Data Science in Healthcare" by authors Sergio Consoli, Diego Reforgiato Recupero, and Milan Petkovic is an insightful guide that delves into the intersection of data science and healthcare. As a first-year student in Pharmaceutical Management, I found this book to be a valuable resource for understanding how data-driven approaches are transforming the healthcare industry, offering fresh perspectives and practical insights for future professionals like myself.

1. Data Science in Healthcare
Author : Sergio Consoli / Diego Reforgiato Recupero / Milan Petkovic
Presented by
Mangesh Patil PM 30

2. Introduction
and
Importance of
Healthcare
Data
Key Data
Science
Techniques
Challenges in
Healthcare Data
Science
Data
Science in
Healthcare
and used
Cases
Contents
Importance
of healthcare
data and
Future
Scope

3. Introduction
 A number of factors make data science indispensable to healthcare in the present day, the most important
of them being the competitive demand for valuable information in the health market.
 The collection of patient data through proper channels can help provide improved quality healthcare to
consumers
 Nowadays, diseases can be predicted at the earliest stage with the help of data science in healthcare, that
too remotely with innovative appliances powered by Machine learning
Role of Data Science in Healthcare:-
1)Disease Prediction and Prevention: Data science helps identify patterns and risk factors from patient data
to predict disease outbreaks and enable preventive measures.
2)Diagnosis : It enhances diagnostic accuracy by analyzing medical images (e.g., X-rays, MRIs) and
detecting abnormalities.
3)Treatment Personalization: Data science tailors treatment plans based on individual patient data,
optimizing outcomes and minimizing side effects.

4. 1)Drug Discovery: It accelerates drug development by analyzing molecular data to identify potential
candidates and predict their efficacy.
2)Telemedicine: Data science supports remote healthcare delivery, facilitating consultations, and monitoring
patients at a distance.
3)Epidemiology and Public Health: It aids in tracking and controlling disease outbreaks and monitoring
public health trends.
Sources of Healthcare Data:-
1)Healthcare apps and Wearables:-
Data collected from health and fitness apps. Smartwatch for detecting cardiac dysfunction. Smartwatches
record vital signs like heart rate, SpO2, skin temperature, step count and more. This critical data, when shared
with healthcare providers, can help in detecting underlying diseases at an early stage.
2)Medical Imaging:-
Perhaps the most common project of data science in the healthcare industry is medical imaging for detecting
internal issues. The X-Rays, MRIs, and CT scans which are so widely used technology are all types of medical
imaging for scanning the internal organs of the human body. Doctors can now provide a far more accurate
diagnosis of the patient's conditions with medical data science than they could with traditional methods.

5. Application and Use Cases of Data Science in Healthcare
 Discovering drugs
Data science helps the pharmaceutical industry by using Artificial Intelligence to create new medicines. It
does this by studying changes in genes and information about patients to make drugs that work well based
on the data.
Virtual assistance
Today, data scientists create chatbots and AI systems to help you understand your health. You can share
some information about your health, and these tools can give you a correct diagnosis. They can also help
you with your health insurance and give you advice on how to live a healthier life.

6. Data science is a field that uses various techniques to extract insights and knowledge from data.
Three fundamental techniques in data science are data preprocessing, machine learning, and deep learning.
Data Preprocessing
•Data preprocessing is the initial step in data analysis.
•It involves cleaning and transforming raw data into a usable format.
•Common tasks in data preprocessing include handling missing data, removing outliers, and normalizing data.
•Clean data is essential for accurate analysis and modeling.
Example : ARGUS, Sapphire, ARIS-G
Machine Learning
•Machine learning is a subset of data science that focuses on developing algorithms that can learn from data.
•It enables computers to make predictions or decisions without being explicitly programmed.
•Machine learning algorithms can be categorized into supervised (predicting labels), unsupervised (finding patterns),
and reinforcement learning (decision making).
Example : Machine Learning in Medical Assistance
Machine Learning in Decision Making

7. Challenges in Healthcare Data Science
• Data Privacy : Protecting patients' sensitive information while using it for analysis is a big challenge.
• Data Quality : Ensuring that medical data is accurate and complete is crucial for reliable insights.
• Regulations : Complying with healthcare regulations like HIPAA adds complexity to data handling.
• Bias and Fairness : Avoiding bias in algorithms and ensuring fairness in healthcare predictions is vital.
• Ethical Concerns : Deciding how to use data ethically and responsibly is an ongoing challenge.
• Scalability : As healthcare data grows, the tools and infrastructure must keep up with demands.
• Clinical Adoption : Convincing healthcare professionals to trust and use data-driven insights can be
challenging.
• Security : Protecting healthcare data from cyber threats is a constant concern.

8. Application and Use Cases of Data Science in Healthcare
Data Science
 What is data science So the data science is a field that comprises of everything related to data cleansing
preperation and analysis dealing with unstructured and structured data
Predictive analytics in healthcare involves using data analysis and machine learning to make predictions
and improve patient care. Some applications include:
 Fraud Detection: Identifying potential healthcare fraud by analyzing billing and claims data.
 Disease Risk Prediction: Identifying individuals at higher risk of certain diseases based on factors like
genetics, lifestyle, and medical history.

9. Application in healthcare.
Tracking Patient Health : Did you know that the human body generates 2TB of data daily? Data scientists
for public health have developed wearable devices that allow doctors to collect most of this data like heart
rate, sleep patterns, blood glucose, stress levels, and even brain activity. With the help of data science tools
and machine learning algorithms, doctors can detect and track common conditions, like cardiac or
respiratory diseases
Identifying patient risks
As a data scientist, you can create machine learning algorithms to combine variables like socioeconomic
background, lab values, and other individual information to create results of a person’s health condition
3. Disease Prediction and Prevention: Data science helps identify patterns and risk factors from patient
data to predict disease outbreaks and enable preventive measures.
4. Electronic Health Records (EHR) : Data science improves the management and analysis of electronic
health records, making patient information more accessible and valuable.
5. Patient Engagement : It enables patient monitoring through wearables and apps, promoting proactive
healthcare.
6. Data Security and Privacy : Ensuring the confidentiality and security of patient data is a critical role,
especially given the sensitivity of healthcare information.

10. Healthcare data is crucial for improving patient care and advancing medical
research.
•It enables evidence-based decision-making, personalized treatment plans,
population health management, healthcare quality improvement, and medical
innovation.
•Additionally, it aids in health policy development and resource allocation,
ultimately leading to better patient outcomes and improved public health.
Importance of healthcare data

11. Benefits of data science in healthcare include
1. Improved Patient Outcomes: Data analysis helps diagnose diseases early and
personalize treatments.
2. Cost Reduction: Predictive analytics streamlines resource allocation and
identifies cost-effective treatments.
3. Medical Advancement: Data science uncovers new medical insights, aiding drug
development and genomics research.

12. Future Scope

13. In general, the core responsibilities of a healthcare data scientist are as follows :
1.Performing data analysis with various analytical tools.
2.Collection data/ health data.
3.Analyzing hospital requirements.
4.Organizing and sorting data for use.
5.Implementing algorithms to extract insights.
6.Building predictive models with the development team.
7.Database management including data collection, retrieval, storage, and security.
Scope

14. THANK
YOU