Identifying a Medical Department based on Unstructured Data

A Big Data Application in Healthcare

Veena Bansal

, Abhishek Poddar

and R. Ghosh-Roy

Indian Institute of Technology Bhilai, Raipur, India

Indian Institute of Technology Kanpur, India

IBM UK Limited, London, U.K.

Keywords: Healthcare, Big Data, Unstructured Data, Tertiary Healthcare.

Abstract: Health is an individual’s most precious asset and healthcare is one of the vehicles for preserving it. The Indian

government’s spend on healthcare system is relatively low (1.2% of GDP). Consequently, Secondary and

Tertiary government healthcare centers in India (that are presumed to be of above average ratings) are always

crowded. In Tertiary healthcare centers, like AIIMS, patients are often unable to articulate correctly their

problems to the healthcare center’s Reception staff for these patients to be directed to the correct healthcare

department. In this paper, we propose a system based on Big Data and Machine Learning to direct the patient

to the most relevant department .We have implemented and tested parts of this system wherein a patient enters

his symptoms and/or provisional diagnosis; the system suggests a department based on this user input. Our

system suggests the correct department 68.05% of the time. Our system presently makes its suggestions using

gradient boosting algorithm that has been trained using two information repositories- symptoms and disease

data, functional description of each medical department. It is our informed assumption that, once we have

incorporated medicine information and diagnostics imaging data to train the system and the complete medical

history of the patient, performance of the system will improve significantly.

1 INTRODUCTION

Everyone strives to be healthy and stay away from

hospitals but occasionally one must visit a healthcare

facility. Healthcare in India is a three-tier system;

Primary care is the first line of contact, often between

a patient and a doctor. Secondary and Tertiary

healthcare centers require a referral from a Primary

healthcare center. Tertiary healthcare centers cater for

complicated medical conditions and require

specialized medical consultations.

A sample referral is shown in Figure 1. The

referral has the name of a patient, provisional

diagnosis and the hospital name to which the patient

has been referred to but more often without the details

of the department within the hospital. The Tertiary

healthcare centers such as AIIMS (All India Institute

of Medical Science) have multiple departments, with

near unique capabilities in each department for

treating ailments. Even medically literate patients

often have difficulty in identifying the correct

department. The healthcare center’s Reception staff is

often the first port of call and these staff often quickly

browse through the medical documents of a patient to

identify the appropriate department; this is not fool

proof and mistakes are often made, leading to

inconveniences caused downstream to all parties

concerned. This is a major bottleneck, especially as

the system must deal with many thousands of patients

each day.

People who have access to the Internet, and have

the required skill sets, can collate information about

each department before making an online

appointment. However, for many people in India,

they do not even have access to the Internet and/or not

literate enough to make an online appointment.

Irrespective of the channel used for booking, all

walk-in patients face very similar challenges of

identifying the correct department to proceed to. We

have therefore focused on the walk-in process where

most of the errors have been noticed. It was our

conclusion that we need to first augment the manual

appointment booking process to identify the correct

department, thereby make the overall booking

process easier and error free for the patients. In this

work, we propose a system that will automatically

Bansal, V., Poddar, A. and Ghosh-Roy, R.

Identifying a Medical Department based on Unstructured Data.

DOI: 10.5220/0006773904750482

In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 475-482

ISBN: 978-989-758-298-1

475

Figure 1: A Sample referral to a tertiary healthcare system.

recommend an appropriate department to the patients

by looking at their medical documents.

We have reviewed the related work in §2 and

presented a formal model of our proposed system in

§3. An implementation of our system has been

detailed in §4. Results and conclusions have been

presented in §5.

2 RELATED WORK

Over the years, several computational systems for

decision making have been used in healthcare. These

have either helped humans in reducing their workload

or helped in decision making or both. Expert systems

built to diagnose a disease (Naser et al, 2010; Tenorio,

2011; Rahman and Hossain, 2013; Ibrahim, 2014)

have faced a challenge in clearly representing medical

history of a patient. Supervised learning techniques

such as decision trees, Bayesian classifiers, artificial

neural networks, support vector machines and k-

nearest neighbors have also been used in building

expert systems. A decision support system can also be

rules or fuzzy rules based (Rahman and Hossian,

2013). These systems are used for diagnosing the

presence of a disease, or predicting adverse effect of

a drug (Fosamax), or predicting the onset of a disease

(Ibrahim, 2014; Ephzibah and Sundarapandian, 2012;

Jain and Raheja, 2015).

