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Mestrado Integrado em Medicina Introdução à Medicina II

Mestrado Integrado em Medicina Introdução à Medicina II. ARTIFICIAL INTELLIGENCE FOR CRITICAL CARE MONITORING AND DECISION SUPPORT. IMPACT ON PATIENT OUTCOMES. Final Presentation. Turma 6 6fmup0910@gmail.com. Professor Doutor Altamiro Pereira. 18-05-2010. Introduction. Previously….

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Mestrado Integrado em Medicina Introdução à Medicina II

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  1. Mestrado Integrado em Medicina Introdução à Medicina II ARTIFICIAL INTELLIGENCE FOR CRITICAL CARE MONITORING AND DECISION SUPPORT IMPACT ON PATIENT OUTCOMES Final Presentation Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  2. Introduction Previously… Systematic review from 2001 Artificial intelligence applications in the intensive care unit[1] We found important to review once more the progresses in this subject as it’s rapidly evolving. We’ve concluded that this study was more a state of the art than an Systematic Review and knowing that we’ve decided to search articles with no date restriction [1] Hanson CW 3rd, Marshall BE; Artificial intelligence applications in the intensive care unit; Critical care medicine; 2001 Feb; 29 (2); 427-35 Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  3. Introduction AI: Definition Artificial intelligence can be defined as a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behaviour, and with the creation of artefacts that exhibit such behaviour [2]. Intensive Care Unit: Definition An intensive care unit (ICU) is a specialized department in hospitals that provides life support or organ support systems in patients who are critically ill and who usually require constant monitoring. Critical Care: Definition Critical care is the permanent and thorough care provided to the critical patients in intensive care units (ICUs). [2] Ramesh, A.N.; Kambhamti, C.; Monson, J.R.T.; Drew, P.J.; Artificial intelligence in medicine; Ann R Coll Surg Engl, 2004; 86: 334–338. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  4. Introduction AI and ICU • Intensive care medicine frequently involves making rapid decisions on the basis of a large and disparate array of information [3]. • Since the technology of monitoring astronauts’ vital signs in space was transferred to the bedside in the 1960s, patient monitoring systems have become an indispensable part of critical care [4]. [3] Jason H. T. Bates and Michael P. Young; Applying Fuzzy Logic to Medical Decision Making in the Intensive Care Unit; 2003 Apr [4] Ying Zhang, MEng Real-Time Development of Patient-Specific Alarm Algorithms; Proceedings of the 29th Annual International; Conference of the IEEE EMBS; Cité Internationale, Lyon, France August 23-26, 2007 Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  5. Introduction Intensive care unit (ICU) is a hospital unit staffed and equipped to provide intensive monitoring closely for a critical medical condition 5. The bedside data must be extracted and organized to become useful information for clinical decisions . In ICU the amount of data makes its integration and interpretation time-consuming and inefficient[5]. Artificial intelligence (AI) systems are pointed as a direction to follow for the development of patient care. [5]-HELDT, Thomas; LONG, Bill; VERGHESE, George C. ; SZOLOVITS, Peter; MARK, Roger G.; Integrating Data, Models, and Reasoning in Critical Care; Conf Proc IEEE Eng Med Biol Soc. 2006;1:350-3 Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  6. Introduction Development of artificial intelligence (AI) in medicine Development of AI programs intended to: - help the clinician in the formulation of a diagnosis; - help the clinician in making of therapeutic decisions; - helping prediction outcome. AI in medicine is designed to support healthcare workers in their every day duties, assisting with tasks that rely on the manipulation of data and knowledge[6]. [6] RAMNARAYAN, Padmanabhan; KAPOOR, Ritika R.; COREN, Michael; NANDURI, Vasantha; TOMLINSON, Amanda L.; TAYLOR, Paul M.; WYATT, Jeremy C.; RITTO, Joseph F.; Measuring the Impact of Diagnostic Decision Support on the Quality of Clinical Decision Making: Development of a Reliable and Valid Composite Score; Journal of the American Medical Informatics Association, 2003; Volume 10 Number 6. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  7. Introduction Modern intensive care units (ICUs) employ an impressive array of technologically sophisticated instrumentation to provide detailed measurements of the pathophysiological state of each patient [5], [7]. Such systems can: - Reduce the problem of information overload, - Providing alarms more specific than today’s single-variable limit alarms, - Decrease healthcare cost in ICU, (…) Such systems include: - Artificial neural networks (ANNs), • Fuzzy expert systems, • Evolutionary computation and hybrid intelligent systems. [5]-HELDT, Thomas; LONG, Bill; VERGHESE, George C. ; SZOLOVITS, Peter; MARK, Roger G.; Integrating Data, Models, and Reasoning in Critical Care; Conf Proc IEEE Eng Med Biol Soc. 2006;1:350-3 [7]-MAHFOUF, M.; ABBOD, M.F.; LINKENS, D.A., A survey of fuzzy logic monitoring and control utilisation in medicine; Artificial Intelligence in Medicine, 2001; 21: 27-42. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  8. Research question Does the usage of monitoring or clinical decision support systems that include AI technology improve the quality of patient care? Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  9. Aim To review the usefulness of AI monitoring and Clinical Decision Support (CDS) systems when applied to patients in the ICU by the interpretation of the patient outcomes. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  10. Specific Objectives • To study the benefits and drawbacks of artificial intelligence monitoring and CDS systems for critical care when compared to non AI-methods. • To find out the impact of the usage of AI systems in the different patient outcomes in ICU's. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  11. Methods Study design: Systematic review    1 - An exhaustive search, in electronic databases, and inclusion of primary studies. 2 - Quality assessment of included studies and data extraction (review, by two persons, of the title and the abstract or the article. Same process for the full article. A third opinion may be requested). 3 - Synthesis of study results (SPSS and Review Manager). 4 - Interpretation of results and report writing. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  12. Methods Population: Articles which report AI applications for monitoring (including warning (alert)), decision support or prescription support in the intensive care unit. Articles which report AI applications for monitoring (including warning (alert)), decision support or prescription support in the intensive care unit. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  13. Methods Data collection methods: → Search strategy Articles included by reviewer Articles were searched in: • PubMed; • ISI Web of Knowledge; • SCOPUS. with no date restriction Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  14. Methods Data collection methods - Query: • Our query was based in the following keywords with the represented basic relationship between them: “Artificial Intelligence” AND (“Critical Care” or “Intensive Care Unit”) AND “Trial”. • Using this structure, we used synonyms and acronyms based on MeSH terms related to the keywords mentioned above, keeping the described relationship, in order to increase our pool of articles. Articles included by reviewer Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  15. Methods Data collection methods - Query: • We were very benevolent with the terms included, in order to reduce the chance of excluding important articles through an insufficient search. • Due to restrictions in size and structure imposed by the different search engines, the query used in each engine had to be molded accordingly. • Since PubMed was the one with the less restrictions and with the biggest article pool, we decided to design our query through there and then make the necessary adjusments to the other search engines. Articles included by reviewer Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  16. Methods Query terms: Artificial intelligence: Computer reasoning, machine intelligence, machine learning, computer vision system, knowledge acquisition, fuzzy logic, expert systems, knowledge bases, neural networks (computer), neural network model, perception, direct support system, robotic, telerobotic; Intensive care unit: Critical care (unit), surgical intensive care (unit), neonatal intensive care (unit), infant newborn intensive care (unit), pediatric intensive care (unit), ICU, PICU, NICU, CC, burn(s) unit, respiratory care unit, coronary care unit. We also used the term “trial” in order to narrow down and specify our research. