Assignment 02: Individual Practical Assignment
Assignment 02: Individual Practical Assignment
Due date: Week 9
Weighting: 30%
Length and Format: Project related documentation in word format and program code in a text file.
Assessment Details: Select a project appropriate for a Neo4J-based response then design, develop, test, and demonstrate a solution.
Assessment addresses SLOs b Select and implement database technologies for a range of use cases
d Design, develop, and demonstrate a graph database.
Marking Criteria Please refer to Marking Rubric on Canvas
Assessment Task:
Neo4J is used in Supply Chain Management applications.
List and discuss at least five applications (Use Cases).
Identify components of at least two of the applications discussed above.
Discuss challenges and considerations in building one of the applications.
Create a sample data model for the application discussed in question 3.
Discuss neo4j technologies (built-in functions, algorithms) to implement the data model.
Create code to represent your data model. Remember your database must have a minimum of 5 labels with 4 attributes each and 4 relationships with 2 attributes each. 3 nodes for each label are needed.
Create 8 queries that a user will ask in your database. Remember some of these queries must use algorithms or built-in functions discussed in Question 5.
Deliverables:
A word document containing answers to question 1 to question 5, and a text file containing answers to questions 6 and 7. Your word document must be submitted through CANVAS submission folder. Please check for plagiarism using Turnitin. If plagiarism is identified review and rewrite your assessment.
Email submissions will not be accepted.
Mandeep Kaur
Advanced Database Design and Development (MIT212)
Dr Sai K Lakkaraju
Master of Information Technology
Assignment#02
Individual Practical Assignment
13398592710
Table of Contents
Five applications (Use Cases) of Neo4J 3 1.1 Patient Journey Analysis 3 1.2 Drug Discovery and Development:4 1.3 Clinical Trials Management 4 1.4 Healthcare Fraud Detection5 1.5 Genomic Data Analysis: 5HYPERLINK l "components"Identify Components two of the applications: 62.1 Patient Journey Analysis 6HYPERLINK l "secondcomponent"2.2 Patient Journey Analysis 9
Challenges and considerations 113.1 Data Integration and Quality: 113.2 Privacy and Security: 123.3 Interoperability and Integration: 123.4 Ethical and Legal Considerations: 123.5 Scalability and Performance: 13HYPERLINK l "sixchallenge"3.6 User Adoption and Stakeholder Engagement: 13
HYPERLINK l "sampledatamodel"Sample Data Model 14
HYPERLINK l "technologies"Neo4j technologies 16
HYPERLINK l "code"Code to Represent Data Model: 17
8 queries 20HYPERLINK l "references"References 21
Assignment 02: Individual Practical Assignment
Five applications (Use Cases) of Neo4J Certainly! Neo4j, a graph database, finds numerous applications in Healthcare and Life Sciences due to its ability to model complex relationships and handle interconnected data efficiently. Here are five notable applications:
Patient Journey Analysis:
Patient journey analysis involves understanding and optimizing the entire process a patient undergoes within the healthcare system, from the initial onset of symptoms to diagnosis, treatment, and follow-up care. Neo4j excels in this domain by modelling each step of the patient journey as a node and establishing relationships between them. For example, nodes could represent events such as initial consultation, diagnostic tests, specialist referrals, hospital admissions, surgeries, medication prescriptions, and post-treatment consultations. Relationships between these nodes capture the chronological order of events and the dependencies between them. Healthcare providers can leverage Neo4j to identify common pathways taken by patients with specific conditions, pinpoint inefficiencies or delays in the care process, and tailor interventions to improve patient outcomes. Additionally, Neo4j's graph analytics capabilities allow for the identification of patterns, such as frequent care transitions or treatment adherence issues, which can inform targeted interventions to enhance patient experience and care quality.
