Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications. Digital therapeutics have proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes, and both patients and health care professionals are benefitting from clinical decision support. AI allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource use in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.
Artificial Intelligence in Diabetes Care GOPAL KHODVE
Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications.
Short overview over possibilities and challenges of using artificial intelligence in health care. Presentation from the MultiHelix ThinkTank, May 14 2020.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications. Digital therapeutics have proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes, and both patients and health care professionals are benefitting from clinical decision support. AI allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource use in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.
Artificial Intelligence in Diabetes Care GOPAL KHODVE
Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications.
Short overview over possibilities and challenges of using artificial intelligence in health care. Presentation from the MultiHelix ThinkTank, May 14 2020.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
Imeglimin, What is new?
By Dr. Usama Ragab Youssif
Lecturer of Medicine - Zagazig University
Agenda
Mitochondrial function and dysfunction
Mitochondrial (dys)function in diabetes
Diabetes core defects and Imeglimin
Imeglimin drug development and approval
Imeglimin and Heart
Today developments in the healthcare industry, especially the integration of Artificial Intelligence (AI), have revolutionised the future of diabetes treatment. As technology and research continue to progress, it shows clearly that the future of diabetes is now.
The application of AI to diabetes treatment and management has been one of the key advancements that have molded the future of diabetes treatment in today’s time
The future of diabetes treatment is indeed promising and we can expect more and more innovations coming into existence. Scientists are already speculating about the involvement of nanotechnology in the future of diabetes research.
Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
Digital Health Market has exploded in the last few years. Will that continue? What are the main areas of growth in digital days and what the future will bring us.
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
Imeglimin, What is new?
By Dr. Usama Ragab Youssif
Lecturer of Medicine - Zagazig University
Agenda
Mitochondrial function and dysfunction
Mitochondrial (dys)function in diabetes
Diabetes core defects and Imeglimin
Imeglimin drug development and approval
Imeglimin and Heart
Today developments in the healthcare industry, especially the integration of Artificial Intelligence (AI), have revolutionised the future of diabetes treatment. As technology and research continue to progress, it shows clearly that the future of diabetes is now.
The application of AI to diabetes treatment and management has been one of the key advancements that have molded the future of diabetes treatment in today’s time
The future of diabetes treatment is indeed promising and we can expect more and more innovations coming into existence. Scientists are already speculating about the involvement of nanotechnology in the future of diabetes research.
Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
Digital Health Market has exploded in the last few years. Will that continue? What are the main areas of growth in digital days and what the future will bring us.
Designing Causal Inference Studies Using Real-World DataInsideScientific
In this webinar, experts provide an overview of causal inference, along with step-by-step guidance to designing these studies using real-world healthcare data.
Causal inference is used to answer cause and effect research questions and yield estimates of effect. Causal study design considerations and statistical methods address the effects of confounding variables and other potential biases and allow researchers to answer questions such as, “Does treatment A produce better patient outcomes compared to Treatment B?”
Causal study interpretations have traditionally been restricted to randomized controlled trials; however, causal inference applied to observational healthcare data is growing in importance, driven by the need for generalizable and rapidly delivered real-world evidence to inform regulatory, payer, and patient/provider decision making. The application of causal inference methods leads to stronger and more powerful evidence. When these techniques are applied to observational data, the results generated are both from and for the real world.
Presenters walk through several real-world case studies including the PCORI-funded BESTMED study and a collaborative study with a prominent pharmacy payer.
