MERI NAZAR--Contribution of Solcrats in my SBL JourneySBL DIGITAL
An Overview with FUN showing Contribution of Solcrats(Employees) on another one Solcrat(
Employee) in her Solace journey in enhancing her Personality with all the Flying Colours.
The ECRINAL range of products is specially designed to meet the needs of your nails, eyelashes and eyebrows naturally.
To normalize and maintain their vital well being, ECRINAL offers a number of solutions specifically designed for each concern.
- PARABEN FREE
- ACETONE FREE
- FORMALDEHYDE FREE
- TOLUENE FREE
CEO's Guide to Sound Decision Making in 21st CenturyVivek Sood
Technology revolution is making decision sciences more complex than ever before. Here is a short 9 slide deck to look at the latest and greatest tools at your disposal
MERI NAZAR--Contribution of Solcrats in my SBL JourneySBL DIGITAL
An Overview with FUN showing Contribution of Solcrats(Employees) on another one Solcrat(
Employee) in her Solace journey in enhancing her Personality with all the Flying Colours.
The ECRINAL range of products is specially designed to meet the needs of your nails, eyelashes and eyebrows naturally.
To normalize and maintain their vital well being, ECRINAL offers a number of solutions specifically designed for each concern.
- PARABEN FREE
- ACETONE FREE
- FORMALDEHYDE FREE
- TOLUENE FREE
CEO's Guide to Sound Decision Making in 21st CenturyVivek Sood
Technology revolution is making decision sciences more complex than ever before. Here is a short 9 slide deck to look at the latest and greatest tools at your disposal
«Журнал "Мій Бізнес"» № 19, березень 2017DIXI Group
У свіжому номері журналу "Мій Бізнес":
- фінанси;
- ресурсоефективність;
- консультація;
- торгівля;
- маркетинг;
- ІТ для бізнесу;
- огляд ринку;
- ідея для бізнесу;
- власний досвід.
Mercado Local
La plaza bursátil limeña mostró avances en sus principales indicadores.
El índice principal tocaba máximos en un mes impulsada por los valores vinculados a la Construcción ante las expectativas crecientes de una fuerte inversión en obras de reconstrucción de las zonas afectadas por las intensas lluvias. Destacaban los papeles de Graña y Montero (+13.37%) tras señalar que no sería afectada por un decreto del Gobierno que busca impedir a empresas corruptas obtener contratos de
obras públicas, ya que ninguno de sus ejecutivos ha sido condenado.
Mercados Globales
Asia
Las acciones japonesas avanzó con fuerza y recuperó parte de las pérdidas registradas en la sesión previa. Destacaron las alzas de Fujitsu y Lenovo Group que anunciaron el aplazamiento de la fecha para un acuerdo final sobre su negocio de computadoras personales hasta la mitad del 2017. En tanto, el índice surcoreano Kospi avanzó levemente
luego de que el país revisara al alza su PBI trimestral hasta 2.4% A/A.
Europa
Las bolsas europas cerraron hoy sus operaciones con ganancias generales, alentadas por el repunte en las acciones del sector minero y el alza en el precio del petróleo. Destacaron las plazas de Alemania y Portugal. En materia bancaria, Credit Suisse indicó que tomará una decisión sobre los planes de aumento de capital tan pronto sea posible.
Estados Unidos
La Bolsa de Nueva York registró alzas tras una fuerte caída del lunes relacionada con los temores de que el fracaso del "Trumpcare" para obtener votos suficientes en la Cámara pueda repercutir en el resto de la agenda económica del presidente Trump. No obstante, los republicanos
indicaron que aún no han abandonado su intención de derogar y reemplazar la Ley de Cuidado de Salud ("Obamacare"). En materia económica, destacó el índice de confianza del consumidor de The Conference Board que tocó en marzo su mayor nivel en 16 años, en tanto los precios de casas crecieron en enero a máximos de 31 meses.
¿Se debatirá y se aprobará en el Pleno Municipal del Ayuntamiento de Alcobendas sobre la idoneidad y los proyectos concretos a los que serán destinados los fondos e inversiones?
Modern Indian School, Nepal, Improvement: Making the Best School.. Betteramulya123
Amulya offers Improvement Tips to the Best School Better: Modern Indian School where all three of my children were educated for the Best Value for the Money with extraordinary results, can still be made Better, based on my Experience and Knowledge of Education in St Xavier's, Raato Bangala, Tribhuvan University, Kathmandu University School of Arts and School of Education, India, USA etc....for over three decades!
