SlideShare uma empresa Scribd logo
1 de 111
Medical Data Mining

Lars Juhl Jensen
unstructured data
structured data
Jensen et al., Nature Reviews Genetics, 2012
individual hospitals
central registries
opt-out
opt-in
Danish registries
civil registration system
CPR number
established in 1968
Jensen et al., Nature Reviews Genetics, 2012
national discharge registry
14 years
6.2 million patients
45 million admissions
68 million records
119 million diagnosis
ICD-10
Jensen et al., Nature Reviews Genetics, 2012
reimbursement
not research
diagnosis trajectories
naïve approach
comorbidity
Jensen et al., Nature Reviews Genetics, 2012
confounding factors
“known knowns”
gender
age
type of hospital encounter
Male

Emergency room

Out-patient

In-patient

Female

Jensen et al., submitted, 2013
“known unknowns”
smoking
diet
“unknown unknowns”
reporting biases
disease clustering
temporal correlation
Jensen et al., submitted, 2013
diagnosis trajectories
Jensen et al., submitted, 2013
epilepsy
Jensen et al., submitted, 2013
gout
Jensen et al., submitted, 2013
electronic health records
structured data
Jensen et al., Nature Reviews Genetics, 2012
unstructured data
free text
Danish
busy doctors
psychiatric patients
delusions
text mining
computer
as smart as a dog
teach it specific tricks
named entity recognition
custom dictionaries
diseases
drugs
adverse drug events
expansion rules
orthographic variation
typos
“negative modifiers”
negations
family members
detailed disease profiles
Text mined codes

Assigned codes

4947 3825

32626

Roque et al., PLOS Computational Biology, 2011
comorbidity
Roque et al., PLOS Computational Biology, 2011
patient stratification
Roque et al., PLOS Computational Biology, 2011
cluster characterization
Roque et al., PLOS Computational Biology, 2011
adverse drug reactions
structured data
medication
clinical narrative
possible ADRs
semi-structured data
SPC
Summary of Product Characteristics
drug indications
known ADRs
temporal correlation
link drugs to ADRs
complex filtering
Eriksson et al., submitted, 2013
new ADRs
Drug substance
ADE
Chlordiazepoxide Nystagmus
Simvastatin
Personality
changes
Dipyridamole
Visual impairment
Citalopram
Psychosis
Bendroflumethiazi Apoplexy
de

p-value
4.0e-8
8.4e-8
4.4e-4
8.8e-4
8.5e-3

Eriksson et al., submitted, 2013
ADR frequencies
Eriksson et al., submitted, 2013
heavily medicated
Eriksson et al., submitted, 2013
ADR dose dependency
Eriksson et al., submitted, 2013
ADR similarity
Eriksson et al., submitted, 2013
drug repurposing
Campillos, Kuhn et al., Science, 2008
Acknowledgments
EHR text mining
Peter Bjødstrup
Jensen
Robert Eriksson
Henriette Schmock
Francisco S. Roque
Anders Juul
Marlene Dalgaard
Massimo Andreatta
Sune Frankild
Eva Roitmann
Thomas Hansen
Karen Søeby
Søren Bredkjær
Thomas Werge
Søren Brunak

Disease trajectories
Anders Bøck Jensen
Tudor Oprea
Pope Moseley
Søren Brunak

Adverse drug reactions
Robert Eriksson
Thomas Werge
Søren Brunak

Mais conteúdo relacionado

Semelhante a Medical Data Mining

Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsMedical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Data and text mining of Danish electronic health records
Data and text mining of Danish electronic health recordsData and text mining of Danish electronic health records
Data and text mining of Danish electronic health recordsLars Juhl Jensen
 
Data and text mining of electronic health records
Data and text mining of electronic health recordsData and text mining of electronic health records
Data and text mining of electronic health recordsLars Juhl Jensen
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical network analysis: Linking diseases and genes through data and text mi...
Medical network analysis: Linking diseases and genes through data and text mi...Medical network analysis: Linking diseases and genes through data and text mi...
Medical network analysis: Linking diseases and genes through data and text mi...Lars Juhl Jensen
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsMedical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsMedical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical text mining: Linking diseases, drugs, and adverse reactions
Medical text mining: Linking diseases, drugs, and adverse reactionsMedical text mining: Linking diseases, drugs, and adverse reactions
Medical text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsMedical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsLars Juhl Jensen
 

Semelhante a Medical Data Mining (20)

Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsMedical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactions
 