K-Nearest Neighbours based diagnosis of hyperglycemiaijtsrd
AI or artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. As a result, Artificial Intelligence is gaining Importance in science and engineering fields. The use of Artificial Intelligence in medical diagnosis too is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, hepatitis, lung diseases, etc... The main aim of this paper is to build an Artificial Intelligent System that after analysis of certain parameters can predict that whether a person is diabetic or not. Diabetes is the name used to describe a metabolic condition of having higher than normal blood sugar levels. Diabetes is becoming increasingly more common throughout the world, due to increased obesity - which can lead to metabolic syndrome or pre-diabetes leading to higher incidences of type 2 diabetes. Authors have identified 10 parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithm. Keeping in view this training data authors developed a system that uses the artificial neural networks algorithm to serve the purpose. These are capable of predicting new observations (on specific variables) from previous observations (on the same or other variables) after executing a process of so-called learning from existing training data (Haykin 1998).The results indicate that the performance of KNN method when compared with the medical diagnosis system was found to be 91%. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. Abid Sarwar"K-Nearest Neighbours based diagnosis of hyperglycemia" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7046.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/7046/k-nearest-neighbours-based-diagnosis-of-hyperglycemia/abid-sarwar
Data Driven is just the beginning, why the details of evidence matter by Dr. ...James McCarter
At Virta Health, our values include being evidence-based and prioritizing data and science over opinion in our decision-making. But how does this apply to the data we provide employers? Here are three questions we think employers should be asking healthcare providers and vendors offering health solutions to make smarter data-driven decisions (and some examples of vendor data that doesn’t stand up to scrutiny).
Ascendable Clarification for Coronary Illness Prediction using Classification...ijtsrd
Coronary disease is predicted by classification technique. The data mining tool WEKA has been exploited for implementing Naïve Bayes classifier. Proposed work is trapped with a specific end goal to enhance the execution of models. For improving the classification accuracy Naïve Bayes is combined with Bagging and Attribute Selection. Trial results demonstrated a critical change over in the current Naïve Bayes classifier. This approach enhances the classification accuracy and reduces computational time. D. Haripriya | Dr. M. Lovelin Ponn Felciah "Ascendable Clarification for Coronary Illness Prediction using Classification Mining and Feature Selection Performances" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26690.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26690/ascendable-clarification-for-coronary-illness-prediction-using-classification-mining-and-feature-selection-performances/d-haripriya
Going Beyond Genomics in Precision Medicine: What's NextHealth Catalyst
Precision medicine processes, while involving genomics, are not confined to working with data about an individual’s genes, environment, and lifestyle. Precision medicine also means putting patients on the right path of care, taking into consideration other individual tolerances, such as participation and cost. Precision medicine processes incorporate data beyond the individual, pulling in socio-economic data, as well as relevant internal and external data, to create an entire patient data ecosystem. With reusable data modules, this information is processed within a closed-loop analytics framework to facilitate clinical decision making at the point of care. This optimizes clinical workflow, thus leading to more precise medicine.
Wholist - Individual and Corporate Lifestyle Transformation- Lifestyle MedicineHeather Hammerstedt
Lifestyle Medicine for individuals and for corporations. Transformation at all levels. Wholist is a lifestyle medicine coaching and consulting company for individuals and corporations. Telehealth ID OR CA. Virtual coaching programs. Speaking engagements. www.wholisthealth.com
Running Head LITERATURE REVIEW 1LITERATURE REVIEW 5.docxhealdkathaleen
Running Head: LITERATURE REVIEW
1
LITERATURE REVIEW
5
Literature Review
Name: Liliana Faura
Course: NRS-490
Professor: Tish Dorman
Date: 1/12/2020
Introduction
The continued prevalence of type II diabetes has been blame d on sedentary lifestyle, but for a long time now, health experts have suggested dietary and lifestyle changes to reverse the trend, which may include but no limited to healthy eating and regular exercise. Scholars and clinicians have been evaluating the impact of the obesity on individuals and resources dedicated to curb the problem as not confined only to health impacts such as various types of diabetes and high-blood pressure, but also economic-wise. To shed more light on the issue of type II diabetes, this review compares the research questions, sample population, and limitation of various research studies regarding the topic of dietary and lifestyles changes for type 2 diabetic patients.