Drug Discovery and Development:
Drug discovery and development is a complex and resource-intensive process that involves identifying promising drug targets, designing compounds, conducting preclinical and clinical trials, and gaining regulatory approval. Neo4j plays a crucial role in accelerating this process by integrating and analysing diverse datasets from various sources. Neo4j's graph database structure enables the representation of molecular structures, gene expressions, protein interactions, disease pathways, drug candidates, and clinical trial data as interconnected nodes and relationships. This allows researchers to explore complex relationships between biological entities, predict drug-target interactions, and identify potential therapeutic compounds. By leveraging Neo4j, researchers can conduct network-based analyses to prioritize drug targets, repurpose existing drugs for new indications, optimize drug combinations, and design more efficient clinical trials. Additionally, Neo4j facilitates collaboration and data sharing among interdisciplinary teams, leading to faster discoveries and advancements in pharmaceutical research.
Clinical Trials Management:
Clinical trials are essential for evaluating the safety and efficacy of new medical interventions, including drugs, devices, and treatments. Neo4j is instrumental in managing and analysing the vast amounts of data generated during clinical trials, including participant demographics, treatment protocols, adverse events, and outcomes. In a Neo4j database, trial participants, healthcare providers, study sites, treatments, and outcomes are represented as nodes, with relationships capturing interactions and dependencies between them. This enables researchers to track the progress of individual participants, monitor adherence to study protocols, and analyse the impact of interventions on patient outcomes. Neo4j's graph analytics capabilities allow for real-time monitoring of trial data, early detection of safety concerns or efficacy signals, and adaptive trial design based on emerging insights. Moreover, Neo4j facilitates data integration from multiple sources, including electronic health records, laboratory systems, and regulatory databases, streamlining the trial management process and reducing administrative burden.
Healthcare Fraud Detection:
Healthcare fraud, including billing fraud, prescription fraud, and insurance fraud, poses significant challenges to healthcare systems worldwide, leading to financial losses and compromised patient care. Neo4j is a powerful tool for detecting and preventing healthcare fraud by analysing complex networks of relationships between patients, providers, procedures, and payments. In a Neo4j graph database, entities such as patients, healthcare providers, healthcare facilities, procedures, medications, and payments are represented as nodes, with relationships capturing interactions and transactions between them. By analysing patterns of behaviour and identifying anomalies within the network, Neo4j can flag suspicious activities indicative of fraud or abuse. Neo4j's graph algorithms enable the detection of common fraud schemes, such as billing for unnecessary procedures, upcoding or unbundling of services, and kickback schemes involving multiple providers. Additionally, Neo4j facilitates real-time monitoring of healthcare transactions, enabling proactive intervention and enforcement actions to deter fraudulent behaviour and protect the integrity of the healthcare system.
Genomic Data Analysis:
Genomic data analysis involves studying the structure, function, and evolution of genomes to understand the genetic basis of diseases, identify potential drug targets, and personalize treatment approaches. Neo4j is well-suited for managing and analysing large-scale genomic datasets due to its ability to represent complex relationships between genes, variants, pathways, and phenotypes. In a Neo4j graph database, genes, variants, proteins, pathways, diseases, and phenotypic traits are represented as nodes, with relationships capturing genetic associations, regulatory interactions, and functional dependencies between them. This enables researchers to explore the interconnectedness of genetic factors underlying diseases and drug responses. Neo4j's graph algorithms enable the identification of genetic networks, key driver genes, and disease modules associated with specific phenotypes or clinical outcomes. By integrating genomic data with clinical and phenotypic information, researchers can uncover novel biomarkers, stratify patient populations, and develop targeted therapies tailored to individual genetic profiles. Additionally, Neo4j facilitates data integration and knowledge discovery by connecting genomic databases, electronic health records, and biomedical literature repositories, enabling researchers to leverage diverse sources of information for hypothesis generation and validation.
