Insights extraction through knowledge graph of medical booking data
Referrals and medical services, within the public healthcare in Italy, are accessed through a Booking Centre administered by local health authorities and controlled by regional governments. In our research lab, we are studying and analyzing this kind of data. Within the CUP-i-ONE, a research project funded by the Campania region, which involves the Local Health Department of Naples, we are working on extracting insights and useful knowledge for stakeholders. Our lab involves different expertise that can help us make an impact on such problems. This is thanks to the combined work of the University of Naples Federico II and CINI consortium. We are working on data generated from the last 6 years of medical prescriptions, booking appointments, either cancellation and rescheduling that sums up to more than 10M entries. The data comes from legacy databases and web services. The main issue is that the database has no entity-relation structure. The data is just saved as a sequence of rows, usually repeating many values in similar columns and rows, with possible mistakes and flows from data generated from tests, and no limits or controls on many types of data. How do we analyze such data and gather insights? Grakn makes it possible to start from a schema and generate a graph. On top of it, with its APIs, it is possible to integrate and study such data, using different Python modules. In particular, the recently released KGLIB, by the means of a research project conducted at Grakn Labs, we can apply machine learning to graphs. In this presentation, we will show our research in applying Knowledge Graph to the domain of medical references, prescriptions and booking data. We will guide you in our research trying to tackle the many research issues that are still open.
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Insights extraction through knowledge graph of medical booking data
- 1. Insights extraction through Knowledge Graph of medical booking data Giampaolo Casolla - Vincenzo Schiano Di Cola University of Naples Federico II Department of Mathematics and Applications "Renato Caccioppoli" (DMA) Department of Electrical Engineering and Information Technology (DIETI) MODAL research group
- 3. To be: ● Apply AI on legacy data ● Generate a knowledge graph form tuple data ● Real time analytics and prediction 3 Challenges: ● Quickly build a knowledge base ● Apply Machine Learning (ML) on Knowledge Graph (KG) ● Shift to knowledge graph complex applications: ○ entity resolution ○ link prediction ○ pattern discovery Why? ● Booking processes are widely used ● Predict load of health service provider or next patient access ● Medical booking data is semantical
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- 5. 5 referral book appointment health service Credits: https://www.vectorstock.com/ https://sbmi.uth.edu/blog/apr-15/04152015.htm practitioner patientpatient patient patient
- 6. 6 years of appointments booked for medical prescriptions SUM UP TO: 10M rows. Main table: 33 attributes, +2 tables,with the pairs: ● medical-branch-id branch-description ● health-service-id health-service-description 6 Field- Name booked -date reservation-date encrypted- nin-id gender patient- age booking-age nt-id medical-bra nch-id health-se rvice-id practitioner-id referral-cent re-id referral-id
- 7. 7 Code medical-branch 02 Cardiologia 002 CARDIOLOGIA CAR Cardiologia Main issues: ● Spelling errors ● Naming convention changes ● Text contraction Tabular data manually modified by human operators Solution: ● String matching algorithm ● Added flag-columns Code health-service-provision 800191 Visita Cardiologica VISCAR Visita di Controllo Cardiologica VIS CA VISIT CARD
- 8. 8 Referral id Patient id Practitioner id 1371797 729104 24236 1371797 789722 35664 Last Reservation Change Date Health service id Referral id 15/06/2016 94.09 5729131 11/07/2016 94.09 5729131
