Can-eHealth

Creating, serializing and deploying a machine learning model for healthcare: Part 1

Post author By Bell Eapen
Post date September 13, 2018
1 Comment on Creating, serializing and deploying a machine learning model for healthcare: Part 1

Machine Learning (ML) and Artificial Intelligence (AI) are the buzzwords lately and it is heartening to find local HSPs scrambling to get on the bandwagon. The emphasis is mostly on creating models which require technical as well as clinical expertise. The quintessential ‘blackbox’ model is a good healthcare analytics exercise, but deploying the model to be useful at the bedside belongs to the IT domain.

This article is about creating a simple model using discharge abstract database (DAD) as the database and Apache spark as the framework, serialize it into a format that can be used externally and building a simple website that deploys the model for users to make predictions. To make this interesting, we will create the website using Java 11 and Spring boot 2.1 that are yet to be released at the time of writing. Both will be released when we reach there. But, please note that this is about deploying a model/pipeline created with spark (which may be an overkill for most projects). Here are some good resources if have small data/simple model.

https://github.com/mtobeiyf/keras-flask-deploy-webapp

https://towardsdatascience.com/deploying-keras-deep-learning-models-with-flask-5da4181436a2

https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html

This post is actually a note to myself as I explore the process. As always the focus is on understanding the process and not on the utility of the model. Feel free to comment below and add your own notes/ideas.

TL;DR the code will be available on our GitHub repository as we progress.

E-Health

Hamilton, ON.

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First, let us start with a brief description of Apache Spark. Apache spark is an open-source big-data API with inbuilt cluster computing ability. Spark is highly accessible and offers simple APIs in Python, Java, Scala, and R. I have picked python as I can use the python interpreter at CC right from pycharm IDE. Pyspark is the python library for interacting with spark which can be linked to sys.path at runtime using the findspark library. Most machine learning pipelines are available in pyspark. We will be building a simple logistic regression model. The necessary libraries can be imported as below.

import logging

import findspark
import pyspark.sql.functions as F
from pyspark import SparkContext
from pyspark.mllib.classification import LogisticRegressionWithLBFGS
from pyspark.mllib.util import MLUtils

import logging

import findspark

import pyspark.sql.functions as F

from pyspark import SparkContext

from pyspark.mllib.classification import LogisticRegressionWithLBFGS

from pyspark.mllib.util import MLUtils

I will be back again with more next week. In the meantime have a look at DAD and the data dictionary. As always the customary disclaimer below:

Read Part 2.

Parts of this material are based on the Canadian Institute for Health Information Discharge Abstract Database Research Analytic Files (sampled from fiscal years 2014-15). However the analysis, conclusions, opinions and statements expressed herein are those of the author(s) and not those of the Canadian Institute for Health Information.

Tags AI, Keras, Model, Spark, Tensorflow

HIS

How to create a Neural Network model for business in 10 minutes

Post author By Facilitator
Post date July 1, 2018
No Comments on How to create a Neural Network model for business in 10 minutes

Neural Network and deep-learning are the buzzwords lately. Machine learning has been in vogue for some time, but the easy availability of storage and processing power has made it popular. The interest is palpable in business schools as well. The ML related techniques have not percolated much from the IT departments to business, but everybody seems to be interested. So, let us build a Neural Network model in 10 minutes.

This is the scenario:

You have a collection of independent variables (IV) that predict a dependent variable (DV). You have a theoretical model and want to know if it is good enough. Remember, we are not testing the model. We are just checking how good the IVs are in predicting DV. If they are not good predictors to start with, why waste time conjuring a fancy model! Sounds familiar? Let’s get started.

Setup

Do you have some preliminary knowledge of python? If not spend another 10 minutes here learning python. Now you have to spend some time to set up your system once. Just follow these instructions.

Code

The first step is to import few modules. If you don’t know what these are, just copy paste and ignore. Consider them as a header that you require.

# Modules
import sys
import numpy
from imblearn.over_sampling import RandomOverSampler
from keras.layers import Dense
from keras.models import Sequential
from pandas import read_csv

# Modules

import sys

import numpy

from imblearn.over_sampling import RandomOverSampler

from keras.layers import Dense

from keras.models import Sequential

from pandas import read_csv

Create a CSV file with your data with the last column as your DV. Now import that file.

# Import data
dataset = read_csv(sys.argv[1], header=1)
(nrows, ncols) = dataset.shape

# Import data

dataset = read_csv(sys.argv[1], header=1)

(nrows, ncols) = dataset.shape

nrows and ncols are the numbers of rows and columns. Now separate DV (y) from IVs (X) as below.