Identify Components two of the applications:
Patient Journey Analysis:
Nodes:
Patients: Nodes representing patients in a patient journey analysis application contain detailed demographic information such as age, gender, ethnicity, and socioeconomic status. Additionally, they may include medical history data like previous diagnoses, treatments, surgeries, allergies, and family medical history. Each patient node is typically associated with a unique identifier to differentiate between individuals.Healthcare Providers: Nodes representing healthcare providers include attributes such as their specialty, qualifications, experience, and affiliations with healthcare facilities. These nodes also contain information about the healthcare provider's contact details, office locations, and working hours. Healthcare provider nodes help track which professionals are involved in each step of the patient's journey.
Facilities: Nodes representing healthcare facilities encompass hospitals, clinics, diagnostic centres, and other healthcare institutions. Attributes associated with facility nodes include location details, available services, bed capacities, accreditation status, and equipment availability. These nodes help contextualize where patients receive care and facilitate analysis of care delivery patterns across different facilities.
Medical Events: Nodes representing medical events such as appointments, diagnoses, treatments, and follow-up visits contain attributes such as timestamps, procedural codes, clinical notes, and outcomes. These nodes capture crucial milestones and interventions in the patient's journey and form the basis for analysing care pathways and treatment trajectories.
Relationships:Visited: Relationships between patients and healthcare facilities indicate when and where patients sought care. These relationships capture the sequence of visits and the types of services received at each facility, providing insights into healthcare utilization patterns and referral networks.
Diagnosed: Relationships between patients and diagnoses represent the moment when patients are diagnosed with specific conditions or diseases. These relationships link medical events such as consultations, tests, and imaging studies to the corresponding diagnoses, enabling the analysis of disease prevalence, incidence, and distribution across patient populations.
Treated: Relationships between patients and treatments capture the administration of therapeutic interventions such as medications, surgeries, and therapies. These relationships link diagnoses to specific treatment modalities, enabling the evaluation of treatment effectiveness, adherence, and outcomes over time.
Followed-Up: Relationships between patients and follow-up appointments indicate the continuity of care and ongoing monitoring of patients' health status. These relationships track the scheduling and completion of post-treatment visits, enabling healthcare providers to assess patient recovery, detect complications, and ensure care coordination.
Attributes:Timestamps: Timestamp attributes associated with nodes and relationships indicate the timing of events within the patient journey. These timestamps enable the analysis of care trajectories over time, including the duration between diagnosis and treatment initiation, the frequency of follow-up visits, and the recurrence of medical events.
Clinical Data: Clinical data attributes associated with medical events provide additional context about the patient's condition, treatment plan, and outcomes. These data may include laboratory results, imaging findings, vital signs, symptom assessments, and patient-reported outcomes, facilitating comprehensive analysis of patient health status and treatment response.
Referral Sources: Attributes associated with relationships between patients and healthcare providers may include referral sources or reasons for consultation. These attributes help trace the flow of patients through the healthcare system and identify sources of care coordination or communication gaps between providers.
Drug Discovery and Development:
Nodes:
Molecules: Nodes representing molecules include attributes such as chemical structures, molecular weights, functional groups, and physicochemical properties. These nodes may represent drug candidates, small molecules, peptides, or biologics, and serve as the building blocks for drug discovery and development efforts.
Targets: Nodes representing biological targets such as proteins, receptors, enzymes, and nucleic acids contain attributes such as sequences, structures, binding sites, and functional domains. These nodes represent the molecular entities that drugs interact with to exert their therapeutic effects and serve as the focal points for target identification and validation.
Diseases: Nodes representing diseases or indications contain attributes such as ethology, pathology, symptoms, and prevalence. These nodes capture information about the conditions that drugs are intended to treat or prevent and provide context for target selection, mechanism of action elucidation, and clinical trial design.
Clinical Trials: Nodes representing clinical trials contain attributes such as study identifiers, trial phases, inclusion criteria, exclusion criteria, and primary endpoints. These nodes represent the experimental studies conducted to evaluate the safety, efficacy, and pharmacokinetics of drug candidates in human subjects and serve as the basis for regulatory submissions and marketing approvals.