Comparison of Research Questions
According to Czupryniak et al (2010), the underlying question for the question is the impact of bariatric surgery on morbidly obese type II patients. However, as compared to Brun et al (2008), seek to answer the question of the targeted endurance training as weight reduction as well as fitness strategy on type II diabetic patients. Similarly, Umpierre (2011) seeks to determine the difference between physical activity exercise and structured exercise training on the regulation of glucose on type two patients. While prior scholars have dealt with lifestyles changes and surgery, Asemi et (2011) sought to answer the question of the impacts of “multispecies probiotic supplements on metabolic profiles, hs-CRP, and oxidative stress in diabetic patients.” While surgery is not a common way of managing weight for diabetic patients Picot et al (2012) echoes Czupryniak et al (2010) in trying to answer the question of the effectiveness of bariatric surgery as a way of managing weight on diabetic patients. Evidently, both lifestyle and dietary changes are some of the strategies used to manage complications associated with type II diabetes but there are other uncongenial ways such as bariatric surgery to manage weight in diabetic patients.
Comparison of Sample Populations
Picot et al (2012) searched 17 electronic sources, which is an according to the scholars, the meta-analysis was carried out strictly on studies that met criteria of the subject matter. Conversely Asemi et al (2013) randomly selected a sample size of 54 diabetic patients for their research. While the two studies use different reach methods, it is evident that quantitative research is more reliable in terms of sample size that qualitative research. Also, Brun and colleagues randomly selected 25 diabetic patients for their study, which pales only three (3) patients selected by Czupryniak and colleagues. Comparatively, Ninot et al (2011) randomly selected a total of 38 diabetic patients for their study, but the difference between this study and o ...
More than 15.3 million Canadians have overweight and obesity. That means that 40% of the population is at risk of type 2 diabetes, cardiovascular diseases, chronic joint pain, mental health and premature death.
To prevent, treat and manage obesity and mental health-related conditions at scale the Canadian obesity industry needs to adopt digital health technologies in a new, accelerated way.
This is an informative and inspirational talk by Michael Bidu, Founder and CEO of MYND Therapeutics, about how technology, AI and ChatGPT will democratize healthcare, digitize medicine and change the patient-physician relationship in obesity healthcare forever.
The talk covers issues and ideas around the need to focus on customer or patient first, digital health, digitization of medicine, digital therapies, immersive technologies like VR/AR/XR and MXR, AI and ChatGPT, and Quantum Computing.
The talk was delivered at the 8th annual Canadian Obesity Summit, May 14-17, 2023 in Whistler, Canada.
For more info about MYND visit: myndtx.com
Presentation at the annual scientific conference of the DOST-National Research Council of the Philippines, 12 Mar 2024. Philippine International Convention Center, Manila.
Artificial Intelligence: Ethical Issues in Residency TrainingIris Thiele Isip-Tan
Symposium presentation at the annual convention of the Philippine Academy of Family Physicians, 8 March 2024. Philippine International Convention Center.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
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
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
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Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
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.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- 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
Maxilla, Mandible & Hyoid Bone & Clinical Correlations by Dr. RIG.pptx
Artificial Intelligence and Diabetes
1. ARTIFICIAL INTELLIGENCE
AND DIABETES
Iris Thiele Isip Tan MD, MSc
Professor 3, UP College of Medicine
Chief, UP Medical Informatics Unit
Director, UP Manila Interactive Learning Center
2. NOTHING TO DISCLOSE
I give consent for the audience to tweet this talk
and give me feedback (@endocrine_witch).
Feel free take pictures of my slides (though the
deck will be at www.slideshare.net/isiptan).
3. What is AI?
Use of AI in diabetes
Will AI replace
physicians?