Another line of research led to the development of

systems that helped patients in managing their diet

and medicines (Caballero-Ruiz et al, 2017; Goethe

and Bronzino, 1995); some helped Health Insurance

Providers with pre-authorization of insurance

requests (Araújo, 2016); others helped doctors in

identifying the best possible treatment for a given

disease (Delias, 2015) or even recommending

pathological tests (Alonso-Amo, 1995); or check the

efficacy of an ongoing treatment (McAndrew, 1996).

All these systems require a vast amount of data

(Davenport, 2014) and with the advent of Big Data

(Aruna Sri and Anusha, 2016), a new set of

possibilities in healthcare have emerged (Schultz,

2013). Prevention strategies and treatment

recommendations are all based on vast amount of data

(Saravan, 2015). The medical world has not yet

evolved a standard terminology to describe medical

conditions and medical departments. Work is being

carried out to create a standard medical language to

be used across applications and platforms

(Handerson, 2016).

We extensively searched for an application or a

system that can provide a description of all diseases

and respective departments of hospitals that treat

these diseases. To the best of our knowledge, no such

application exists. Such an application or a system

can help a patient identify the appropriate department

of a hospital for a specific treatment. We spoke with

the doctors in Secondary and Tertiary healthcare

facilities, and they all confirmed that patients are

often directed to the wrong department by the

Reception staff. Sometimes, patients are not even able

to describe their problems. Often the Reception staff

are unable to decipher the medical reports/documents

provided by the patients. A patient often therefore

ends up wasting his own time; the hospital also ends

up wasting its own resources if the patient ends up at

the wrong department. We have, hence, decided to

build a system that will direct patients to the most

appropriate department of the healthcare facility. Our

system is based on Big Data techniques and is

described next.

3 THE PROPOSED MODEL

The block diagram of our proposed system is given in

Figure 2. When patients walk into a Tertiary

healthcare center, their documents can be scanned

including:

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

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 previous prescriptions from doctors

 medicines taken

 diagnostic reports & medical images

The scanner would then digitize the documents.

The digitized documents would then be used by the

Digital Utility Module (referred to as DUM). The

DUM would pre-process and extract the information

presented in the documents and images. The images

will then be processed, extract metadata if available,

to identify the organs and other relevant details

available in the images (Filipovych and Davatzikos,

2001; Kucheryavski, 2007; Antoio at al, 2001).

Prescriptions, reports, bills etc. will be processed by

OCR and ICR engines to convert them into searchable

and editable text (Ciregan, 2012; Patel et al, 2012).

The extracted information will then be passed to

the next module, called DIRECT. The DIRECT

module will recommend a hospital department based

on the input. DIRECT is at the heart of our system,

has the knowledge base and processes the inputs

provided by patients to recommend an appropriate

department. DIRECT employs a machine learning

model that is trained and validated offline. The

training process involves the following steps that we

explain next.

 Data Cleaning & Preprocessing

 Scalable Model Building

 Model Validation & Selection

 Preprocessing & integration for updates

Data Cleaning and Preprocessing

We need a labeled dataset to train the system. For

instance, we can train the system to learn the disease

that each hospital department treats by using data

containing diseases mapped to an appropriate hospital

department. This process includes creating a profile

for each disease based on its symptoms, medicines,

and diagnostic reports and then mapping each disease

profile to a department. This includes extracting the

useful parts of the text, purging the stop-words from

the text (Ullman and Rajaraman, 2011), converting

the words into a common form by using stemming

(Lovins, 1968), feature extraction from the texts

(Guyon and Elisseff, 2003) and converting the data

into a vector space model (Ripley, 1996). The

challenging part of the problem is that apart from text

data, there are also image data to deal with. According

to data types, we have loosely three classes of

extracted features – the symptoms or disease name,

the medicines taken and processed images. While

training, the model will learn to assign weights to

each class of features.

Scalable Model Building

Gradient Boosting Machine (Click et al, 2017), Deep

Learning (a multi-layer neural network model

trained using back-propagation algorithm) (Candel

et al, 2015) and Distributed Random Forest (H2O

website) are the models that we have selected based

on their potential and performance. Our main task is

multinomial classification (Aly, 2005).

