Transforming healthcare through cloud-native machine learning architecture: A case study in AWS, Spark, and Kubernetes Implementation

 Corresponding author: Naveen Srikanth Pasupuleti. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Transforming healthcare through cloud-native machine learning architecture: A case study in AWS, Spark, and Kubernetes Implementation Naveen Srikanth Pasupuleti * Komodo Health, USA. World Journal of Advanced Research and Reviews, 2025, 26(02), 1622-1631 Publication history: Received on 30 March 2025; revised on 09 May 2025; accepted on 11 May 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.26.2.1649 Abstract This article examines a transformative case study in healthcare data infrastructure, where a skilled data engineer revolutionized operations by implementing an integrated technology stack with advanced machine learning capabilities. Facing challenges of processing diverse and voluminous patient data, the engineer architected a comprehensive solution leveraging AWS services, including S3, Redshift, and Lambda to create a cloud-based data lake optimized for AI workloads. This foundation was augmented with Apache Spark for distributed processing and MLlib for scalable machine learning, Hadoop clusters for specialized workloads, and Kubernetes for container orchestration— creating a flexible, resilient system capable of supporting sophisticated predictive models. The implementation featured automated ETL processes within a robust data pipeline alongside purpose-built feature stores and model serving infrastructure. A strategic combination of SQL and NoSQL databases provided flexible storage solutions optimized for various machine learning algorithms, from natural language processing for clinical notes to computer vision for medical imaging. Despite obstacles including data inconsistency and latency issues, the solution delivered substantial improvements in operational efficiency and clinical outcomes through AI-powered predictive capabilities, demonstrating the transformative potential of modern data engineering and machine learning approaches in healthcare settings. Keywords: Data Lake Architecture; Distributed Computing; Container Orchestration; ETL Automation; Healthcare Analytics 1. Introduction the healthcare data challenge 1.1. The Data Explosion in Healthcare The healthcare industry is experiencing an unprecedented data revolution, with providers now managing exponentially growing volumes of patient information. According to Stanford Medicine's 2018 Health Trends Report, the digitization of healthcare has created an environment where the health sector generates approximately 30% of the world's data volume [1]. This dramatic increase stems from the widespread adoption of electronic health records (EHRs), with adoption rates rising from 9.4% to 83.8% in hospitals over a recent seven-year period. The challenge extends beyond volume alone, as healthcare organizations must integrate data from clinical notes, medical imaging, genomic sequencing, and connected medical devices—each generating information in different formats, requiring distinct processing approaches. This data complexity creates both a challenge and an opportunity for machine learning applications, which can extract meaningful patterns from diverse healthcare datasets but require sophisticated infrastructure to operate at scale. World Journal of Advanced Research and Reviews, 2025, 26(02), 1622-1631 1623 1.2. Case Study: Infrastructure Limitations Our case study examines a multi-facility healthcare provider struggling with dated infrastructure that had become increasingly inadequate for modern analytical needs. The organization's primary data processing framework originated from a traditional data warehouse design predating the emergence of complex feature engineering requirements. Similar to the evolution described in modern ML platforms research, the organization's data architecture needed to progress beyond simple extract-transform-load (ETL) workflows to accommodate more sophisticated data transformations and real-time feature extraction [2]. The existing system required over 30 hours to process comprehensive analytics reports, creating critical delays in decision-making. The limitations were even more pronounced for machine learning workloads, with data scientists waiting up to 72 hours for model training cycles to complete on large patient cohorts. With their patient database growing at 27% annually, leadership recognized that their infrastructure scalability—limited to 8-10% annual capacity increases—represented an unsustainable trajectory that would further constrain their ability to implement advanced predictive analytics. 1.3. Vision for Transformation The healthcare provider's leadership commissioned a complete infrastructure overhaul, guided by the democratization principles outlined in Stanford Medicine's report. This vision aligned with the trend that 79% of healthcare professionals anticipate more open data sharing environments in the coming years [1]. The proposed transformation centered on building a comprehensive data platform incorporating cloud services, distributed processing frameworks, and containerization technologies—all designed to support advanced machine learning capabilities. The senior data engineer leading this initiative designed an architecture capable of supporting the full spectrum of healthcare analytics—from traditional business intelligence to sophisticated predictive modeling applications. This approach embraced the recent architectural evolution of feature stores in machine learning platforms, enabling both batch processing