The document discusses a clinical decision support system called SIADe for diagnosing dementia, Alzheimer's disease, and mild cognitive impairment. It lists participating institutions and outlines the agenda which includes motivation, objectives, clinical decision modeling, achievements, and future work. The motivation discusses the aging population and high prevalence of dementia. The objective is to design a decision support system to aid in diagnosis. It will use a knowledge base, inference engine, and mobile app for physicians. Clinical decision modeling involves identifying diagnostic guidelines, preprocessing patient records, building a Bayesian network model, and evaluating model performance. The system aims to address information overload and integrate evidence-based knowledge to help physicians with clinical decision making.
Decision Support System for clinical practice created on the basis of the Un...blejyants
The company Socmedica developing an expert system of decision support for medical information systems. The product is aimed at solving the problem of medical errors.
How Clinical Decision Support Systems (CDSS) is the right tool for physicians?Eurostars Programme EUREKA
We believe that CDSS delivered using information systems, ideally with the electronic medical record as the platform, will finally provide decision makers with tools making it possible to achieve large gains in performance, narrow gaps between knowledge and practice, and improve safety.
Decision Support System for clinical practice created on the basis of the Un...blejyants
The company Socmedica developing an expert system of decision support for medical information systems. The product is aimed at solving the problem of medical errors.
How Clinical Decision Support Systems (CDSS) is the right tool for physicians?Eurostars Programme EUREKA
We believe that CDSS delivered using information systems, ideally with the electronic medical record as the platform, will finally provide decision makers with tools making it possible to achieve large gains in performance, narrow gaps between knowledge and practice, and improve safety.
The Inferscience introduce Infera, a clinical decision support engine that improves decision making, assisting clinicians to work more quick-witted. In this presentation, you can get the detailed information about this Advanced Clinical Decision Support System.
The Pre-Anesthesia Evaluation Module is designed to manage the data and workflow of pre-anesthesia evaluation, either at the pre-admission testing visit or at the surgeon’s office. Medical history is collected from patients via a self-administered Tablet questionnaire, and available data regarding that patient is also downloaded from the EHR. This data is used to determine what testing is needed prior to anesthesia. This system can be used in the surgeon’s office, to help avoid anesthesia complications and help prevent canceled or delayed cases. A set of screenshots and an overview of the module can be reviewed via this downloadable PowerPoint presentation.
Virtual Worlds Paper Ni09 Hansen Murray Erdm2hansen
A brief overview of Virtual Worlds as a pedagogical tool for the education of health care education. By: Margaret Hansen, Associate Professor, School of Nursing, at NI09 Congress, June 29, 2009, Helsinki, Finland
The Inferscience introduce Infera, a clinical decision support engine that improves decision making, assisting clinicians to work more quick-witted. In this presentation, you can get the detailed information about this Advanced Clinical Decision Support System.
The Pre-Anesthesia Evaluation Module is designed to manage the data and workflow of pre-anesthesia evaluation, either at the pre-admission testing visit or at the surgeon’s office. Medical history is collected from patients via a self-administered Tablet questionnaire, and available data regarding that patient is also downloaded from the EHR. This data is used to determine what testing is needed prior to anesthesia. This system can be used in the surgeon’s office, to help avoid anesthesia complications and help prevent canceled or delayed cases. A set of screenshots and an overview of the module can be reviewed via this downloadable PowerPoint presentation.
Virtual Worlds Paper Ni09 Hansen Murray Erdm2hansen
A brief overview of Virtual Worlds as a pedagogical tool for the education of health care education. By: Margaret Hansen, Associate Professor, School of Nursing, at NI09 Congress, June 29, 2009, Helsinki, Finland
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Crowdsourced annotations data offers cognitive computing systems insights in lay semantics. This is especially important in health care, where medical terminology is often not aligned with patients `lay' language. However, the general crowd often has limited medical knowledge. Therefore this research investigated the opportunities of social health websites for obtaining ground truth annotations data for cognitive computing systems including clinical decision support systems. By identifying these websites and analyzing their data, it offers a starting point for the future utilization of user-generated health content for cognitive systems. However, the opportunities of social health data are currently limited by various legal regulations. Therefore this paper also dwells on the legal aspects of implementing social health data for cognitive computing systems.
