Possibilities and pitfalls of AI in PICU
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This document discusses the possibilities and pitfalls of using artificial intelligence in pediatric intensive care units. It outlines key considerations for developing AI models using healthcare data, including choosing appropriate data and statistical techniques, preparing data by addressing issues like missing values and confounding factors, developing and evaluating models, and ensuring models are implemented ethically. Small initial applications of AI in PICUs are highlighted as proof of concept to motivate further exploration and research.
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4222020 Originality Reporthttpsucumberlands.blackboar.docx
4/22/2020 Originality Report
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=c27f66fc-c5df-47dc-9f48-d005b34b35d9… 1/4
%59
%0
SafeAssign Originality Report
Spring 2020 - Data Science & Big Data Analy (ITS-836-9) - Second Bi-T… • Final Exam (project)
%59Total Score: High riskNaveeda Reddy Anugu
Submission UUID: eb75ebf8-071b-3035-cadd-c5c1220ab248
Total Number of Reports
1
Highest Match
59 %
Final.pptx
Average Match
59 %
Submitted on
04/22/20
10:09 PM CDT
Average Word Count
1,130
Highest: Final.pptx
%59Attachment 1
Institutional database (6)
Student paper Student paper Student paper
Student paper Student paper Student paper
Internet (1)
hospitalcareers
Top sources (3)
Excluded sources (0)
View Originality Report - Old Design
Word Count: 1,130
Final.pptx
2 7 5
6 1 3
4
2 Student paper 7 Student paper 5 Student paper
FINAL SPONSOR PRESENTATION
Naveeda Reddy Anugu
University of the Cumberlands
Professor: Dr Kelly Wibbenmeyer
DATE: 04/22/2020
SITUATION
The hospital intends to utilize patient data gathers to enhance the health of its members and offer services at relatively lower costs
This is in response to patients’ previous complaints about the high cost of treatment and associated low health results
The impacts points toward ineffective treatment plans and harmful prescription of medication to susceptible populations
Data collected include diseases treated and billed for, prescriptions were given, and the types of treatments given
The data may be used in big data analytics to enhance treatment options, customize patients’ risks and to support customized treatment intervention
Advanced and frontier big data analytics has the potential to improve health. Data can be used to distinguish between effective and ineffective medical practices
depending on an individual patient. Aetna, is a company that collects data for more than 20 million customers and from its pilot project studies, it is evident that
health institutions can promote health and reduce costs by utilizing Big Data Analytics. 2
PROJECT GOALS
The project entails unveiling an invention that will permit big data analytics to buttress decisions and intervention in healthcare settings
The aim of the intervention include: Creation of patient centered decision making support systems
1
2
3
4
2
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport?attemptId=c27f66fc-c5df-47dc-9f48-d005b34b35d9&course_id=_114115_1&download=true&includeDeleted=true&print=true&force=true
4/22/2020 Originality Report
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=c27f66fc-c5df-47dc-9f48-d005b34b35d9… 2/4
The aim of the intervention include: Creation of patient-centered decision making support systems
Utilization of patient's data contained in clinical trails, insurance companies records and in patient’s health reco.
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Patients should be able to trust their doctors blindly. However, for most of the masses getting a second opinion is has become a necessity. A doctor, albeit a human, should be able to guarantee their diagnoses of the patient. With the current advancements of technology, it is shocking that technology is not being utilized to give this guarantee. Technology is present and ever evolving to remove human error.
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Diabetes mellitus is a common disease caused by a set of metabolic ailments
where the sugar stages over drawn-out period is very high. It touches diverse organs
of the human body which therefore harm a huge number of the body's system, in
precise the blood strains and nerves. Early prediction in such disease can be exact
and save human life. To achieve the goal, this research work mainly discovers
numerous factors associated to this disease using machine learning techniques.
Machine learning methods provide effectual outcome to extract knowledge by building
predicting models from diagnostic medical datasets together from the diabetic
patients. Quarrying knowledge from such data can be valuable to predict diabetic
patients. In this research, six popular used machine learning techniques, namely
Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), C4.5 Decision
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4222020 Originality Reporthttpsucumberlands.blackboar.docx
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https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=c27f66fc-c5df-47dc-9f48-d005b34b35d9… 1/4
%59
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Submitted on
04/22/20
10:09 PM CDT
Average Word Count
1,130
Highest: Final.pptx
%59Attachment 1
Institutional database (6)
Student paper Student paper Student paper
Student paper Student paper Student paper
Internet (1)
hospitalcareers
Top sources (3)
Excluded sources (0)
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Final.pptx
2 7 5
6 1 3
4
2 Student paper 7 Student paper 5 Student paper
FINAL SPONSOR PRESENTATION
Naveeda Reddy Anugu
University of the Cumberlands
Professor: Dr Kelly Wibbenmeyer
DATE: 04/22/2020
SITUATION
The hospital intends to utilize patient data gathers to enhance the health of its members and offer services at relatively lower costs
This is in response to patients’ previous complaints about the high cost of treatment and associated low health results
The impacts points toward ineffective treatment plans and harmful prescription of medication to susceptible populations
Data collected include diseases treated and billed for, prescriptions were given, and the types of treatments given
The data may be used in big data analytics to enhance treatment options, customize patients’ risks and to support customized treatment intervention
Advanced and frontier big data analytics has the potential to improve health. Data can be used to distinguish between effective and ineffective medical practices
depending on an individual patient. Aetna, is a company that collects data for more than 20 million customers and from its pilot project studies, it is evident that
health institutions can promote health and reduce costs by utilizing Big Data Analytics. 2
PROJECT GOALS
The project entails unveiling an invention that will permit big data analytics to buttress decisions and intervention in healthcare settings
The aim of the intervention include: Creation of patient centered decision making support systems
1
2
3
4
2
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport?attemptId=c27f66fc-c5df-47dc-9f48-d005b34b35d9&course_id=_114115_1&download=true&includeDeleted=true&print=true&force=true
4/22/2020 Originality Report
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=c27f66fc-c5df-47dc-9f48-d005b34b35d9… 2/4
The aim of the intervention include: Creation of patient-centered decision making support systems
Utilization of patient's data contained in clinical trails, insurance companies records and in patient’s health reco.
