mestrado integrado em medicina introdução à medicina ii

Mestrado Integrado em MedicinaIntrodução à Medicina II

18-05-2010

Professor

Doutor Altamiro Pereira

ARTIFICIAL INTELLIGENCE FOR

CRITICAL CARE MONITORING AND

DECISION SUPPORT

Final Presentation

IMPACT ON PATIENT OUTCOMES

Turma [email protected]

Professor


Introduction

Previously…

[1] Hanson CW 3rd, Marshall BE; Artificial intelligence applications in the intensive care unit; Critical caremedicine; 2001 Feb; 29 (2); 427-35

Artificial intelligence applications in the

intensive care unit [1]

Systematic review

from 2001

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

We found important to review once more the progresses in this

subject as it’s rapidly evolving.

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Professor


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 is the permanent and thorough care provided to the critical patients in intensive care

units (ICUs).

Introduction

Critical Care: Definition

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].

[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.

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Professor


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 theIntensive 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, FranceAugust 23-26, 2007

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Professor


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

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Professor


Introduction

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].

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.

[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.

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Professor


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 include:

- Artificial neural networks (ANNs),

-Fuzzy expert systems,

-Evolutionary computation and hybrid

intelligent systems.

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,

(…)[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.

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Does the usage of monitoring or clinical decision support systems

that include AI technology improve the quality of patient care?

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Research question

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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.

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Aim

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• 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.

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Specific Objectives

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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.

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Professor


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.

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Data collection methods:

→ Search strategy

ProfessorDoutor Altamiro Pereira

Methods

Articles included by reviewerArticles were searched in:

PubMed;

ISI Web of Knowledge;

SCOPUS.

with no date restriction

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Data collection methods - Query:


Methods

Articles included by reviewer

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• 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.


Data collection methods - Query:


Methods

Articles included by reviewer

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• 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.


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.Professor

Doutor Altamiro PereiraTurma 6

[email protected]

Methods

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Professor


Methods

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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


Professor


Methods

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("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


Professor


Methods

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"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]


Professor


Methods

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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]))


Query used in the ISI Web of Knowledge Database

Professor


Methods

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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)


Query used in the SCOPUS Database

Professor


Methods

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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)


Professor


Methods

Inclusion/exclusion criteria:Inclusion criteria

1.Study design (clinical trials);

2.Study participants of included articles are patients in the intensive care unit;

3.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 stay

or 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.

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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).

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Methods

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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

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Methods

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Search Results

Search was conducted on March 7th, 2010

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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.

. 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.

Abstract's Review

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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%.

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Publication

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Study Type

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Outcomes

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Outcomes

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Results

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• 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.

Merouani2008 [8]


Professor


Results

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• 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.

Merouani2008


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Results

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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).


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Results

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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.


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Results

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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.


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Results

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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.


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Results

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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.


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Results

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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.


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Results

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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.


Professor


Results

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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.



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.

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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].

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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].

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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.

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Professor


Limitations

• Inability to access the full text of two articles due to the lack of response from their authors.

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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

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References

[1] Hanson CW 3rd, Marshall BE; Artificial intelligence applications in the intensive care unit; Critical caremedicine; 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 theIntensive 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, FranceAugust 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.

18-05-2010


ProfessorDoutor Altamiro Pereira Turma 6

[email protected]

18-05-2010

References

[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.

[8] Mehdi Merouani, Bruno Guignard, François Vincent, Stephen W Borron, Philippe Karoubi,Jean-Philippe Fosse, Yves Cohen, Christophe Clec'h, Eric Vicaut, Carole Marbeuf-Gueye, Frederic Lapostolle and Frederic Adnet; Norepinephrine weaning in septic shock patients by closed loop controled based fuzzy logic; 2008 Dec;

[9] Davide Dazzi, Francesco Taddei, Alessandra Gavarini, Enzo Uggerib, Roberto Negro, Antonio Pezzarossa; The control of blood glucose in the critical diabetic patient. A neuro-fuzzy method; 2000; 80-87

[10] Hao Ying, Member, IEEE, Michael McEachern, Donald W. Eddleman, Member IEEE, Louis C. Sheppard, Fellow, IEEE; Fuzzy Control of Mean Arterial Pressure in Postsurgical Patients with Sodium Nitroprusside Infusion; 1992 Oct;

[11] Sheng-Jean Huang, Jiann-Shing Shieh, Mu Fu, Ming-Chien Kao; Fuzzy logic control for intracranial pressure via continuous propofol sedation in a neurosurgical intensive care unit; 2006; 639-647;

[12] Mouloud A. Denaı, Mahdi Mahfouf, Jonathan J. Ross; A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part I: Physiological modelling and decision supportsystem design; 2009; 35-52;

mestrado integrado em medicina introdução à medicina ii

Documents

critical care monitoring

introductioncritical

thorough care

theintensive care unit

critical care conf

development of patient

icu intensive care medicine

intensive care units