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Evidence based decision making in periodonticsHardi Gandhi
INTRODUCTION TO EVIDENCE BASED DENTISTRY
EVIDENCE BASED PERIODONTOLOGY
NEED, PRINCIPLES, GOALS AND ADVANTAGES OF EBDM
SKILLS NEEDED FOR EBDM
ASSESING THE EVIDENCE
INCORPORATING INTO THE PRACTICE
Epidemiological Analysis Workshop By Dr Suzanne Campbell COUNTDOWN on NTDs
This workshop was held in Yaounde, Cameroon on 24th March 2017 as part of the 'Towards Elimination of Schistosomiasis: A Paradigm Shift' Conference organised by Prof. Louis Albert Tchuem Tchuente, Director of the Centre for Schistosomiasis and Parasitology.
The 10th Annual Utah Health Services Research Conference: Iterative Development of Sepsis Detection Algorithms for the Emergency Department. By: Peter Haug - Intermountain Healthcare
Health Services Research Conference: March 16, 2015
Patient Centered Research Methods Core, University of Utah, CCTS
Guest Lecturer at the University of Dayton - 03 April 2013
Agenda:
- What is IBM Watson and why is it important?
- How is IBM putting Watson to work?
- What can we expect in the future?
The Learning Health System: Thinking and Acting Across ScalesPhilip Payne
A Learning Health System (LHS) can be defined as an environment in which knowledge generation processes are embedded into daily clinical practice in order to continually improve the quality, safety, and outcomes of healthcare delivery. While still largely an aspirational goal, the promise of the LHS is a future in which every patient encounter is an opportunity to learn and improve that patient’s care, as well as the care their family and broader community receives. The foundation for building such an LHS can and should be the Electronic Health Record (EHR), which provides the basis for the comprehensive instrumentation and measurement of clinical phenotypes, as well as a means of delivering new evidence at the patient- and population levels. In this presentation, we will explore the ways in which such EHR-derived phenotypes can be combined with complementary data across a spectrum from biomolecules to population level trends, to both generate insights and deliver such knowledge in the right time, place, and format, ultimately improving clinical outcomes and value.
Using real-world evidence to investigate clinical research questionsKarin Verspoor
Adoption of electronic health records to document extensive clinical information brings with it the opportunity to utilise that information to support clinical research, and ultimately to support clinical decision making. In this talk, I discuss both these opportunities and the challenges that we face when working with real-world clinical data, and introduce some of the strategies that we are adopting to make this data more usable, and to extract more value from it. I specifically discuss the use of natural language processing to transform clinical documentation into structured data for this purpose.
This presentation is meant to help choose the appropriate statistical analysis for IBDP Biology IAs. It was created as support for teachers but also useful for students.
Within the presentation, we discuss different types of biological data, and how to describe and analyse it using mathematics.
A presentation covering research fraud, and some basic concepts for interpreting papers. The presentation was made at the annual congress of PainSA, Johannesburg, South Africa, 2015.
Anurati Mathur & Propeller Health @ Madison's Big Data MeetupAnurati Mathur
Using healthcare data - context & considerations for collecting, cleansing, analyzing, and displaying geospatial and temporal data, with a focus on Propeller Health's program in Louisville, KY.
Similar to A Clinical Decision Support System For Alzheimer´s Disease and Other Related Mental Disorders (20)
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
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JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
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Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
2. Participating Institutions
• Center for Studies and Research on Aging (CEPE-Rio),
Vital Brazil Institute, Rio de Janeiro.
• Center for Alzheimer's Disease and Related Disorder (CDA-IPUB-UFRJ),
Institute of Psychiatry, Federal University of Rio de Janeiro.
• Institute of Computing, Federal Fluminense University (IC-UFF), Niterói.
• Midiacom Lab, Federal Fluminense University, Niterói.
• Medical Sciences College, Rio de Janeiro State University, Rio de Janeiro.
• National Laboratory for Scientific Computing (INCT), Brazil.
• Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro.
• King’s College London (KCL).
4. Motivation
• Alzheimer’s disease represents 50-80% of dementia cases.