# Separate DV from IVs 
values = dataset.values
X = values[:, 0:ncols-1]
y = values[:, ncols-1]

# Separate DV from IVs

values = dataset.values

X = values[:, 0:ncols-1]

y = values[:, ncols-1]

In most cases, you will be trying to predict a rare event. So add some oversampling for taste 🙂

# Oversampling
ros = RandomOverSampler(random_state=0)
X_R, y_R = ros.fit_sample(X, y)

# Oversampling

ros = RandomOverSampler(random_state=0)

X_R, y_R = ros.fit_sample(X, y)

Create, compile and fit the model.

# create model
model = Sequential()
model.add(Dense(12, input_dim=vnum, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X_R, y_R, epochs=150, batch_size=10, verbose=2)

# create model

model = Sequential()

model.add(Dense(12, input_dim=vnum, kernel_initializer='uniform', activation='relu'))

model.add(Dense(8, kernel_initializer='uniform', activation='relu'))

model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))

# Compile model

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Fit the model

model.fit(X_R, y_R, epochs=150, batch_size=10, verbose=2)

The three model.add statements represent the three layers in Neural Network. The number after Dense is the number of neurons in each layer. You can play with these values a bit. These settings should work in most business cases. Read this for more information.

Now evaluate the model.

# evaluate the model
scores = model.evaluate(X_R, y_R)
print("\n")
print("\n Accuracy of the model")
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
print("\n --------------------------------------------------")

# evaluate the model

scores = model.evaluate(X_R, y_R)

print("\n")

print("\n Accuracy of the model")

print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))

print("\n --------------------------------------------------")

Put this code in a file (say nnet.py) and use it as below.

python nnet.py mydata.csv

1	python nnet.py mydata.csv

TL;DR

Just use QRMine. nnet.py is in there.

Operationalizing Neural Network models

Shortly, I will show you how to operationalize a model using flask.

…

Originally published at nuchange.ca on June 21, 2018. If you have some feedback, reach out to the author on Twitter, LinkedIn or Github.

Tags Neural Network

OpenSource

Are you ready to ‘Git’ into Open Source

Post author By Bell Eapen
Post date October 18, 2017
No Comments on Are you ready to ‘Git’ into Open Source

Open Source health information systems provide cost-effective tools for healthcare. Even if you are not a coder, you may be able to contribute to open source projects. As a matter of fact, some open-source projects find it difficult to get volunteers to document and test the code. E-Health enthusiasts from the clinical and management fields often want to contribute to popular open source projects, but do not know how.

Open source projects involve a collaboration of people with various skills, often with no way of physically meeting each other. In a complex software product, even a misplaced comma can break the system. How do open source projects effectively collaborate avoiding such code-breaking mistakes? Well, they use some specialized tools and workflows to manage code, many of which are not familiar to non-programmers. In the next few posts, I will introduce you to the most important tool that coders use; the versioning system. We shall discuss Git (the most popular versioning system), from a non-programmers perspective.

This is not for those who are familiar with Git and we will not be discussing advanced Git usage. Hence, let me state the assumptions that I am making about you as the reader. You have not heard of Git before. You are as scared of code as you are scared of python. When you hear Java, the first thing that comes to your mind is the island in Indonesia. You don’t know what ‘typing on the command line’ means. But you own a computer, know how to download and install programs, know how to navigate the web, wants to learn more about contributing to open-source projects and above all want to help save lives especially in resource-deprived areas. Watch the video below for inspiration.

At the end of this journey, you will know how to follow open-source projects and make minor code contributions. This might initiate you into learning computer programming, but that is not my intention. You might even win a free T-Shirt from DigitalOcean. If you are ready to jump right in, follow the steps here: http://wiki.canehealth.com/index.php/GIT:_First_Steps_(Creating_GitHub_Account_and_Downloading_SourceTree)

Tags git, GitHub, Open source, openmrs

Health Research Methodology Information Systems

Grounded Theory – QRMine: Qualitative Research support tools in Python.

Post author By Bell Eapen
Post date September 28, 2017
No Comments on Grounded Theory – QRMine: Qualitative Research support tools in Python.

Grounded theory (GT) emerged as a research methodology from medical sociology following the seminal work by Barney Glaser and Anselm Strauss. However, they later developed different views on their original contribution with their supporters leading to the establishment of a classical Glaserian GT and a pragmatic Straussian Grounded Theory. Constant comparison is central in Classical Grounded Theory, and it involves incident to incident comparison for identifying categories, incident to category comparison for refining the categories and category to category comparison for the emergence of the theory.