1Big Data Analytics forHealthcareChandan K. ReddyD.docxaulasnilda
1
Big Data Analytics for
Healthcare
Chandan K. Reddy
Department of Computer Science
Wayne State University
Jimeng Sun
Healthcare Analytics Department
IBM TJ Watson Research Center
2Jimeng Sun, Large-scale Healthcare Analytics
Healthcare Analytics using Electronic Health Records (EHR)
Old way: Data are expensive and small
– Input data are from clinical trials, which is small
and costly
– Modeling effort is small since the data is limited
• A single model can still take months
EHR era: Data are cheap and large
– Broader patient population
– Noisy data
– Heterogeneous data
– Diverse scale
– Complex use cases
3Jimeng Sun, Large-scale Healthcare Analytics
Heterogeneous Medical Data
DiagnosisDiagnosis
MedicationMedication
LabLab
Clinical
notes
Clinical
notes
ImagesImages
Genetic
data
Genetic
data
4Jimeng Sun, Large-scale Healthcare Analytics
Challenges of Healthcare AnalyticsScalability ChallengesChallenges in Healthcare Analytics
Collaboration across domains
Analytic platform
Intuitive results
Scalable computation
5
PARALLEL MODEL BUILDING
6Jimeng Sun, Large-scale Healthcare Analytics
Motivation – Predictive modeling using EHR is growing
Need for scalable predictive modeling platforms/systems due to increased
computational requirements from:
– Processing EHR data (due to volume, variability, and heterogeneity)
– Building accurate models
– Building clinically meaningful models
– Validating models for accuracy and generalizability
Explosion in
interest
7Jimeng Sun, Large-scale Healthcare Analytics
What does it take to develop a predictive model using EHR?
Marina: IBM
Analytics Consultant
1
2
3
4
5
Within 3 months, we need to
1. understand business case
2. obtain the data
3. prepare the data
4. develop predictive models
5. deliver the final model
David Gotz, Harry Starvropoulos, Jimeng Sun, Fei Wang.
ICDA: A Platform for Intelligent Care Delivery Analytics, AMIA 2012
8Jimeng Sun, Large-scale Healthcare Analytics
A Generalized Predictive Modeling Pipeline
Cohort Construction: Find an appropriate set of patients with the specified
target condition and a corresponding set of control patients without the
condition.
Feature Construction: Compute a feature vector representation for each
patient based on the patient’s EHR data.
Cross Validation: Partition the data into complementary subsets for use in
model training and validation testing.
Feature Selection: Rank the input features and select a subset of relevant
features for use in the model.
Classification: The training and evaluation of a model for a specific classifier.
Output: Clean up intermediate files and to put results into their final locations.
Model specification
9Jimeng Sun, Large-scale Healthcare Analytics
Cohort Construction
A
ll
pa
tie
nt
s
D1
Disease Target samples
D1 Hypertension control 5000
D2 Heart failure onset 33K
D3 Hypertension diagnosis 300K
Cases
Controls
D3
D2
10Jimeng Sun, Large- ...
Elsevier Medical Graph – mit Machine Learning zu Precision MedicineRising Media Ltd.
Elsevier Health Analytics entwickelt den Medical Knowledge Graph, welcher Korrelationen zwischen Krankheiten und zwischen Krankheiten und Behandlungen darstellt. Auf einem Gesamtdatensatz von sechs Millionen anonymisierten Patienten, beobachtbar über sechs Jahre, haben wir über 2000 Modelle erstellt, welche die Entwicklung von Krankheiten prognostizieren. Jedes Modell ist adjustiert für mehr als 3000 Kovariablen. Dazu kam ein Boosting Algorithmus mit Variablenselektion zum Einsatz. Die Betas der selektierten Variablen wurden extrahiert, getestet hinsichtlich Kausalität und Signifikanz, und daraus wurde die erste Version des Medical Graphen mit über 2000 Krankheitsknoten und 25.000 Effekt-Kanten gebaut. Der Graph wird aktuell in der Praxis getestet, mit dem Ziel, dem Arzt eine patienten-individuelle Entscheidungsunterstützung für die Behandlung zu geben.
Deep learning methods applied to physicochemical and toxicological endpointsValery Tkachenko
Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.
