individual “particles” – distributed in a search space – each located at a solution particles move – each particle has a velocity – each moves to a new solution at each step particles communicate – share information about good locations memory – memory of previous good locations particles swarm towards good solutions Particle Swarm Optimisation • each particle has a: – position, x (solution currently being examined) – velocity, v (direction and speed of motion) – memory of “best” position wrt objectives • swarm remembers best known position(s) • Swarm moves synchronously over a series of timesteps • Each particle updates x and v based on current knowledge The PSO metaheuristic Evaluate current positions Change velocities and positions Update and exchange information particle swarm optimisation 1. 2. 3. 4. initialise swarm random positions, zero velocities, best = initial update the swarm 1. update the positions 2. evaluate current positions 3. update the memory (best positions) 4. update the velocities if not stopping condition do 2. stop and return population and memory swarm update rules update the velocities* vik 1 wv ik c1r1 xi* xik c2 r2 xi** xik particle best bias inertia global/nbd best bias * velocities are typically “clamped” within maximum possible values update the positions k 1 i x k 1 i x v k i swarm topologies • global topology – all particles intercommunicate – the best position found by any particle is communicated to the entire swarm – gbest (position, value) • neighbour topology – particles communicate with neighbours – best position known to neighbours is shared – nbest (position, value) swarm topologies 5 5 2 4 7 6 2 4 3 7 4 6 1 neighbour best topology 4 3 1 global best topology particle swarm visualisation • Java applet by Mark Sinclair – code available for download – PSO Java Applet v1.0 – November 2006 – http://uk.geocities.com/markcsinclair/pso.html • Visualises a 2-D test function – Schaffer F5 function • global optimum marked by a blue cross • current global best marked by a red dot Swarm visualisation Cancer Chemotherapy • • • • Systemic treatment with toxic drugs Often used in combination with surgery and radiotherapy Attacks primary and secondary tumours Also attacks healthy tissues, leading to toxic side effects Chemotherapy Simulation potency response dN f N c ct N dt growth plasma conc. pop. size Objectives of Cancer Chemotherapy N(t) Nmax Nfinal Ncure T0 Tfinal PST t Minimise: final tumour size; overall tumour burden (shaded); side effects. Prolong the patient survival time Chemotherapy Constraints • Maximum instantaneous dose • Maximum cumulative dose g1 (c) Cmax j Cij 0 i 1, n, j 1, d • Maximum permissible size of the tumour • Restriction on the toxic side-effects n g 2 (c) Ccum j Cij 0 j 1, d i 1 g3 (c) N max N (ti ) 0 i 1, n d g 4 (c) Cs-eff k kj Cij 0 i 1, n, k 1, m j 1 Optimisation of Cancer Chemotherapy • Two optimisation objectives – tumour eradication – prolongation of the patient survival time • Decision vectors are the concentration levels of anti-cancer drugs in the bloodplasma C ij , i 1, n , j 1, d • State Equation n d dN j Cij H (t ti ) H (t ti 1 ) N (t ) ln dt N (t ) j 1 i 1 Experiments (Petrovski, Sudha, McCall 2004) • • • Search for feasible chemotherapies – All constraints met Compare Genetic Algorithm with – PSO Global Best – PSO Local Best Desirable properties – Find a feasible solution with 100% success rate – Find a feasible solution with as few tumour simulations as possible – Low variation in time taken to achieve satisfaction PSO Parameters Number of particles N 50 Topologies global Initial velocities Random in [0,2] Inertia coefficient Random. in [0.5, 1] , local with nbds of size 10 . Social and cognitive components c1 = c2 = 4 Velocity clamping |vmax| <=1 Probability of finding feasible solution Results 110 100 90 80 70 60 50 40 30 20 10 0 GA PSO (Gbest) PSO (Lbest) 0 100 200 300 Generations 400 500 600 Box plots of run length 600 550 500 29 450 400 350 300 250 200 150 100 13 28 50 0 -50 -100 N= 27 27 27 FGENSGA FGENSGB FGENNH GA G-best L-best Multi-objective optimisation x2 f2(x) f1(x) x1 Decision variable space Objective function space maximise F (x ) ( f1 (x ), f 2 (x ),, f k (x ))T subject t o G (x ) ( g1 (x ), g 2 (x ),, g m (x )) 0 Pareto Optimality f2(x) x2 Feasibility region Dominated region f1(x) • Pareto dominance x1 Non-dominated region x x, i.e. x dominatesx, iff i 1, , k fi (x) fi (x) j 1, , k f j (x) f j (x) • Pareto optimality x is Pareto - optimal if f x is non - dominated, i.e. x x x • Pareto optimal set (front) Multi-objective PSO (MOPSO) ( Coello Coello, Lechuga 2004) • Map particles onto objective space • Identify the non-dominated set • Store these positions • Assign other particles to these leaders geographically • Update particle veocities: – Inertia component – Cognitive component – Social component based on • Selection from repository • Avoiding crowding