4. ARTIFICIAL
INTELLIGENCE
Allow machines to sense,
reason, act and adapt like
humans do - or in ways
beyond our abilities
https://newsroom.intel.com/news/many-ways-define-artificial-intelligence/#gs.0msuwc
5. More computing power
More data
Better algorithms
Broad investment
AI is not new …
https://newsroom.intel.com/news/many-ways-define-artificial-intelligence/#gs.0msuwc
12. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
Case-based reasoning model
for T1DM bolus insulin advice
13. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
CASE FEATURES
Determine which parameters are
required by bolus calculators
Carbohydrate intake
Pre-meal blood glucose
Target blood glucose level
Insulin-on-board
Exercise
Time
Insulin Sensitivity Factor (ISF)
Carbohydrate-to-Insulin Ratio (CIR)
15. RETRIEVE
Use the date/time of event to infer
ISF and CIR
Factors in preceding bolus doses
REUSE
Adaptation rule which resolves
differences between insulin-on-
board (IOB) in the problem and
retrieved case(s)
Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
16. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
REUSE step: Average bolus prediction of
retrieved cases then adapt
Equation for averaging bolus prediction of retrieved cases
k = number of retrieved cases
in = bolus solution provided by a retrieved case
17. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
REUSE step: Average bolus prediction of
retrieved cases then adapt
Equations for adapting bolus suggestion
18. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
REVISE
If postprandial BG is equal
or close to target BG then
recommendation is optimal
and not revised
19. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
Advanced Bolus Calculator for Diabetes (ABC4D)
CBR approach: tuning of ISF
and CIR for a small set of meal
scenarios
ISF and CIR from the most
similar case used in a standard
bolus calculator to suggest a
bolus dose
No temporal approach
20. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
Focus on helping patient directly
(instead of aiding the clinician)
RETAINS all
successful cases
Derives bolus
suggestion from
similar cases
21. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin
decision support. Artificial Intelligence in Medicine 2018;85:28-42.
CBR method can be
adopted by insulin
pumps, blood glucose
monitors, PCs and as
a web service
22. CBR service in the cloud opens possibility
of case sharing between subjects
Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin
decision support. Artificial Intelligence in Medicine 2018;85:28-42.
23. OBJECTIVE
Apply gradient forest analysis to identify subgroups of
ACCORD participants with increased/decreased risk of all-
cause mortality attributable to intensive therapy
24. Action to Control Cardiovascular Risk in Diabetes
(ACCORD) Trial halted due to increase in all-cause
mortality in intensive therapy arm
Median A1c
Intensive: 6.4%
Standard: 7.5%
ACCORD study group. N Engl J Med 2008; 358:2545-2559
25. Which subgroup in ACCORD was
most likely to benefit or have increased
mortality?
Heterogeneous
treatment effects
26. Basu et al. Diabetes Care 2017
doi.org/10.2337/dc17-2252
27. Basu et al. Diabetes Care 2017
doi.org/10.2337/dc17-2252
Summary risk stratification decision tree
Hemoglobin Glycosylation Index
(Observed - Predicted A1c)
Predicted A1c
0.009 x FPG [mg/dl] + 6.8
28. Basu et al. Diabetes Care 2017 doi.org/10.2337/dc17-2252
Survival curves for all-cause mortality among
subsets identified by each subgroup in the decision tree
A: HGI <0.44, BMI <30 kg/m2 and age <61 years
B: HGI <0.44, BMI <30 kg/m2 and age >61 years
29. Basu et al. Diabetes Care 2017 doi.org/10.2337/dc17-2252
Survival curves for all-cause mortality among
subsets identified by each subgroup in the decision tree
C: HGI <0.44 and BMI >30 kg/m2
D: HGI >0.44
30. What is AI?
Use of AI in diabetes
Will AI replace
physicians?
31. Machine learning represents a
shifting clinical paradigm from rigidly
defined management strategies to
data-driven precision
medicine.
Buch et al. Diabet Med 2018;35:495-7.
32. Buch et al. Diabet Med 2018;35:495-7.
Clinical guidelines will be
delivered through apps
rather than static documents.
33. Buch et al. Diabet Med 2018;35:495-7.
Healthcare professionals will require adequate
training to operate AI-based solutions
Appreciate the limitations of technology
Over-reliance on AI risks de-skilling the profession
34. “The pinnacle of AI is being fully
autonomous. But I don’t think
that will happen in medicine;
AI will always need human
backup.
- Eric Topol MD
35. A robot may not injure a human
being or, through inaction, allow a
human being to come to harm.
A robot must obey orders given it
by human beings except where
such orders would conflict with
the First Law.
A robot must protect its own
existence as long as such
protection does not conflict with
the First or Second Law.
Isaac Asimov’s Three Laws of Robotics