Medical Data Mining
Medical Data MiningMedical Data Mining
Medical Data Mining
 
Data and text mining of Danish electronic health records
Data and text mining of Danish electronic health recordsData and text mining of Danish electronic health records
Data and text mining of Danish electronic health records
 
Data and text mining of electronic health records
Data and text mining of electronic health recordsData and text mining of electronic health records
Data and text mining of electronic health records
 
Medical Data Mining
Medical Data MiningMedical Data Mining
Medical Data Mining
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
 
Medical network analysis: Linking diseases and genes through data and text mi...
Medical network analysis: Linking diseases and genes through data and text mi...Medical network analysis: Linking diseases and genes through data and text mi...
Medical network analysis: Linking diseases and genes through data and text mi...
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
 
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsMedical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactions
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
 
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsMedical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactions
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
 
Medical text mining: Linking diseases, drugs, and adverse reactions
Medical text mining: Linking diseases, drugs, and adverse reactionsMedical text mining: Linking diseases, drugs, and adverse reactions
Medical text mining: Linking diseases, drugs, and adverse reactions
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
 
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsMedical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
 
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactionsMedical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactions
 

Mais de Lars Juhl Jensen

One tagger, many uses: Illustrating the power of dictionary-based named entit...
One tagger, many uses: Illustrating the power of dictionary-based named entit...One tagger, many uses: Illustrating the power of dictionary-based named entit...
One tagger, many uses: Illustrating the power of dictionary-based named entit...Lars Juhl Jensen
 
One tagger, many uses: Simple text-mining strategies for biomedicine
One tagger, many uses: Simple text-mining strategies for biomedicineOne tagger, many uses: Simple text-mining strategies for biomedicine
One tagger, many uses: Simple text-mining strategies for biomedicineLars Juhl Jensen
 
Extract 2.0: Text-mining-assisted interactive annotation
Extract 2.0: Text-mining-assisted interactive annotationExtract 2.0: Text-mining-assisted interactive annotation
Extract 2.0: Text-mining-assisted interactive annotationLars Juhl Jensen
 
Network visualization: A crash course on using Cytoscape
Network visualization: A crash course on using CytoscapeNetwork visualization: A crash course on using Cytoscape
Network visualization: A crash course on using CytoscapeLars Juhl Jensen
 
STRING & STITCH : Network integration of heterogeneous data
STRING & STITCH: Network integration of heterogeneous dataSTRING & STITCH: Network integration of heterogeneous data
STRING & STITCH : Network integration of heterogeneous dataLars Juhl Jensen
 
Biomedical text mining: Automatic processing of unstructured text
Biomedical text mining: Automatic processing of unstructured textBiomedical text mining: Automatic processing of unstructured text
Biomedical text mining: Automatic processing of unstructured textLars Juhl Jensen
 
Network Biology: A crash course on STRING and Cytoscape
Network Biology: A crash course on STRING and CytoscapeNetwork Biology: A crash course on STRING and Cytoscape
Network Biology: A crash course on STRING and CytoscapeLars Juhl Jensen
 
Cellular Network Biology: Large-scale integration of data and text
Cellular Network Biology: Large-scale integration of data and textCellular Network Biology: Large-scale integration of data and text
Cellular Network Biology: Large-scale integration of data and textLars Juhl Jensen
 
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...Lars Juhl Jensen
 
STRING & related databases: Large-scale integration of heterogeneous data
STRING & related databases: Large-scale integration of heterogeneous dataSTRING & related databases: Large-scale integration of heterogeneous data
STRING & related databases: Large-scale integration of heterogeneous dataLars Juhl Jensen
 
Tagger: Rapid dictionary-based named entity recognition
Tagger: Rapid dictionary-based named entity recognitionTagger: Rapid dictionary-based named entity recognition
Tagger: Rapid dictionary-based named entity recognitionLars Juhl Jensen
 
Network Biology: Large-scale integration of data and text
Network Biology: Large-scale integration of data and textNetwork Biology: Large-scale integration of data and text
Network Biology: Large-scale integration of data and textLars Juhl Jensen
 
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and textNetwork biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and textLars Juhl Jensen
 
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and textNetwork biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and textLars Juhl Jensen
 
Biomarker bioinformatics: Network-based candidate prioritization
Biomarker bioinformatics: Network-based candidate prioritizationBiomarker bioinformatics: Network-based candidate prioritization
Biomarker bioinformatics: Network-based candidate prioritizationLars Juhl Jensen
 