• Dementia has a prevalence of 7.8% of elderly from a local
community of São Paulo. Herrera et. al. (2002)
• Another survey indicated 6.9% of elderly from São Paulo.
Alzheimer’s represented 59% of dementia cases. Bottino et. al. (2006)
• Dementia has a prevalence from 4.6% to 9.7% of elderly.
Rodriguez et. al. (2008).
• In 2020, Brazil will occupy the sixth worldwide ranking in
terms of elderly population.
5. Motivation
Decision support systems have been designed for helping
physician in clinical decision making.
Benefits:
• Ability to address the information overload that
physicians face.
• Integrating evidence-based knowledge.
6. Objective
Design and develop a clinical decision support system for
diagnosis of Dementia, Alzheimer`s Disease and Mild
Cognitive Impairment.
Why?
• World-wide population aging.
• High prevalence of Dementia among elderly.
• Early diagnosis of Alzheimer’s Disease can improve
the treatment efficiency, patient quality of life and
reduce the costs for public health systems.
7. CDSS - Principal Components
Physician
Mobile
application
Communication
interface
Inference engine
Knowledge base
Ask for a decision
support for diagnosis.
Internet
HTTP messages
Provides suggestions
for possible diagnosis
that match a patient
signs and symptoms.
Clinical decision
support system
Published references
related to diagnosis
criteria
Knowledge
acquisition
Normal controls and
patients’ clinical
records
8. Decision Modeling Process
Decision
modeling for
a disease Identify the
diagnosis
guideline for
the disease
Diagnosis
criteria for
the disease
Preprocess the
clinical records
of patients and
normal controls
Training
database
Build a
Bayesian
network
structure
Perform
Bayesian
parameter
learning
Evaluate the
Bayesian
learning
Deploy the
decision
model Acceptable
performance
measures?
Review the
decision
model
Additional
attributes
Additional
clinical
records
Decision model
modeled
No
Yes
9. Patient care
requested
Take patient medical
history and/or carry out
clinical examinations for
dementia screening
Does the
patient have
possible
dementia?
Carry out
neuropsycholo
gical tests for
Dementia
Carry out
treatment for
other diseases
Treatment
follow-up (*)
If diagnosis
of Dementia
confirmed?
Carry out psychological
tests exams for Mild
Cognitive Impairment
Carry out
neuropsychological tests
and exams for Dementia
due to Alzheimer s
Disease
Treatment
follow-up (*)
Treatment for
Dementia due to
Alzheimer s Disease
follow-up (*)
Treatment
follow-up (*)
Treatment for
Mild Cognitive
Impairment
follow-up (*)
If diagnosis of
Alzheimer s Disease
confirmed?
No Yes
Yes
No
If diagnosis of Mild
Cognitive
Impairment
confirmed?
No Yes No Yes
DiagnosisofDementia,AlzheimersDiseaseand
MildCognitiveImpairment
(*) A treatment should be defined by a physician
Diagnosis Process for Dementia, AD and MCI
10. Decision Modeling Process
Decision
modeling for
a disease Identify the
diagnosis
guideline for
the disease
Diagnosis
criteria for
the disease
Preprocess the
clinical records
of patients and
normal controls
Training
database
Build a
Bayesian
network
structure
Perform
Bayesian
parameter
learning
Evaluate the
Bayesian
learning
Deploy the
decision
model Acceptable
performance
measures?
Review the
decision
model
Additional
attributes
Additional
clinical
records
Decision model
modeled
No
Yes
11. Preprocess the
patients’ health
records Integrate the patients’
health records spread
across multiple
spreadsheets in one
training database
Database
balancing
Attributes
selection
Discretize
numerical
attributes
Training
database
preprocessed
Preprocessing the Health Records
12. positive
135
negative
45
Alzheimer’s Disease
Dementia
Mild Cognitive Impairment
negative
67
positive
180
negative
35
positive
32
Composed by:
• Normal controls and patients’ health records provided by Center for Alzheimer's
Disease and Related Disorder, Institute of Psychiatry, UFRJ.
Project approved by Research Ethics Committee (2012).
