Presentation on similarities and differences between statistical and machine learning research fields for the @UM_MiCHAMP Big Data & AI in Health Seminar Series; October 21, 2022
A critical reflection on the role of Machine Learning in medical research, with specific comparisons to more classical statistical approaches to learning from data
Symposium Data-driven risk-based decisions; PhD defense Tom Hueting
Wednesday 22 juni 2022
Universiteit Twente
I present some issues on improving prediction models by updating and adding markers
A critical reflection on the role of Machine Learning in medical research, with specific comparisons to more classical statistical approaches to learning from data
Symposium Data-driven risk-based decisions; PhD defense Tom Hueting
Wednesday 22 juni 2022
Universiteit Twente
I present some issues on improving prediction models by updating and adding markers
Improving predictions: Lasso, Ridge and Stein's paradoxMaarten van Smeden
Slides of masterclass "Improving predictions: Lasso, Ridge and Stein's paradox" at the (Dutch) National Institute for Public Health and the Environment (RIVM)
Dichotomania and other challenges for the collaborating biostatisticianLaure Wynants
Conference presentation at ISCB 41 in the session
"Biostatistical inference in practice: moving beyond false
dichotomies"
A comment in Nature, signed by over 800 researchers, called for the scientific community to “retire statistical significance”. The responses included a call to halt the use of the term „statistically significant”, and changes in journal’s author guidelines. The leading discourse among statisticians is that inadequate statistical training of clinical researchers and publishing practices are to blame for the misuse of statistical testing. In this presentation, we search our collective conscience by reviewing ethical guidelines for statisticians in light of the p-value crisis, examine what this implies for us when conducting analyses in collaborative work and teaching, and whether the ATOM (accept uncertainty; be thoughtful, open and modest) principles can guide us.
Clinical trials are about comparability not generalisability V2.pptxStephenSenn2
Lecture delivered at the September 2022 EFSPI meeting in Basle in which I argued that the patients in a clinical trial should not be viewed as being a representative sample of some target population.
Measuring clinical utility: uncertainty in Net BenefitLaure Wynants
Introduction and Objective(s)
The impact of introducing a prediction model in clinical practice to inform clinical decisions on interventions (eg. treat patient vs. do not treat patient) can be quantified by Net Benefit (NB). NB is calculated as TP/N - FP/N * w, where TP is the number of true positives, FP is the number of false positives, and w is a weight reflecting the benefit of a TP and the harm of a FP. NB and decision curves (where NB is plotted for a range of w) are population-level quantities that can tell policymakers whether using a prediction model is better than using alternative strategies (such as treat all or treat none). Nonetheless, the NB estimate itself is uncertain. The objective of this talk is to investigate the origins and measures of NB uncertainty.
Method(s) and Results
Sampling variability and heterogeneity between populations are sources of uncertainty about NB. We will show that despite wide confidence and prediction intervals around NB, the choice of optimal strategy may be unaffected. A first measure of uncertainty is the probability of usefulness. It is the probability that the model is the optimal strategy among competing strategies and can be calculated through a random effects meta-analysis. The probability of usefulness has conceptual links with a second measure, the Net Benefit Value of Information (NB VOI). VOI is a concept borrowed from decision theory that quantifies the expected loss due to not confidently knowing which of competing strategies is the best. The methods will be illustrated with case studies in ovarian cancer diagnosis and prognosis after myocardial infarction.
Conclusions
Uncertainty in NB can be large. The probability of usefulness from a random-effects meta-analysis reflects heterogeneity in clinical utility across populations, while the NB VOI can be used to determine whether more validation data from a certain population is needed.
This slideshow provides a brief introduction to the concepts of epidemiology, the key historical figures and events that played a role in the evolution of epidemiology and finally an overview of key epidemiological study designs.
This talk presents areas of investigation underway at the Rensselaer Institute for Data Exploration and Applications. First presented at Flipkart, Bangalore India, 3/2015.
