Journal club and talk given to Health Data Analytics MSc, February 2023. Reflecting on how to do good machine learning over biomedical data, the pitfalls and good practices
Machine learning, health data & the limits of knowledgePaul Agapow
Lecture for Imperial College London's MSc in Health Data Analytics, critiquing a recent paper on COVID diagnosis and moving out to talk about good practices (& limits) in ML and model building
Where AI will (and won't) revolutionize biomedicinePaul Agapow
Presented AI & Big Data Expo, London, December 2022.
Given the hype and success of machine learning and AI in other fields, its application in healthcare is only natural.
- However, the actual successes in medicine have been limited, with a number of high-profile failures.
- Here, I propose that biology is uniquely complex, with our lack of domain knowledge limiting the application of AI.
- However, there is reason for cautious optimism, with AI-lead approaches shifting the odds in our favour.
Machine learning, health data & the limits of knowledgePaul Agapow
Lecture for Imperial College London's MSc in Health Data Analytics, critiquing a recent paper on COVID diagnosis and moving out to talk about good practices (& limits) in ML and model building
Where AI will (and won't) revolutionize biomedicinePaul Agapow
Presented AI & Big Data Expo, London, December 2022.
Given the hype and success of machine learning and AI in other fields, its application in healthcare is only natural.
- However, the actual successes in medicine have been limited, with a number of high-profile failures.
- Here, I propose that biology is uniquely complex, with our lack of domain knowledge limiting the application of AI.
- However, there is reason for cautious optimism, with AI-lead approaches shifting the odds in our favour.
Prediction, Big Data, and AI: Steyerberg, Basel Nov 1, 2019Ewout Steyerberg
Title"Clinical prediction models in the age of artificial intelligence and big data", presented at the Basel Biometrics Society seminar Nov 1, 2019, Basel, by Ewout Steyerberg, with substantial inout from Maarten van Smeden and Ben van Calster
Our second webinar in the MDC Connects Series 2021 | A Guide to Complex Medicines.
This slide deck takes a closer look at developing the assay cascade for complex medicines.
Tilly Bingham, Concept Life Sciences
2023-11-09 HealthRI Biobanking day_Amsterdam_Alain van Gool.pdfAlain van Gool
Examples of lessons learned in Omics-based biomarker studies from myself and colleagues in X-omics and EATRIS, for an audience of biobankers, researchers and diagnostic/clinical chemistry experts.
Atul Butte's presentation to the Association of Medical School Pediatric Department Chairs #AMSPDC on March 3, 2018.
Some pre-publication data slides have been removed from this deck.
The reality of moving towards precision medicineElia Stupka
How do we move towards precision medicine? How can we deliver on the big data in health promise? Who will be the enablers and players? Pharma, Big Tech, or newcomers?
Can drug repurposing be saved with AI 202405.pdfPaul Agapow
Presented at DigiTechPharma, London May 2024.
What is drug repurposing. Why is it needed? What systematic approaches are there? Is AI a solution? Why not?
IA, la clave de la genomica (May 2024).pdfPaul Agapow
A.k.a. AI, the key to genomics. Presented at 1er Congreso Español de Medicina Genómica. Spanish language.
On the failure of applied genomics. On the complexity of genomics, biology, medicine. The need for AI. Barriers.
Prediction, Big Data, and AI: Steyerberg, Basel Nov 1, 2019Ewout Steyerberg
Title"Clinical prediction models in the age of artificial intelligence and big data", presented at the Basel Biometrics Society seminar Nov 1, 2019, Basel, by Ewout Steyerberg, with substantial inout from Maarten van Smeden and Ben van Calster
Our second webinar in the MDC Connects Series 2021 | A Guide to Complex Medicines.
This slide deck takes a closer look at developing the assay cascade for complex medicines.
Tilly Bingham, Concept Life Sciences
2023-11-09 HealthRI Biobanking day_Amsterdam_Alain van Gool.pdfAlain van Gool
Examples of lessons learned in Omics-based biomarker studies from myself and colleagues in X-omics and EATRIS, for an audience of biobankers, researchers and diagnostic/clinical chemistry experts.
Atul Butte's presentation to the Association of Medical School Pediatric Department Chairs #AMSPDC on March 3, 2018.
Some pre-publication data slides have been removed from this deck.
The reality of moving towards precision medicineElia Stupka
How do we move towards precision medicine? How can we deliver on the big data in health promise? Who will be the enablers and players? Pharma, Big Tech, or newcomers?
Can drug repurposing be saved with AI 202405.pdfPaul Agapow
Presented at DigiTechPharma, London May 2024.
What is drug repurposing. Why is it needed? What systematic approaches are there? Is AI a solution? Why not?
IA, la clave de la genomica (May 2024).pdfPaul Agapow
A.k.a. AI, the key to genomics. Presented at 1er Congreso Español de Medicina Genómica. Spanish language.
On the failure of applied genomics. On the complexity of genomics, biology, medicine. The need for AI. Barriers.
Digital Biomarkers, a (too) brief introduction.pdfPaul Agapow
Presentation at the Artid workshop, U. Bristol, March 2024, on digital biomarkers for improved clinical trials and monitoring of complex diseases, including neurological & movement disorders.
Analysing biomedical data (ers october 2017)Paul Agapow
Presented at European Respiratory Society, Berlin, October 2017. High level talk to mix of clinicians and scientists on the difficulties of biomedical analysis, including practical, statistical and data issues.