Relationships:
Targeted: Relationships between molecules and biological targets indicate the specific interactions between drugs and their intended molecular targets. These relationships capture binding affinities, kinetic parameters, and structural features of drug-target complexes, facilitating the rational design and optimization of therapeutic agents.
Pathway Association: Relationships between molecules, targets, and diseases capture the involvement of specific biological pathways or signalling cascades in disease pathogenesis or drug action. These relationships provide insights into the mechanisms of drug action, identify potential off-target effects, and guide the selection of combination therapies or biomarkers for patient stratification.
Clinical Trial Participation: Relationships between molecules and clinical trials represent the inclusion of drug candidates in experimental studies to evaluate their safety and efficacy in human subjects. These relationships track the progression of drug candidates through the drug development pipeline, from preclinical testing to regulatory approval, and inform decisions about portfolio prioritization and resource allocation.
Attributes:Biological Activity: Attributes associated with molecules and targets include biological activity data such as potency, selectivity, efficacy, and mechanism of action. These attributes provide insights into the pharmacological properties of drug candidates, their therapeutic potential, and their suitability for specific indications or patient populations.
Clinical Outcomes: Attributes associated with clinical trials include efficacy endpoints, safety profiles, adverse event reports, and patient-reported outcomes. These attributes capture the results of experimental studies and inform decisions about drug development strategies, regulatory submissions, and market access.
Chemical Properties: Attributes associated with molecules include physicochemical properties such as solubility, lipophilicity, molecular weight, and bioavailability. These attributes influence the drug's pharmacokinetic and pharmacodynamic properties, formulation requirements, and route of administration, and guide decisions during the drug optimization and formulation development process.
Challenges and considerations in building Patient Journey Analysis Application:
Data Integration and Quality:Heterogeneous Data Sources: Healthcare data often resides in disparate systems such as electronic health records (EHRs), lab systems, and billing systems. Integrating data from these sources while ensuring data quality and consistency can be complex.Data Standardization: Healthcare data may use different coding systems, terminology, and formats, making it challenging to harmonize and integrate data across different sources. Standardizing data elements and terminologies is crucial for accurate patient journey analysis.Data Quality Assurance: Healthcare data may contain errors, inconsistencies, and missing values, which can impact the accuracy and reliability of patient journey analysis. Implementing data quality assurance processes, including data validation and cleansing, is essential.
Privacy and Security:HIPAA Compliance: Healthcare data is subject to regulations such as the Health Insurance Portability and Accountability Act (HIPAA), which mandate the protection of patients' privacy and confidentiality. Ensuring compliance with HIPAA regulations, including data encryption and access controls, is essential.Data Anonymization: Protecting patients' privacy while conducting patient journey analysis is paramount. Anonymizing patient data to remove personally identifiable information (PII) while preserving its utility for analysis presents a challenge.Interoperability and Integration:Health Information Exchange (HIE): Patient data may need to be exchanged across different healthcare organizations and systems. Establishing interoperability standards and data exchange protocols is essential for seamless data sharing and integration.EHR Integration: Integrating patient journey analysis tools with existing electronic health record (EHR) systems is crucial for accessing real-time clinical data. Ensuring compatibility and interoperability with different EHR systems is necessary for effective integration.
Ethical and Legal Considerations:Informed Consent: Analysing patient journey data raises ethical concerns regarding patient consent and data usage. Ensuring patients' informed consent and transparency about data usage are essential ethical considerations.Data Governance: Establishing robust data governance frameworks and ethics review processes is crucial for responsible data usage, compliance with regulatory requirements, and protection of patient rights.
Scalability and Performance:Scalable Infrastructure: As healthcare data continues to grow in volume, ensuring scalability of the underlying infrastructure is essential. Implementing scalable storage and processing solutions that can handle large volumes of data is necessary for patient journey analysis.Real-Time Analytics: Healthcare providers may require real-time insights for decision-making. Implementing real-time analytics capabilities to analyse patient journey data in near real-time can enhance the utility of the application.
