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Complexity Management: una nuova prospettiva

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  • 1. Complexity management: una nuova prospettiva Anticipare le crisi misurando la complessità e semplificando il sistema © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 2. COMPLEXITY MANAGEMENT: A TECHNOLOGY FOR THE CHALLENGES OF THE 21-st CENTURY ONTONIX© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 3. DisclaimerThe concepts and methods presented in this document are for illustrative purposes only, and are not intended to be exhaustive. Ontonix assumes no liability or responsibility toany person or company for direct or indirect damages resulting from the use of any information contained herein.Any reproduction or distribution of this document, in whole or in part, without the prior written consent of Ontonix is prohibited.Reverse-engineering of the concepts, methods or ideas contained in this document is strictly forbidden. The methods described in the present document are protected by USpatents.OntoSpace , OntoNet and OntoDyn are trademarks of Ontonix. All other trademarks are the property of their respective owners.Copyright © 2011, Ontonix S.r.l. All Rights Reserved. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 4. Nassim Taleb – Cigno Nero Sulla base del comportamento nel passato di un processo derivarne delle indicazioni – stime, previsioni – sul suo comportamento nel futuroMEDIOCRISTAN ESTREMISTAN«Statistiche» funzionano Incertezza, non linearità prevalgono.Statistiche comprendono sia quelle Le statistiche non funzionano.basate su math, sia quelle basate Il processo non è prevedibile, alsu modelli mentali (logica) massimo ci si può organizzare perModello = mappa delle relazioni tra minimizzare effetti negativi ele variabili che caratterizzano un massimizzare effetti positivi del «cignosistema, di come interagiscono tra nero»di loro e di come impattano sul«risultato» finale atteso.Gli assunti dei modelli sono il«fattore chiave», da essi dipende labontà del modello © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 5. Nassim Taleb – Cigno Nero Sulla base del comportamento nel passato di un processo derivarne delle indicazioni – stime, previsioni – sul suo comportamento nel futuroMEDIOCRISTAN ESTREMISTANIl mondo è pieno di casi in cui i POSSIBILE AVERE SEGNALI«modelli» hanno fallito: DELL’APPROSSIMARSI DELLA CRISI? OVVERO DI QUANDO I MIEISubprime MODELLI NON SONO PIU’Lemahn brothers ADEGUATI?Immobliaire….. SI’ , NON IN ASSOLUTO, LIMITATAMENTE AI DATI DISPONIBILI (se i dati non comprendono i parametri che in realtà sono importanti, ….) © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 6. Sulla base del comportamento nel passato di un processo derivarne delle indicazioni – stime, previsioni – sul suo comportamento nel futuro MODEL FREE Non bisogna continuamente verificare laESTREMISTAN correttezza degli assunti sottostanti ANZI mi segnala quando smettono di essere validiPOSSIBILE AVERE SEGNALIDELL’APPROSSIMARSI DELLA QUANTITATIVO – BENCHMARKCRISI? OVVERO DI QUANDO I MIEI Misura confrontabile nel tempo e con modelliMODELLI NON SONO PIU’ del processo (validazione del modello,ADEGUATI? benchmark anche con dati parziali e differenti) NO STATISTICHE Non vulnerabile nell’estremistan (prevede i cigni neri?) KEY CONCEPTS: Complessità è un attributo misurabile di ogni sistema. Un sistema molto complesso non è necessariamente fragile. La fragilità dipende dal rapporto tra complessità e complessità massima sostenibile. Forti variazioni di complessità e/o l’approssimarsi della complessità al valore massimo (di un dato sistema) sono precursori di crisi repentine © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 7. CONTENTS•  About Ontonix•  Why Manage Complexity•  What is Complexity?•  Beyond the Concept of Risk•  Our Products - Our Services•  Applications © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 8. About Ontonix       © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 9. Publications© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 10. Our Global Presence CPS Frankfurt, D Ontonix Sp z o.o. Business Dimensions Warsaw, PolandOntonix LLC Geneva, CHNovi, USA BLUE Eng. Bursa, Turkey Soyotec VAS Beijing, PRC Hinteregg, CHOntoMed LLCAnn Arbor, USA Ontonix S.r.l. Saconsulting FlexSci Como, Italy Madrid, Spain Beijing, PRC Ontonix RSA Pretoria, RSA Ontonix Brasil Sao Paulo, Brazil © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 11. References© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 12. Why Complexity Management© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 13. What is Complexity? Complexity is a function of structure and uncertainty (entropy). It quantifies the degree of sophistication and the “amount of chaos” within a system. It is a fundamental property of dynamical systems, just like energy. Structure Uncertainty(Topology of information flow) (Noise content in information) COMPLEXITY © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 14. Some Properties of Complexity•  Rapidly rising complexity is observed prior to a crisis or collapse•  Peak complexity generally corresponds to maximum crisis intensity•  High complexity corresponds to high risk of contagion and stress propagation•  Highly varying complexity points to a situation of low stability and high unpredictability. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 15. Complex or Complicated?