of historical data and real-time streaming capabilities to support point-of-care predictive decision-making [2]. The infrastructure was specifically designed to accommodate diverse machine learning workloads, including computer vision models for radiology image analysis, natural language processing for clinical documentation, and timeseries models for patient monitoring data. 2. Cloud foundation: building the AWS data lake architecture 2.1. Assessment and Planning The healthcare organization's migration to an AWS-based data lake architecture began with a comprehensive data infrastructure assessment. Similar to findings in recent industry research, the organization discovered their data engineers were spending approximately 71% of their time on data preparation and infrastructure maintenance rather than value-generating activities [3]. This inefficiency stemmed from their fragmented legacy architecture consisting of 17 distinct storage systems with varying access protocols and inconsistent metadata management. The assessment team identified several critical technical requirements, including HIPAA-compliant security controls, standardized data governance, and the ability to process both structured clinical data and unstructured imaging files exceeding 500MB per study. Through detailed infrastructure mapping and workload analysis, the team established baseline performance metrics to guide architectural decisions and measure future improvements. 2.2. Implementing S3-Based Storage Hierarchy Amazon S3 served as the foundation for the new data lake architecture, providing the organization with virtually unlimited scalability. The implementation utilized S3's tiered storage classes to optimize costs across the data lifecycle. For frequently accessed patient records, S3 Standard storage provided immediate retrieval capabilities with 99.999999999% durability. For archival data—such as medical imaging studies older than one year—the organization implemented S3 Glacier Deep Archive, achieving storage costs as low as $0.00099 per GB per month [4]. This represented a significant operational expenditure reduction compared to their previous on-premises storage infrastructure. The architecture incorporated strict data partitioning strategies based on data domain, source system, and time periods, facilitating efficient data retrieval without full-dataset scans. S3 object tagging and metadata catalogs provided comprehensive data lineage tracking, essential for regulatory compliance and audit purposes. 2.3. Data Processing and Analytics Infrastructure To transform the raw data lake into an actionable analytics platform, the organization implemented a multi-layered processing architecture. Amazon Redshift formed the core analytics engine, with an initial deployment of 8 ra3.4xlarge nodes providing sufficient computational capacity for complex analytical workloads. The data engineering team implemented Redshift Spectrum to create a unified query layer across both hot and cold data stores. This architecture World Journal of Advanced Research and Reviews, 2025, 26(02), 1622-1631 1624 aligned with industry best practices identified in research demonstrating that 65% of organizations with advanced data engineering maturity utilize separation between storage and compute resources [3]. Complementing the data warehouse, 47 Lambda functions handled specialized ETL processes, metadata synchronization, and data validation. These serverless components provided automatic scaling during peak processing periods, such as month-end reporting cycles when query volumes increased fivefold. The Lambda functions integrated with AWS Step Functions to orchestrate complex workflow sequences, providing transaction-like semantics for multi-step data transformations that previously required custom application code. Figure 1 AWS Cloud Foundation for the Healthcare Data Lake Architecture [3, 4] 3. Distributed Processing: Harnessing Spark and Hadoop for Machine Learning 3.1. Performance Analysis and Framework Selection The healthcare organization's distributed processing infrastructure was designed based on comprehensive benchmarking of available technologies against their specific workload characteristics, including machine learning requirements. Initial analysis revealed that their legacy system fell significantly short of performance targets, with dataintensive clinical analytics queries experiencing latency up to 26 times greater than acceptable thresholds. Machine learning workloads were particularly impacted, with model training pipelines frequently failing due to memory constraints and inefficient resource allocation. This performance gap echoed findings from benchmark studies showing that traditional data processing approaches struggle with healthcare analytics workloads where data locality becomes critical for performance optimization [5]. The evaluation team conducted extensive comparative analysis across multiple distributed processing frameworks, focusing on memory utilization efficiency, latency characteristics, and throughput capacity under various data distribution patterns. Apache Spark emerged as the optimal solution due to its unified processing model, in-memory computation capabilities, and robust machine learning libraries (MLlib), which benchmark studies have shown can deliver performance improvements of up to 100x compared to disk-based processing for iterative algorithms common in healthcare analytics and machine learning [5]. The implementation architecture was designed around a primary EMR cluster with memory-optimized nodes to accommodate the complex data transformations required for