Assignment 5 Multi-ClassificationDue date Mar 13th, 2020.docx

Assignment 5: Multi-Classification
Due date: Mar 13th, 2020 (Friday)
Total Points: 100
Please put your name, student ID, date and time here
Name:
Student ID:
Date:
Time:
In this assignment, you will investigate the handwritten digits
dataset.
Sample images:
Please apply the folowing eight methods to classify the
handwritten digits dataset.
Split the dataset into training sets and test sets
Fit the training data sets to the following eight algorithms
Print the classification report on the test data sets
Method 1: KNN

Method 2: Linear SVM
Method 3: Gaussian Kernel SVM
Method 4: Naive Bayes
Method 5: Decision Tree
Method 6: Random Forest
Method 7: Voting Classifier
Method 8: Bagging
Assignment 5 file:///C:/Users/Al-Ja/Downloads/Assignment
5.html
1 of 5 3/11/2020, 6:57 PM
In [4]: # Importing the dataset
from sklearn.datasets import load_digits
digits = load_digits()
print(digits)
5.html
2 of 5 3/11/2020, 6:57 PM
{'data': array([[ 0., 0., 5., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 10., 0., 0.],
[ 0., 0., 0., ..., 16., 9., 0.],

...,
[ 0., 0., 1., ..., 6., 0., 0.],
[ 0., 0., 2., ..., 12., 0., 0.],
[ 0., 0., 10., ..., 12., 1., 0.]]), 'target': array([0, 1, 2, ..., 8,
9, 8]), 'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'images': array
([[[ 0., 0., 5., ..., 1., 0., 0.],
[ 0., 0., 13., ..., 15., 5., 0.],
[ 0., 3., 15., ..., 11., 8., 0.],
...,
[ 0., 4., 11., ..., 12., 7., 0.],
[ 0., 2., 14., ..., 12., 0., 0.],
[ 0., 0., 6., ..., 0., 0., 0.]],
[[ 0., 0., 0., ..., 5., 0., 0.],
[ 0., 0., 0., ..., 9., 0., 0.],
[ 0., 0., 3., ..., 6., 0., 0.],
...,
[ 0., 0., 1., ..., 6., 0., 0.],
[ 0., 0., 1., ..., 6., 0., 0.],
[ 0., 0., 0., ..., 10., 0., 0.]],
[[ 0., 0., 0., ..., 12., 0., 0.],
[ 0., 0., 3., ..., 14., 0., 0.],
[ 0., 0., 8., ..., 16., 0., 0.],
...,
[ 0., 9., 16., ..., 0., 0., 0.],
[ 0., 3., 13., ..., 11., 5., 0.],
[ 0., 0., 0., ..., 16., 9., 0.]],
...,
[[ 0., 0., 1., ..., 1., 0., 0.],
[ 0., 0., 13., ..., 2., 1., 0.],
[ 0., 0., 16., ..., 16., 5., 0.],
...,

[ 0., 0., 16., ..., 15., 0., 0.],
[ 0., 0., 15., ..., 16., 0., 0.],
[ 0., 0., 2., ..., 6., 0., 0.]],
[[ 0., 0., 2., ..., 0., 0., 0.],
[ 0., 0., 14., ..., 15., 1., 0.],
[ 0., 4., 16., ..., 16., 7., 0.],
...,
[ 0., 0., 0., ..., 16., 2., 0.],
[ 0., 0., 4., ..., 16., 2., 0.],
[ 0., 0., 5., ..., 12., 0., 0.]],
[[ 0., 0., 10., ..., 1., 0., 0.],
[ 0., 2., 16., ..., 1., 0., 0.],
[ 0., 0., 15., ..., 15., 0., 0.],
...,
[ 0., 4., 16., ..., 16., 6., 0.],
[ 0., 8., 16., ..., 16., 8., 0.],
[ 0., 1., 8., ..., 12., 1., 0.]]]), 'DESCR': "..
_digits_dataset:n
nOptical recognition of handwritten digits datasetn
--------------------------------------------------nn**Data Set
Characteristic
s:**nn :Number of Instances: 5620n :Number of
Attributes: 64n :Attr
ibute Information: 8x8 image of integer pixels in the range
0..16.n :Missing
Attribute Values: Nonen :Creator: E. Alpaydin (alpaydin '@'
boun.edu.tr)n
:Date: July; 1998nnThis is a copy of the test set of the UCI
ML hand-written d
igits
datasetsnhttp://archive.ics.uci.edu/ml/datasets/Optical+Recogni
tion+of+Ha