Impact of Big Data & Artificial Intelligence in Drug Discovery & Development ...Nick Brown
Oral Presentation given at European Drug Discovery Innovation & Outsourcing Programme on 12th September 2023 in Barcelona. Overview around impact for AstraZeneca R&D from examples in the past 5+ years, including machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, and examples applying AI for right dose - identifying risk factors for CV patients and automated Population PK model prediction.
«Журнал "Мій Бізнес"» № 19, березень 2017DIXI Group
У свіжому номері журналу "Мій Бізнес":
- фінанси;
- ресурсоефективність;
- консультація;
- торгівля;
- маркетинг;
- ІТ для бізнесу;
- огляд ринку;
- ідея для бізнесу;
- власний досвід.
Mercado Local
La plaza bursátil limeña mostró avances en sus principales indicadores.
El índice principal tocaba máximos en un mes impulsada por los valores vinculados a la Construcción ante las expectativas crecientes de una fuerte inversión en obras de reconstrucción de las zonas afectadas por las intensas lluvias. Destacaban los papeles de Graña y Montero (+13.37%) tras señalar que no sería afectada por un decreto del Gobierno que busca impedir a empresas corruptas obtener contratos de
obras públicas, ya que ninguno de sus ejecutivos ha sido condenado.
Mercados Globales
Asia
Las acciones japonesas avanzó con fuerza y recuperó parte de las pérdidas registradas en la sesión previa. Destacaron las alzas de Fujitsu y Lenovo Group que anunciaron el aplazamiento de la fecha para un acuerdo final sobre su negocio de computadoras personales hasta la mitad del 2017. En tanto, el índice surcoreano Kospi avanzó levemente
luego de que el país revisara al alza su PBI trimestral hasta 2.4% A/A.
Europa
Las bolsas europas cerraron hoy sus operaciones con ganancias generales, alentadas por el repunte en las acciones del sector minero y el alza en el precio del petróleo. Destacaron las plazas de Alemania y Portugal. En materia bancaria, Credit Suisse indicó que tomará una decisión sobre los planes de aumento de capital tan pronto sea posible.
Estados Unidos
La Bolsa de Nueva York registró alzas tras una fuerte caída del lunes relacionada con los temores de que el fracaso del "Trumpcare" para obtener votos suficientes en la Cámara pueda repercutir en el resto de la agenda económica del presidente Trump. No obstante, los republicanos
indicaron que aún no han abandonado su intención de derogar y reemplazar la Ley de Cuidado de Salud ("Obamacare"). En materia económica, destacó el índice de confianza del consumidor de The Conference Board que tocó en marzo su mayor nivel en 16 años, en tanto los precios de casas crecieron en enero a máximos de 31 meses.
¿Se debatirá y se aprobará en el Pleno Municipal del Ayuntamiento de Alcobendas sobre la idoneidad y los proyectos concretos a los que serán destinados los fondos e inversiones?
Modern Indian School, Nepal, Improvement: Making the Best School.. Betteramulya123
Amulya offers Improvement Tips to the Best School Better: Modern Indian School where all three of my children were educated for the Best Value for the Money with extraordinary results, can still be made Better, based on my Experience and Knowledge of Education in St Xavier's, Raato Bangala, Tribhuvan University, Kathmandu University School of Arts and School of Education, India, USA etc....for over three decades!
1Big Data Analytics forHealthcareChandan K. ReddyD.docxaulasnilda
1
Big Data Analytics for
Healthcare
Chandan K. Reddy
Department of Computer Science
Wayne State University
Jimeng Sun
Healthcare Analytics Department
IBM TJ Watson Research Center
2Jimeng Sun, Large-scale Healthcare Analytics
Healthcare Analytics using Electronic Health Records (EHR)
Old way: Data are expensive and small
– Input data are from clinical trials, which is small
and costly
– Modeling effort is small since the data is limited
• A single model can still take months
EHR era: Data are cheap and large
– Broader patient population
– Noisy data
– Heterogeneous data
– Diverse scale
– Complex use cases
3Jimeng Sun, Large-scale Healthcare Analytics
Heterogeneous Medical Data
DiagnosisDiagnosis
MedicationMedication
LabLab
Clinical
notes
Clinical
notes
ImagesImages
Genetic
data
Genetic
data
4Jimeng Sun, Large-scale Healthcare Analytics
Challenges of Healthcare AnalyticsScalability ChallengesChallenges in Healthcare Analytics
Collaboration across domains
Analytic platform
Intuitive results
Scalable computation
5
PARALLEL MODEL BUILDING
6Jimeng Sun, Large-scale Healthcare Analytics
Motivation – Predictive modeling using EHR is growing
Need for scalable predictive modeling platforms/systems due to increased
computational requirements from:
– Processing EHR data (due to volume, variability, and heterogeneity)
– Building accurate models
– Building clinically meaningful models
– Validating models for accuracy and generalizability
Explosion in
interest
7Jimeng Sun, Large-scale Healthcare Analytics
What does it take to develop a predictive model using EHR?