patient cohort analyses and large-scale feature engineering for predictive models. World Journal of Advanced Research and Reviews, 2025, 26(02), 1622-1631 1625 3.2. Optimizing Spark for Healthcare Machine Learning Workloads The Spark implementation required significant customization to address the unique characteristics of healthcare data processing and machine learning workflows. The team implemented a multi-tenant architecture with dynamic resource allocation, allowing the system to efficiently serve both scheduled batch processes and computationally intensive machine learning training jobs. Performance was optimized through careful tuning of executor conFigureurations, with memory allocation set to 85% of available RAM on worker nodes based on detailed profiling of garbage collection patterns during model training operations. The organization implemented specialized serialization frameworks to handle the complex nested data structures common in FHIR-based clinical records, achieving a 37% reduction in serialization overhead compared to default implementations. For machine learning pipelines, the team developed custom Spark transformers and estimators to handle healthcare-specific feature engineering tasks, such as medical terminology normalization and temporal feature extraction from longitudinal patient records. Spark SQL served as the primary interface for structured data analytics, with a predefined library of over 200 parameterized queries optimized through logical plan analysis. This approach aligns with research findings that emphasize the importance of query optimization in analytic benchmark performance, where even a 20% improvement in query efficiency can translate to substantial operational benefits in healthcare settings and accelerate machine learning development cycles [5]. The Spark Streaming implementation utilized time windowing techniques with a 15-second processing interval to balance latency requirements against processing efficiency, enabling near-real-time feature calculation for predictive models operating on streaming healthcare data. 3.3. Integration of Hadoop Ecosystem Components for End-to-End ML Pipelines Figure 2 Distributed Processing Spark and Hadoop Architecture [5, 6] While Spark formed the processing core, the architecture incorporated several Hadoop ecosystem components to create a comprehensive data platform with robust machine learning capabilities. The integration approach followed a systematic methodology similar to that outlined in enterprise data platform research, with interfaces between components designed around well-defined contracts and standardized data formats [6]. Apache Hive served as a metastore with a unified catalog of data assets across the organization, implementing a governance model with clearly World Journal of Advanced Research and Reviews, 2025, 26(02), 1622-1631 1626 defined ownership and quality metrics for each data domain. This governance framework was extended to machine learning assets, with model metadata, training datasets, and evaluation metrics tracked in a central registry. Data lineage was tracked through specialized metadata tags propagated throughout the processing pipeline, enabling comprehensive audit trails for regulatory compliance and machine learning model explainability. The resource management layer utilized YARN with hierarchical scheduling queues established based on service level requirements, with critical clinical prediction systems assigned guaranteed minimum resource allocations of 40% of cluster capacity. This approach to resource governance aligns with research showing that effective multi-tenant resource management is crucial for large-scale data platforms, where resource sharing must be balanced against predictable performance for mission-critical machine learning models [6]. The complete infrastructure incorporated failover mechanisms with a recovery time objective of 5 minutes, achieved through checkpoint-based state management and stateless processing design, ensuring continuous availability of predictive services that had become integral to clinical workflows. 4. Orchestration and Scaling: Kubernetes Implementation 4.1. Container Adoption and Orchestration Strategy The healthcare organization's container orchestration journey aligned with broader industry trends, where Kubernetes has emerged as the de facto standard for managing containerized applications at scale. According to the Cloud Native Computing Foundation's 2021 survey, 96% of organizations are either using or evaluating Kubernetes, reflecting its dominance in the container orchestration landscape [7]. The healthcare provider's initial assessment identified significant operational inefficiencies in their traditional infrastructure, with deployments requiring an average of 7.4 hours to complete and environment inconsistencies causing nearly two dozen production incidents quarterly. Their implementation strategy focused on containerizing the entire data processing pipeline, beginning with stateless components that presented the lowest migration complexity. The organization's approach mirrored industry patterns identified in the CNCF survey, where 79% of respondents reported running Kubernetes in production environments, demonstrating the maturity of the technology for mission-critical workloads [7]. The container implementation standardized all images on Alpine Linux with comprehensive security scanning integrated into the CI/CD pipeline, which automatically rejected builds containing vulnerabilities with CVSS scores above 7.0. This security-first approach proved critical for maintaining HIPAA compliance while modernizing the infrastructure. 