Glaser’s Classical GT (1) provides guidelines for evaluation of the GT methodology. The evaluation should be based on whether the theory fits the data, whether the theory is understandable to the non-professionals, whether the theory is generalizable to other situations, and whether the theory offers control over the structure and processes.

Strauss and Corbin (2) recommended a strict coding structure elaborating on how to code and structure data. The seminal article by Strauss and Corbin describes three stages of coding: open coding, axial coding, and selective coding. Classical Grounded Theory offers more flexibility than Straussian GT while the latter may be easier to conduct especially for new researchers.

Open coding is the first step where data is broken down analytically, and conceptually similar chunks are grouped together under categories and subcategories. Once the differences between the categories are established, properties and dimensions of each are dissected. Coding in GT may be overwhelming, and scaling up of categories from open coding may be difficult. This leads to the generation of low-level theories. With natural language processing, information systems can help young researchers to make sense of the of data that they have collected during the stage of open coding. QRMine is a software suite for supporting qualitative researchers using NLP. Gtdict is a module that identifies Categories, Properties, and Dimensions in the interview transcript.

QRMine is opensource and is available here. Ideas, comments and pull requests welcome.

Last 3 commits to GitHub Repo:

References:

Glaser BG. The Constant Comparative Method of Qualitative Analysis. Social Problems [Internet]. 1965 Apr;12(4):436–45. Available from: http://dx.doi.org/10.1525/sp.1965.12.4.03a00070

Corbin JM, Strauss A. Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology [Internet]. 1990;13(1):3–21. Available from: http://dx.doi.org/10.1007/BF00988593 [Source]

Tags Grounded Theory, Qualitative Research

Resources

Oscar eForm Generator

Electronic capture of patient data is vital in any health information system. It ‘s hard to bundle every form that a clinician will ever need along with an EMR. The EMRs adopt various strategies to solve this problem, but a general standard is lacking.

Eforms is OSCAR’s solution to this problem. The OSCAR eForms are arguably one of the most useful features of OSCAR and is being used in many settings beyond which it was initially designed for. Community eForms can be downloaded from the OSCAR Canada Users Society.

EForm is not an elegant solution and creating complex eforms require programming expertise. Reporting of data collected through eForms is difficult because of the way in which the data is abstracted as key-value pairs in the database.

Oscar provides basic eForm generator functionality built-in using a form image in the background with controls transposed on top. However, it is not user-friendly and lacks the ability to save and continue the work later.

Oscar EForm Generator

I have created an online OSCAR eForm generator that solves most of the above-mentioned problems. Here is an advanced OSCAR eform generator with drag and drop controls. You can save the form as a text file and continue editing later after loading the content. It also supports radio-buttons by internally mapping to OSCAR supported code. You can pull OSCAR demographic fields and define complex show/hide rules. You can cut and paste the generated code into the OSCAR eform editor. The form is generated in your browser using javascript and it is not sent or saved on our server.

Watch the video below to see how it is done. This is still being tested and is not ready for production. Contact us for more details. Please report bugs, function requests, and feed backs.

The application is available here: http://nuchange.ca/oscar-eform-generator .

Tags eForms, OSCAR

HIS

HIPA, Strengthening The Foundation of The Privacy Law

Post author By Sunil Mammen
Post date May 19, 2016
No Comments on HIPA, Strengthening The Foundation of The Privacy Law

Most of us would have heard about PHIPA, the Personal Health Information Protection Act and QCIPA, the Quality of Care Information Protection Act. The foundation laws that are aimed to protect your Personal Health Information from those hackers and crooks who would love to get their hands on your personal information. Even with the establishment of these laws we have seen many breaches, some of which really shot to the limelight in the past recent years.

However, the word in town is that a new Bill had recently passed the third reading. The Bill 119 or the Health Information Protection Act is all set to make amendments to the PHIPA AND QCIPA. Amendments range from the mandatory reporting of privacy breaches to doubling the existing fines for offenders. According to Dr. Eric Hoskins, the Minister of Health and Long-Term Care “This legislation gives our health care system the necessary tools to protect the private health information of patients in Ontario…” and hopefully it does.

The advancement in health information technology has ushered a new era in providing quality care for people but at a very expensive price, privacy of one’s personal information. Systems should be designed, developed and deployed keeping in mind that security of the personal health information is kept a key criteria. Workflows should be redesigned in healthcare institutions and other organizations to include a culture of reporting privacy breaches and to study such situations to avoid the breaches in the future. Laws and regulations can play only so much to enforce protection, it is the duty of the people involved to follow and practice the law.

