Alliance Health Chief Data Scientist Deep Dhillon presentation to UW CS students on mining unstructured healthcare data. This talk describes technical information on a system designed to empower patients and health care professionals by automatically generating health care content that is fresh, authoritative, statistically relevant and not available via other means.
Text and data mining in UK and France (ADBU - 13 Dec 16)Rob Johnson
Slides from my presentation in Paris on 13 Dec 2016, summarising the findings of our study on text and data mining in public research for the ADBU. Full report available at http://adbu.fr/etude-tdm/.
Doctoral Thesis Proposal: An Automatic Knowledge Discovery Strategy In Biomed...Universidad de los Llanos
The growing on variety, volume and velocity of public biomedical databases in the last years have generate an explosion of big data in biology and medicine. Most of these databases comprise structural, molecular and genetic information from different kind of images acquisition modalities and associated metadata having a great potential, not yet exploited, as a source of information and knowledge which could impact biomedical research in different application fields. In fact, new research areas are emerging in this direction, known as bioimage informatics and computational pathology, which are areas basically attempting to apply different methods of image processing, pattern recognition, machine learning and data mining, in multimodal biomedical databases. However, the proposed tools and methods for image collection analysis have some research challenges coming with deluge of big data in biomedicine such as: visual appearance variability, semantic gap between image content and high-level meaning, structural and interpretable representation of image content, semantic inclusion of multimodal information sources, and scalability support with the increasing volume of databases. In this way, the research proposal is addressing the problem of automatic extraction of knowledge from biomedical image collections. Specifically, the goal is to devise methods to automatically find: visual patterns that compactly explain the visual richness of biomedical images, relationships between visual patterns, and relationships between visual patterns and their meaning in a particular biomedical context. In order to solve it, the proposed methodology has three main stages: part-based bioimage representation, semantic bioimage representation and biomedical knowledge discovery. Each stage of methodology state-of-the-art methods from computer vision, image processing, machine learning and data mining will be explored to provide interpretable learning methods supported by high-performance computing.
Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s GoingHealth Catalyst
Health system leaders have questions about big data: When will I need it? How should I prepare? What’s the best way to use it? It’s important to separate the hype of big data from the reality. Where big data stands in healthcare today is a far cry from where it will be in the future. Right now, the best use cases are in academic- or research-focused healthcare institutions. Most healthcare organizations are still tackling issues with their transactional databases and learning how to use those databases effectively. But soon—once the issues of expertise and security have been addressed—big data will play a huge role in care management, predictive analytics, prescriptive analytics, and genomics for everyday patients. The transition to big data will be easier if health systems adopt a late-binding approach to the data now.
Text and data mining in UK and France (ADBU - 13 Dec 16)Rob Johnson
Slides from my presentation in Paris on 13 Dec 2016, summarising the findings of our study on text and data mining in public research for the ADBU. Full report available at http://adbu.fr/etude-tdm/.
Doctoral Thesis Proposal: An Automatic Knowledge Discovery Strategy In Biomed...Universidad de los Llanos
The growing on variety, volume and velocity of public biomedical databases in the last years have generate an explosion of big data in biology and medicine. Most of these databases comprise structural, molecular and genetic information from different kind of images acquisition modalities and associated metadata having a great potential, not yet exploited, as a source of information and knowledge which could impact biomedical research in different application fields. In fact, new research areas are emerging in this direction, known as bioimage informatics and computational pathology, which are areas basically attempting to apply different methods of image processing, pattern recognition, machine learning and data mining, in multimodal biomedical databases. However, the proposed tools and methods for image collection analysis have some research challenges coming with deluge of big data in biomedicine such as: visual appearance variability, semantic gap between image content and high-level meaning, structural and interpretable representation of image content, semantic inclusion of multimodal information sources, and scalability support with the increasing volume of databases. In this way, the research proposal is addressing the problem of automatic extraction of knowledge from biomedical image collections. Specifically, the goal is to devise methods to automatically find: visual patterns that compactly explain the visual richness of biomedical images, relationships between visual patterns, and relationships between visual patterns and their meaning in a particular biomedical context. In order to solve it, the proposed methodology has three main stages: part-based bioimage representation, semantic bioimage representation and biomedical knowledge discovery. Each stage of methodology state-of-the-art methods from computer vision, image processing, machine learning and data mining will be explored to provide interpretable learning methods supported by high-performance computing.
Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s GoingHealth Catalyst
Health system leaders have questions about big data: When will I need it? How should I prepare? What’s the best way to use it? It’s important to separate the hype of big data from the reality. Where big data stands in healthcare today is a far cry from where it will be in the future. Right now, the best use cases are in academic- or research-focused healthcare institutions. Most healthcare organizations are still tackling issues with their transactional databases and learning how to use those databases effectively. But soon—once the issues of expertise and security have been addressed—big data will play a huge role in care management, predictive analytics, prescriptive analytics, and genomics for everyday patients. The transition to big data will be easier if health systems adopt a late-binding approach to the data now.
Whats The Role Of Social Media In Bcm Dominique C Brack, SBCIReputelligence
Social media presents a pool of opportunities for the growth of businesses. It is now easier for enterprises to market their products easily and cheaply, promptly respond to customer’s enquiries, among other benefits. On the other hand, it also poses huge risks to them. It is now easier for unhappy customers to complain and share their bad experiences with anyone on the web. Former employees can spread falsehood or some company secrets that may not be fit for public ears. Competitors can easily take advantage of your company’s crisis by escalating the woes of another and throwing a minor crisis out of proportion using the social media.
If it looks like a bear and smells like a bear. It's a bear. Marketers exist to sell. In the 2-3 hysterical haze of the social runup that inalienable truth, as unpleasant as it may be, has been left behind. It's time for marketers to wake up and take control of social and make it an effective part of the marketing reality.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
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Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Whats The Role Of Social Media In Bcm Dominique C Brack, SBCIReputelligence
Social media presents a pool of opportunities for the growth of businesses. It is now easier for enterprises to market their products easily and cheaply, promptly respond to customer’s enquiries, among other benefits. On the other hand, it also poses huge risks to them. It is now easier for unhappy customers to complain and share their bad experiences with anyone on the web. Former employees can spread falsehood or some company secrets that may not be fit for public ears. Competitors can easily take advantage of your company’s crisis by escalating the woes of another and throwing a minor crisis out of proportion using the social media.
If it looks like a bear and smells like a bear. It's a bear. Marketers exist to sell. In the 2-3 hysterical haze of the social runup that inalienable truth, as unpleasant as it may be, has been left behind. It's time for marketers to wake up and take control of social and make it an effective part of the marketing reality.
Similar to Mining Unstructured Healthcare Data (16)
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Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
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2. alliance health networks
HCP/advanced patients
medify.com – 10,000% user growth past year
patients
diabeticconnect.com - # 1 online diabetes site @ ~1.4M uniques
*connect.com – content sites + guided social networks
health care industry
patient surveying, matchmaking and analysis
3. why mine healthcare text?
anatomy of what patients currently use: webmd (e.g. drugs.com, yahoo health, etc.)
q/a topic pages news + media experts discussions
• original content
• addresses emotional needs
• simple to understand
• provides answers
• original content
• moderately authoritative
• simple to understand
• original content • good coverage in the head
• fresh • provides answers
• simple to understand
• good coverage in the head
• original content
• typically aligned well with google searches,
i.e. treatments for X, symptoms for Y original content=important
• good coverage in the head providing answers=important
• original content head=developed
• aligns well with google searches fresh=important
• provides answers authority=important
4. why mine healthcare text?
