Precision Health: The Role of the US National Library of Medicine - - PowerPoint PPT Presentation

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Precision Health: The Role of the US National Library of Medicine - - PowerPoint PPT Presentation

May 13, 2023 349 likes •837 views

Precision Health: The Role of the US National Library of Medicine in Broadening the Conversation Patricia Flatley Brennan, RN, PhD, FAAN Director National Library of Medicine Abstract Its useful but limiting to think of precision medicine

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Precision Health:

The Role of the US National Library of Medicine in Broadening the Conversation

Patricia Flatley Brennan, RN, PhD, FAAN Director National Library of Medicine

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Abstract

It’s useful but limiting to think of precision medicine as a conversation between genes and drugs. However, this narrow conversation belies the great advances in human health arising from genomic discoveries and data-driven

  • science. Integrating knowledge from other health practice disciplines, such as

nursing, expands Precision Medicine to Precision health, characterizing people by more than their genes and characterization of the person to one not limited to genes and extending nature of precision interventions beyond medicine to

  • health. Grounded with examples from nursing research, this talk will identify the

role of the US National Library of Medicine in transforming precision medicine to precision health.

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Precision Cardiology

Precision oncology Precision stomotology Precision nursing

Precis isio ion n engine neerin ring Precision Health Economics Precisio cision Imaging aging Precision Drug Development

Precision stereotactic surgery

Precision radiotherapy

Precision psychiatry Precision diabetes Precision health care

Precision drug design Precision agriculture

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  • Case: 64 year old man diagnosed with familial

adenomatous polyposis

  • Sequence to determine the allele location
  • Evidence-based decision making

– Surgery or medicine? – Insurance coverage – Patient preferences and assets

Precision Therapeutics

Targeting Therapies

APC gene
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  • Case: 54-year-old woman, perimenopausal

with 20 years use of Birth Control Pills; 3 positive 2nd-degree relations; sedentary; had 13 mammograms

  • Three tools:

– Data mining to find important relationships – Simulation for policy, capacity, population – Optimization: “smartly” choose among 1,000s of pathways

Precision Screening

Optimal Mammography Protocol

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What might a nurse know about these two people ?

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Cell/Molecular Organ Person Environment

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it’s about broadening the conversation

Precision Health…

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What do Nurses know that others don’t know?

  • Human response
  • Action directed

information

  • Care between the care
  • Being present in the

intimate moments of life

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Characterizing the human response

  • Inflammation –

– Cox – systemic inflammatory process mediates chronic critical illness – Fredrickson and colleagues note that our genes are wired for adversity, but eudamonia (and not hedonia) mitigates CRTA

  • Fatigue – men who experience fatigue

demonstrate differential gene expression

NINR Study Identifies Genes Involved in Cancer Treatment- related Fatigue US Navy

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Action Directed Information

  • Isoflavones reduce hot

flashes and some help mood and sleep disturbance; amino acids don’t help

  • Information visualization

improves understanding; aids people in knowing what to do

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The Care Between the Care

Go where the care happens.

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Care between the Care

  • Caring for a spouse with end-stage heart failure

through implantation of a left ventricular assist device as destination therapy. (Kitko et al 2017)

  • Frontloading and intensity of skilled home health

visits: a state of the science. Oconnor et al 2014

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Being present in the intimate movements of life

  • Advance Care Planning and End-of-Life Decision

Making in Dialysis helps caregivers feel less burdensome, more in control

  • What happens to women in prison at the end of

life?

  • Comprehending patient vernacular improves

understanding of symptoms and fear in heart failure patients approaching end of life

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Precision Health Management

Optimizing Medication Effectiveness

  • 19 year old college freshman tackling self-management of cancer

recovery

  • What influences medication absorption?
  • Data-driven investigations

– Metabolomics – Metagenomics – Circadian rhythms

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What does the NLM do to support transforming Precision Medicine to Precision Health

  • Enhances Information Delivery
  • Promotes Access to Research Data
  • Fosters Common Data Elements
  • Conducts Research to Build Methods
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SLIDE 21 s t a n d a r d s 01 01 010 101 01 110 1011 0110

Enhance information delivery

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SLIDE 22 Protocols Funding Code Models Clinical Data Library Study Data People Pathways Instruments

Fostering a ecosphere of discovery

digital research objects

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PubMed

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Literature is the primary repository of knowledge
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Relevance based ranking & Snippets

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Data Discovery in PubMed Central & PubMed

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Data Citations and Supplementary Data in PMC

Examples of what you will find in supplementary material:

