This document discusses healthcare big data and its integration with artificial intelligence. It covers several types of healthcare big data including genetic data, electronic health records, national healthcare data, medical images, and sensor/mobile data. It describes how these different data sources can be integrated and analyzed using techniques like natural language processing, machine learning, and data mining to generate insights. It also provides examples of how healthcare big data is currently being used for applications like precision medicine, pharmacogenomics, and evaluating drug safety and risk.
- 의료 빅데이터 - 개념 입문과 임상의사가 할 수 있는 일
빅데이터 개념 입문과
의료와 관계된 빅데이터 종류와 활용 방법
진료와 연구에 활용할 수 있는 방법을
임상의사의 관점에서 다루었습니다.
1. What is Big Data?
2. Healthcare Big Data
1) Electrical Health Records (EHR) Structured/Unstructured Data
2) Medical Images
3) National Healthcare Data
4) Behavior/Sensor Data
5) Genetic Data
3. Clinical and Research Applications
Presentación auspiciada por Janssen en el marco del 5to congreso de actualización de Hematología y Oncología, 26.08.2017, Centro de Convenciones, Blue Garden, Barranquilla
- 의료 빅데이터 - 개념 입문과 임상의사가 할 수 있는 일
빅데이터 개념 입문과
의료와 관계된 빅데이터 종류와 활용 방법
진료와 연구에 활용할 수 있는 방법을
임상의사의 관점에서 다루었습니다.
1. What is Big Data?
2. Healthcare Big Data
1) Electrical Health Records (EHR) Structured/Unstructured Data
2) Medical Images
3) National Healthcare Data
4) Behavior/Sensor Data
5) Genetic Data
3. Clinical and Research Applications
Presentación auspiciada por Janssen en el marco del 5to congreso de actualización de Hematología y Oncología, 26.08.2017, Centro de Convenciones, Blue Garden, Barranquilla
Prof. Nicholas Harvey's presentation from Osteoporosis 2016: Calcium, with or without vitamin D supplementation, is not associated with ischaemic heart disease or cardiac death: the UK Biobank cohort.
Find out more at: https://nos.org.uk/conference
Global Medical Cures™ | Crestor- Post Marketing Adverse Event ReviewGlobal Medical Cures™
Global Medical Cures™ | Crestor- Post Marketing Adverse Event Review
DISCLAIMER-
Global Medical Cures™ does not offer any medical advice, diagnosis, treatment or recommendations. Only your healthcare provider/physician can offer you information and recommendations for you to decide about your healthcare choices.
Update on the 18th International Conference on Co-morbidities and Adverse Drug Reactions in HIV
Daniel Lee, M.D.
January 20th, 2017
UCSD HIV & Global Health Rounds
Prof. Nicholas Harvey's presentation from Osteoporosis 2016: Calcium, with or without vitamin D supplementation, is not associated with ischaemic heart disease or cardiac death: the UK Biobank cohort.
Find out more at: https://nos.org.uk/conference
Global Medical Cures™ | Crestor- Post Marketing Adverse Event ReviewGlobal Medical Cures™
Global Medical Cures™ | Crestor- Post Marketing Adverse Event Review
DISCLAIMER-
Global Medical Cures™ does not offer any medical advice, diagnosis, treatment or recommendations. Only your healthcare provider/physician can offer you information and recommendations for you to decide about your healthcare choices.
Update on the 18th International Conference on Co-morbidities and Adverse Drug Reactions in HIV
Daniel Lee, M.D.
January 20th, 2017
UCSD HIV & Global Health Rounds
Biomedical big data and research clinical application for obesityHyung Jin Choi
1. What is Biomedical Big Data?
2. Biomedical Big Data
1) Genetic Data
2) Electrical Health Records
3) National Healthcare Data
4) Medical Images
5) Sensor/Mobile Data
6) Data Integration
3. Biomedical Big Data + Artificial Intelligence
4. Research/Clinical Application for Obesity
The Present and Future of Personal Health Record and Artificial Intelligence ...Hyung Jin Choi
1. Why Personal Health Record and Artificial Intelligence ?
2. Obesity Example
3. Personal Health Record
1) Genetic Data
2) Electrical Health Records
3) National Healthcare Data
4) Medical Images
5) Sensor/Mobile Data
6) Data Integration
4. PHR+AI Applications
"How Scientific Wellness will Drive The Future of Health" - Nathan Price (Pro...Hyper Wellbeing
"How Scientific Wellness will Drive The Future of Health" - Nathan Price (Professor, Institute of Systems Biology)
Delivered at the inaugural Hyper Wellbeing Summit, 14th November 2016, Mountain View, California.