Marina: IBM
Analytics Consultant
1
2
3
4
5
Within 3 months, we need to
1. understand business case
2. obtain the data
3. prepare the data
4. develop predictive models
5. deliver the final model
David Gotz, Harry Starvropoulos, Jimeng Sun, Fei Wang.
ICDA: A Platform for Intelligent Care Delivery Analytics, AMIA 2012
8Jimeng Sun, Large-scale Healthcare Analytics
A Generalized Predictive Modeling Pipeline
Cohort Construction: Find an appropriate set of patients with the specified
target condition and a corresponding set of control patients without the
condition.
Feature Construction: Compute a feature vector representation for each
patient based on the patient’s EHR data.
Cross Validation: Partition the data into complementary subsets for use in
model training and validation testing.
Feature Selection: Rank the input features and select a subset of relevant
features for use in the model.
Classification: The training and evaluation of a model for a specific classifier.
Output: Clean up intermediate files and to put results into their final locations.
Model specification
9Jimeng Sun, Large-scale Healthcare Analytics
Cohort Construction
A
ll
pa
tie
nt
s
D1
Disease Target samples
D1 Hypertension control 5000
D2 Heart failure onset 33K
D3 Hypertension diagnosis 300K
Cases
Controls
D3
D2
10Jimeng Sun, Large- ...
Elsevier Medical Graph – mit Machine Learning zu Precision MedicineRising Media Ltd.
Elsevier Health Analytics entwickelt den Medical Knowledge Graph, welcher Korrelationen zwischen Krankheiten und zwischen Krankheiten und Behandlungen darstellt. Auf einem Gesamtdatensatz von sechs Millionen anonymisierten Patienten, beobachtbar über sechs Jahre, haben wir über 2000 Modelle erstellt, welche die Entwicklung von Krankheiten prognostizieren. Jedes Modell ist adjustiert für mehr als 3000 Kovariablen. Dazu kam ein Boosting Algorithmus mit Variablenselektion zum Einsatz. Die Betas der selektierten Variablen wurden extrahiert, getestet hinsichtlich Kausalität und Signifikanz, und daraus wurde die erste Version des Medical Graphen mit über 2000 Krankheitsknoten und 25.000 Effekt-Kanten gebaut. Der Graph wird aktuell in der Praxis getestet, mit dem Ziel, dem Arzt eine patienten-individuelle Entscheidungsunterstützung für die Behandlung zu geben.
Deep learning methods applied to physicochemical and toxicological endpointsValery Tkachenko
Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.
Impact of Big Data & Artificial Intelligence in Drug Discovery & Development ...Nick Brown
Oral Presentation given at European Drug Discovery Innovation & Outsourcing Programme on 12th September 2023 in Barcelona. Overview around impact for AstraZeneca R&D from examples in the past 5+ years, including machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, and examples applying AI for right dose - identifying risk factors for CV patients and automated Population PK model prediction.
Meaningful (meta)data at scale: removing barriers to precision medicine researchNolan Nichols
Randomized controlled trials (RCTs) are the gold standard for evaluating therapeutics in patient populations. The data collected during RCTs include a wealth of clinical measures, biomarkers, and tissue samples – the analysis of which can lead to the approval of new medicines that improve the lives of patients. The secondary use of these data can also fuel the discovery of novel targets and biomarkers that support precision medicine, but a lack of metadata standards creates substantial barriers to reuse.
For this talk, I will discuss the challenges that arise when aggregating diverse types of data from a large number of RCTs and present a case study in how to apply (meta)data standards for the scalable curation and integration of these data into an analysis ready form.