Training Database
13. positive
135
negative
45
Alzheimer’s Disease Dementia Mild Cognitive Impairment
negative
67
positive
180
negative
35
positive
32
BeforebalancingAfterbalancing
negative
35
positive
32
negative
134
positive
180positive
135
negative
90
Data Balancing
Method:
SMOTE (Synthetic Minority Over-sampling Technique)1
1: Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; Kegelmeyer, W. P. SMOTE: Synthetic Minority Over-Sampling Technique.
Journal of Artificial Intelligence Research, v. 16, p. 321-357, 2002.
14. Attribute( MD( Entropy(
Mini$mental*state*examination*score* 5* 0.2791*
Clinical*Dementia*rating*scale* 11* 0.2441*
Pfeffer*questionnaire*score* 12* 0.2074*
Verbal*fluency*test*score* 8* 0.1665*
Clock*drawing*test*scale* 12* 0.0881*
Trial*making*test*scale* 40* 0.0829*
Age* 4* 0.0684*
Lawton*scale* 58* 0.0342*
IQCode*score* 56* 0.0324*
Stroop*color*word*test* 60* 0.0209*
Gender* 9* 0.0001*
Depression* 16* 0.0001*
Education*level* 2* 0.0423*
Rey*Complex*Figure* 78* 0.0181*
Cambridge*Cognitive*Examination* 79* 0.0000*
Digit*symbol* 81* 0.0000*
Neuropsychiatric*inventory* 56* 0.0000*
Cornell*depression*scale* 62* 0.0000*
Timed*Up*and*Go* 64* 0.0000*
POMA* 85* 0.0000*
Sit$to$Stand*test* 97* 0.0000*
Digit*span*test* 62* 0.0000*
Rey*Auditory$Verbal*Learning* 93* 0.0000*
Brain*anatomical*structures*volume* 83* 0.0000*
Criteria:
Attributes filtered by missing
data rate (MD<60%)
AND
Information Gain
(Entropy>0.00001)
MD = Missing data ratio. It is calculated by
the ratio between the number of missing
data records and the total number of
records of the corresponding attribute.
Attributes Selection
15. Bayes’ Rule
Bayes’'rule:'
P(h | e) =
P(e | h)⋅ P(h)
P(e) !
the probability of a hypothesis h conditioned upon some evidence e is equal to its
likelihood P(e | h)
!
times its probability prior to any evidence P(h), normalized by
dividing P(e).
Definition: after applying Bayes’ theorem to obtain P(h | e) adopt that as your
posterior degree of belief in h, or Bel(h) = P(h | e).
Given dichotomous random variables (takes on one of only two possible values when
observed or measured):
P(h | e) =
P(e | h)⋅ P(h)
P(e | h)⋅ P(h)+ P(e |¬h)⋅ P(¬h) !
17. Example:
Suppose that we have this very simple model of flu causing a high temperature with
the following prior and conditional probabilities distribution values.
If an individual has a high temperature (i.e., the evidence available is Hi=True), the
computation for this diagnostic reasoning is as follows:
Bel(Flu = True) =α ⋅ P(Hi = True | Flu = True)⋅ P(Flu = True) =α ⋅0.05⋅0.9 =α ⋅0.045
Bel(Flu = False) =α ⋅ P(Hi = True | Flu = False)⋅ P(Flu = False) =α ⋅0.95⋅0.2 =α ⋅0.19
!
Pr(Flu=True) 5%
Pr(Flu=False) 95%
Pr(Hi=True | Flu=True) 90%
Pr(Hi=False | Flu=True) 10%
Pr(Hi=True | Flu=False) 20%
Pr(Hi=False | Flu=False) 80%
Bayesian Network
18. If an individual has a high temperature (i.e., the evidence available is Hi=True), the
computation for this diagnostic reasoning is as follows:
Bel(Flu = T) =α ⋅ P(Hi = T | Flu = T)⋅ P(Flu = T) =α ⋅0.05⋅0.9 =α ⋅0.045
Bel(Flu = F) =α ⋅ P(Hi = T | Flu = F)⋅ P(Flu = F) =α ⋅0.95⋅0.2 =α ⋅0.19
Bel(Flu = T)+ Bel(Flu = F) =1 given that variable states are mutually exclusive.