•  A system may be complicated, but have very low complexity.•  A large number of parts doesn’t generally imply high complexity. It does, in general, imply a complicated system.•  Complexity implies capacity to surprise, to deliver unexpected behaviour.•  In order to measure the amount of complexity it is necessary to take uncertainty into account, not just the number of parts. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 16. Principle of Incompatibility High precision is incompatible with high complexity. L. Zadeh, UCLALow complexity High complexitySystem is: System is:• Predictable • Unpredictable• Easy to understand • Difficult to understand• Can be described with precision • Cannot be described precisely• Easy to control • Difficult to control• Unable to surprise • Can surprise• Structure dominated • Entropy dominated• Robust • Vulnerable © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 17. Representing Structure Node Link Node Link© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 18. Examples of Complexity Maps© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 19. Identifying Structure In Data Variable 1 Variable 2 Variable 3 Variable 4 Variable 5 Variable 6 Variable 7 Variable 8 ........ Sample 1 Sample 2Variable 1 Sample 3 . . . . Steps: Variable 2Variable 3 For all combinations of variables: •  Build (x;y) scatter plot •  Transform plot to 2D image •  Analyze image and measure its information content and “amount of structure” •  If image contains structure, create a link in the map – this corresponds to a relationship between two variables Variable 5 © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 20. From Data to Images and Structure y Image with little or no structure: no information is exchanged between x and y. y x Image with evident structure: much information is exchanged between x and y. y x Image with evident structure: much information is exchanged between x and y. x© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 21. Critical Complexity    Mode 1 Mode 2   Mode 3 Mode 4 © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 22. Complexity and Robustness     Upper complexity bound (critical complexity) Current system complexity Lower complexity bound © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 23. What Happens at Critical Complexity Critical complexity (upper complexity bound) y Entropy dominates – system is “chaotic” y y x x y Current complexity x Structure dominates – system is deterministic 0The x-y scatter plots shown in this slide represent typical relationshipsbetween pairs of variables in a system which functions in the proximity xof the lower and upper bounds of complexity. Lower complexity bound © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 24. Complexity vs Time Step  i   In systems which evolve in time, complexity changes, as well as the corresponding lower and upper bounds.Step  i+1   The same may be said of the Complexity Map, its structure, density, hubs, etc.Step  i+2   In such cases data is analyzed using a moving-window approach. t1 t2 Time Time t1 Time t2 Complexity = 28.5 Complexity = 44.8 © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 25. Characteristics of a Highly Complex Business or System © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 26. The Two Facets of Fragility   © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 27. A New Approach To Risk   © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 28. Beyond Risk Management© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 29. Complexity of the World’s Economy© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 30. Beyond Risk Management Conventional Modern Risk management Uncertainty-based Complexity-based paradigm Rating mechanism Probability of Default (PoD) Current State of Health Temporal horizon Mid/long term Short term Economy Smooth Turbulent, globalized Measure of Success Growth Survival Approach Fragmented Holistic Math techniques Statistical/MCS models Model-freeThe table presents a comparison between the uncertainty and complexity-based philosophiesbehind risk management. The fundamental difference lies in the idea of a rating:Conventional PoD rating: a Probability of Default within a certain period of time ( i.e. years).Complexity-based rating: measure of the present state of health, no future projection (e.g. likedoctors). A less complex company is less exposed and can better confront an uncertain market. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 31. SOFTWARE PRODUCTS Our Software ProductsOntoSpace™The World’s first complexity management system.(OntoSpace™ is an interactive tool)OntoDyn™A system for measuring the stability of portfolios ofcompanies and customer retention.(OntoDyn™ can run as a batch tool)OntoNet™An on-line system for on-demand complexity analysisand self-rating of corporations.(OntoNet™ is available exclusively for on-line execution) © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 32. SERVICES OFFERING Our Services Offering•  Three levels of complexity rating•  Complexity/vulnerability analysis of companies or products•  Crisis anticipation•  Customer retention•  Merger simulation and analysis•  All our services produce quantitative results © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 33. SERVICES: RATING Our Rating Services On-line self-rating: an entry level solution accessible to even small companies. Subscription rating: a specific solution for publicly-traded companies. Holistic rating: an advanced solution in which a corporation is analyzed with its ecosystem.© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 34. SERVICES: CRISIS ANTICIPATION Pre-Alarms and Crisis-Anticipation Complexity may be measured and monitored versus time. Sudden fluctuations of complexity point to instabilities, traumas or imminent crises. The magnitude of the trauma may be quantified by the difference in complexity before and after the event. Based on similar information it is possible to identify thresholds of complexity beyond which one may expect a crisis and therefore take measures in order to mitigate its effects.System with mildly increasing Highly unstable system – the System with step-wise Collapsing system. Loss ofcomplexity (middle orange case is relative to a hospital increases complexity. This complexity is equivalent tocurve is complexity, the other patient in an ICU. Each case corresponds to the US loss of functionality. Whencurves correspond to lower complexity fluctuation housing market. The time complexity reaches a zeroand upper complexity corresponds to crisis. Time span is 5 years. The 2007 value, the system has nobounds. span is 8 hours. sub-prime bubble is indicated longer any structure and by red arrow. ceases all activity. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 35. SERVICES: CRISIS ANTICIPATION Pre-Alarms and Crisis-Anticipation Complexity evolution of four US banks as confronted with that of the US housing market. Of the four banks only bank C did not default – notice decrease of complexity due to change of business model.In many cases jumps in complexity may becaused by deliberate actions such as: During 2008 and 2009 the complexity of the US housing market has been increasing in a way similar to• Mergers/Aquisitions the the period which preceed the 2007 bubble.• Spin-offs• New regulationsThe above case illustrates the evolution ofthe complexity of the Israeli banking systemover a period of four years. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 36. Rating StratificationIn highly complex turbulent environments, such as the global economy, stratifying risk ratingsinto 30+ classes is deprived of physical significance. See Principle of Incompatibility. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 37. Robustness RatingBelow are examples of five systems, having very low (left) to very high robustness (right).Typical scatter plots are illustrated for each case. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 38. SELF-RATINGSelf-rating: Objective Rating Available Globally User • Process is fully automatic, 24/7 • FREE templates available for download • Employees have no access to eServer • Parameters may be unnamed – guarantees confidentiality • Login to www.ontonix.com • Upload P&L data • MS-Excel format • Flat processing fee • Pay via Credit Card/PayPal • Invoice sent via e-mail • e-mail notification when analysis is done • Very fast turn-around time • Comprehensive rating report • Original data is destroyed ONTONIX eSERVER Complexity-Rating Report (see example here) © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 39. SELF-RATING Self-rating: Analysis Templates© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 40. Example: On-line Rating of a Public Company © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 41. Example: On-line Rating of a Public Company– Complexity & Risk Map © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 42. Example: On-line Rating of a Public Company– Variable Ranking © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 43. Example: On-line Rating of a Public Company– Failure Analysis © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 44. The OntoDyn Early-warning Concept•  The exposure of banks depends largely on the state of health of their clients (corporate and retail), but: –  Companies can hide their real situation when applying for credit. –  Ratings are unreliable and issued on a yearly basis. –  Balance sheets and financial statements are: •  Subjective •  Often unreliable •  Issued on a quarterly or yearly basis (too slow in a turbulent world!) © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 45. The OntoDyn Early-warning Concept•  However, the information on the interaction (i.e. transactions) between a client and his bank is available. It is: –  Objective –  Reliable –  Available on a daily/weekly or monthly basis•  Using OntoDyn this information may be used to infer the state of health of the client.•  Based on bank-client transaction data OntoDyn measured the stability of the client as fast as the data is available and warn of changes therein.•  Changes in client’s stability point to potential problems. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 46. The OntoDyn Early-warning Concept•  The stability of a client is computed as a function of the rate of change of his complexity (in analogy to blood sugar levels, or cholesterol or other bio-markers).•  Large and sudden changes in complexity point to an unstable system. This is sufficient to issue a pre-alarm.•  Similar information may be used in two ways: –  Assist in credit risk analysis (less stable clients will get credit at more stringent conditions) –  Customer retention (clients leaving bank to go with competitor bank or client is defaulting). © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 47. A Bank and Its Clients – A Dynamic SystemA bank interacts with amultitude of clients.Clients also interact witheach other. This givesrise to a huge dynamicsystem which the bankmonitors with a fairly highfrequency (daily/weekly)or monthly. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 48. Bank-Client Interaction and Early Warnings Client Complexity of Client Transactions dataBank-client Transactions Complexity & Risk Map of Client Concept: A bank doesn’t need to know a client’s Balance Sheet in order to be able to infer his state of health. Bank Based on the objective transactional data, the client’s complexity is measured on a daily/weekly or monthly basis. Sudden changes in complexity are identified and signalled. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 49. SERVICES: CRISIS ANTICIPATION Measuring the Stability of Bank Clients – Customer Retention Courtesy, Banca Popolare di SondrioTracking the complexity of the clients of a bank provides an advanced mechanism for customer retention analysis.Based on the customer’s transactions (see examples of parameters in the Complexity Maps) it is possible to track hiscomplexity and to measure the stability based on how this complexity varies over time. Sudden changes point topotential customer retention problems. In the above example, the complexity of a client went from 14.77 to 8.81indicating a potential problem which the bank may decide to investigate. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 50. SERVICES: CRISIS ANTICIPATIONStability Profile of Bank Clients and Customer Retention Example of stability profile of a portfolio of 230 corporate customers of a retail bank. A Stability Profile is generated every month. Potentially unstable clients are listed. Each client hs a stability index ranging from 0 to 1. A value close to 1 indicates a stable client. The orange vertical line corresponds to a first level of attention – all clients falling below the corresponding value of stability are potentially at risk of being lost. All clients with stability to the left of the red vertical line must be investigated. Both levels of attention depend on the type of business and type of clients and are established in collaboration with Ontonix. The skyline of the profile should be checked at least every month. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 51. SERVICES: CRISIS ANTICIPATION Measuring the Stability of Bank Branches Branch A Branch B Courtesy, Banca Popolare di Sondrio Profitability Complexity Courtesy, Mizrahi-Tefahot Bank Courtesy, Banca Popolare di SondrioJust like in the case of bank customers, it is possible to track the complexity of a bank’s branches. Performed on amonthly basis this provides an innovative mechanism for the anticipation of problems. A stability profile like the oneindicated in the previous slide can be produced based on conventional parameters branches use to report theirperformance. The system is able to show also how the complexity and profitability of a branch are strongly related –see plot on the right. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 52. APPLICATIONSUNA SERIE DI CASI ED ESEMPI PER FAR COMPRENDERE LE POTENZIALITA’RAFFINERIA : … e inaspettatamente la pompa «critica» lunedì si fermò (a dispettodei sistemi di prevenzione messi in campo)COMMERCIALISTA: nei bilanci trimestrali è nascosta una informazione che con leanalisi economiche finanziarie, anche le più innovative, non riesco a cogliere?PRODUZIONE FILATI NYLON : … com’è che con 15 addetti al controllo deiprocessi produttivi , mi faccio sempre sorprendere …. e non capisco perché èsuccesso il fermo impiantoPORTAFOGLIO CREDITI IN CONTENZIOSO: mi fidavo delle statistiche e deimodelli predittivi che le utilizzavano (nonostante la variabilità/incertezza del contestofosse tanta), ma adesso come faccio a sapere se sto utilizzando i parametri correttiper valutare (prezzi, rischi,..)?ENAV - per dimensionare adeguatamente le risorse in funzione della densità ditraffico e anche per segnalare con congruo anticipo l’avvicinarsi di situazionipotenzialmente rischiose. © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.
  • 53. UNA SERIE DI CASI ED ESEMPI PER FAR COMPRENDERE LE POTENZIALITA’CONFINDUSTRIA GENOVA: aggregare imprese è probabilmente l’unica via diuscita per le PMI italiane, come faccio a sapere, prima, se il sistema che vado acreare (PMI aggregate) è più o meno fragile di quello di partenza (PMI isolate)PONTE PEDONALE: la creatività non è infinita, anche i geni riconosciuti tendono aripetersi e cmq le soluzioni vengono trovate all’interno dello spazio del «giàspèrimentato, già conosciuto». Come faccio a scoprire se esistono soluzioniprogettuali migliori (meno complesse) di quelle «tradizionali»?UCC LOMBARDIA : 2 progetti pilota per riprodurre i risultati già ottenuti inesperienze fatte negli USA e in altri ospedali italiani. L’interpretazione dei segnali(cardiologici, pressori, ecc..) dei pazienti monitorizzati è complessa, spesso ladifferenza la fa l’esperienza di chi legge i segnali. La complessità si è dimostrata unbuon indicatore (80-85% di affidabilità) dello stato generale del paziente. Secondoprogetto riguarda utilizzo della misura di complessità come indicatore della buonariuscita di una terapia, in tutti quei casi dove ancora una volta i segnali dainterpretare sono molti e complessi (defibrillatori) © Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.

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