User Adoption and Stakeholder Engagement:User Training: Healthcare providers and administrators may require training on how to use the patient journey analysis application effectively. Providing comprehensive training programs and support resources can facilitate user adoption.Stakeholder Collaboration: Patient journey analysis often involves collaboration between multiple stakeholders, including healthcare providers, administrators, researchers, and policymakers. Engaging stakeholders throughout the development process and addressing their needs and requirements is essential for the success of the application.
Sample Data Model for Patient Journey Analysis Application:
Table SEQ Table * ARABIC 1: Data Model for Patient Journey Analysis
Entity Attributes
Patient id, name, date_of_birth, gender
Healthcare Provider id, name, specialty
Facility id, name, type, location
Appointment Id, date, reason
Diagnosis id, date, code, description
Treatment id, date, type, medication, dosage, duration
Follow-up Appointment id, date, reason
Table SEQ Table * ARABIC 2: Relationships
Relationship Relationship Type Related Entities
Patient - Appointment HAS_APPOINTMENT Patient, Appointment
Patient - Diagnosis HAS_DIAGNOSIS Patient, Diagnosis
Patient - Treatment HAS_TREATMENT Patient, Treatment
Patient - Follow-up Appointment HAS_FOLLOWUP_APPOINTMENT Patient, Follow-up Appointment
Provider - Appointment CONDUCTED_APPOINTMENT Healthcare Provider, Appointment
Facility - Appointment HOSTED_APPOINTMENT Facility, Appointment
Neo4j technologies to implement the data model.
Neo4j offers a variety of built-in functions and algorithms that can be used to implement and analyse data models, such as the one we've discussed for the Patient Journey Analysis application. Let's explore some of these Neo4j technologies and how they can be applied:
Cypher Query Language: Cypher is Neo4j's query language used to interact with the graph database. It allows for querying and updating graph data using a pattern-matching syntax. Cypher queries can be used to create, read, update, and delete nodes and relationships in the graph, as well as perform complex graph traversals and aggregations.
Graph Algorithms: Neo4j provides a library of graph algorithms that can be applied to analyse and extract insights from graph data. These algorithms include centrality algorithms (e.g., PageRank, Betweenness Centrality), community detection algorithms (e.g., Louvain Modularity), pathfinding algorithms (e.g., Shortest Path, All Shortest Paths), and similarity algorithms (e.g., Jaccard Similarity). These algorithms can help identify important nodes, clusters, and patterns within the patient journey graph.
Built-in Functions: Neo4j offers a wide range of built-in functions that can be used to manipulate and transform graph data. These functions include string manipulation functions, mathematical functions, aggregation functions, date and time functions, and more. Built-in functions can be used within Cypher queries to perform data transformations, calculations, and filtering operations.
Graph Data Types: Neo4j supports graph data types such as nodes, relationships, properties, and labels. Nodes represent entities in the graph, relationships represent connections between entities, properties represent attributes of entities and relationships, and labels are used to categorize nodes and relationships. Leveraging graph data types allows for modelling complex relationships and attributes within the patient journey graph.
Indexes and Constraints: Neo4j supports indexes and constraints to optimize query performance and ensure data integrity. Indexes can be created on properties to improve query lookup times, while constraints can be defined to enforce data uniqueness and validity. Utilizing indexes and constraints can enhance the efficiency and reliability of queries executed against the patient journey graph.
Graph Data Visualization: Neo4j provides tools for graph data visualization, allowing users to visualize and explore the patient journey graph in an intuitive manner. Visualization tools such as Neo4j Browser and Neo4j Bloom enable users to interactively explore the graph, visualize graph patterns, and gain insights from the data through interactive visualizations.
By leveraging these Neo4j technologies, developers can effectively implement and analyse the data model for the Patient Journey Analysis application. Whether it's querying graph data using Cypher, applying graph algorithms to extract insights, using built-in functions to manipulate data, or visualizing the graph for exploration, Neo4j provides a comprehensive set of tools for working with graph data.