f2(x) f1(x) MOPSO for cancer chemotherapy optimisation Petrovski, McCall, Sudha (2009) Tumour reduction 1,20E-04 1,00E-04 8,00E-05 6,00E-05 4,00E-05 2,00E-05 0,00E+00 0 0,2 0,4 solutions0,6 0,8 MOPSO algorithm 1 A set of non-dominated found by the 1,2 1,4 Patient Survival • • • Good spread along Pareto front High quality global best solution Rapid discovery of feasible solutions Agenda • • • • • Concepts of swarm intelligence Particle Swarm Optimisation for cancer chemotherapy design Medical Data Modelling and metaheuristic approaches Ant Colony Optimisation for medical data modelling Open Questions Prostate Cancer Management • • • • Cancer of the prostate gland Affects late middle-aged to elderly men Second most common cause of cancer death of men Most common cancer in UK men Prostate cancer patient pathway • • Symptoms → Referral Initial testing – – • Scan and Biopsy – • Prostate Specific Antigen Digital Rectal Examination Gleason score Treatment choices – – – – Watchful waiting Hormone therapy Radiotherapy Surgery Uncertainties • • • • Symptoms may be from benign conditions – Benign prostatic hyperplasia, prostatitis Invasive investigation has side effects – Impotence, incontinence Surgical /radiotherapy treatment – Strong side effects on quality of life – Success rates relatively low Prostate cancer generally slow-growing – Age of patient is important – Is the cancer localised or metastatic? Motivation • • • Model the prostate cancer patient pathway Predict likely outcomes of decisions Assist clinicians to: – Formulate and recommend management strategies – Explore decisions and consequences with patients Bayesian Networks for Prostate Cancer Management • • Bayesian Networks for medical applications – Long history in expert systems (from 1970s) – Good fit to probability-based medical decision-making – Mainly used for diagnosis and prognosis Prostate cancer modelling – Statistical techniques (notably Partin tables) – ANN approaches (e.g. Prostate Calculator – Crawford et. al.) What is a Bayesian network? • • • • A representation of the joint probability distribution of a set of random variables Represents causal dependencies Can be learned from a data set Two components to learn: – Structure • Directed Acyclic Graph – Parameters Example - The Asia Network- Lauritzen & Spiegelhalter (1988) G= (V, E) Where: Visit to Asia? Smoking? V: vertices represent variables of interest E: edges represent conditional dependencies among the variables Tuberculosis? Lung Cancer? Tuberculosis or Lung Cancer? X-Ray Result Structure factorises the joint probability distribution Dyspnea? n P ( X 1 ,..., X n ) P X i | Pa( X i ) i 1 Bronchitis? Parameters define conditional probabilities Visit No Visit 1.00% 99.0% Visit to Asia? Tuberculosis? Smoking? Smoker 50.00% Non-Smoker 50.00% Lung Cancer? Tuberculosis or Lung Cancer? Tuberculosis Present Cancer Present Absent Present Absent True 100.0% 100.0% 100.0% 0.00% False 0.00% 0.00% 0.00% 100.0% n Absent P ( X 1 ,..., X n ) P X i | Pa( X i ) i 1 BN Structure Learning • – – – – – – • A Number of possible networks grows super exponentially with the number of variables 1 variable 1 2 variables 3 3 variables 25 4 variables 543 5 variables 29,281 6 variables 3,781,503 37 variables in prostate cancer data A A A A B B B B A A A B A A C B C B C B A C A A B C B C B C Approaches to learning BN Structures • Dependency test based – Conditional independency tests (CI) – Edges correspond to correlations between the variables – Example algorithms: PC, NPC.. • Search and score based – Local/ greedy search strategies – Scoring metrics Search and Score • • Search through the space of possible networks scoring each network The solution is the network which maximises the score – Search strategies (greedy/local search/etc.) – Scoring metrics - goodness of fit • Maximum Likelihood • BDe Metric. Computes the relative posterior probability of a network structure given the data • BIC (Bayesian Information Criterion). Coincides with the MDL score BN Structure Learning Local greedy search : K2 [Cooper & Herskovitz, 1992] – Assume a node ordering – Start with root node – Add the parent set that maximizes the score – K2 Score (CH) aims to maximize P(Structure|Data) ri ( ri 1)! P( Bs , D) P( Bs ) N ij N ijk ! i 1 j 1 ( N ij ri 1)! k 1 n qi EA for BN structure learning • [Larrañaga] first to use Genetic Algorithm to learn BN structures • [Wong] Hybrid Evolutionary Programming to discover BN structures • [van Dijk] Build a skeleton graph (undirected graph) using CI (X2) test. Then GA to turn the skeleton into a DAG by evolving a population of DAG’S using skeleton as template (Repair operator for illegal structures) • [Habrant] Application to time series prediction in finance . Used K2 score, with and without ordering assumption. • [Novobilski] Establish a population of legally fixed length encoded DAG’s Chain-Model GA (Kabli, McCall, Herrmann 2007) Population