5.html
3 of 5 3/11/2020, 6:57 PM
In [27]: import matplotlib.pyplot as plt
digits.images[0].shape
list = [10,100,100,45]
fig = plt.figure()
for i,j in enumerate(list):
plt.subplot(2,2,i+1)
plt.imshow(digits.images[j],cmap='gray')
In [2]: X = digits.data
y = digits.target
Step 1. Split the dataset into training data and testing data ( 10
points )
In [ ]:
Step 2. Algorithm Analysis ( 80 points )
Method 1. KNN
In [ ]:
Method 2. Linear SVM
In [ ]:
Method 3. Gaussian Kernal SVM
In [ ]:

Method 4. Naive Bayes
5.html
4 of 5 3/11/2020, 6:57 PM
In [ ]:
Method 5. Decision Tree
In [ ]:
Method 6. Random Forest
In [ ]:
Method 7. Voting Classifier
In [ ]:
Method 8. Bagging
In [ ]:
Step 3: Accuracy Results Table ( 8 points )
KNN L_SVM RBF_SVM NB DT RF Voting Bagging
Accuracy
Weighted Precision

Weighted Recall
Step 4: Conclusion ( 2 Points )
In [ ]:
In [ ]:
In [ ]:
In [ ]:
5.html
5 of 5 3/11/2020, 6:57 PM
Chapter 13: Mobile Technology and mHealth
Robert Hoyt MD
John Sharp

Learning Objectives
After reviewing the presentation, viewers should be able to:
Describe the evolution from personal digital assistants to
smartphones and the emergence of mHealth
List the various ways mobile technology is currently being used
in healthcare
Compare and contrast mobile technology for clinicians and
patients
Identify the limitations of mobile technology
Introduction
Mobile technologies, particularly smartphones, are extremely
popular to all members of the healthcare team
Adding to the popularity:
Improved speed, memory, wireless connectivity and shrinking
form factor (size and shape)
Affordable
Constantly improving features
Phone capability, email and access to Internet
A myriad of mobile apps for consumers and clinicians

Evolution of Mobile Technology
2G in 1990
3G in 2001
4 G in 2006
5 G ? 2020
Mobile health (mHealth) is the “medical and public health
practice supported by mobile devices, such as mobile phones,
patient monitoring devices, personal digital assistants and
wireless devices”
Global Observatory for e-Health for the WHO (2011)
mHealth

Personal Digital Assistants (PDAs)
1990: Apple Newton $700 and bulky
1996: Palm Pilot
1999 Epocrates, free popular drug program
Later: PDAs with phone capability,
Internet access, WiFi
With huge consumer demand the transition to
smartphones was rapid by any standard
Smartphones
Defined as having an operating systems capable of hosting
medical software or it Internet capable
Cloud computing allowed more medical programs of higher
complexity to be accessed

Vast majority of adults and physicians carry a smartphone
Many access medical issues on the phone and a minority have at
least one medical app and/or receive text messages from a
healthcare system
7
The iPad was the first tablet to make an impact in healthcare
This very well liked platform has been used in exam rooms and
the hospital to provide a light weight means of reaching the
Internet and EHR access
The reality is that tablets have shortcomings as well, such that
the actual impact of tablets in healthcare is largely unknown
Tablet PCs
m-Health Conceptual framework

Mobile Technology and Patients
Devices: smartphone or tablet PC with apps connected to
Internet
Text Messaging or Short Message Service (SMS). Used for:
Appointment reminders
Education
Disease management
Behavior modification
Medication compliance
Laboratory results notification
Public Health – Immunization
SMS messaging more likely to be tried in developing countries
Mobile Technology and Patients
(Software categories)
Personal health record
Telemedicine