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  17. Methods Query used in the PubMed Database ("trial"[all fields]) AND ((("critical care"[MeSH Terms] OR ("critical"[All Fields] AND "care"[All Fields]) OR "critical care"[All Fields]) OR (critical[All Fields] AND cares[All Fields]) OR ("intensive care"[MeSH Terms] OR ("intensive"[All Fields] AND "care"[All Fields]) OR "intensive care"[All Fields]) OR (intensive[All Fields] AND cares[All Fields]) OR ("intensive care"[MeSH Terms] OR ("intensive"[All Fields] AND "care"[All Fields]) OR "intensive care"[All Fields] OR ("surgical"[All Fields] AND "intensive"[All Fields] AND "care"[All Fields]) OR "surgical intensive care"[All Fields]) OR (("surgical procedures, operative"[MeSH Terms] OR ("surgical"[All Fields] AND "procedures"[All Fields] AND "operative"[All Fields]) OR "operative surgical procedures"[All Fields] OR "surgical"[All Fields]) AND Intensive[All Fields] AND Cares[All Fields]) OR ("intensive care, neonatal"[MeSH Terms] OR ("intensive"[All Fields] AND "care"[All Fields] AND "neonatal"[All Fields]) OR "neonatal intensive care"[All Fields] OR ("neonatal"[All Fields] AND "intensive"[All Fields] AND "care"[All Fields])) OR (("infant, newborn"[MeSH Terms] OR ("infant"[All Fields] AND "newborn"[All Fields]) OR "newborn infant"[All Fields] OR "neonatal"[All Fields]) AND Intensive[All Fields] AND Cares[All Fields]) OR (("infant, newborn"[MeSH Terms] OR ("infant"[All Fields] AND "newborn"[All Fields]) OR "newborn infant"[All Fields] OR ("infant"[All Fields] AND "newborn"[All Fields]) OR "infant newborn"[All Fields]) AND ("intensive care"[MeSH Terms] OR ("intensive"[All Fields] AND "care"[All Fields]) OR "intensive care"[All Fields])) OR (("infant, newborn"[MeSH Terms] OR ("infant"[All Fields] AND "newborn"[All Fields]) OR "newborn infant"[All Fields] OR ("infant"[All Fields] AND "newborn"[All Fields]) OR "infant newborn"[All Fields]) AND Intensive[All Fields] AND Cares[All Fields])) OR (("intensive care"[MeSH Terms] OR ("intensive"[All Fields] AND "care"[All Fields]) OR "intensive care"[All Fields]) OR ICU[All Fields] OR icus[All Fields] OR picu[All Fields] OR picus[All Fields] OR nicu[All Fields] OR nicus[All Fields] OR ("intensive care units"[MeSH Terms] OR ("intensive"[All Fields] AND "care"[All Fields] AND "units"[All Fields]) OR "intensive care units"[All Fields] OR ("critical"[All Fields] AND "care"[All Fields] AND "unit"[All Fields]) OR "critical care unit"[All Fields]) OR ("critical care"[MeSH Terms] OR ("critical"[All Fields] AND "care"[All Fields]) OR "critical care"[All Fields]) OR ("intensive care"[MeSH Terms] OR ("intensive"[All Fields] AND "care"[All Fields]) OR "intensive care"[All Fields]) OR ("burn units"[MeSH Terms] OR Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  18. Methods Query used in the PubMed Database ("burn"[All Fields] AND "units"[All Fields]) OR "burn units"[All Fields]) OR ("burn units"[MeSH Terms] OR ("burn"[All Fields] AND "units"[All Fields]) OR "burn units"[All Fields] OR ("burns"[All Fields] AND "unit"[All Fields]) OR "burns unit"[All Fields]) OR ("respiratory care units"[MeSH Terms] OR ("respiratory"[All Fields] AND "care"[All Fields] AND "units"[All Fields]) OR "respiratory care units"[All Fields]) OR ("respiratory care units"[MeSH Terms] OR ("respiratory"[All Fields] AND "care"[All Fields] AND "units"[All Fields]) OR "respiratory care units"[All Fields] OR ("respiratory"[All Fields] AND "care"[All Fields] AND "unit"[All Fields]) OR "respiratory care unit"[All Fields]) OR ("coronary care units"[MeSH Terms] OR ("coronary"[All Fields] AND "care"[All Fields] AND "units"[All Fields]) OR "coronary care units"[All Fields]) OR ("coronary care units"[MeSH Terms] OR ("coronary"[All Fields] AND "care"[All Fields] AND "units"[All Fields]) OR "coronary