Evaluate If fitter than worst individual Insert in population X1X2X3X4 X2X3X1X4 X1X4X3X2 X1 X2 X3 X4 X2 X3 X1 X4 X1 X4 X3 X2 score data assign fitness Selection Crossover One offspring Breed Mutation End of Evolution X1X2X3X4 X2X3X1X4 X1 K2 Search data X1 X3 X2 X4 X3 X2 X4 Prostate Cancer Data • • • Patients treated at Aberdeen Royal Infirmary Retrospective data – 320 patients diagnosed and treated for prostate cancer – 2.5 year period 2002-2004 Data Collection – Selection of representative samples by clinician – Data from manual patient records collected in a bespoke database – Overall 37 patient factors collected – Data was discretised using medical knowledge BN structure learned from PC data Applications • • • • • • Scanning and biopsy decision – Uncomfortable process – BN helps assess value of scan and biopsy Treatment choice – Surgery probability of success vs after-care life Hospital resource planning – Sample model to predict future demand – Explore policy changes Patient disease education – Allow patient to explore what disease means for them Pathological staging – Partin tables Follow-on £50k project funded by NHS Grampian / NRP in association with BAUS Agenda • • • • • Concepts of swarm intelligence Particle Swarm Optimisation for cancer chemotherapy design Medical Data Modelling and metaheuristic approaches Ant Colony Optimisation for medical data modelling Open Questions natural inspiration for ant colony optimisation • ant colony behaviour – foraging for food • stigmergy – ants communicate indirectly through the environment – pheromone (scent trails) laid down as ants move • problem solving potential – ant colonies can find shortest paths to food ant colony intelligence • foraging behaviour – many distinct individuals solving a common problem – accumulation of pheromone biases future search • search properties – the ants explore many paths in the early stages – the ants converge on a common path as evidence builds up Which search spaces? • • • Construction spaces – Each ant constructs a solution – Series of construction steps Ants select steps based on pheromone amounts Pheromone deposited backwards along ant path – Amount depends on solution evaluation – Pheromone evaporates over time Example: Travelling Salesman Problem Find the shortest path needed to visit all of n cities precisely once, then returning to the starting city. Solution: an ordering in which to visit the cities Evaluation: the length of the route corresponding to the ordering Solution construction by ants • • • each ant has a: – partial solution, s (solution under construction) – choice of next construction step – knowledge of pheromone associated with each step ants chooses next step probabilistically – more pheremone increases chance of selecting step pheromone dropped equally along solution steps once full solution is evaluated notation m s(i, j ) ij Tk Ck ijk number of ants in colony step (i, j) in construction space pheromone on step (i,j) pheromone evaporation rate solution path constructed by ant k cost of solution constructed by ant k pheromone dropped on step(i,j) by ant k pheromone update rules 1. 2. evaporate calculate new pheromone ij (1 ) ij 1 k if s (i, j) belongs to T ijk C k 0 otherwise m 3. distribute pheromone ij ij ijk k 1 ant colony optimisation 1. initialise ant colony set parameters, initialise pheromone 2. while (stopping criterion not met) { 1. construct ants solutions 2. update pheromones } 3. stop: return best solution and pheromones ACO for Bayesian Network Structure Learning • Growing research area • ACO-B (L. de Campos 2002) – ants construct DAGs then hill climb best solutions – Ants construct orderings of nodes and hill climb • ACO-E (Daly et. al. 2006, 2009) – search space of DAG equivalence classes – Different operators added to extend possible construction steps • MMACO (P. Pinto 2008) – Ants used to refine skeleton BN constructed by MMPC ChainACO, K2ACO (Wu, McCall, Corne – 2010) • chainACO – Ants construct orderings – Orderings are evaluated as chains using CH score – Phase I ends with orderings producing best chains – Phase II runs K2 on best orderings • K2ACO – Ants construct orderings – K2 constructs BN on ordering. – BNs evaluated by CH score • Currently being evaluated on: – Benchmarks (ASIA, CAR, ALARM, …) – Medical data Agenda • • • • • Concepts of swarm intelligence Particle Swarm Optimisation for cancer chemotherapy design Medical Data Modelling and metaheuristic approaches Ant Colony Optimisation for medical data modelling Open Questions Open Questions • Accuracy / computational cost trade-offs – How much data is enough? – When does it pay off to use cheap evaluation? • algorithms vs structures – How do particular metaheuristics interact with problem structure – Network topology – Inductive bias • more nodes, more data – How can these methods scale effectively? 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