Tags HIPA, Privacy

HIS

A Quick Intro To Interoperability

Post author By Sunil Mammen
Post date May 16, 2016
No Comments on A Quick Intro To Interoperability

Interoperability is one of the highest trending words that one might hear if they are stepping into the wonderful world of EHealth. It has become so necessary that one would probably have a chapter or two on it in every health informatics degree.

Interoperability in simple terms is the way in which one system communicates with another to help ease the decision making process and thus aide in improving the care process. Imagine one day Ms. Jane Doe is scheduled for an MRI by Hospital A. Now in the absence of an interoperable environment the radiology facility would have to either physically transport the scans through a person, fax or courier. However, imagine if there was a situation where the radiology facility could update the hospital A of the status of the MRI scan as well as send the scans directly to the medical record associated with Ms. Jane. Such a situation would save Hospital A a lot of money, time and help improve the quality and time of the decision making process. This situation shows one of the benefits of implementing an interoperable system and there are many more !

However, there are some tricky areas one needs to eliminate (which is quite hard) to achieve the true benefit of having an integrated health information system. Prominent being that of privacy and security. An advance in technology also means an advance in techniques for hacking. There is always a threat lurking to steal one’s personal health information to earn some big bucks or use it for some other illegal purpose especially in developing countries where there aren’t as stringent privacy laws to protect health information.

CAS-002
350-018
9A0-385
CISSP
000-104
300-208
M70-101
200-310
642-999
CISM
1Z0-144
9A0-385
70-177
70-486
OG0-091
300-206
70-243
300-206
350-001
210-260
300-320
200-355
MB2-704
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1z0-434
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N10-006
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PR000041
N10-006
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JN0-360
100-101
200-101
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CAS-002
EX300
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70-417
101
200-125 ,
74-678
2V0-621D
1Y0-201
9L0-012

Research

LaTex template for eHealth Thesis

Post author By Facilitator
Post date March 29, 2016
No Comments on LaTex template for eHealth Thesis

I have shared below the latex template for the MSc eHealth thesis (McMaster University). At the outset, let me state the standard disclaimer: This is not an official template, use this at your own risk. If you find any mistakes, fork it on GitHub and improve it.

Latex is a system where “What You Get Is What You Mean“. So you can precisely “program” the typesetting and save lots of time in formatting large documents. However, it may not be ideal for small documents. Though you can install LaTex in you system by following the instructions here, there is an excellent free online service that you can use called http://sharelatex.com. Sharelatex also has the IEEE template available in their library.

Please be aware that your supervisor might ask you to submit revisions in a Word document for tracking changes. So LaTeX formatting is typically done before final submission and not during supervisor readings. You need to collect references in the BibTeX format. Mendeley reference manager has a BibTex export facility. I recommend creating a folder for your thesis references and using Mendeley’s web importer.

Steps to use eHealth Thesis template

1. Download the zip file here. (Please star the repository if you have a GitHub account.)
2. Register at Share latex, Create a ‘New Project’ and ‘Upload project’.
3. Add the details on the preliminaries.tex file
4. replace references.bib with your references. Retain the file name.
5. Cut and paste your thesis content to the respective ‘Chapters’.
6. See ‘Materials and Methods’ file for the correct way of inserting and referencing figures, tables, equations, and citations. Table of contents, the list of figures and tables, etc. would be automatically generated.
7. Compile it and download the pdf.

The template is open-source. Feel free to improve it. Pull-requests to merge your improvements are welcome. Contact me if you are in trouble on our IRC channel ##ehealth

Do you believe in the open-source eHealth culture? Join Us on PRO{DENTS} and contribute to the Wiki Textbook of eHealth.

Tags ehealth thesis, latex, McMaster University

HIS

Up and running with activiti in 20 minutes

Post author By Bell Eapen
Post date March 14, 2016
No Comments on Up and running with activiti in 20 minutes

Activiti-Explorer — Image Credit: Unsplash @ pixabay

Activiti is a BPMN automation tool that makes communication between business analyst and the developers easy. Activiti has a web-based graphical interface for business analysts to prepare workflows that can be enhanced by developers adding Java code using an Eclipse plugin. It has a lightweight engine that can be embedded in Java applications to deploy the workflow and an explorer for deploying the process definitions online. Activiti also has a REST interface.