Improving predictions: Lasso, Ridge and Stein's paradoxMaarten van Smeden
Slides of masterclass "Improving predictions: Lasso, Ridge and Stein's paradox" at the (Dutch) National Institute for Public Health and the Environment (RIVM)
Dichotomania and other challenges for the collaborating biostatisticianLaure Wynants
Conference presentation at ISCB 41 in the session
"Biostatistical inference in practice: moving beyond false
dichotomies"
A comment in Nature, signed by over 800 researchers, called for the scientific community to “retire statistical significance”. The responses included a call to halt the use of the term „statistically significant”, and changes in journal’s author guidelines. The leading discourse among statisticians is that inadequate statistical training of clinical researchers and publishing practices are to blame for the misuse of statistical testing. In this presentation, we search our collective conscience by reviewing ethical guidelines for statisticians in light of the p-value crisis, examine what this implies for us when conducting analyses in collaborative work and teaching, and whether the ATOM (accept uncertainty; be thoughtful, open and modest) principles can guide us.
Clinical trials are about comparability not generalisability V2.pptxStephenSenn2
Lecture delivered at the September 2022 EFSPI meeting in Basle in which I argued that the patients in a clinical trial should not be viewed as being a representative sample of some target population.
Measuring clinical utility: uncertainty in Net BenefitLaure Wynants
Introduction and Objective(s)
The impact of introducing a prediction model in clinical practice to inform clinical decisions on interventions (eg. treat patient vs. do not treat patient) can be quantified by Net Benefit (NB). NB is calculated as TP/N - FP/N * w, where TP is the number of true positives, FP is the number of false positives, and w is a weight reflecting the benefit of a TP and the harm of a FP. NB and decision curves (where NB is plotted for a range of w) are population-level quantities that can tell policymakers whether using a prediction model is better than using alternative strategies (such as treat all or treat none). Nonetheless, the NB estimate itself is uncertain. The objective of this talk is to investigate the origins and measures of NB uncertainty.
Method(s) and Results
Sampling variability and heterogeneity between populations are sources of uncertainty about NB. We will show that despite wide confidence and prediction intervals around NB, the choice of optimal strategy may be unaffected. A first measure of uncertainty is the probability of usefulness. It is the probability that the model is the optimal strategy among competing strategies and can be calculated through a random effects meta-analysis. The probability of usefulness has conceptual links with a second measure, the Net Benefit Value of Information (NB VOI). VOI is a concept borrowed from decision theory that quantifies the expected loss due to not confidently knowing which of competing strategies is the best. The methods will be illustrated with case studies in ovarian cancer diagnosis and prognosis after myocardial infarction.
Conclusions
Uncertainty in NB can be large. The probability of usefulness from a random-effects meta-analysis reflects heterogeneity in clinical utility across populations, while the NB VOI can be used to determine whether more validation data from a certain population is needed.
This slideshow provides a brief introduction to the concepts of epidemiology, the key historical figures and events that played a role in the evolution of epidemiology and finally an overview of key epidemiological study designs.
This talk presents areas of investigation underway at the Rensselaer Institute for Data Exploration and Applications. First presented at Flipkart, Bangalore India, 3/2015.
A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...Editor IJCATR
In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural
Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results
based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the
consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of
implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
Data; Data manipulation, sorting, grouping, rearranging. Plotting the data. D...jybufgofasfbkpoovh
Data; Data manipulation, sorting, grouping, rearranging. Plotting the data. Descriptive statistics. Inferential statistics. Python Libraries for Data Science.
• Improve Data Management with Semantic Data Integration
• Discuss the issues of data variety and data uncertainty
• Moving from Big Data to Big Analysis
• How to apply Analysis to Big Data (Big Analysis)
• Benefits of Advanced Analytics in Life Science
Open Science Better Science? Steyerberg 2June2022.pptxEwout Steyerberg
Is Open Science Better Science?