So,α ⋅0.045+α ⋅0.19 =1∴α =
1
0.045+ 0.19
Bel(Flu = True) =
0.045
0.19+ 0.045
= 0.19
Bel(Flu = False) =
0.19
0.19+ 0.045
= 0.81
Bayesian Network
19. Decision Modeling Process
Decision
modeling for
a disease Identify the
diagnosis
guideline for
the disease
Diagnosis
criteria for
the disease
Preprocess the
clinical records
of patients and
normal controls
Training
database
Build a
Bayesian
network
structure
Perform
Bayesian
parameter
learning
Evaluate the
Bayesian
learning
Deploy the
decision
model Acceptable
performance
measures?
Review the
decision
model
Additional
attributes
Additional
clinical
records
Decision model
modeled
No
Yes
21. Decision Modeling Process
Decision
modeling for
a disease Identify the
diagnosis
guideline for
the disease
Diagnosis
criteria for
the disease
Preprocess the
clinical records
of patients and
normal controls
Training
database
Build a
Bayesian
network
structure
Perform
Bayesian
parameter
learning
Evaluate the
Bayesian
learning
Deploy the
decision
model Acceptable
performance
measures?
Review the
decision
model
Additional
attributes
Additional
clinical
records
Decision model
modeled
No
Yes
23. Discretize Numerical Attributes
Minimum&Description&Length&(MDL)&(1):&
Occam’s razor: choose the shortest explanation for the observed data.
hMAP = argmaxP(D | h)⋅ P(h)
hMAP = argmax lgP(D | h)+ lgP(h)[ ]
hMAP = argmin −lgP(D | h)− lgP(h)[ ]
This equation can be interpreted as a statement that short hypotheses are preferred.
Assuming that LC(i) ≅ description length of message i with respect to C.
LCD|H
(D | h) = −logP(D | h) , where CD|h is the optimal code for describing data D.
LCH
(h) = −logP(h) , where CH is the optimal code for hypothesis space H.
So:
hMAP ∝argmin
H∈h
LCD|h
(D | h)+ LCH
(h)#$ %&
1: Kononenko, I. On biases in estimating multi-valued attributes. International Joint Conference on Artificial Intelligence, 1995.
Lawrence Erlbaum Associates. p.1034-1040.
24. Bayesian Learning: EM Algorithm
Expectation-Maximization algorithm (1)
(1/3):
• Find a maximum likelihood estimates for θ when given dataset is incomplete.
• Starts with random probability distributions.
• Alternates between two steps.
• Expectation step: “complete” the data set by using the current parameter
estimates ˆθ (calculate expectations for missing values).
• Maximization step: use the “complete” data set to find a new maximum
likelihood estimate ˆθ ' for the parameters.
1: Dempster, A. P.; Laird, N. M.; Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the
Royal Statistical Society. Series B (Methodological), v. 39, n. 1, p. 1-38, 1977. ISSN 0035-9246.
25. Bayesian Learning: EM Algorithm
Expectation-Maximization algorithm (2/3):
Let:
yi – observable variables.
zi – latent variables.
θ – all possible parameters in the model.
Goal is to find:
ˆθ = argmax
θ
P(θ | D)
P(θ | yi,..., yn )∝ P(y1...yn |θ)⋅ P(θ)∝ P(y1...yn |θ)
As P(y1...yn |θ) = P(y1...yn, z1...zn |θ)∫ dz
26. Bayesian Learning: EM Algorithm
Expectation-Maximization algorithm (3/3):
Using the auxiliary function:
Q(θ |θt ) = P(z1...zn |θt, y1...yn )∫ logP(θ, z | y1...yn )dz
What EM algorithm does is:
θt+1 = argmaxQ(θ |θt ), with random starting point.
E-Step: find the probabilities for z1…zn if all parameters are fixed to θt
M-Step: now that P(z1...zn |θt, y1...yn ) is fixed, find θ that maximizes the integral.
27. Utility
Dementia?