Medication reminders
Fitness coach
Immunization guides
Disease management
Prevention guides
Diagnostics
Vital sign monitoring
Mental health
Connect with healthcare system
Mobile Technology to Track Health Habits and Physiological
Signs
New movement (“wearable HIT” and “quantified self”)
New devices and sensors to monitor diet, exercise, sleep, heart
rate, respiratory rate, oxygen level, skin temperature, hydration,
etc.
Oriented towards patients
Communicate with smartphone via Bluetooth LE
Smart watches a new platform

It remains to be seen how fitness and home monitoring data can
be uploaded, analyzed and archived in an EHR. Who is
reimbursing for this?
Should the data be analyzed automatically by machine learning
algorithms and posted on a patient dashboard?
Does PGHD change behavior? Probably not
Only a minority of health apps are successful and persist
Patient Generated Health Data (PGHD)
Mobile Technology and Clinicians
Smartphones synchronized with office or hospital. Popular, but
raises significant security and storage concerns
Several EHR vendors offer a specific iPad software package for
clinicians
Medical Software categories for clinicians: drug information,
calculators, databases, immunization guides, medical resources,
prevention guides, diagnostics, sensors, image viewers, journal
access, Medline searches, monitoring, coding, medical
translator, EHR access, telehealth, dictation and remote data
collection

Apple
HealthKit: for iOS and Watch OS. Use APIs to integrate app
with OS
CareKit: open source SDK for patient monitoring
ResearchKit: iOS app can be used for research
Android
Google Fit: SDK to build apps using APIs
ResearchStack: for research
Research Droid: research using Android smartphones
Software Development Kits (SDKs) for mHealth
Devices and apps which are used for treatment or CDS must
have FDA approval
All others do not need FDA approval
Regulatory Requirements

Mobile Technology Challenges
Distraction: at work and everywhere else
Technical: inputting, screen size and interoperability issues
Security: need BYOD policies
Lack of quality control: Mobile App Rating Scale
Lack of evidence (low quality studies)
Will new sensors and devices be reimbursed by payers or will
the patient have to pay?
Mobile technology has evolved at a blistering pace
The era of mHealth is here but too early to know what the
impact on healthcare system will be
Healthcare-related apps are popular and available for all
platforms, but are they used?
Enterprise integration of mobile technology is evolving;
smartphones integrating with EHRs
Conclusions

Chapter 14: Evidence Based Medicine and Clinical Practice
Guidelines
Robert Hoyt MD
William Hersh MD
After reviewing the presentation, viewers should be able to:
State the definition and origin of evidence based medicine
Define the benefits and limitations of evidence based medicine
Describe the evidence pyramid and levels of evidence
State the process of using evidence based medicine to answer a
medical question
Recall important online and smartphone evidence based
medicine resources
Describe the interrelationship between clinical practice
guidelines, evidence based medicine, and electronic health

records
Define the processes required to create and implement a clinical
practice guideline
Learning Objectives
Information technology has the potential to improve decision
making through online medical resources, electronic clinical
practice guidelines, electronic health records (EHRs) with
decision support, online literature searches, digital statistical
analysis and online continuing medical education
Healthcare reform is dependent on quality healthcare and that is
dependent on the best evidence available
Introduction
Revised 2000: is a systematic approach to clinical problem
solving which allows the integration of the best available
research evidence with clinical expertise and patient values
EBM Definition

Why EBM is important from the viewpoint of the National
Academy of Medicine: “Patients should receive care based on
the best available scientific knowledge. Care should not vary
illogically from clinician to clinician or from place to place”
We are moving slowly from anecdotal evidence to randomized
controlled trials
Importance of EBM
Current methods of keeping medically or educationally up-to-
date do not work
Translation of research into practice is often very slow
Lack of time and the volume of published material results in
“information overload”
The pharmaceutical industry bombards clinicians and patients
every day; often with misleading or biased information

