Health And Big Data

Front 1

Motivation in Big Data

Back 1

-Availability of Information Systems

-Optimize clinical and financial data

Front 2

Information systems (4)

Back 2

1. EMR

2. HIS

3. Laboratory Inventory Systems

4. Pharmacy Systems

Front 3

Optimize clinical and financial data (5)

Back 3

1.Well informed decisions (effective treatment choices)

2.Directed programs

3. Cost – effective approach in managing treatment

4. Forecast / Predict

5. Policies grounded on data

Front 4

What do you use Big Data for?

Back 4

-Statistics

-Mining

-Models

-Simulations

Front 5

Types of Mining (2)

Back 5

1. Data Mining

2. Process Mining

Front 6

Types of Models (3)

Back 6

1. Workflow or Clinical Processes

2. Knowledge

3. Diseases

Front 7

What is Big Data?

Back 7

Volume

Variety

Velocity

Veracity

Front 8

How do we get big data?

Back 8

-Primary sources (surveys, information systems, social networking applications)

-Secondary sources (websites, public databases, social networking applications)

Front 9

Primary sources

What can you generate from surveys and/or information systems?

Back 9

Datasets (XLS, CSV)

Front 10

Primary sources

Information systems (EMRs, HIS) can be ____ or _____

Back 10

closed or open

Front 11

Primary sources

social networking applications go through ________ before data mining

Back 11

APIs (Application Program Interface)

Front 12

Secondary sources

You can either ____________ or ______________ these data

Back 12

download fixed datasets (xls, csv)

or

download from specified range (xls, csv)

Front 13

Transformation from data to information

Back 13

extraction - store - preprocess - analyze - visualize or narrate

Front 14

Extraction

Back 14

APIS

CODING (e.g. R, PYTHON)

Front 15

Store

Back 15

Dynamic

Fixed

Curate

Front 16

Preprocess

Back 16

Frequencies

Demographic

Front 17

Analyze

Back 17

Statistical

Machine Learning

SNA

Front 18

Visualize

Back 18

D3.js

charts

graphs

Front 19

Narrate

Back 19

insights

user experience

sticky

Front 20

an example of a participatory surveillance app

Back 20

EB Tracker

Front 21

Modeling tools

Back 21

Mathematical Modeling

Agent Based Modeling

Front 22

STEM (by IBM) model

Back 22

SEIR

Susceptible-Exposed-Infected-Recovered

Front 23

health informatics in line of rainstorm and flood disasters

Back 23

eBayanihan

Front 24

FASSSTER

Back 24

-??

Front 25

Pros of Big Data

Back 25

informed decision making

modeling, prediction and forecasting

simulations

Front 26

Cons of Big Data

Back 26

challenges data privacy

Front 27

What would help in facilitating use and promotion?

Back 27

-Clear policies from government

-Find that balance between use of data and securing data

Front 28

Responsible big data management on health information

Back 28

-Public Awareness

-Transparency

-New Regulatory Framework / Certifications

-Standardization

-Training and Education

Front 29

types of mining

Back 29

1. exploratory mining

2. guided mining

Front 30

mining wherein an algorithm can tell you that your data can have categories

Back 30

exploratory

Front 31

mining that has types already beforehand

Back 31

guided

Front 32

where classification algorithms are

Back 32

Blackboxes