Patients and HCPs need long tail, statistically meaningful, consumer friendly,
authoritative and fresh health content.
q/a topic pages news + media experts discussions
• manually written, not authoritative
• not thorough, i.e. not long tail
• not consistently credible, i.e. minimal
• accredation
• not statistically meaningful
• sparse
• manually written, moderately authoritative
• not thorough, i.e. not long tail
• manually written, not authoritative
• not consistently credible, i.e. minimal accredation
• not thorough, i.e. not long tail
• evergreen, rarely change
• manually written, not authoritative
• not consistently credible, i.e. minimal accredation
• not thorough, i.e. not long tail manual=expensive
manual=head focused
• manually written, not authoritative
• not consistently credible, i.e. minimal accredation manual=not authoritative
• not statistically meaningful manual=old and dated
5. automated content generation
• cost effective
• structured content
• statistically based
• scales to millions of patients
• scales to long tail treatments + conditions
• authoritative / citation driven
• fresh
8. how do we mine text?
assignments
curators
examples
knowledge parser Index
external repos Page
like UMLS Search
Module
9. annotate in the product:
• Easier for people to give explicit GT feedback
• Channels high visibility error angst productively
• Channels more GT toward areas most seen by users
• Override high visibility system mistakes with human data
gt demo:
• http://www.diabeticconnect.com/discussions/4790
• https://www.medify.com/internal/annotate/abstract?abstractId=8181575
14. Distribution of Signals Mined From Diabetic Connect Discussion
Threads
Medical History - Newly Lifestyle Adjustment
Diagnosed Medical History - Symptom 6%
0% 3%
Medical History - Condition
10%
Helper/Influencer
33%
Personal Account
12%
Treatment Experience
15%
Medical History
21%
16. abstract parser work flow
document sentence selection 1
1. sentences w/ high conclusion presence selected
2. shallow entity tagging applied based on umls
2 3. sentence text is modified to retain entities, optimize
shallow tagging for performance, and eliminate unnecessary filler.
4. deep typed dependency parsing of modified text
5. 3 tier text classification applied on BOW + entities
3
sentence 6. SVO triples extracted from dependencies
modification 7. rule based conclusions generated from triples
8. confidence models applied to rule and classifier
based conclusions
9 9. knowledge base used to represent domain and
dependency tree discourse style.
knowledge parse 4 10. errors measured against curated data applied for
improvements
5
triple extraction 6
3 tier classification 10
concluder 7
8
confidence assignment annotations
17. discussion parser work flow
message sentence split 1 1. sentences split from discussion thread messages
2. shallow entity tagging applied based on umls
3. sentence text is modified to retain entities, optimize
2 for performance, and eliminate unnecessary filler.
shallow tagging 4. deep typed dependency parsing of modified text
for select conclusion types
5. select conclusion type based text classification
3 applied on BOW + entities after dimensionality
sentence reduction
modification 6. SVO triples extracted from dependencies
7. rule based conclusions generated from triples
9 8. rule based KB driven anaphora resolution applied.
Classification based conclusions added.
dependency tree 9. knowledge base used to represent domain and
knowledge parse 4 discourse style.
10. errors measured against curated data applied for
improvements
5
triple extraction 6
classification 10
concluder 7
8
anaphora resolution conclusions
18. feature engineering
• entities, i.e. normalize synonyms, id new
entity types, like social relations
• entity types, i.e. metformin >
treatment_medication
• phrase driven cues, i.e. [have] [you]
[considered] > suggestion_indicator
19. anaphora resolution
• relation structure, i.e. [person]>takes>it
it refers to treatment (i.e. not condition/symptom), and specifically: medication but not device
• statistically driven, manually curated cues
i.e. drug > treatment/medication
• filter
– non matching antecedent candidates
– singular/plural agreement
• score candidates:
– antecedent occurrence frequency
– distance (#sents) from antecedent to anaphor
– co-occurrence of anaphor w/ antecedent
36. Advair: Experts vs. Patients
• “medicalese” vs. patients words
• more granularity
• a story like perspective w/ words
of inspiration
My Pulmonologist today said that he had just come from
the hospital bedside of a patient with my exact symptoms
who is on oxygen and not doing well. If I hadn't been so
diligent in following my asthma plan that could easily have
been me. His telling me that really hit home.
By the way, my Pulmonologist surprised me with his
view on many of his patients. He actually got excited
and thanked me for being knowledgeable about
asthma, understanding my own body and health needs
and for following my treatment plan. He said he doesn't
get many patients who can actually talk about their
disease, symptoms, time
frames, treatments/medications, and non-medical
measures they are taking. He also doesn't get many
people who ask questions. My response to this: What
are people thinking? They need to take control of their
own health or noone else will.