  • Computer code
  • Mathematical or computational

models

  • Audio or video clips
  • Oversized tables
  • Intervention protocols
  • Primary or supplementary data

sets

  • Expanded methodology sections
  • Additional figures
Source: Publication Manual of APA (6th ed.) PMC Search: has suppdata[filter]
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Data Links in PubMed: Secondary Source ID

Example PubMed Searches: hasdatabanklist genbank[si] OR figshare[

Secondary Source ID data sources:

  • Publishers
  • NLM indexers
  • PMC

Challenges:

  • NLM indexing resources were

reduced in 2016, leading NLM to explore alternative options

  • No incentives for

journals/publishers to supply these metadata to NLM

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SLIDE 30 loprovfigshare[Filter] OR loprovdryaddb[Filter]

Data Links in PubMed: LinkOut

Links to the materials directly supporting the research discussed in the cited article, including data sets from experiments/studies accessory graphics, images, sound, and multimedia files related to the article.

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Common Data Elements Make data findable, interoperable

◼ Structured human & machine readable definitions of NIH CDEs allowing ◆Search for individual CDE or sets per programs ◆Compare & harmonize similar but distinct CDEs ◆Select or create CDEs with minimal duplication

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NINR CDE Project

  • 1. Anxiety
  • 2. Cognition
  • 3. Demographics
  • 4. Depression
  • 5. Diagnosis
  • 6. Fatigue
  • 7. Global Health Assessment
  • 8. Pain
  • 9. Positive Affect & Well-being

10.SF-36 11.Self-Efficacy 12.Self-regulation 13.Sleep Disturbance 14.Pediatric Global Health Assessment 15.Pediatric Parent Proxy Fatigue 16.Pediatric Parent Proxy Global Health 17.Pediatric Short Form Fatigue

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Fosters Research

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Video

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Deep Learning for

Cervical Cancer Screening

  • Leading cause of cancer mortality

in low-resource settings

  • Limited usefulness of visual

inspection with acetic acid (VIA), whether in-person or via telemedicine.

Goal: apply deep learning to automate diagnosis

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Deep Learning for

Cervical Cancer Screening

  • Highly accurate

– Identifies prevalent precancer/ cancer (AUC = 0.95) – Predicts incident cases several years in advance

  • Outperforms human

interpretation

  • Requires minimal clinical training

and cost

  • R&D ongoing

Automated visual evaluation (AVE) algorithm

AUC = 0.9540

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CRISPR-Cas

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SLIDE 38 Makarova et al, 2015. Nature rev Microbiol. Koonin, Makarova, Zhang, Curr Opin Microbiol 2017

Discovered by computational methods and partially validated experimentally (2016-2018)

New CRISPR-Cas classification

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THYME Project

Goal: Extract temporal relationships from clinical text

  • Recognizes disorders
  • Normalizes clinical narrative
  • Integrates individual episodes into an

aggregate patient timeline

  • Annotates within a document and across

documents

  • Incorporated into Apache via cTAKES

ctakes.apache.org

Discovery in Clinical Text

Guergana Savova, PhD [U54LM008748 R01LM010090]

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thyme.healthnlp.org

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Reaching NLM

patti.brennan@nih.gov @NLMdirector

emey87 / IconArchive / CC BY-NC-ND-4.0

@NLM_NEWS

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Accelerate discovery and advance health through data-driven research Reach more people in more ways through enhanced dissemination and engagement Build a workforce for data-driven research and health

Transforming Information into Discovery

NLMTownHall@mail.nih.gov
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Accelerate discovery and advance health through data- driven research

1.1 Connect the resources of a digital research enterprise 1.2 Advance research and development in biomedical informatics and data science 1.3 Foster open science policies and practices 1.4 Create a sustainable institutional, physical, and computational infrastructure

Goal 1

NLMTownHall@mail.nih.gov
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Reach more people in more ways through enhanced dissemination and engagement

2.1 Know NLM users and engage with persistence 2.2 Foster distinctiveness of NLM as a reliable, trustable source of health information and biomedical data 2.3 Support research in biomedical and health information access methods and information dissemination strategies 2.4 Enhance information delivery

Goal 2

NLMTownHall@mail.nih.gov
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  • Innovative attribution
  • Automated curation
  • Personalized presentation

& delivery

The 21st Century Collection

COLLECT | CONNECT | KNOW
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Build a workforce for data-driven research and health

3.1 Expand and enhance research training for biomedical informatics and data science 3.2 Assure data science and open science proficiency 3.3 Increase workforce diversity 3.4 Engage the next generation and promote data literacy

Goal 3

NLMTownHall@mail.nih.gov