- 9. 9 Patient id Patient gender Practitioner id 510423 M 23901 510424 M 23902 510425 F 23901 510426 F 23901 510423 M 23902 510426 F 23903 510426 F 23901 510426 F 23903 510423 M 23903 510424 M 23902 Single Patient id Patient gender Practitioner id Unique 4 2 3 Couple (Patient id, Patient gender) (Patient gender, Practitioner id) (Practitioner id, Patient id) Unique 4 5 7 4 = 4 ENTITY ATTRIBUTE Patient id Patient gender
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- 17. 17 ● Ubuntu 18.04 LTS ● Python 3.7 ● Grakn core 1.5.9 ● Grakn Workbase 1.2.7 ● R 3.5.3 ● Tensorflow GPU 1.14 ● Ampligraph 1.2.0 Credits: https://www.nvidia.it/Download/index.aspx, https://hd.tudocdn.net/851711?w=660&h=440, https://i.ebayimg.com/images/g/dbYAAOSwIShbhvQq/s-l300.jpg ● Core i9-9900K ● 128 GB DDR4 ● Geforce RTX 2080 Ti, 11GB ● 1 TB M.2 NVMe
- 18. Inspired by phone-calls migration example on GitHub Loaded 1747 patients. Each patient: 60 < health services < 100. 18 Entities & meta-Relations # of instances reservation 123k referral 32k health-care 10k provision 9k practitioner 2k appointment-provider 2k patient 2k booking-staff 1k medical-branch 1k Credits: https://github.com/graknlabs/docs/blob/master/08-examples/04-phone-calls-migration-python.md
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- 20. 20 relation_template(data): query = "match " for r, roleplayer in enumerate(data["roleplayers"]): **** **** if "roleplayers_relation" in data: **** ****
- 21. 21 Patient Rel: Referral Rel: Reservation Triplets Time 1k 1,8k 5,6k 52k 04 min 2k 3,5k 12k 110k 10 min 4k 7k 24k 214k 24 min 8k ~14k ~48k ~450k ~1 h 16k ~28k ~96k ~900k ~2 h Selected patients: 1-3 health services
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- 23. Goal: Map each node in a network into a low-dimensional space 23 Why network embedding? ● Distributed representations for nodes ● Similarity of embeddings between nodes indicates their network similarity ● Encode network information and generate node representation Credits: http://web.stanford.edu/class/cs224w/slides/07-noderepr.pdf
- 26. @has-type relations extracted from Grakn 26 @has-relations # of instances @has-booked-date 149k @has-reservation-date 123k @has-booking-type 123k @has-referral-id 32k @has-patient-age 32k @has-exemption-code 22k … ... 1 558 700Total triples:
- 28. 28 Score function Bilinear ComplEx Credits: https://www.researchgate.net/publication/267428940_Querying_Factorized_Probabilistic_Triple_Databases/figures?lo=1, https://arxiv.org/pdf/1606.06357
- 29. 29 PCA Key points: ➔ Good partitioning ➔ Grouped by relations t-SNE
- 30. Goal: Find hidden groups of different entities 30 Methods: ● K-Means ● Hierarchical Clustering ● DbScan
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- 33. 33 Cluster 7 Cluster 8
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- 42. 42 match $1 isa patient, has encrypted-nin-id 777937; $2 isa patient, has encrypted-nin-id 706294; $3 isa practitioner, has practitioner-id 23909; get; limit 100;
- 43. 43 Methods: ● Nearest Neighbors ● Linear SVM ● RBF SVM ● Gaussian Process ● Random Forest ● Neural Network ● Naive Bayes ● QDA ● XGBoost Goal: Predict patients’ visits
- 44. 44 MEAN
- 45. Conclusions: ● Analyzed Booking of Medical data ● From SQL to KG ● Machine Learning on embedding 45 Future Work: ● Investigate other embedding algorithms ● DeepMind's Graph Nets + KGLIB Grakn useful for: ● KG schema creation ● Reasoning ● Triples extraction ● Link analysis
- 46. 1. R&D AI solutions 2. Any data can be exploited 3. Not an easy task (human hours, high specs) 4. Many tools out there to be used correctly 5. Producing insights can be cumbersome 46
- 47. 47 University of Naples Federico II Department of Mathematics and Applications "Renato Caccioppoli" (DMA) Department of Electrical Engineering and Information Technology (DIETI) M.O.D.A.L. research group