Model Validation & Selection

This is the process where we select a final model

based on varying criteria like log loss (Collier, 2015)

or misclassification rates among all the different

models. There are many hyper parameters in each

machine learning model that get tuned during this

training.

Processing & Integration Updation

This process of the DIRECT module will enhance the

accuracy of the system over time as it sees and learns

from more and more real use cases. The predicted

department and the inputs from the patients are pre-

processed in the agreed format of our training data. It

is then added to our knowledge base for continuous

learning.

4 SYSTEM IMPLEMENTATION

We have implemented part of the proposed system

called The Tertiary Healthcare Center Directing

System. The complete system needs four information

repositories for training: Symptoms & Disease Data

(names of diseases and their symptoms),

Departmental Functional Description, Drug &

Medicine Information, Diagnostics Imaging Data.

The form of Symptoms & Disease data is as follows.

<symptom

, symptom

, …, symptom

> <disease

Functional description of each department is

represented as follows.

<disease

, disease

, …, disease

><medical_deptt>

Drug & Medicine information consists of the

following information.

<drug

, drug

, …, drug

><disease

Diagnostics and Imaging data has two

components: Image and corresponding diagnosis. We

have used Symptoms and Disease data as well as

Departmental Functional Descriptions to train and

test the system. We have not yet incorporated Drug

and Medicines Information, Diagnostics Imaging

data.

Identifying a Medical Department based on Unstructured Data

477

The data for training and validating the system is

not available in the required form and requires pre-

processing. Hence, the system implementation

includes the following phases:

a. Finding a dataset that has disease

information (possibly including their

names, associated symptoms, types,

synonyms etc.) and name of the concerned

medical department.

b. Converting the above dataset into vectors.

c. Identify suitable machine learning models

and train them.

d. Test the models and select the best

performing model.

We created a labeled dataset using a disease-

description dataset and a document on functional

descriptions of hospital departments using heuristics.

We used dataset from the Disease Ontology project

called doid-non-classified.obo (DOID, 2017)

(referred to as disease_description). Each disease has

an assigned identifier, name, symptoms and some

other details. We also created functional descriptions

(referred to as functional_description) of healthcare

departments from two different sources (Henderson,

2016; Mayoclinic Website, 2017). The datasets

disease_description and functional_description

contain information about 10612 diseases and 20

healthcare departments respectively. The system

compares each disease description with all the

departmental descriptions to assign each disease to a

department. This is a challenging task and involves

text processing. We had to remove stop words,

perform stemming, used heuristics to handle

synonymous, homonymous, etc. For instance, the pair

of words electrocardiogram and cardiomyopathy are

essentially the same whereas hypertension and

hyperbola are totally unrelated. We used python to

implement the preprocessing phase of the system.

We obtained a labeled dataset where each disease

is mapped to a hospital department. The dataset is

converted into a vector space model using term-

frequency-index and document-frequency technique

(tfidf). The labelled dataset presented as vectors have

been used to train and test machine learning models.

Our problem is essentially a multinomial

classification task (Aly, 2005). We had department

names as our classes and the objective of our model

was to learn a mapping between disease descriptions

and departments. We split the datasets into two parts:

65% for training, 35% for testing. We trained three

machine learning algorithms: Gradient Boosting

Machine, Distributed Random Forest and Deep

Learning. We implemented the system using an open-

source big data analysis platform (H2O, 2016).

Figure 2: The block diagram of our system.

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

478

Table 1: List of medical departments in a hospital.

Serial

No.

Department Name

Anesthetics

Breast Screening

Cardiology

Ear, nose and throat (ENT)

Elderly services department

Gastroenterology

General Surgery

Gynecology

Hematology

Neonatal Unit

Neurology

Nutrition and dietetics

Obstetrics and gynecology

units

Oncology

Ophthalmology

Orthopedics

Physiotherapy

Renal Unit

Sexual Health

Urology

We choose the model with the lowest

misclassification rate as our final model. The results

from our model building, validation and selection

phase have been discussed in the next section.

5 RESULTS AND DISCUSSIONS

We have used three machine learning models, namely

Gradient Boosting Machine, Deep Learning and

Distributed Random Forest. Each of these models

require learning that essentially amounts to tuning

some of the hyper parameters.