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Possibilities and pitfalls of AI in PICU
- 1. Possibilities and pitfalls of AI in PICU Joppe Nijman Pediatric Intensivist / Clinician Researcher University Medical Center Utrecht EAPS congress 2022 Slideshare.net/joppenijman
- 2. Faculty Disclosure Company Name Honoraria/ Expenses Consulting/ Advisory Board Funded Research Royalties/ Patent Stock Options Ownership/ Equity Position Employee Other (please specify) X No, nothing to disclose Yes, please specify:
- 3. “ Big data is data that contains greater variety, arriving in increasing volumes and with more velocity, making it difficult or impossible to process using traditional methods. 3
- 4. 4 Photo archive Wilhelmina Kinderziekenhuis, Utrecht, The Netherlands
- 5. 14 patients Admitted daily 266,112,000 meas Every single day ~220 parameters Measured every second 5
- 6. 6 Beam et al. JAMA 2018.
- 8. “ Artificial intelligence will not replace doctors. But doctors who use a rtificial intelligence will replace those who don’t. 8 Berci Meskó - https://medicalfuturist.com/
- 9. How (not) to make your AI model 9 1 3 5 6 4 2 Healthcare data Chosing the right statistical method Model evaluation & prediction Data preparation Modelling Implementation, legal & ethical issues
- 10. Type of data ⬡ (Semi)-structured vs unstructured ⬡ Text, numbers, images, etc. Data availability? FAIR data (Findability, Accessibility, Interoperability, Reusability) Healthcare data 10 Johnson et al. JACC 2018
- 11. Research for new breast cancer biomarkers ⬡ Structured data ⬡ Mainly numbers (imaging) ⬡ Machine learning? ∙ Amount of data ∙ Complex interactions Example 11 Rodrigues-Ferreira et al. Cancer Lett 2022.
- 12. Garbage in = garbage out ⬡ Measurement error ⬡ Missing data ⬡ Normalization ⬡ Unclassified data ⬡ Confounding (e.g. treatment effects)? Data preparation 12 Johnson et al. JACC 2018 https://xkcd.com/1838/
- 13. Measurement error, missing data and treatment effects 13 Zoodsma et al. Unpublished data 2022.
- 14. 14 Sepanski et al. Front Pediatr 2018. Heterogeneity & normalization
- 15. Automated sepsis prediction algorithm in EHR EPIC Unclassified data 15 https://www.statnews.com/2021/09/27/epic-sepsis-algorithm- antibiotics-model/ Huat Goh et al. Nature Comm 2021
- 16. Technique selection ⬡ Conventional ∙ E.g. Regression ⬡ Advanced data science / machine learning / artificial intelligence 16 Johnson et al. JACC 2018
- 17. 17 Lorberfeld. Towards Data Science (towardsdatascience.com) 2019.
- 18. Modelling & performance 18 Johnson et al. JACC 2018
- 19. Performance 19 Swalin. KDNuggets 2018 Auyeung. CK-12 foundation.
- 20. Your model is finished! 20 Johnson et al. JACC 2018
- 21. Your model is finished! 21 Wynants et al. BMJ 2020 Sperrin et al. BMJ 2020
- 22. 22 Plana et al. JAMA Net Open 2022.
- 23. 23 Van Royen et al. Eur Resp J 2022.
- 24. Small steps in PICU… 24 Aczon et al. Pediatr Crit Care Med 2021. Ehrman et al. Front Pediatr 2022. Matam et al. J Clin Mon Comp 2019. Shah al. J Clin Med 2020.
- 25. Small steps in PICU… 25 & the next talks in this session
- 26. Interested? Join the Data Science Working Group of ESPNIC ⬡ https://www.equator-network.org/ (TRIPOD-AI) ⬡ https://www.clinical-trials.ai/ (CONSORT-AI) ⬡ https://www.go-fair.org/ (FAIR data use) ⬡ https://datacamp.com 26
- 27. 27 Thanks! Slideshare.net/joppenijman Any questions? j.nijman@umcutrecht.nl https://picudatalab.com @joppe_nijman Design: slidecarnival.com; icons: thenounproject.com
- 28. Feature selection 28 Mourad et al. Scientific Reports 2020