Ewout W. Steyerberg, PhD
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
Abstract
The Open Science movement has many components, including Open Access to scientific publications, sharing of research data, and providing open source software. These components are expected to contribute to better science. In this seminar I aim to reflect on the strength and limitations of Open Science in the context of epidemiological research.
First, I note that by making research more open, the scale of research increases; this might enable addressing some research questions better. This allows us to recognize that different researchers use different scientific approaches; Open science makes that we become increasingly aware of different styles in research.
Second, we may hope to learn more about the value of modern approaches to data analysis such as machine learning. Indeed, neutral comparison studies benefit from the open availability of multiple data sets that can be analyzed with standardized approaches to the analysis, adding realism compared to analytical and simulation studies.
Third, I note that more data sharing is a positive development, especially to highlight heterogeneity between settings. In sum, I remain optimistic that open science will lead to better science, with the caveat that we recognize complexities that limit the interpretation of increasing amounts of data, such as the medical context, study design, measurement and data analysis.
These slides were presented in a series of lectures organized by Prof Marianna Huebner, June 2, 2022
Automating Data Science over a Human Genomics Knowledge BaseVaticle
# Automating Data Science over a Human Genomics Knowledge Base
Radouane Oudrhiri, the CTO of Eagle Genomics, will talk about how Eagle Genomics is building a platform for automating data science over a human genomics knowledge base. Rad will dive into the architecture Eagle Genomics and also discuss how Grakn serves as the knowledge base foundation of the system. Rad also give a brief history of databases, semantic expressiveness and how Grakn fits in the big picture.
# Radouane Oudrhiri, CTO, Eagle Genomics
Radouane has an extensive experience in leading world-class software and data-intensive system developments in different industries from Telecom to Healthcare, Nuclear, Automotive, Financials. Radouane is Lean/Six Sigma Master Black Belt with speciality in high-tech, IT and Software engineering and he is recognised as the leader and early adaptor of Lean/Six Sigma and DFSS to IT and Software. He is a fellow of the Royal Statistical Society (RSS) and member of the ISO Technical Committee (TC69: Applications of Statistical methods) where he is co-author of the Lean & Six Sigma Standard (ISO 18404) as well as the new standard under development (Design for Six Sigma). He is also part of the newly formed international Group on Big Data (nominated by BSI as the UK representative/expert). Radouane has also been Chair of the working group on Measurement Systems for Automated Processes/Systems within the ISPE (International Society for Pharmaceutical Engineering).
Supervised Multi Attribute Gene Manipulation For Cancerpaperpublications3
Abstract: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems.
They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery.
Introduction to feature subset selection methodIJSRD
Data Mining is a computational progression to ascertain patterns in hefty data sets. It has various important techniques and one of them is Classification which is receiving great attention recently in the database community. Classification technique can solve several problems in different fields like medicine, industry, business, science. PSO is based on social behaviour for optimization problem. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Rough Set Theory (RST) is a mathematical tool which deals with the uncertainty and vagueness of the decision systems.
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AISeth Grimes
Dan Lee from Dentuit AI presented an Intro to Deep Learning for Medical Image Analysis at the Maryland AI meetup (https://www.meetup.com/Maryland-AI), May 27, 2020. Visit https://www.youtube.com/watch?v=xl8i7CGDQi0 for video.
AI TESTING: ENSURING A GOOD DATA SPLIT BETWEEN DATA SETS (TRAINING AND TEST) ...ijsc
Artificial Intelligence and Machine Learning have been around for a long time. In recent years, there has been a surge in popularity for applications integrating AI and ML technology. As with traditional development, software testing is a critical component of a successful AI/ML application. The development methodology used in AI/ML contrasts significantly from traditional development. In light of these distinctions, various software testing challenges arise. The emphasis of this paper is on the challenge of effectively splitting the data into training and testing data sets. By applying a k-Means clustering strategy to the data set followed by a decision tree, we can significantly increase the likelihood of the training data set to represent the domain of the full dataset and thus avoid training a model that is likely to fail because it has only learned a subset of the full data domain.