4.2. Resource Management Framework The Kubernetes deployment incorporated sophisticated resource management principles to ensure optimal performance across diverse workload profiles. The implementation leveraged Kubernetes' native resource specification capabilities, defining precise CPU and memory requirements for each component in the data processing pipeline. The organization implemented a standardized approach to resource requests and limits as documented in Kubernetes' resource management best practices, with requests defining the minimum guaranteed resources and limits establishing usage boundaries [8]. This granular approach to resource definition enabled the platform to make intelligent scheduling decisions, particularly during high-demand periods when resource contention could impact critical services. The team established a three-tier Quality of Service (QoS) classification aligned with clinical priorities: Guaranteed class for patient-facing analytics, Burstable for internal operational workflows, and BestEffort for non-time-sensitive batch processing. The resource governance framework incorporated LimitRanges to enforce minimum and maximum resource allocations within namespaces, preventing resource monopolization while ensuring efficient infrastructure utilization. This approach-maintained resource utilization above 78% while preserving headroom for demand spikes, significantly improving the economics of the platform compared to the previous static allocation model. 4.3. Scaling and High Availability Architecture The organization implemented a comprehensive scaling architecture designed to maintain performance under variable workloads while ensuring high availability for critical healthcare analytics. The production environment utilized Amazon EKS with worker nodes distributed across three availability zones, creating infrastructure redundancy that maintained service availability even during zone failures. The implementation incorporated both Horizontal Pod Autoscaling (HPA) and Cluster Autoscaler, creating a two-dimensional scaling capability that adjusted both application instances and underlying infrastructure based on demand patterns [8]. Custom scaling metrics derived from application telemetry enabled intelligent scaling decisions, with response time percentiles and queue depths serving as primary scaling triggers rather than raw CPU utilization. The organization implemented Pod Disruption Budgets (PDBs) to ensure minimum service availability during infrastructure maintenance, preventing degradation of critical analytics capabilities during upgrades. This approach maintained 99.97% availability for clinical decision support systems throughout the transition period, exceeding the organization's service level objectives. The multi-cluster architecture incorporated sophisticated traffic management with weighted routing capabilities, enabling gradual workload World Journal of Advanced Research and Reviews, 2025, 26(02), 1622-1631 1627 transitions during deployments and creating a resilient foundation capable of supporting the healthcare provider's expanding analytical requirements. Figure 3 Orchestration and Scaling: Kubernetes Implementation [7, 8] 5. Database Strategy: SQL and NoSQL Integration for Machine Learning 5.1. Strategic Data Architecture for Healthcare Machine Learning The healthcare organization's database modernization initiative addressed fundamental challenges within their fragmented data ecosystem while establishing a robust foundation for machine learning applications. According to the analysis of healthcare data management, organizations typically struggle with data siloed across multiple systems, with many healthcare providers maintaining between 15 and 20 distinct data repositories for clinical information alone [9]. The organization's landscape mirrored this industry pattern, operating 27 separate database instances spanning various technologies and vendors, creating significant barriers to implementing cohesive machine learning models that required cross-domain data access. Their modernization approach incorporated Gartner's recommended data fabric architecture, implementing a unified semantic layer that harmonized terminology and relationships across domains while preserving source-specific implementation details. This architectural pattern proved essential for maintaining semantic cohesion across structured clinical data, unstructured documentation, and specialized healthcare datasets— a critical requirement for developing accurate machine learning models. The data modeling methodology incorporated healthcare-specific reference models aligned with industry standards, creating logical constructs for patient, provider, encounter, and clinical observation entities. This domain-driven approach enabled the organization to maintain traceability between business concepts and technical implementations, facilitating data governance and quality management that directly supported machine learning model explainability, a critical requirement for AI applications in healthcare settings. 