care units"[All Fields] OR ("coronary"[All Fields] AND "care"[All Fields] AND "unit"[All Fields]) OR "coronary care unit"[All Fields]))) AND (("artificial intelligence"[MeSH Terms] OR ("artificial"[All Fields] AND "intelligence"[All Fields]) OR "artificial intelligence"[All Fields]) OR ("artificial intelligence"[MeSH Terms] OR ("artificial"[All Fields] AND "intelligence"[All Fields]) OR "artificial intelligence"[All Fields] OR ("computer"[All Fields] AND "reasoning"[All Fields]) OR "computer reasoning"[All Fields]) OR ("artificial intelligence"[MeSH Terms] OR ("artificial"[All Fields] AND "intelligence"[All Fields]) OR "artificial intelligence"[All Fields] OR ("machine"[All Fields] AND "intelligence"[All Fields]) OR "machine intelligence"[All Fields]) OR AIs[All Fields] OR ("artificial intelligence"[MeSH Terms] OR ("artificial"[All Fields] AND "intelligence"[All Fields]) OR "artificial intelligence"[All Fields] OR ("machine"[All Fields] AND "learning"[All Fields]) OR "machine learning"[All Fields]) OR (("knowledge"[MeSH Terms] OR "knowledge"[All Fields]) AND Representation[All Fields]) OR (("knowledge"[MeSH Terms] OR "knowledge"[All Fields]) AND ("Representations (Berkeley)"[Journal] OR "representations"[All Fields])) OR ("artificial intelligence"[MeSH Terms] OR ("artificial"[All Fields] AND "intelligence"[All Fields]) OR "artificial intelligence"[All Fields] OR ("computer"[All Fields] AND "vision"[All Fields] AND "systems"[All Fields]) OR "computer vision systems"[All Fields]) OR ("artificial intelligence"[MeSH Terms] OR ("artificial"[All Fields] AND "intelligence"[All Fields]) OR "artificial intelligence"[All Fields] OR ("computer"[All Fields] AND "vision"[All Fields] AND "system"[All Fields]) OR Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  19. Methods Query used in the PubMed Database "computer vision system"[All Fields]) OR (("knowledge"[MeSH Terms] OR "knowledge"[All Fields]) AND Acquisitions[All Fields]) OR ("fuzzy logic"[MeSH Terms] OR ("fuzzy"[All Fields] AND "logic"[All Fields]) OR "fuzzy logic"[All Fields]) OR ("expert systems"[MeSH Terms] OR ("expert"[All Fields] AND "systems"[All Fields]) OR "expert systems"[All Fields] OR ("expert"[All Fields] AND "system"[All Fields]) OR "expert system"[All Fields]) OR ("expert systems"[MeSH Terms] OR ("expert"[All Fields] AND "systems"[All Fields]) OR "expert systems"[All Fields]) OR ("knowledge bases"[MeSH Terms] OR ("knowledge"[All Fields] AND "bases"[All Fields]) OR "knowledge bases"[All Fields] OR ("knowledge"[All Fields] AND "base"[All Fields]) OR "knowledge base"[All Fields]) OR ("knowledge bases"[MeSH Terms] OR ("knowledge"[All Fields] AND "bases"[All Fields]) OR "knowledge bases"[All Fields]) OR ("knowledge bases"[MeSH Terms] OR ("knowledge"[All Fields] AND "bases"[All Fields]) OR "knowledge bases"[All Fields] OR "knowledgebases"[All Fields]) OR ("knowledge bases"[MeSH Terms] OR ("knowledge"[All Fields] AND "bases"[All Fields]) OR "knowledge bases"[All Fields] OR "knowledgebase"[All Fields]) OR ("neural networks (computer)"[MeSH Terms] OR ("neural"[All Fields] AND "networks"[All Fields] AND "(computer)"[All Fields]) OR "neural networks (computer)"[All Fields] OR ("neural"[All Fields] AND "network"[All Fields]) OR "neural network"[All Fields]) OR ("Neural Netw"[Journal] OR "IEEE Trans Neural Netw"[Journal] OR ("neural"[All Fields] AND "networks"[All Fields]) OR "neural networks"[All Fields]) OR ("neural networks (computer)"[MeSH Terms] OR ("neural"[All Fields] AND "networks"[All Fields] AND "(computer)"[All Fields]) OR "neural networks (computer)"[All Fields] OR ("neural"[All Fields] AND "network"[All Fields] AND "model"[All Fields]) OR "neural network model"[All Fields]) OR ("neural networks (computer)"[MeSH Terms] OR ("neural"[All Fields] AND "networks"[All Fields] AND "(computer)"[All Fields]) OR "neural networks (computer)"[All Fields] OR ("neural"[All Fields] AND "network"[All Fields] AND "models"[All Fields]) OR "neural network models"[All Fields]) OR ("neural networks (computer)"[MeSH Terms] OR ("neural"[All Fields] AND "networks"[All Fields] AND "(computer)"[All Fields]) OR "neural networks (computer)"[All Fields] OR "perceptron"[All Fields]) OR ("neural networks (computer)"[MeSH Terms] OR ("neural"[All Fields] AND "networks"[All