Much of what is considered the “standard of care” in every day
practice has yet to be challenged and could be wrong
Importance of EBM
Continuing Medical Education (CME): we know this is often
ineffective
Clinical Practice Guidelines (CPGs): will be covered later but
CPGs may not produce change
Expert advice: much better than no information but not always
evidence based
Reading: “like drinking water from a fire hose”. Need focus so
the best and most pertinent content is read
Traditional Methods for Gaining
Medical Knowledge
7
.
A clinical question is generated. Use PICO tools *
Seek the best evidence using an EBM resource

Critically appraise the evidence using tools we will cover later
in the presentation
Apply the evidence to the patient, taking into account their
values, preferences and circumstances
Example: For patients over age 65 with a suspected stroke or
TIA, which is the preferred initial imaging technique (MRI or
CT or both)?
The EBM Process to Answering a Clinical Question
8
* P for patient or problem. Define the patient or group; I for
intervention or therapy of interest; C for comparison with other
drug or placebo?; O for outcome of interest, mortality? Some
add T for type of study = PICOT. We will focus on therapy
questions
Validity:
Internal: Is the study believable? Look for apparent biases or
errors in selecting patients, measuring outcomes, conducting the
study, or analyzing the results
External: are the results generalizable to your population of
interest? If you have a geriatric population results on teenagers
might be irrelevant
Results:
Results should be assessed in terms of the magnitude of
treatment effect and precision which we will comment on later

Terminology Used in Answering
Clinical Questions
Therapy Question: does the addition of Plavix to aspirin reduce
the incidence of future strokes?
Prognosis Question: will lowering the average blood pressure to
less than 120/80 reduce the likelihood of a stroke?
Diagnosis Question: which is more sensitive and specific for
detecting a heart attack, CPK or troponin?
Harm Question: how many patients will have renal insufficiency
due to an ace inhibitor compared to a beta blocker?
Cost question: which is more cost effective to reduce hospital
readmissions for heart failure, inpatient case management or
home health nursing care?
Most Common Types of Clinical Questions
10

Evidence Pyramid
Better evidence
But fewer published
articles
Case reports/case series: collections of reports on treatment of
patients without control groups; much less scientific
significance
Case control studies: study patients with a specific condition
compared with people who do not the condition; less reliable
than randomized controlled trials
Cohort studies: evaluate and follow patients who have a specific
exposure or receive a particular treatment over time and
compare them with another group that is similar but has not
been affected by the exposure being studied; the two groups
may differ in ways other than the variable under study
Randomized controlled trials (RCTs): subjects are randomly
assigned to a treatment or a control group that received placebo
or no treatment; only difference between the two groups is the
intervention being studied.
“Double blinded” – both the investigators and the subjects do
not know whether they received an active medication or a
placebo
RCTs are the gold standard design to test therapeutic

interventions
Evidence Pyramid Studies
Systematic reviews: answer a focused question by including
multiple RCTs. Several reviewers scan the medical literature
with specific criteria
Meta-analyses: quantitative summary of systematic reviews
using statistical techniques to combine the results of several
studies, thereby leading to larger numbers (more statistical
significance) and a wider range of patients
Evidence Pyramid Studies
GRADE: Grading of Recommendations, Assessment,
Development and Evaluation is a common framework, but many
others exist
Level 1: High quality evidence derived from consistent RCTs
Level 2: Moderate quality evidence inconsistent or less
methodologically strong RCTs; or exceptionally strong

observational evidence
Level 3: Low quality evidence, usually from observational
studies
Level 4: Very low quality evidence from flawed observational
studies, indirect evidence or expert opinion
Levels of Evidence
A study can be statistically but not medically significant. There
are tools that shed more practical light on the results, such as
NNT
The risk for people who are in the experimental group is called
the experimental event rate (EER); whereas the risk for people
who are in the control group is called the control event risk
(CER)
The relative risk is the EER/CER or a ratio
The relative risk reduction (RRR) is the EER-CER/CER
The absolute risk reduction (ARR) is the EER-CER
Number Needed to Treat (NNT)

