A cutting edge Open Source HealthCare app(Android+Web) which utilizes Machine Learning to predict diseases based on Symptoms or Natural Language. Also includes features such as Live Chat with Doctors,Appointment Booking,Possible Drugs you can take and Specialist you can refer to.
With the amount of data present in today's age, we can diagnose common ailments and help reduce the workload on hospitals and doctors. Prognosis of common ailments through ML and NLP will also help patients save time and money by not having to travel long distances and physically consult doctors which is also tedious and time consuming. Moreover we still feel that having a doctor's final opinion is priceless and that ML cannot completely replace doctors. Hence our services provides virtual doctor consultations. Moreover in cases of emergency when going to a hospital is absolutely necessary we cannot waste time on booking appointments after reaching the hospital. Our service lets users check for doctors' availabiltiy in hospitals neraby and prebook appointments for emergency consultations. It also shows vacancies for particular doctors (those who sign-up) in nearby hospitals. We also provide basic medication suggestions for common aiements.
Feel free to navigate to any section you want through the navbar below.
This project is not meant for use in real life but rather provides a base on which a solid service can be built with the help of individuals from the healthcare industry. This project was created solely for educational purposes and is not intended for commercial use. Please do not take any decisions without consulting a real doctor first.
- Meet Our Team
- Dataset
- Context Diagram
- Database Overview
- Prognosis Flow
- Appointment booking and hospitals
- Live Chat With Doctor
- Open-Source API
- Results and Predictions
- References
Meet Our Team ↩
Apratim Shukla , Mayank Tolani , Raj Sangani, Swapnil Mishra , Vandit Sheth , Prince Singh
Dataset ↩
This dataset is provided by Columbia University and is a knowledge database of disease-symptom associations generated by an automated method based on information in textual discharge summaries of patients at New York Presbyterian Hospital admitted during 2004. The first column shows the disease, the second the number of discharge summaries containing a positive and current mention of the disease, and the associated symptom. Associations for the 150 most frequent diseases based on these notes were computed and the symptoms are shown ranked based on the strength of association
For NLP Based similarity search
Drug Review Dataset (Drugs.com) Data Set
The dataset provides patient reviews on specific drugs along with related conditions and a 10 star patient rating reflecting overall patient satisfaction. The data was obtained by crawling online pharmaceutical review sites. The intention was to study
(1) sentiment analysis of drug experience over multiple facets, i.e. sentiments learned on specific aspects such as effectiveness and side effects, (2) the transferability of models among domains, i.e. conditions, and (3) the transferability of models among different data sources (see 'Drug Review Dataset (Druglib.com)').
The data is split into a train (75%) a test (25%) partition (see publication) and stored in two .tsv (tab-separated-values) files, respectively.
Citation
Felix Gräßer, Surya Kallumadi, Hagen Malberg, and Sebastian Zaunseder. 2018. Aspect-Based Sentiment Analysis of Drug Reviews Applying Cross-Domain and Cross-Data Learning. In Proceedings of the 2018 International Conference on Digital Health (DH '18). ACM, New York, NY, USA, 121-125. DOI: Web Link
Context Diagram ↩
Database Overview ↩
Prognosis Flow ↩
Appointment booking and hospitals ↩
Live Chat With Doctor ↩
Open-Source API ↩
| Method | URL | USE |
|---|---|---|
GET |
http://127.0.0.1:5000/api/details/apitoken | Shows your PharmaCat Account Details |
GET |
http://127.0.0.1:5000/api/login/username~password | Generates your PharmaCat API Token upon successful login |
GET |
http://127.0.0.1:5000/api/symptoms | Generates list of all Symptoms in your Database |
GET |
http://127.0.0.1:5000/api/diagnosesym/n~symptom1~symptom2 | Diagnoses Disease,Medicine and Specialist |
GET |
http://127.0.0.1:5000/api/diagnosetext/word1 ~ word2 ~ word3 | Diagnoses Disease |
GET |
http://127.0.0.1:5000/api/hospital/apitoken | Generates list of Hospitals near you |
GET |
http://127.0.0.1:5000/api/register/username ~ password ~ email ~ fullname ~ address ~ bloodgroup ~ age | Registers the Patient in the PharmaCat Database |
Results and Predictions ↩
References ↩
[1] MinChen, Yixue Hao, Kai Hwang, Lu Wang and Lin Wang (2020). Disease Prediction by Machine Learning Over Big Data from Healthcare Communities - IEEE Access.
[2] Syed Javeed Pasha and E. Syed Mohamed (October 2020). Novel Feature Reduction Model with Machine Learning and Data Mining Algorithms for Effective Disease Risk Prediction - IEEE Access
[3] Shahadat Uddin, Arif Khan, Md Ekramul Hossain and Mohammad Ali Moni (2019). Comparing Different Supervised Machine Learning Algorithms for Disease Prediction – BMC Medical Informatics
[4] K. Subhadra, Vikas B (2019). Neural network based Intelligent System for predicting heart disease - International Journal of Innovative Technology and Exploring Engineering Volume 8
[5] Runzhi Li, Wei Liu, Yusong Lin, Hongling Zhao and Chaoyang Zhang (2017). An Ensemble Multilabel Classification for Disease Risk Prediction - Journal of Healthcare Engineering
[6] Jaymin Patel, Prof.Tejal Upadhyay, Dr. Samir Patel (March 2016). Heart Disease Prediction Using Machine Learning and Data Mining Technique – IJSC Journal
[7] M. Srividya & S. Mohanavalli & N. Bhalaji, Behavioral Modeling for Mental Health using Machine Learning Algorithms, 11 March 2018
[8] Liu, Shengyu, et al. "Drug-drug interaction extraction via convolutional neural networks." Computational and mathematical methods in medicine 2016 (2016).
[9] Ursula Schmidt-Erfurth , Amir Sadeghipour , Bianca S Gerendas, Sebastian M Waldstein , Hrvoje Bogunović (2018)
[10] Keniya, Rinkal & Khakharia, Aman & Shah, Vruddhi & Gada, Vrushabh & Manjalkar, Ruchi & Thaker, Tirth & Warang, Mahesh & Mehendale, Ninad. (2020). Disease Prediction From Various Symptoms Using Machine Learning. SSRN Electronic Journal. 10.2139/ssrn.3661426.
[11] M.Rajeswari, A.Chandrasekar, Nasiya PM, "Disease Prognosis by Machine Learning Over Big Data from Healthcare Communities" , International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019
[12]Pal, Ashish & Rawal, Pritam & Ruwala, Rahil & Patel, Vaibhavi. (2019). Generic Disease Prediction using Symptoms with Supervised Machine Learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 1082-1086. 10.32628/CSEIT1952297.
[13] Comparing different supervised machine learning algorithms for disease prediction (Shahadat Uddin, Arif Khan, Md Ekramul Hossain and Mohammad Ali Moni)
[14] DISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATA (Vinitha S, Sweetlin S, Vinusha H and Sajini S)
[15] Probabilistic Machine Learning for Healthcare(Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi, and Rajesh Ranganath)
[16] Split Learning for collaborative deep learning in healthcare (Maarten G. Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, Ramesh Raskar)
[17] Mining Association Rules from Large Datasets Towards Disease Prediction (K. Srinivas, G. Raghavendra Rao, A. Govardhan)
[18] Reinforcement Learning in Healthcare: A Survey (Chao Yu, Jiming Liu, Shamim Nemati)
[19] Secure and Robust Machine Learning for Healthcare: A Survey (Adnan Qayyum, Junaid Qadir, Muhammad Bilal, and Ala Al-Fuqaha) 50