6%
94%
>13
0-13
Education
82%
18%
Female
Male
Gender
56%
44%
>72
0-72
Age
58%
42%
Positive
Negative
Diagnosis
1%
1%
16%
21%
50%
12%
5
4
3
2
1
0
Clock Drawing Test
(CDT) scale
20%
41%
39%
27-30
18-26
0-17
Mini Mental State Exam
(MMSE) score
51%
46%
16%
>11
5-11
0-4
Verbal Fluency Test
(VFT) score
19%
15%
32%
29%
6%
3-severe
2-moderate
1-mild
0.5-very mild
0-normal control
Clinical Dementia Rating (CDR)
scale
72%
28%
>3.55
0-3.55
IQCode (Informant
Questionnaire on Cognitive
Decline in the Elderly) score
74%
26%
>9
0-9
Lawton scale
71%
29%
>15
0-15
Stroop color word test
72%
18%
10%
>59
17-59
0-16
Trial Making Test (TMT)
39%
61%
>51
0-51
Berg balance scale
78%
8%
14%
>2
1-2
0
Pfeffer questionnaire
32%
68%
Presence
Absence
Depression
30. Decision Modeling Process
Decision
modeling for
a disease Identify the
diagnosis
guideline for
the disease
Diagnosis
criteria for
the disease
Preprocess the
clinical records
of patients and
normal controls
Training
database
Build a
Bayesian
network
structure
Perform
Bayesian
parameter
learning
Evaluate the
Bayesian
learning
Deploy the
decision
model Acceptable
performance
measures?
Review the
decision
model
Additional
attributes
Additional
clinical
records
Decision model
modeled
No
Yes
31. Bayesian Learning: Results Evaluation
1. Using cross-validation with 4 folds, we compared
Bayesian Network performance with other well-known
classifiers:
• Näive Bayes
• Logistic Regression
• Multilayer Perceptron
• Decision Table
• Decision Stump using Boost algorithm
• J48 Decision Tree
2. Qualitative evaluation of sensitivity analysis results.
32. Bayesian Learning: Results Evaluation
Classification performance measures:
Performance measure Acronym Domain Best score
Area under ROC curve AUC [0, 1] 1
Harmonic mean of
precision and recall
F1 [0, 1] 1
Mean square error MSE [0, 1] 0
Mean cross-entropy MXE [0, ∞) 0
39. 1. Design and develop a prototype application.
http://siade.midiacom.uff.br
Future Works
40.
41. 2. Evaluate the decision support system in a real clinical
daily routine.
3. Improve the decision model with a continuous Bayesian
network learning process.
4. Extend the clinical decision model to other domains.
Future Works
42. About Bayesian modeling:
1. How to establish a continuous parameters adjustment method for Bayesian
models?
2. A higher missing data ratio may cause bias, imprecision or confounding. Is it
possible finding out a model for missing data? What should be a reasonable
level of missing data ratio?
3. The independence between random variables with same parent is an
assumption from Bayesian-based models. What is the better way to deal
with it? What are its effects in the Bayesian results?
Questions
43. About Dementia and other related mental disorders:
4. How could we define a health cost-effective analysis for utility node?
5. Is there any other patients database with normal controls that could be used
as training database for Bayesian learning?
6. How could we integrate the identified decision points of the current clinical
guidelines with the decision boxes of Bayesian networks?
Questions
44. About Decision-Support System:
7. Is there any health information system that we could integrate with our
decision-support model?
8. Depends on (7), how could we assure the semantic interoperability between
the knowledge base mapped on decision-support model and the health
information system?
9. Our decision-support system has focused on clinical diagnosis process. Is
there another health care area that is relevant for designing and developing
a similar decision-support system? (e.g., patient-centered treatment
planning, health monitoring system...)
Questions
45. This research was partially supported by:
• FAPERJ (Research Support Foundation of the State of Rio de Janeiro).
• CNPQ (National Council for Scientific and Technological Development).
Acknowledgements
46. Acknowledgements
I would like to thank…
Robin Morris, Daniel Stahl (King’s College London),
Jerson Laks (Federal University of Rio de Janeiro), and
Daniel Mograbi (Pontifical Catholic University of Rio de Janeiro)
for such opportunity.
47. And I thank you for the
audience!
…any question?
Acknowledgements
seixas_flavioluiz@gmail.com