Mastering Data Science A Comprehensive Introduction.docxworkshayesteh
Mastering Data Science: A Comprehensive Introduction" is your ultimate guide to the dynamic and ever-evolving world of data science. With over 3000 words of in-depth content, this book takes you on a journey from the foundational concepts of data science to its practical applications in various industries.
Discover the significance of data in the modern age and the role of data scientists in transforming raw information into actionable insights. Explore the data science lifecycle, from problem formulation to model deployment and ongoing maintenance. Learn how to acquire, clean, and preprocess data effectively, ensuring data quality and reliability.
Delve into the art of exploratory data analysis (EDA), where data visualization and statistical techniques help uncover patterns and relationships. Gain expertise in feature engineering and selection, as well as the fundamentals of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning.
Uncover the specifics of various machine learning algorithms, from linear regression to neural networks, and understand when and how to use them. Learn how to rigorously evaluate and select models using performance metrics, cross-validation, and hyperparameter tuning while considering bias and fairness.
The book goes beyond model building by addressing the deployment of models in real-world applications, including recommendation systems, fraud detection, and natural language processing. It also explores ethical considerations related to data science.
As data continues to grow, the chapter on big data and data science introduces you to the challenges and tools used to handle large-scale datasets efficiently.
Data science isn't confined to one industry. Explore practical applications in business and industry, such as data-driven decision-making, customer analytics, and predictive maintenance. Understand the role of data science in research and academia, including data-intensive research and data ethics.
Stay ahead of the curve by examining the future of data science, including emerging trends like artificial intelligence, deep learning, and responsible data science practices.
"Mastering Data Science" isn't just a book; it's your key to unlocking the potential of data in the 21st century. Whether you're a novice or an experienced data scientist, this comprehensive guide equips you to navigate the ever-expanding world of data and make meaningful contributions to various domains.
With the recent growth of the graph-based data, the large graph processing becomes more and more important. In order to explore and to extract knowledge from such data, graph mining methods, like community detection, is a necessity. The legacy graph processing tools mainly rely on single machine computational capacity, which cannot process large graphs with billions of nodes. Therefore, the main challenge of new tools and frameworks lies on the development of new paradigms that are scalable, efficient and flexible. In this paper, we review the new paradigms of large graph processing and their applications to graph mining domains using the distributed and shared nothing approach used for large data by Internet players.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
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Statistics and ML 21Oct22 sel.pptx
1. Oct 21, 2022
Statistics and machine learning:
friends or foes?
Ewout W. Steyerberg, PhD
Professor of Clinical Biostatistics and
Medical Decision Making
Dept of Biomedical Data Sciences
Leiden University Medical Center
Thanks to many, including Ben van Calster, Leuven;
Maarten van Smeden, Utrecht
2. Statistics and machine learning:
friends or foes?
21-Oct-22
2 Insert > Header & footer
• Introduction for debate
• Friction points: foes
• Commonalities between statistics and ML: friends
3. Statistics and Machine Learning (ML)
In medical research, “artificial intelligence” usually just means “machine learning” or
“algorithm”
21-Oct-22
3 Insert > Header & footer
14. Friction points between statistics and ML: foes
1. ML claims to be new and supersede statistics
2. ML claims any data is relevant
3. ML makes promises it cannot keep
21-Oct-22
14 Insert > Header & footer
15. 1. ML claims to be new and supersede statistics
21-Oct-22
15 Insert > Header & footer
22. 1. ML claims to be new and supersede statistics
ML has developed from statistics
ML as part of statistics
Statistics as part of ML
ML:
models roughly outside of the traditional regression types of analysis:
• decision trees (and descendants, XGBoost, ..)