Fields] Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  20. Methods Query used in the PubMed Database AND "(computer)"[All Fields]) OR "neural networks (computer)"[All Fields] OR "perceptrons"[All Fields]) OR ("neural networks (computer)"[MeSH Terms] OR ("neural"[All Fields] AND "networks"[All Fields] AND "(computer)"[All Fields]) OR "neural networks (computer)"[All Fields] OR ("connectionist"[All Fields] AND "model"[All Fields]) OR "connectionist model"[All Fields]) OR ("neural networks (computer)"[MeSH Terms] OR ("neural"[All Fields] AND "networks"[All Fields] AND "(computer)"[All Fields]) OR "neural networks (computer)"[All Fields] OR ("connectionist"[All Fields] AND "models"[All Fields]) OR "connectionist models"[All Fields]) OR ("robotics"[MeSH Terms] OR "robotics"[All Fields]) OR ("robotics"[MeSH Terms] OR "robotics"[All Fields] OR "telerobotic"[All Fields]) OR (Remote[All Fields] AND ("surgery"[Subheading] OR "surgery"[All Fields] OR "operations"[All Fields] OR "surgical procedures, operative"[MeSH Terms] OR ("surgical"[All Fields] AND "procedures"[All Fields] AND "operative"[All Fields]) OR "operative surgical procedures"[All Fields])) OR (Remote[All Fields] AND ("surgical procedures, operative"[MeSH Terms] OR ("surgical"[All Fields] AND "procedures"[All Fields] AND "operative"[All Fields]) OR "operative surgical procedures"[All Fields] OR "operation"[All Fields])) OR Telerobotic[All Fields] OR robotic[All Fields] OR (direct[All Fields] AND support[All Fields] AND system[All Fields]) OR (direct[All Fields] AND support[All Fields] AND systems[All Fields])) Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  21. Methods Query used in the ISI Web of Knowledge Database Ts=(artificial intelligence OR Computer Reasoning OR Machine Intelligence OR AI OR AIs OR Machine Learning OR Knowledge Representation OR Computer Vision System OR Knowledge Acquisition OR fuzzy logic OR expert system OR Knowledge Base OR Knowledgebase OR Neural Network OR Connectionist Model OR robotic OR Telerobotic OR Remote Operation OR direct support system) AND Ts=(intensive care OR ICU OR icus OR picu OR picus OR nicu OR nicus OR critical care unit OR critical care OR intensive care OR burn units OR burns unit OR respiratory care units OR respiratory care unit OR coronary care units OR coronary care unit OR Critical care OR critical cares OR intensive care OR intensive cares OR Surgical Intensive Care OR Surgical Intensive Cares OR Neonatal Intensive Care OR Neonatal Intensive Cares OR Infant Newborn Intensive Care OR Infant Newborn Intensive Cares) AND TS=(trial) Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  22. Methods Query used in the SCOPUS Database TITLE-ABS-KEY(("intensive care" OR icu OR icus OR picu OR picus OR nicu OR nicus OR "critical care unit" OR "critical care" OR "intensive care" OR "burn units" OR "burns unit" OR "respiratory care units" OR "respiratory care unit" OR "coronary care units" OR "coronary care unit" OR "Critical care" OR "critical cares" OR "intensive care" OR "intensive cares" OR "Surgical Intensive Care" OR "Surgical Intensive Cares" OR "Neonatal Intensive Care" OR "Neonatal Intensive Cares" OR "Infant Newborn Intensive Care" OR "Infant Newborn Intensive Cares") AND ("artificial intelligence" OR "computer reasoning" OR "machine intelligence" OR ai OR ais OR "machine learning" OR "knowledge representation" OR "knowledge representations" OR "computer vision systems" OR "computer vision system" OR "knowledge acquisition" OR "knowledge acquisitions" OR "fuzzy logic" OR "expert system" OR "expert systems" OR "knowledge base" OR "knowledge bases" OR "knowledge base" OR "neural network" OR "neural networks" OR "neural network model" OR "neural network models" OR perceptron OR perceptrons OR "connectionist model" OR "connectionist models" OR "robotics" OR "telerobotics" OR "remote operations" OR "remote operation" OR "direct support system" OR "direct support systems" OR "robotic" OR "telerobotic") AND "trial") AND SUBJAREA(mult OR agri OR bioc OR immu OR neur OR phar OR mult OR medi OR nurs OR dent OR heal OR mult OR ceng OR CHEM OR comp OR eart OR ener OR engi OR envi OR mate OR math OR phys)   Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  23. Methods Inclusion/exclusion criteria: Inclusion criteria • Study design (clinical trials); • Study participants of included articles are patients in the intensive care unit; • Studies that describe AI systems’ intervention on monitoring, warning (alert), decision support or prescription support; 4. Study outcomes include mortality, morbidity, quality of life, length of stayor other patient outcomes. Exclusion criteria Articles that use data from the ICU as secondary data for the demonstration of AI systems based only on system's performance outcomes. Articles not able to be filtered due to absence of abstract or full text were excluded. Those that didn’t meet the inclusion criteria were also excluded. Finally, those that met the inclusion and the exclusion criteria were excluded. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  24. Methods Study variables: • Characteristics of the articles (year, author and country of publishing, etc…) • Type of study (number of participants, duration, etc…) • Domain of application (neurological, respiratory, cardiovascular, etc…) • Area of application (monitoring, clinical decision support.) • The patients‘ outcomes described in each article (mortality rate, length of stay, quality of stay, morbidity, quality of life, cost of stay or other patient outcomes). Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  25. Methods Statistical analysis: • Storage of the data using SPSS; • Analysis of the study variables using the appropriate frequency measures; • Agreement statistic and Kappa statistic for the agreement analysis Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  26. Search Results Search was conducted on March 7th, 2010 Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  27. Reviewers' Agreement Abstract's Review In order to evaluate if the analysis of the articles during the first review was well executed, we’ve analysed the agreement between reviewers. . Global agreement was 86%; . Negative agreement was 94%; . Positive agreement was 56%. The fact that we had a high value in the negative agreement means that we’ve correctly excluded the articles in the first review. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  28. Reviewers' Agreement In order to evaluate if the analysis of the articles during the first review was well executed, we’ve analysed the agreement between reviewers. The agreement levels remain relatively high. Still, the relatively low level of the Kappa statistic may be due to the fact that we had a low number of articles in the second stage of the review. Full Text review . Global agreement was 73%; . Negative agreement was 46%; . Positive agreement was 82%. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  29. Publication Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  30. Study Type Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  31. Outcomes Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  32. Outcomes Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  33. Outcomes Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  34. Results Merouani2008 [8] • It’s a decision/prescription support system articles. In this study, the objective was to test if infusion rates of norepinephrine controled by a closed-loop fuzzy logic system would reduce the duration of septic shock • Duration of shock proved to be significantly shorter (P < 0.001) in the fuzzy group than in the control group. The total amount of norepinephrine infused was significantly lower (P < 0.01) in the fuzzy group than in the control group. This resulted in a lower total dose being administered and is related with a shorter duration of septic shock, although no statistical corroboration was provided in the article. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  35. Results Merouani2008 • Moreover, it was shown that the fuzzy logic group showed advantages regarding the mean arterial blood presure (MAP). The patient's MAP slowly oscillates around the target value set by the intensivist in the fuzzy group, while in the control patients it tended to drift, with more marked amplitudes. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  36. Results Dazzy2001 [9] • Fuzzy logic is the base of a monitoring system. Fuzzy logic principles and neural network techniques were applied to control intravenous insulin administration rates during glucose infusion in critical diabetic patient. • The results showed that the neuro-fuzzy nomogram allowed a faster decrease of blood glucose (BG) levels below 10 mmol/l (A:7.8 vs. B: 13.2 h; P < .02). Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  37. Results Dazzy2001 • It also allowed to reach and maintain a strict control condition around a specific point (6.7 mM/l) as safely as the old one did around a larger glycemic target (between 6.7 and 10 mM/l); P < .0001. • Also, it was concluded that this nomogram can be efficiently and safely used to reach faster and to maintain a near normal BG level in critically ill diabetic patients during intravenous glucose and insulin infusion. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  38. Results Ying1992 [10] • It’s a fuzzy logic-monitoring study. It uses a fuzzy control system to provide closed-loop control of mean arterial pressure (MAP) in post-surgical patients in a cardiac surgical intensive care unit setting by regulating sodium nitroprusside (SNP) infusion. • To evaluate the ability of the fuzzy control system handling the patients with different sensitivity, computer simulation was conducted. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  39. Results Ying1992 • It showed that the clinically fine-tuned fuzzy control SNP system could adapt to a wide range of patient sensitivity, from the sensitive patients (K = -2.88) to the insensitive patients (K = -0.18), a ratio of 16: 1. • MAP is tightly controlled around the desired MAP level. The results of the clinical trials on 12 patients revealed that the performance of the fuzzy control SNP delivery system was clinically acceptable. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  40. Results Huang2006 [11] • Uses fuzzy logic control to provide continuous propofol sedation in order to reduce the effect of agitation on intracranial pressure (ICP) in neurosurgical intensive care unit patients. Fuzzy logic is used on monitoring patients. • The results show that for mean and RMSD (root mean square deviation) of ICP errors, the values of SOFLC group were significantly lower than those of RBC (rule-based controller) and FLC (fuzzy logic controller) groups (P < 0.05), but the values of the RBC and FLC groups showed no significant difference. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  41. Results Huang2006 • It is also concluded that FLC can easily mimic the rule-base of human experts to achieve stable sedation similar to the RBC group. • Furthermore, the results also show that a SOFLC (self-organizing fuzzy logic controller) can provide more stable sedation of ICP pattern because it can modify the fuzzy rule-base to compensate for inter-patient variations. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  42. Results Denai2009 [12] • This study used a fuzzy logic control system on a clinical decision support system (CDSS) in the cardiac intensive care unit (CICU). • The study used a model that was able to reproduce conditions experienced by 7 post operative cardiac surgery patients (hypertension, hypovolemia, vasodilation, systemic inflammatory response system (SIRS)) and Simulated patient scenarios were developed in collaboration with the expert/study anesthetist (3rd author) to reproduce a range of pathophysiological conditions resembling those observed in post-cardiac surgery patients. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  43. Results Denai2009 • All in all, the interventions in this study resulted in a good control of the. Therefore, the preliminary simulation studies showed good feasibility for the application of CDSS for controlling the patients’ cardiovascular system following surgery in a real ICU. • The results demonstrated steady-state performances of the fuzzy rule-based controller for the range of hypertension considered. It was also demonstrated that the multi-drug fuzzy controller produced good responses and target tracking with acceptable overshoots in the MAP (Mean Arterial Pressure). Aditionnaly, as shown in the figure, the other hemodynamic parameters reached reasonable final values. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  44. Discussion The review of the 5 articles found in our research showed us that the results of the clinical trials we’ve found about this specific subject were considered to be positive by the authors. Our results show us that this is an area of medical interest that can be successfully used in the care of critical care patients from several areas of the intensive care units. The articles found reported clinical trials in which the use of the fuzzy logic systems reached better results than the non Artificial Intelligence systems and in many cases with statistically significant results. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  45. Discussion The results of Denai et al and Merouani et al showed that the use of clinical decision support systems in the ICU can be positive, and regarding that it should be seen as a way of fighting the growing difficulty found by clinicians and other medical staff to make fast decisions base on a great amount of information. The articles written by Ying et al, Huang et al and Dazzy et al, although with different domains of application, show a very positive response in the use of AI-systems in monitoring. These systems can represent a large benefit in the ICUs either in the decrease of errors [15], the better adaptation to each particular patient or even in the maintaining of a strict control of outcomes such as glucose levels [17]. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  46. Discussion About the articles included we can also say that all of them used fuzzy logic and that the majority of them were used in monitoring. The domain of applications of the studies found was also something that concerned us from the beginning and in the articles included the domains where artificial intelligence systems were used were cardiac intensive care [16][19], insulin administration[17], neurosurgical intensive care[15] and also in the control of septic shock[18]. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  47. Discussion In the areas and domains of application in which AI systems, in these cases fuzzy-logic, were studied, evidence showed that it can be used efficiently and safely. Although the results obtained by our research were not sufficient to state that the use of Artificial Intelligence systems in the intensive care units is benefitial in every single aspect, it seems to us that there is a need to go further on this subject as it might represent a good breakthrough to enhance the quality and efficiency of the treatment provided to Intensive Care Units’ patients. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  48. Limitations • Inability to access the full text of two articles due to the lack of response from their authors. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  49. Authors 6th Class Alves, J.; Conceição, M.; Dama, C.; Estevão-Costa, N.; Jesus, M.; Lopes, S.; Neto, R.; Pereira, M.; Queirós, P.; Silva, V.; Videira, P. Adviser: Pedro Pereira Rodrigues Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

  50. References [1] Hanson CW 3rd, Marshall BE; Artificial intelligence applications in the intensive care unit; Critical care medicine; 2001 Feb; 29 (2); 427-35 [2] Ramesh, A.N.; Kambhamti, C.; Monson, J.R.T.; Drew, P.J.; Artificial intelligence in medicine; Ann R Coll Surg Engl, 2004; 86: 334–338. [3] Jason H. T. Bates and Michael P. Young; Applying Fuzzy Logic to Medical Decision Making in the Intensive Care Unit; 2003 Apr [4] Ying Zhang, MEng Real-Time Development of Patient-Specific Alarm Algorithms; Proceedings of the 29th Annual International; Conference of the IEEE EMBS; Cité Internationale, Lyon, France August 23-26, 2007 [5]-HELDT, Thomas; LONG, Bill; VERGHESE, George C. ; SZOLOVITS, Peter; MARK, Roger G.; Integrating Data, Models, and Reasoning in Critical Care; Conf Proc IEEE Eng Med Biol Soc. 2006;1:350-3 [6] RAMNARAYAN, Padmanabhan; KAPOOR, Ritika R.; COREN, Michael; NANDURI, Vasantha; TOMLINSON, Amanda L.; TAYLOR, Paul M.; WYATT, Jeremy C.; RITTO, Joseph F.; Measuring the Impact of Diagnostic Decision Support on the Quality of Clinical Decision Making: Development of a Reliable and Valid Composite Score; Journal of the American Medical Informatics Association, 2003; Volume 10 Number 6. Turma 6 6fmup0910@gmail.com Professor Doutor Altamiro Pereira 18-05-2010

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