For more information including details of subsequent events, please visit http://hyperwellbeing.com
The summit was created to foster a community around an emerging industry - Wellness as a Service (WaaS). Consumer technologies, in particular wearables and mobile, are powering a consumer revolution. A revolution to turn health and wellness into platform delivered services. A revolution enabling consumer data-driven disease risk reduction. A revolution extending health care past sick care towards consumer-led lifelong health, wellness and lifestyle optimization.
WaaS newsletter sign-up http://eepurl.com/b71fdr
@hyperwellbeing
Obesity PHR Big Data - Research and Clinical ApplicationsHyung Jin Choi
1. Obesity
2. Obesity Big Data
1) Genetics
2) Environment
3) Electrical Health Record
4) Imaging
5) Mobile/Sensor
6) National Health Record
7) Social Network Service
3. Research
4. Clinical Application
Translational Genomics towards Personalized medicine - Medhavi Vashisth.pptMedhavi27
Every individual is unique, and so is his/her body's affinity and reaction towards diseases and their treatment methods. The science of personalized takes into account biology of one individual at a time and relates it with established databases for devising or optimizing suitable treatment strategies.
Presenter: Marina Sirota, UCSF
Recent advances in genome typing and sequencing technologies have enabled quick generation of a vast amount of molecular data at very low cost. The mining and computational analysis of this type of data can help shape new diagnostic and therapeutic strategies in biomedicine. In this talk, I will discuss how such technological advances in combination with data science and integrative analysis can be applied to drug discovery in the context of drug target identification, computational drug repurposing, and population stratification approaches.
"Hacking the Software for Life" - Brad Perkins (Chief Medical Officer, Human ...Hyper Wellbeing
"Hacking the Software for Life" - Brad Perkins (Chief Medical Officer, Human Logevity, Inc.)
Delivered at the inaugural Hyper Wellbeing Summit, 14th November 2016, Mountain View, California.
For more information including details of subsequent events, please visit http://hyperwellbeing.com
The summit was created to foster a community around an emerging industry - Wellness as a Service (WaaS). Consumer technologies, in particular wearables and mobile, are powering a consumer revolution. A revolution to turn health and wellness into platform delivered services. A revolution enabling consumer data-driven disease risk reduction. A revolution extending health care past sick care towards consumer-led lifelong health, wellness and lifestyle optimization.
WaaS newsletter sign-up http://eepurl.com/b71fdr
@hyperwellbeing
SILS 2015 - Connecting Precision Medicine to Precision Wellness Sherbrooke Innopole
By: Joel Dudley, Mount Sinai School of Medicine
At Sherbrooke International Life Sciences Summit - 2nd edition | September 28/29/30 2015
www.sils-sherbrooke.com
우리 몸은 체중, 체온, 혈압, 혈당, 전해질, 체액양 등을 일정하게 유지하는 항상성 조절 기능이 있다. 특히 특정 체중 조절점 (body weight set point)으로 체중을 일정하게 유지하는 기능은 비만의 병인에도 중요한 역할을 하고, 비만 치료에 있어서 문제가 되는 요요현상에서도 중요한 역할을 한다. 체중 조절점은 어떤 생물학적 기전으로 이루어지며, 잘 변하지 않는 체중 조절점이 어떤 기전으로 가변적이 될 수 있는지 알아보고자 한다. 또한, 이런 기전을 실제 비만클리닉에서 어떻게 적용할 수 있을지 살펴보고자 한다.
1. What is Stress?
2. Mechanism
Neuro-Anatomy of Stress
Neuro-Endocrine of Stress
Inflammation and Stress
3. Stress and Disease
Stress and Food Addiction
4. Stress and Central Regulation of Metabolism
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
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.
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.
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.
CDSCO and Phamacovigilance {Regulatory body in India}NEHA GUPTA
The Central Drugs Standard Control Organization (CDSCO) is India's national regulatory body for pharmaceuticals and medical devices. Operating under the Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India, the CDSCO is responsible for approving new drugs, conducting clinical trials, setting standards for drugs, controlling the quality of imported drugs, and coordinating the activities of State Drug Control Organizations by providing expert advice.
Pharmacovigilance, on the other hand, is the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems. The primary aim of pharmacovigilance is to ensure the safety and efficacy of medicines, thereby protecting public health.