5.2. Relational Database Implementation with Feature Store Capabilities The organization's relational database strategy emphasized high availability and performance for mission-critical clinical data while incorporating specialized feature store capabilities to support machine learning workflows. The implementation selected Amazon Aurora PostgreSQL as the primary platform based on intensive performance benchmarking that demonstrated throughput improvements of 73% compared to their legacy systems [10]. The World Journal of Advanced Research and Reviews, 2025, 26(02), 1622-1631 1628 database architecture incorporated a multi-tier design with separate clusters optimized for transactional and analytical workloads, with an additional feature store layer designed specifically for machine learning use cases. This feature store implementation maintained pre-computed features for common predictive modeling scenarios, significantly reducing feature engineering overhead and ensuring consistency between model training and inference stages. The analytical tier leveraged Aurora's parallel query capabilities, enabling complex population health queries and feature extraction operations to execute across distributed processing nodes with near-linear scaling characteristics. Schema design incorporated healthcare-specific patterns, including entity-attribute-value structures for flowsheet data, temporal tables for longitudinal patient history, and specialized indexing strategies for clinical terminology hierarchies. The implementation included sophisticated query optimization techniques, utilizing execution plan management to ensure consistent performance for both clinical workflows and machine learning inference services that required real-time feature calculation. 5.3. Specialized Database Technologies for Machine Learning Diversity Figure 4 Integration of SQL and NoSQL Technologies [9, 10] The healthcare organization implemented a polyglot persistence strategy to address the diverse characteristics of healthcare data assets and machine learning workloads. According to AWS technical documentation, healthcare workloads benefit significantly from purpose-built database engines aligned with specific data access patterns and structure [10]. For clinical documentation and unstructured content, the organization deployed Amazon DocumentDB with a sharded architecture spanning multiple instances to distribute workload across computing resources. This implementation supported natural language processing models that extracted structured insights from over 12 million clinical documents with an average retrieval time of 83 milliseconds, essential for clinical documentation workflows in high-volume care settings. For high-velocity telemetry data from patient monitoring systems, the organization implemented a specialized time-series database architecture using Amazon Timestream, which reduced storage requirements by 95% through automatic data compression and tiering policies while supporting real-time anomaly detection models with sub-second response times. The time-series implementations incorporated advanced machine learning models for detecting subtle clinical deterioration patterns, with trained models deployed directly within the database environment to minimize prediction latency. For medical imaging studies, the organization developed a hybrid architecture combining Amazon S3 for raw DICOM storage with DynamoDB for metadata indexing and access pattern World Journal of Advanced Research and Reviews, 2025, 26(02), 1622-1631 1629 optimization. This approach enabled sub-second retrieval of study metadata while maintaining cost-effective storage for multi-terabyte imaging archives, creating an optimal foundation for computer vision models that analyzed radiological images for automated disease detection and clinical decision support. 6. Results and Future Directions 6.1. Quantifiable Improvements Through Machine Learning Integration The healthcare organization's data infrastructure transformation yielded substantial operational benefits that directly impacted clinical and financial performance, with machine learning capabilities playing a central role in these improvements. According to McKinsey's analysis of healthcare data initiatives, organizations implementing comprehensive big data strategies with machine learning components have realized between $300 billion and $450 billion in reduced healthcare spending nationally through operational improvements and enhanced clinical outcomes [11]. In alignment with these industry findings, the healthcare provider documented significant efficiency gains across multiple domains powered by predictive modeling. Their clinical workflow optimization, enhanced by machine learning algorithms operating on the new data platform, reduced average patient admission processing time from 127 minutes to 38 minutes by predicting resource requirements and optimizing staff allocations. The organization's machine learning-powered capacity forecasting models improved resource allocation precision, reducing excess staffing costs by $4.2 million annually while simultaneously decreasing emergency department boarding hours by 34% through predictive admission models that anticipated bed availability with 92% accuracy. The claims processing capabilities demonstrated similar improvements, with first-pass claim accuracy increasing from 82% to 96% through AI-powered validation algorithms that identified potential denial issues before submission. This improvement directly contributed to accelerated reimbursement cycles and reduced administrative overhead, with the total financial impact estimated at approximately 11% of annual operating revenue—closely matching McKinsey's observation that data-driven healthcare organizations with mature AI capabilities typically realize an 8-15% improvement in profit margins through optimized operations and enhanced revenue cycle management [11]. 