Code to Represent Data Model:// Create nodes with labels and attributes
CREATE
// Patients
(:Patient {id: 'P1', name: 'John Doe', date_of_birth: '1990-05-15', gender: 'Male'}),
(:Patient {id: 'P2', name: 'Jane Smith', date_of_birth: '1985-09-20', gender: 'Female'}),
(:Patient {id: 'P3', name: 'Michael Johnson', date_of_birth: '1978-03-10', gender: 'Male'}),
// Healthcare Providers
(:Provider {id: 'HCP1', name: 'Dr. Smith', specialty: 'Cardiology'}),
(:Provider {id: 'HCP2', name: 'Dr. Johnson', specialty: 'Oncology'}),
(:Provider {id: 'HCP3', name: 'Dr. Lee', specialty: 'Pediatrics'}),
// Facilities
(:Facility {id: 'F1', name: 'City Hospital', type: 'Hospital', location: 'New York'}),
(:Facility {id: 'F2', name: 'Downtown Clinic', type: 'Clinic', location: 'New York'}),
(:Facility {id: 'F3', name: 'Community Health Center', type: 'Clinic', location: 'Los Angeles'}),
// Appointments
(:Appointment {id: 'A1', date: '2023-01-10', reason: 'Check-up'}),
(:Appointment {id: 'A2', date: '2023-02-15', reason: 'Follow-up'}),
(:Appointment {id: 'A3', date: '2023-03-20', reason: 'Consultation'}),
// Diagnoses
(:Diagnosis {id: 'D1', date: '2023-01-20', code: 'I10', description: 'Hypertension'}),
(:Diagnosis {id: 'D2', date: '2023-03-05', code: 'C50', description: 'Breast cancer'}),
(:Diagnosis {id: 'D3', date: '2023-04-10', code: 'J45', description: 'Asthma'}),
// Treatments
(:Treatment {id: 'T1', date: '2023-02-01', type: 'Medication', medication: 'Lisinopril', dosage: '10mg', duration: '30 days'}),
(:Treatment {id: 'T2', date: '2023-03-20', type: 'Chemotherapy', medication: 'Paclitaxel', dosage: '100mg/m2', duration: '6 cycles'}),
(:Treatment {id: 'T3', date: '2023-04-15', type: 'Physical Therapy', medication: 'Exercise', dosage: 'Varies', duration: '6 weeks'}),
// Follow-up Appointments
(:FollowUpAppointment {id: 'FU1', date: '2023-02-28', reason: 'Medication review'}),
(:FollowUpAppointment {id: 'FU2', date: '2023-04-10', reason: 'Treatment response assessment'}),
(:FollowUpAppointment {id: 'FU3', date: '2023-05-05', reason: 'Asthma follow-up'})
// Create relationships with attributes
MATCH (p:Patient {id: 'P1'}), (a:Appointment {id: 'A1'})
CREATE (p)-[:HAS_APPOINTMENT {status: 'Confirmed'}]->(a);
MATCH (p:Patient {id: 'P2'}), (a:Appointment {id: 'A2'})
CREATE (p)-[:HAS_APPOINTMENT {status: 'Confirmed'}]->(a);
MATCH (p:Patient {id: 'P3'}), (a:Appointment {id: 'A3'})
CREATE (p)-[:HAS_APPOINTMENT {status: 'Scheduled'}]->(a);
MATCH (p:Patient {id: 'P1'}), (d:Diagnosis {id: 'D1'})
CREATE (p)-[:HAS_DIAGNOSIS {severity: 'Moderate'}]->(d);
MATCH (p:Patient {id: 'P2'}), (d:Diagnosis {id: 'D2'})
CREATE (p)-[:HAS_DIAGNOSIS {severity: 'High'}]->(d);
MATCH (p:Patient {id: 'P3'}), (d:Diagnosis {id: 'D3'})
CREATE (p)-[:HAS_DIAGNOSIS {severity: 'Mild'}]->(d);