• Support vector machines (SVMs)
• neural networks (including Deep learning)
21-Oct-22
22 Insert > Header & footer
23. 2. ML claims any data is relevant
Typical context: Electronic Health Records (EHR); large administrative data sets
Uncover patterns in data that are there but remained hidden
Strong point of EHR: large N, large sets of features
Weak point of EHR: ‘quality’
Selection of patients
Start point definition
End point definition
Selective measurement
Missing values
…
21-Oct-22
23 Insert > Header & footer
24. More data is better? Lessons from meta-analysis
Meta-analysis:
Risk of bias assessment
Respect clustering nature
21-Oct-22
24 Personal protective equipment for preventing highly infectious diseases
26. 3. ML makes promises it cannot keep
“Uncover patterns in data that are there but remained hidden”
Unsupervised learning
Clustering unstable and determined by optimization criterion
Supervised learning
Trees / neural networks better for prediction than regression
21-Oct-22
26 Insert > Header & footer
31. Machine learning vs conventional modeling
Text
“We found that random forests did not outperform Cox models despite their inherent ability to
accommodate nonlinearities and interactions. …
Elastic nets achieved the highest discrimination performance …, demonstrating
the ability of regularisation to select relevant variables and optimise model coefficients in an EHR context.”
21-Oct-22
31 Insert > Header & footer
32. Systematic review on ML vs classic modeling
21-Oct-22
32 Insert > Header & footer
35. Commonalities between statistics and ML: friends
4. Research question is key
5. Complex data structures require innovative approaches
6. Some problems are really hard
21-Oct-22
35 Insert > Header & footer
37. 4. Research question is key
From easy to hard questions
- Exploratory / descriptive
- Prediction / classification
- Causal
21-Oct-22
37 Insert > Header & footer
38. 4. Research questions
Separate
- Exploratory: data mining
“enjoy the results, because you will never see these results again”
- Descriptive: patterns in the data to learn about nature;
hypothesis generating; biomarkers – disease
ML provides more flexibility; less interpretability?
- Prediction: machine learning /trees often poor in performance
ML may provide benefits in specific circumstances?
21-Oct-22
38 Insert > Header & footer
40. ML good for prediction?
Large N, small p
“Natural flexibility”?
Versus non-linear terms / interactions in regression?
21-Oct-22
40 Insert > Header & footer
41.
42. ML good for treatment selection rules?
High hopes
“The incorporation of new data modalities such as single-cell profiling, along with techniques that
rapidly find effective drug combinations will likely be instrumental in improving cancer care.”
21-Oct-22
42 Insert > Header & footer
43. Statistics good for treatment selection rules?
21-Oct-22
43 Insert > Header & footer
45. Alternatives
21-Oct-22
45 Insert > Header & footer
1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups,
relying on the mathematical dependency of the absolute risk difference on baseline risk;
2) Treatment effect modeling methods (9 papers): prognostic factors and treatment effect modifiers,
including penalization or separate data sets for subgroup identification / effect
3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers
to classify the trial population into those who benefit from treatment and those who do not
46. 5. Complex data structures require innovative approaches
Examples of succesful ML
- Image analysis: Deep Learning (DL)
- Radiology, pathology, dermatology, opthalmology, gastroenterology, cardiology,
…
- Free text: natural language processing (NLP)
- Mining electronic health records, building blocks for prediction, …
- Pharmacovigilance in social media
21-Oct-22
46 Insert > Header & footer
47. 6. Some problems are really hard
Prediction
Small N, small p regression
Small N, large p hopeless
Large N, small p regression
Large N, large p ?
Treatment selection
Balance bias – precision
Causal interpretation
21-Oct-22
47 Insert > Header & footer
48. Summary 21 Oct 2022
1. ML is not really new and needs to liaise with statistics
2. Data quality and bias: design is key, learn from clinical epidemiology
3. Don’t make too many promises
4. Research questions relate to description, prediction and causality
5. Recognized power for specific complex data structures
6. Work on the truly hard problems together
21-Oct-22
48 Insert > Header & footer