Interpreting transcriptomics (ers berlin 2017)Paul Agapow
Presented at European Respiratory Society, Berlin, October 2017. High level talk to mix of clinicians and scientists on analyzing transcriptomic / gene expression data
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
Best Ayurvedic medicine for Gas and IndigestionSwastikAyurveda
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the 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 lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
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. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Maxilla, Mandible & Hyoid Bone & Clinical Correlations by Dr. RIG.pptx
ML, biomedical data & trust
1. gsk.com
AI & Big Data Expo, London
Machine learning, biomedical data & trust
Paul Agapow (Statistics & Data Science Innovation Hub)
2. Background & disclaimer
• Previously a health informatician, biomedical ML
researcher, bioinformatician, “computer guy”,
disease chaser, epi-informatician,
phylogeneticist, evolutionary biologist,
immunologist, biochemist …
• Now a director @GSK
• This presentation does not reflect thought,
policy or projects in progress at GSK
• There are no conflicts of interest
3. 10 June 2021 3
“AI will not replace
drug hunters, but drug
hunters who don’t use
AI will be replaced by
those who do.”
-Andrew Hopkins, CEO Exscientia
5. 5
07 February 2023
3 hurdles to using AI/ML in therapy development
Biological & physiological
complexity
Insufficient & uneven data
A gap between AI/ML practice &
medical needs
6. To make a
new drug,
you must
first solve for
everything
6
7. 12 July 2021 7
The complexity of biology:
About 50 trillion cells of 200 types
Each cell has 23 pairs of chromosomes
In total 6.4 billion basepairs (positions)
Organised into about 18,000 genes
(Or maybe more like 40,000 genes)
Genetic material elsewhere in the cell
Epigenetic modification
1 million different types of molecules
Lifestyle & history
Exposure & environment
Immune system repertoire & priming
…
Of which we know only a fraction
8. The data types and sources we need are myriad & varied
8
Hughes et al. (2010) ”Principles of early drug discovery”
9. • There are many different
modalities of intervention
• With different (data)
considerations & different
levels of ML experience
07 February 2023 9
There are many different means to the same end
McKinsey, EvaluatePharma 2022
10. It’s often not
the right data
• Difficult / expensive to generate
• Unstructured
• Unlabeled
• The wrong type
• Sparse, unevenly sampled
• WEIRD
• In different formats and silos
10
11. 07 February 2023 11
Melanie Mitchell via Dagmar Monett
A disconnect between AI/ML practice and medical needs
Academic focus on problems with low medical value
12. • There are many models
that work perfectly … in
the lab
• Why?
- Unrealistic or poor
training data
- Emphasis on hitting
metrics
07 February 2023 12
A disconnect between AI/ML practice and medical needs
A tendency to treat biomedicine as simply a data / ML problem
13. The classic
analytical
tension
13
What we need to solve
What we tend to solve
Easy things
Available, ideal data
Ground truth
Simplify
“Interesting”
“Table-land”
Useful things
Incomplete messy data
Unclear biological reality
Uncertain findings
Needful
“Network-land”
14. 14
Laure Wynants via Maarten van Smeden
A disconnect between AI/ML practice and medical needs
Many ”good” models are not fit for production
15. 07 February 2023 15
• The pandemic prompted a flood of publications &
preprints
• Most plagued by the usual biomedical AI problems
• … and also produced by those outside the field
• As a general principle, any paper applying ML to COVID
is terrible
• Bad models in a crisis situation are not neutral, they
distract, expend effort, are an opportunity cost
COVID was a lightning rod for bad biomedical ML
16. 07 February 2023 16
• What does it purport to do: Find risk factors
associated with deterioration of COVID patients
• Why? Better / faster assessment of incoming
patients
• Who? Patients admitted to two hospitals with +ve
PCR test for COVID with CT scan with lesions
• Data? Demographics, bloods, labs, breathing/
oxygen scores, CT scans manually scored
“Interpretable Prediction of Severity & Crucial Factors of COVID Patients”
Zheng et al. BioMed Research International (2021), DOI: 10.1155/2021/8840835
17. 07 February 2023 17
• Conflates diagnosis & prognosis
• The cohort:
- Suggested this can replace PCR but cohort are selected
by PCR result
- The act of taking a CT scan in some ways selects for
cohort
- Unclear when some readings taken, when we are looking
at deterioration
- Are the training set the set that a model might be used on
in the clinic?
- Not many critical – so actually testing for severe cases
- What’s the split between hospitals
- Patients are different already, pre-existing conditions
- Association with age & general health
- Old patients running a temperature with lesioned lungs do
poorly
• Clinical use:
- Will all this data be available in a timely fashion for a
model in the clinic
- If the severity is based of bloods & oxygenation readings,
why not just use them
- Information complexity?
• Validation:
- Would it work for another time period at same hospitals?
At other hospitals?
• Analytics
- “The impenetrable wall of math”
- XGBoost is always a good place to start
- Ensemble methods usually are
- Feature interaction?
- Some features overlap (neutrophils, n. ratio, NLR)
- What features correlate?
- No attempt to simplify model
- Any model is interpretable with SHAP
• Still useful for intrinsic / research purposes
Thoughts and questions
Not necessarily faults, not all easily answerable
18. 07 February 2023 18
• Models will always tell you the truth
- But it’s the truth conditioned on the data they’ve seen
- It might not be the truth you think
• Biomedical data is complex, it always come with a context
• Patients are complex, they always come with a medical history
• How were these patients selected?
• What is this model actually saying and why?
• Does this model replicate in other populations?
• But despite all this, we have to make and actionably interpret
models
Some principles for better biomedical ML