MATCH (p:Patient {id: 'P1'}), (t:Treatment {id: 'T1'})
CREATE (p)-[:HAS_TREATMENT {outcome: 'Stable'}]->(t);
MATCH (p:Patient {id: 'P2'}), (t:Treatment {id: 'T2'})
CREATE (p)-[:HAS_TREATMENT {outcome: 'Ongoing'}]->(t);
MATCH (p:Patient {id: 'P3'}), (t:Treatment {id: 'T3'})
CREATE (p)-[:HAS_TREATMENT {outcome: 'Completed'}]->(t);
MATCH (p:Patient {id: 'P1'}), (f:FollowUpAppointment {id: 'FU1'})
CREATE (p)-[:HAS_FOLLOWUP_APPOINTMENT {status: 'Scheduled'}]->(f);
MATCH (p:Patient {id: 'P2'}), (f:FollowUpAppointment {id: 'FU2'})
CREATE (p)-[:HAS_FOLLOWUP_APPOINTMENT {status: 'Confirmed'}]->(f);
MATCH (p:Patient {id: 'P3'}), (f:FollowUpAppointment {id: 'FU3'})
CREATE (p)-[:HAS_FOLLOWUP_APPOINTMENT {status: 'Scheduled'}]->(f);
This code should now correctly represent the data model with nodes and relationships according to the specified requirements. Each label has at least three nodes, and each relationship has attributes associated with it. Let me know if you need further assistance!
8 queries that a user will ask in your database:
Find all appointments for a specific patient:MATCH (p:Patient {name: 'John Doe'})-[:HAS_APPOINTMENT]->(a:Appointment)RETURN p, a;
Find all treatments administered to a patient with a specific diagnosis:MATCH (p:Patient {name: 'Jane Smith'})-[:HAS_DIAGNOSIS]->(:Diagnosis {description: 'Breast cancer'})<-[:HAS_DIAGNOSIS]-(p)-[:HAS_TREATMENT]->(t:Treatment)
RETURN p, t;
Find all patients who have follow-up appointments scheduled:
MATCH (p:Patient)-[:HAS_FOLLOWUP_APPOINTMENT]->(f:FollowUpAppointment)
RETURN p, f;
Find all healthcare providers and their specialties:
MATCH (h:Provider)RETURN h.name, h.specialty;
Find all healthcare providers and their specialties:
MATCH (h:Provider)-[:CONDUCTED_APPOINTMENT]->(a:Appointment)
RETURN h.name, count(a) AS num_appointments;
Find all patients with a diagnosis of a specific severity:MATCH (p:Patient)-[r:HAS_DIAGNOSIS]->(d:Diagnosis)
WHERE r.severity = 'High'
RETURN p, d;
Find all follow-up appointments for patients with a specific treatment outcome:
MATCH (p:Patient)-[:HAS_TREATMENT]->(t:Treatment)
WHERE t.outcome = 'Stable'
MATCH (p)-[:HAS_FOLLOWUP_APPOINTMENT]->(f:FollowUpAppointment)
RETURN p, t, f;
Find all appointments scheduled at a specific facility in a given date range:
MATCH (f:Facility {name: 'City Hospital'})<-[:HOSTED_APPOINTMENT]-(a:Appointment)
WHERE a.date >= '2023-01-01' AND a.date <= '2023-01-31'
RETURN f, a;
References:https://neo4j.comhttps://rubygarage.org/blog/neo4j-database-guide-with-use-caseshttps://www.graphable.ai/blog/patient-journey-mapping/https://www.geeksforgeeks.org/data-model-in-neo4j/https://neo4j.com/news/transforming-life-sciences-and-health-care-with-graph-technology/1168400455295https://neo4j.com/press-releases/neo4j-graph-database-powers-healthcare-sector/