The Art of Counting: Scoring and ranking co-occurrences in literature
The Art of Counting: Scoring and ranking co-occurrences in literatureThe Art of Counting: Scoring and ranking co-occurrences in literature
The Art of Counting: Scoring and ranking co-occurrences in literatureLars Juhl Jensen
 
Text-mining-based retrieval of protein networks
Text-mining-based retrieval of protein networksText-mining-based retrieval of protein networks
Text-mining-based retrieval of protein networksLars Juhl Jensen
 
Gene association networks: Large-scale integration of data and text
Gene association networks: Large-scale integration of data and textGene association networks: Large-scale integration of data and text
Gene association networks: Large-scale integration of data and textLars Juhl Jensen
 

Mais de Lars Juhl Jensen (20)

One tagger, many uses: Illustrating the power of dictionary-based named entit...
One tagger, many uses: Illustrating the power of dictionary-based named entit...One tagger, many uses: Illustrating the power of dictionary-based named entit...
One tagger, many uses: Illustrating the power of dictionary-based named entit...
 
One tagger, many uses: Simple text-mining strategies for biomedicine
One tagger, many uses: Simple text-mining strategies for biomedicineOne tagger, many uses: Simple text-mining strategies for biomedicine
One tagger, many uses: Simple text-mining strategies for biomedicine
 
Extract 2.0: Text-mining-assisted interactive annotation
Extract 2.0: Text-mining-assisted interactive annotationExtract 2.0: Text-mining-assisted interactive annotation
Extract 2.0: Text-mining-assisted interactive annotation
 
Network visualization: A crash course on using Cytoscape
Network visualization: A crash course on using CytoscapeNetwork visualization: A crash course on using Cytoscape
Network visualization: A crash course on using Cytoscape
 
STRING & STITCH : Network integration of heterogeneous data
STRING & STITCH: Network integration of heterogeneous dataSTRING & STITCH: Network integration of heterogeneous data
STRING & STITCH : Network integration of heterogeneous data
 
Biomedical text mining: Automatic processing of unstructured text
Biomedical text mining: Automatic processing of unstructured textBiomedical text mining: Automatic processing of unstructured text
Biomedical text mining: Automatic processing of unstructured text
 
Network Biology: A crash course on STRING and Cytoscape
Network Biology: A crash course on STRING and CytoscapeNetwork Biology: A crash course on STRING and Cytoscape
Network Biology: A crash course on STRING and Cytoscape
 
Cellular networks
Cellular networksCellular networks
Cellular networks
 
Cellular Network Biology: Large-scale integration of data and text
Cellular Network Biology: Large-scale integration of data and textCellular Network Biology: Large-scale integration of data and text
Cellular Network Biology: Large-scale integration of data and text
 
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
 
STRING & related databases: Large-scale integration of heterogeneous data
STRING & related databases: Large-scale integration of heterogeneous dataSTRING & related databases: Large-scale integration of heterogeneous data
STRING & related databases: Large-scale integration of heterogeneous data
 
Tagger: Rapid dictionary-based named entity recognition
Tagger: Rapid dictionary-based named entity recognitionTagger: Rapid dictionary-based named entity recognition
Tagger: Rapid dictionary-based named entity recognition
 
Network Biology: Large-scale integration of data and text
Network Biology: Large-scale integration of data and textNetwork Biology: Large-scale integration of data and text
Network Biology: Large-scale integration of data and text
 
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and textNetwork biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and text
 
Cellular Network Biology
Cellular Network BiologyCellular Network Biology
Cellular Network Biology
 
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and textNetwork biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and text
 
Biomarker bioinformatics: Network-based candidate prioritization
Biomarker bioinformatics: Network-based candidate prioritizationBiomarker bioinformatics: Network-based candidate prioritization
Biomarker bioinformatics: Network-based candidate prioritization
 
The Art of Counting: Scoring and ranking co-occurrences in literature
The Art of Counting: Scoring and ranking co-occurrences in literatureThe Art of Counting: Scoring and ranking co-occurrences in literature
The Art of Counting: Scoring and ranking co-occurrences in literature
 
Text-mining-based retrieval of protein networks
Text-mining-based retrieval of protein networksText-mining-based retrieval of protein networks
Text-mining-based retrieval of protein networks
 
Gene association networks: Large-scale integration of data and text
Gene association networks: Large-scale integration of data and textGene association networks: Large-scale integration of data and text
Gene association networks: Large-scale integration of data and text
 

Último

Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesZilliz
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machinePadma Pradeep
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Wonjun Hwang
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfRankYa
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 

Último (20)

Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector Databases
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machine
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdf
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 

Medical Data Mining