Random Forest hyper parameters include the total

number of trees to grow, maximum tree depth and the

number of predictors randomly sampled as candidates

for each split.

Neural networks have variants such as hyperbolic

tangent (Kalman and Kwasny, 1992), rectifier

(Hahnloser et al 2000) and maxout (Goodfellow,

2013); each of these could optionally be paired with a

regularization technique called dropout (Srivastava et

al, 2014). There are many hyper-parameters to be

tuned (Han and Kamber, 2001).

Hyper parameters of Gradient Boosting that need

tuning include the number of trees to be constructed,

the maximum depth of each tree, percentage of rows

to be sampled per tree, and learning rate. There are

certain guidelines for tuning these parameters

(Friedman, 1999; 2002).

Table 2 summarizes the results that we have

obtained from these three models. We trained all three

machine learning models using 5 different settings of

the parameters. After training the system, we tested

using the same data. Column 2, 4 and 6 of Table 2

show false positive or misclassification for all 5

parameters settings for all three machine learning

models on the training data. We then tested the three

models with 5 different parameters settings using the

validation data which is new to the models. The

percentage of false positive is shown in columns 3, 5

and 7. The misclassification or false positives have

been plotted for better perception of the three models

with 5 different settings of hyper parameters and

shown in Figure 3. As mentioned in the previous

section, we have used 10,612 disease mapped to 20

hospital departments. It is obvious from the results

that, using just the descriptions of departments and

diseases, the system is able to suggest correct

department 89.82% of the time using Distributed

Random Forest on the training data. However, when

we run Distributed Random Forest on validation data,

it is able to suggest the correct department 60.19% of

the time only. Amongst all models and parameters

settings, the best validation performance is 68.05% of

Gradient Boosting Model. The performance across all

parameters settings and models is close to 70%.

It can therefore be concluded that the information

contained in our dataset cannot give us a performance

better than 70% true positives. Our system as shown

in Figure 2 has many other sources of information that

we need to incorporate for better performance as we

explain in the next section.

6 CONCLUSION AND FUTURE

WORK

We wanted to build a system that will help patients

going to tertiary health care system identify the

correct hospital department. We have implemented

and tested parts of this system wherein a patient enters

his symptoms and/or provisional diagnosis; the

system suggests a department based on this user

input. Our system suggests the correct department

68.05% of the time. To improve the performance

further, we need to incorporate medicine information

and diagnostics imaging data into our system as

shown in Figure 2. The system should take user’s past

prescriptions and diagnostic reports into account

when suggesting a medical department.

Identifying a Medical Department based on Unstructured Data

479

Table 2: Misclassification done by three different machine learning models with five different settings for hyper parameters

for training and validation data (GBM: Gradient Boosting Machine, DL: Deep Learning and DRF: Distributed Random Forest,

T: Training Data, V: Validation Data).

Parameters

Setting

G B M ( T )

G B M ( V )

D L ( T )

D L ( V )

D R F ( T )

D R F ( V )

21.94

39.03

34.80

35.55

15.38

34.14

51.34

55.04

33.42

34.52

15.02

32.91

30.40

43.74

32.90

33.96

12.14

33.57

17.72

31.95

32.43

33.78

10.99

35.96

13.82

39.35

33.3

33.47

10.18

39.81

Figure 3: Misclassification done by three different Machine Learning models for five different settings of hyper-parameters

with training and validation data; GBM: Gradient Boosting Method, DL: Deep Learning, DRF: Distributed Random Forest;

T: Training and V: Validation.

Once we incorporate everything, the performance

of this system will improve. We are now working on

integrating diagnostic image data. We have

experimented with image datasets of eyes and lungs.

We have been able to classify the organ in the image

with near 100% accuracy. We have yet to figure out a

mechanism to integrate the diagnostic image data into

the decision making process. We also want scan the

past prescriptions and run them through OCR/ICR to

convert them into text gain more information about

the treatment that the patient has received. Again, this

information must be integrated into decision making

process. We may have to map branded medicines into

generic medicines to be able to use this information

in the deciding the hospital department. Perhaps, our

knowledge base should contain a list of medicinal

compounds and the common diseases which they

treat, and a list of medicine names from different

brands for the same compound.

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