Chronic Kidney Disease prediction is one of the most important issues in healthcare analytics. The most interesting and challenging tasks in day to day life is prediction in medical field. In this paper, we employ some machine learning techniques for predicting the chronic kidney disease using clinical data. We use three machine learning algorithms such as Decision Tree(DT) algorithm, Naive Bayesian (NB) algorithm. The performance of the above models are compared with each other in order to select the best classifier in predicting the chronic kidney disease for given dataset.
Similar to AMIA Joint Summits 2017 - Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network (20)
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Nucleic Acid-its structural and functional complexity.
AMIA Joint Summits 2017 - Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network
1. Electronic phenotyping with
APHRODITE and the Observational
Health Sciences and Informatics
(OHDSI) data network
S16: Papers - EHR-based Phenotyping
2017 Joint Summits on Translational Science,
March 28th, 2017
J U A N M . B A N D A , P H D 1 , Y O N I H A L P E R N , P H D 2 , D AV I D S O N T A G , P H D 3 , N I G A M
H . S H A H , P H D 1
1 S T A N F O R D C E N T E R F O R B I O M E D I C A L I N F O R M A T I C S R E S E A R C H , S T A N F O R D , C A ;
2 N E W Y O R K U N I V . , N E W Y O R K , N Y ;
3 M I T , C A M B R I D G E , M A
2. DISCLOSURE:
SPEAKER DISCLOSES THAT HE HAS NO RELATIONSHIPS WITH COMMERCIAL
INTERESTS.
1
Electronic phenotyping with APHRODITE and the
Observational Health Sciences and Informatics
(OHDSI) data network
3. Motivation
- Phenotyping is the beginning step to almost every biomedical study, yet
in many cases is very complicated
- Popular modern approaches are rule-based which take very long to
develop
- Can we build a statistical model which provides performance
comparable to a consensus definition and requires less development
effort? …. Turns out we can! (Agarwal V, et al. and Halpern Y, et al.)
- Can we port these two approaches to the Observational Health
Sciences and Informatics (OHDSI) tech stack?
2
Agarwal V, et al. Learning statistical models of phenotypes using noisy labeled training data. JAMIA. 2016. 23 (6), 1166-1173
Halpern Y, et al. Electronic medical record phenotyping using the anchor and learn framework. JAMIA 2016. 23 (4): 731-740
4. Automated PHenotype Routine for Observational
Definition, Identification, Training and Evaluation
(APHRODITE)
- Framework is designed to enable phenotyping via supervised (or semi-
supervised) learning of phenotype models
- Reads patient data from the OHDSI/OMOP CDM version 5
- Combines two recently published phenotyping approaches:
- XPRESS (Agarwal V, et al.)
- Achor learning (Halpern Y, et al.)
- In addition, to enable sharing and reproducibility of the underlying
phenotype 'recipes', APHRODITE allows sharing of either the trained
model or sharing of the configuration settings and anchor selections
across multiple sites
Agarwal V, et al. Learning statistical models of phenotypes using noisy labeled training data. JAMIA. 2016. 23 (6), 1166-1173
Halpern Y, et al. Electronic medical record phenotyping using the anchor and learn framework. JAMIA 2016. 23 (4): 731-740