In India, pharmacovigilance activities are monitored by the Pharmacovigilance Programme of India (PvPI), which works closely with CDSCO to collect, analyze, and act upon data regarding adverse drug reactions (ADRs). Together, they play a critical role in ensuring that the benefits of drugs outweigh their risks, maintaining high standards of patient safety, and promoting the rational use of medicines.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
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
2. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
2
3. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
3
13. Nutrient-wide
association study
13 2016 AJCN Nutrient-wide association study of 57 foods-nutrients and epithelial ovarian cancer in the European Prospective Investigation into Cancer and Nutrition
15. 1000 개의 질병들
Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.
Phenotype-wide Association Study
18. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
18
19. Tissue Specific Expression
Comprehensive Catalogues of Genomic Data
Variation in the human genome
Mendelian (monogenic) diseases
(N=22,432)
Whole genome sequencing (N=1,000)
Four ethnic groups
(CEU, YRI, JPT, CHB, N=270)
GWAS catalog
Complex (multigenic) traits
(Tratis=2000)
Disease-related variations
Functional elements
2017-07-31
19
20. Disease genetic susceptibility
Cancer driver
somatic mutation
Pharmacogenomics
Targeted
Cancer Treatment
(EGFR)
Causal
Variant
Targeted Drug
(MODY-SU)
Drug Efficacy/Side Effect
Related Genotype
(CYP, HLA)
Genetic Diagnosis
(Mendelian,
Cystic fibrosis)
Molecular
Classification
- Prognosis
(Leukemia)
Hereditary
Cancer
(BRCA)
Microbiome
(Bacteria,
Virus)
Genomic Medicine
Risk prediction
(Complex disease,
Diabetes)
Germline Variants
Fetal DNA
23. Influence of Genetics on Human Disease
For any condition the overall balance of g
enetic and environmental determinants ca
n be represented by a point somewhere w
ithin the triangle.
23
Single
Locus /
Mendelian
Multiple
Loci or multi-
chromosomal
Environmental
Cystic Fibrosis
Hemophilia A
Examples:
Alzheimer’s Disease
Type II Diabetes
Cardiovascular Disease
Diet
Carcinogens
Infections
Stress
Radiation
Lifestyle
Gene = F8
Gene= CFTR
F8 = Coagulation Factor VIII
CFTR = Cystic Fibrosis Conductance Transmembrane Regulator
Lung Cancer
44. Future of Genomic Medicine?
Test when neededWithout information Know your type
Blood
type
Geno
type
Here is my
sequence
45. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
45
46. Electronic Health Records
2012 NRG Mining electronic health records- towards better research applications and clinical care
46
47. Common EHR Data
Joshua C. Denny Chapter 13: Mining Electronic Health Records in the Genomics Era. PLoS Comput Biol. 2012 December; 8(12):
International Classification of Diseases (ICD)
Current Procedural Terminology (CPT)
54. 밤동안 저혈당수면 Lt.foot rolling Keep떨림,
식은땀, 현기증, 공복감, 두통, 피로감등의 저혈
당 에 저혈당 이 있을 즉알려주도록 밤사이 특
이호소 수면유지상처와 통증 상처부위 출혈
oozing, severe pain 알리도록 고혈당 처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .당뇨약 이해 잘 하고 수술부위
oozing Rt.foot rolling keep드레싱 상태를 고혈
당 고혈당 의식변화 BST 387 checked.고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 발관리 방법에 .
목표 혈당, 목표 당화혈색소에 .식사를 거르거
나 지연하지 않도록 .식사요법, 운동요법, 약물
요법을 정확히 지키는 것이 중요을 .처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .혈당 정상 범위임rt foot rolling
중으로 pain호소 밤사이 수면양호걱정신경 예
민감정변화 중임감정을 표현하도록 지지하고
경청기분상태 condition 조금 나은 듯 하다고 혈
당 조절과 관련하여 신경쓰는 모습 보이며 혈당
self로 측정하는 모습 보임혈당 조절에 안내하
고 불편감 지속알리도록고혈당 고혈당 의식변
화 고혈당 허약감 지남력 혈당조절 안됨고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 정기점검 내용과
빈도에 .BST 140 으로 저혈당 호소 밤동안 저
혈당수면 Lt.foot rolling Keep떨림, 식은땀, 현
기증, 공복감, 두통, 피로감등의 저혈당 에 저
혈당 이 있을 즉알려주도록 pain 및 불편감 호
소 WA 잘고혈당 고혈당 의식변화 고혈당 허
약감 지남력 혈당조절 안됨식사요법, 운동요법,
약물요법을 정확히 지키는 것이 중요을 .저혈당
/고혈당 과 대처법에 .혈당정상화, 표준체중의
유지, 정상 혈중지질의 유지에 .고혈당 ,,관리
방법 .혈당측정법,인슐린 자가 투여법, 경구투
약,수분 섭취량,대체 탄수화물,의료진의 도움이
필요한 사항에 교혈당 정상 범위임수술부위
oozing Rt.foot rolling keep수술 부위 (출혈, 통
증, 부종)수술부위 출혈 상처부위 oozing
Wound 당겨지지 않도록 적절한 체위 취하기
설명감염 발생 위험 요인 수술부위 출혈 밤동안
간호기록지 Word Cloud
Natural Language Processing (NLP)54
55.