The NNT is calculated by dividing 1 by the ARR (or 100/ARR
if the ARR is expressed as a percentage and not a fraction
NNT calculates the number of patients who need to be treated
with X to prevent one Y, over a specific time
An example: drug A is associated with stroke in 5% of patients,
compared to 7% of patients on placebo over a one year trial
RRR = 5%-7%/7% = 29%; ARR = 7%-5% = 2% (ignore minus
signs)
NNT = 100/2% or 50 patients need to be treated for one year to
prevent one stroke
Number needed to harm (NNH) is calculated by harm
percentage divided into 100
Web site http://www.thennt.com/ devoted to NNT and EBM
Number Needed to Treat (NNT)
Low yield of excellent quality articles in general
Many repeated studies show different results
Results are very frequently inconclusive
Many studies are poorly designed, have inadequate number of
patients, and have some bias
Many medical journals are now requiring registration of all
clinical trials in public registries
Limitations of the Medical Literature

Different rating systems for evidence
Different conclusions drawn by different people
Reviews are very time intensive so reviews don’t exist for every
medical condition
Some view EBM as “cook book medicine”
RCTs are expensive, so often sponsored by drug companies,
introducing bias
Limitations of EBM
Health informatics studies are under the microscope:
Often small, non-randomized, non-controlled studies
Frequently based on before and after implementation studies
Technology studies are hard to double blind
Early studies failed to look at unintended consequences of
technology; they only looked for benefit
Excellent cost analyses are frequently missing
Much more on this in the textbook

Evidence Based Health Informatics
(EBHI)
EBM Resources
JAMA Evidence
Clinical Evidence
Cochrane Library
Cochrane Summaries
Evidence Updates
ACP Journal Club
Practical Pointers for Primary Care
Essential Evidence Plus
TripDatabase
OVID
SUMSearch
Bandolier
Centre for EBM
Best Bets
Evidence Based Health Care

Google (insert “evidence based”)
NNT web site
MDCalc has EBM calculator tools
EBM for mobile technology: MedCalc3000 calculators are web
based and exist for mobile platforms
More EBM Resources
The Institute of Medicine in 1990 defined clinical practice
guidelines (CPGs) as: “systematically developed statements to
assist practitioner and patient decisions about health care for
specific clinical circumstances”
CPGs are based on EBM and are generally embraced by most
specialties around the world
While they are embraced they are not always utilized by
physicians for a variety of reasons
Clinical Practice Guidelines

Panel of subject matter experts convene to evaluate an
important medical problem (high cost, high risk, etc.), usually
with a disease management team, possibly IT, epidemiology,
etc.
Team reviews local issue and evidence and examines national
evidence; balancing costs, benefits and risks
Team evaluates the applicability of the guideline to the local
scenario
Leadership at the top and the medical and nursing staff are
critical for acceptance in a healthcare system
Implementation details are important as well; will it be paper or
electronic based? Part of EHR?
Clinical Practice Guidelines Steps
Practice setting: are the clinicians too busy or indifferent to new
evidence?
Contrary opinion: do the experts agree?
Sparse data: is the evidence conclusive?
Expect low initial acceptance
CPGS can be too long, without a summary
Lack of local champions
CPGs may lack patient input with both the writing of the CPGs
and the implementation
Incentives to adopt may be missing for clinicians
Barriers to CPGs

CPG Example
Hypertension CPG by Kaiser
CPGs available for mobile technology
Web based CPGs (risk calculators)
ATP III
FRAX
Gail breast cancer risk assessment
Stroke risk calculator
EHR-based CPGs:
Library of CPGs embedded in the EHR
Order sets for new admissions
Electronic CPGs

National Guideline Clearinghouse
National Institute for Health and Clinical Excellence
Agency for Health Care Research and Quality
Health Team Works
Institute of Clinical Systems Improvement
CPG Resources
EBM is the pursuit of the best available evidence
EBM is based on a hierarchy or pyramid of evidence
There are multiple limitations of the medical literature and
EBM
The average clinician would benefit from understanding EBM
and having tools available
CPGs help standardize care based on EBM
CPGs are becoming a routine part of EHRs
Conclusions

Assignment 5 Multi-ClassificationDue date Mar 13th, 2020.docx

Assignment 5 Multi-ClassificationDue date Mar 13th, 2020.docx

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