3
5. 4
+140 collaborators
16 countries
Common CDM and
vocabulary
The patient network available includes 84 databases, both clinical and claims,
totaling over 650 million patients
13. How do we know ‘noisy labeling’ works?
Using the evaluation of Agarwal V., et al.:
- Selected one phenotype each from defined by the Electronic Medical
Records and Genomics (eMERGE) and the Observational Medical
Outcomes Partnership (OMOP) initiatives:
- Myocardial infarction
- Type 2 diabetes mellitus
- Created manually reviewed gold standard of cases and controls
Agarwal V., et al. Learning statistical models of phenotypes using noisy labeled training data. JAMIA. 2016. 23 (6), 1166-1173
Cases Controls Accuracy Recall PPV Cases Controls Accuracy Recall PPV
Source Myocardial Infarction (MI) Type 2 Diabetes Mellitus (T2DM)
OMOP / PheKB
Definition 94 94 0.87 0.91 0.84 152 152 0.92 0.88 0.96
XPRESS Noisy
labels 94 94 0.85 0.93 0.8 152 152 0.89 0.99 0.81
APHRODITE
Noisy labels 94 94 0.94 0.87 1.00 152 152 0.91 0.98 0.87
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14. Building phenotype models with APHRODITE
Using the following keywords
Performance of noisly labeled trained models
Performance of gold-standard test set
Myocardial Infarction (MI) Type 2 diabetes mellitus (T2DM)
Old myocardial infarction Type 2 diabetes mellitus with hyperosmolar coma
True posterior myocardial infarction Type 2 diabetes mellitus
Myocardial infarction with complication Pre-existing type 2 diabetes mellitus
Myocardial infarction in recovery phase
Type 2 diabetes mellitus with multiple
complications
Microinfarct of heart Type 2 diabetes mellitus in non-obese
Cases Cont. Acc. Recall PPV Acc. Recall PPV
Myocardial Infarction (MI) Type 2 Diabetes Mellitus (T2DM)
XPRESS 750 750 0.86 0.89 0.84 0.88 0.89 0.87
APHRODITE 750 750 0.9 0.92 0.89 0.89 0.92 0.87
APHRODITE 1,500 1,500 0.9 0.93 0.9 0.91 0.93 0.88
APHRODITE 10,000 10,000 0.91 0.93 0.91 0.92 0.94 0.89
Cases Cont. Acc. Recall PPV Cases Cont. Acc. Recall PPV
Source Myocardial Infarction (MI) Type 2 Diabetes Mellitus (T2DM)
OMOP/PheKB 94 94 0.87 0.91 0.84 152 152 0.92 0.88 0.96
XPRESS 94 94 0.89 0.93 0.86 152 152 0.89 0.88 0.9
APHRODITE
(750) 94 94 0.91 0.93 0.90 152 152 0.91 0.95 0.88
APHRODITE
(1,500) 94 94 0.92 0.93 0.91 152 152 0.92 0.95 0.89
APHRODITE
(10,000) 94 94 0.92 0.94 0.91 152 152 0.93 0.96 0.89
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25. Conclusions
We have successfully implemented the framework proposed by Agarwal
et al. and the Anchor learning framework by Halpern et al. to build and
refine phenotype models
We have demonstrated that it is possible identify anchors during the
model building process to generate a better labeled training set which
leads to a better performing model than just using keyword for noisy
labeling
Our main contribution is the APHRODITE package, which allows for the
potential redistribution of locally validated phenotype models as well as
the sharing of the workflows for learning phenotype models at multiple
sites of the OHDSI data network
Agarwal V, et al. Learning statistical models of phenotypes using noisy labeled training data. JAMIA. 2016. 23 (6), 1166-1173
Halpern Y, et al. Electronic medical record phenotyping using the anchor and learn framework. JAMIA 2016. 23 (4): 731-740
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28. 28
Why do noisy labels work
Learning theory - Generalization error of a model trained using
data with inaccurate labels can be made small
Model trained using m observations with accurate labels has the
same gen error as a model trained using m/(1 – 2τ) 2 observations
with noisy labels.
Model inaccuracies in automatic labeling as random classification
noise given by error rate τ
Unless τ is 0.5, having more observations will help
The cost of getting additional observations is negligible
Less cumbersome: (not requiring long lists of rules)
Nearly unsupervised (minimial user input).
- Multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics
- All our solutions are open-source
Manual review:
Each record was voted a case (or control) if two clinicians agreed, and a third clinician approved
We ensure that the potential cases and controls found in the noisy-labeled sets are completely disjoint from this manually reviewed evaluation set.
Potential question: You will get a question here about why is the case:control 50:50 and not what you would have in real life. (e.g. ~14% cases for diabetes)
140 collaborators in 16 countries
Not surprising. Learning theory - Generalization error of a model trained using data with inaccurate labels can be made small
Model trained using m observations with accurate labels has the same gen error as a model trained using m/(1 – 2τ) 2 observations with noisy labels.
Model inaccuracies in automatic labeling as random classification noise given by error rate τ
Unless τ is 0.5, having more observations will help
The cost of getting additional observations is negligible
With over 20,000 features (on average) the regular APHRODITE models with and without anchors perform the best, but with very close performance to the models that exclude the visits data. For the phenotypes we present, this indicates that most of the coded data is not particularly useful in making the phenotype assignments. It is also quite evident that removing the text features (observations) results in nearly a 15% drop in accuracy demonstrating that access to the unstructured portions of the medical record is crucial for the success of training phenotype models with imperfectly labeled training data.