Activiti uses an in-memory database by default. Installation requires Java and servlet container like Tomcat. I have created a puppet script to automate activity installation in a virtual machine. The script installs activity Explorer and the REST interface with the mySQL database. if you want to connect to an external database you can make the necessary changes in the properties file within the code folder.

Installation instructions for Activiti

There are ways of creating a virtual Linux machine in your laptop (Mac and Windows). Virtualization leaves your operating system untouched, and the virtual machine can be removed without a trace after use. Without further ado, you can install this in 5 easy steps using my puppet script.

1. Install VirtualBox.
2. Install Vagrant.
3. Download and extract the zip file below to any folder.

RM1

4. Windows users double-click run.bat. Mac users run the following command from the download folder.

vagrant up

1	vagrant up

(The script takes approximately 10 minutes to setup the machine. However, no response is needed from your part. An internet connection is required.)
5. Access in your browser:

Access Activiti at http://localhost:8001/activiti-explorer
Access REST interface at http://localhost:8001/activiti-rest/services

To stop the machine on windows, use stop.bat and on Mac:

vagrant suspend

1	vagrant suspend

You can restart the machine as step 4 above. Restarting the machine does not require an internet connection.

If you want to destroy (uninstall) the virtual machine, use the following command in the script folder.

vagrant destroy --force

1	vagrant destroy --force

Feel free to fork and improve this script on GitHub. Pull-requests are welcome. Join E-Health on GitHub if you want direct write access to the repository.

“JK0-022 ”
“MB2-707 ”
“70-177 ”
“70-462 ”
“MB5-705 ”
“CRISC ”
70-417
“350-018 ”
350-060
“1z0-434 ”
70-410
SSCP
“70-411 ”
“70-483 ”
“70-480 ”
“NS0-157 ”
“1Y0-201 ”
“000-106 ”
“JK0-022 ”
70-178
“HP0-S42 ”
CISSP
“70-534 ”
“OG0-093 ”
“100-105 ,”
“JK0-022 ”
“70-417 ”
“70-532 ”
“CRISC ”
“c2010-657 ”
“200-120 ”
“SY0-401 ”
“3002 ”
“100-105 ,”
“OG0-091 ”
“OG0-093 ”
“000-104 ”
“350-050 ”
“400-201 ”
“OG0-093 ”
9L0-066
“ADM-201 ”
“EX300 ”
“LX0-103 ”
“000-104 ”
“000-105 ”
“70-246 ”
“070-461 ”
“MB6-703 ”
“70-347 ”
“CISM ”
1Z0-051
“ADM-201 ”
“70-462 ”
“1V0-601 ”
000-017
“220-901 ”
“9A0-385 ”
“ITILFND ”

Tags Cloud computing, Java virtual machine, Linux, Open source, Virtual machine, VirtualBox, VMware

Research

Research Project on 3D Food Printing

Post author By Facilitator
Post date February 11, 2016
No Comments on Research Project on 3D Food Printing

Students at Humber College are completing a research project on 3D Food Printing. The purpose of this research project is to explore the current knowledge and attitudes millennials possess regarding 3D food printing technology. The study will examine key areas of interest and concern to the participants, as well as assess participant’s potential buying intent.

This research project aims to provide keen insight for potential stakeholders of this revolutionary technology including: (1) the developers, researchers, and investors; (2) the consumer population; and (3) the participants of the study.

Take our quick 5-minute anonymous survey and enter a draw for a chance to win a $50 VISA! We would like to hear from anyone living in the Greater Toronto Area (GTA) between the ages of 18-35 regarding your opinions on this innovative new technology.

To access the survey, please click here: https://humber.qfimr.com/SU0JVOIV6N4

Once you complete the survey, you will be asked to enter the VISA draw. Your participation is important to us.  We greatly appreciate your help and time!

Thank you,

Juhi Agarwal
Jason Szymanski
Tracey Haefele
Anjali Sharma
Ankita Singh

70-410
220-802
70-346
70-486
ITILFND
1Z0-144
220-901
200-125 ,
VCP550
70-410
400-051
SY0-401
1z0-808
2V0-621
70-533
MB6-703
640-911
JK0-022
PEGACPBA71V1
70-463
CAP
200-125 ,
000-105
c2010-652
000-089
70-270
210-065
300-320
70-410
EX300
2V0-621
642-999
200-101
SSCP
101
220-801
1z0-808
3002
1Z0-060
9A0-385
LX0-103
SSCP
CCA-500
350-030
70-462
70-533
LX0-103
70-532
70-270
640-911
300-101
c2010-657
NS0-157
CISSP
300-075