56.
57. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
57
60. Korean Society for
Bone and Mineral
Research
Anti-hypertensive
prescriptions
(2008-2011)
N = 8,315,709
New users
N = 2,357,908
Age ≥ 50 yrs
Monotherapy
Compliant user (MPR≥80%)
No previous fracture
N = 528,522
Prevalent users
N = 5,957,801
Excluded
Age <50
Combination therapy
Inadequate compliance
Previous fracture
N = 1,829,386
Final study population
심평원 빅데이터 연구
고혈압약과 골절
Choi et al., 2015 International Journal of Cardiology
61. Compare Fracture Risk
Comparator?Hypertension
CCB
High
Blood Pressure
Fracture
Risk
BB
Non-
user
Healthy
Non-
user
Cohort study (Health Insurance Review & Assessment Service)
New-user design (drug-related toxicity)
Non-user comparator (hypertension without medication)
2007 20112008
Choi et al., 2015 International Journal of Cardiology61
64. Overview of secondary data in
public health by data source
보험청구자료 건강검진
NHIS (National Health Insurance Corporation): 국민건강보험공단 (보험공단)
HIRA (Health Insurance Review and Assessment Service) :건강보험심사평가원 (심평원)
통계청 암센터
J Korean Med Assoc 2014 May; 근거중심 보건의료의 시행을 위한 빅데이터 활용
65. Big data platform model by Korea Institute of
Drug Safety and Risk Management
66. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
66
69. 69 2017 Scientific Reports. Precision Radiology- Predicting longevity using feature engineering and deep learning methods in a radiomics framework
70. 사망률 높을 것으로 예측되는 영상(왼쪽)과
낮을 것으로 예측되는 영상(오른쪽)
70 2017 Scientific Reports. Precision Radiology- Predicting longevity using feature engineering and deep learning methods in a radiomics framework
71. Quantitative nuclear morphometry
2015 Laboratory Investigation. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis
of whole slide images
77. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
77
98. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
98
105. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
105
106. Healthcare Big Data
+ Artificial Intelligence
106
Healthcare Big Data Machine Learning
Novel Insights and Applications
111. 111
왓슨 도입 후 달라진 점 중 하나는 환자를 대하는
의료진의 긴장도가 높아졌다는 것. 길병원은 의
료진이 왓슨과 다른 치료법을 추천할 땐 분명한
근거를 제시하도록 했다.
1월엔 부산대병원이, 이달 16일엔 건양대병원이
길병원에 이어 왓슨을 도입했다. 현재 왓슨은 결
장·직장, 유방, 폐, 위, 자궁암만을 진단할 수 있지
만 4월엔 난소암이 추가되고 연말엔 방광, 전립
샘, 혈액암(백혈병)까지 추가돼 암종 85%를 커버
할 수 있게 된다.
117. Personalized Nutrition by
Prediction of Glycemic Responses
117
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
Received: October 5, 2015;
Received in revised form:
October 29, 2015;
Accepted: October 30, 2015;
121. Google spin-off deploys wearable
electronics for huge health study
121
4년간 10,000명 - 건강 상태 데이터 축적
심박수, 수면패턴, 유전 정보, 감정 상태, 진료기록, 가족력, 소변/타액/혈액 검사
스탠퍼드 대학병원/듀크 대학병원
https://www.nature.com/articles/d41586-017-01144-1
122. Zuckerberg, Chan give UCSF $10
million for health data research
122
http://www.sfchronicle.com/business/article/Zuckerberg-Chan-give-UCSF-10-million-for-health-11534088.php
Alphabet’s health subsidiary Verily, formerly Google Life Sciences, recently launched a study to track health information from 10,000 people. IBM’s Watson Health uses algorithms to sift through patient records and research papers from medical journals to help doctors diagnose and treat diseases. Amazon has assembled a team to build tools for electronic health records data, CNBC reported this week