6.2. Clinical Outcomes Through Advanced Machine Learning Models The modernized data infrastructure fundamentally transformed the organization's ability to deliver evidence-based, personalized care at scale through sophisticated machine learning applications. The implementation of comprehensive clinical decision support systems operating on the unified data platform enabled advanced predictive modeling and intervention protocols that produced measurable improvements in patient outcomes. The early sepsis detection system leveraged an ensemble machine learning approach combining gradient-boosted trees and recurrent neural networks to identify subtle physiological changes preceding clinical deterioration. This model achieved 89% sensitivity and 92% specificity in identifying sepsis risk approximately 6 hours before clinical manifestation, aligning with published research demonstrating that machine learning models operating on integrated clinical data streams can potentially reduce mortality rates by 18-29% through earlier intervention [12]. Beyond acute care applications, the organization implemented population health management capabilities driven by machine learning risk stratification models. Their diabetic patient management program applied random forest algorithms to predict complication risks based on longitudinal clinical data, increasing compliance with evidence-based care recommendations from 62% to 89% and resulting in a 42% reduction in preventable hospital admissions for this population. This outcome mirrors clinical research findings that integrated data platforms supporting coordinated care delivery with AI-driven risk prediction can reduce hospitalizations for chronic conditions by 35-50% [12]. The enhanced analytics capabilities also accelerated the organization's clinical research initiatives, with their machine learning-based trial matching algorithm increasing clinical trial enrollment by 317% by automatically identifying eligible patients based on comprehensive electronic health record data and genomic profiles stored within the unified data architecture. 6.3. Future Machine Learning Roadmap and Innovation The healthcare organization's future technology roadmap builds upon their successful implementation while incorporating emerging machine learning capabilities that promise to further transform healthcare delivery. Their strategic planning incorporates precision medicine initiatives that integrate genomic, clinical, and social determinants data to create highly personalized care pathways through advanced multimodal learning approaches. This strategy aligns with research indicating that multimodal machine learning integration can improve treatment response rates by 30-40% for certain conditions by matching interventions to specific patient characteristics [12]. The organization is expanding their machine learning architecture to support federated learning frameworks that enable secure collaboration with academic medical centers without compromising patient privacy. These distributed machine learning approaches are expected to accelerate biomedical discovery by increasing available training data volumes for rare disease research by an estimated 850% compared to single-institution studies while maintaining regulatory World Journal of Advanced Research and Reviews, 2025, 26(02), 1622-1631 1630 compliance. The technology roadmap includes implementing advanced transformer-based natural language processing models to extract structured insights from unstructured clinical documentation, with pilot implementations demonstrating extraction accuracy exceeding 95% for key clinical concepts. The organization is also investing in reinforcement learning approaches for treatment optimization, developing models that can recommend personalized treatment pathways by learning from historical outcomes data across their patient population. These initiatives collectively represent the organization's commitment to continuous innovation in healthcare AI, establishing a foundation for increasingly sophisticated machine learning applications that directly impact patient outcomes while maintaining the highest standards of explainability and ethical deployment required in healthcare settings. Table 1 Performance Improvement Metrics After Data Infrastructure Transformation [11, 12] Performance Indicator Before Implementation After Implementation Improvement (%) Clinical Analytics Query Time 26.4 hours 37 minutes 97.7% System Availability 97.2% 99.98% 2.78% Data Integration Latency 4 hours 30 seconds 99.8% Server Utilization 24% 76% 216.7% 7. Conclusion The successful transformation of the healthcare company's data infrastructure demonstrates the profound impact that thoughtfully integrated cloud, distributed processing, and machine learning technologies can have on organizational effectiveness and patient outcomes. By implementing a comprehensive solution centered on AWS services, Spark, Hadoop, and Kubernetes, the data engineering team created a scalable architecture capable of supporting increasingly sophisticated AI models while maintaining performance and reliability. The implementation of specialized features, stores, and dedicated ML pipelines enabled rapid development and deployment of predictive models that directly improved clinical care, from early sepsis detection to optimized resource allocation. The dual database approach addressed the complex reality of healthcare data, accommodating both structured patient records and unstructured medical information while providing optimized access patterns for different machine learning algorithms. Beyond the technical achievements, this case study illustrates the strategic business value of AI-enhanced data pipelines, as evidenced by quantifiable improvements in patient outcomes and operational efficiency. As healthcare continues to generate increasingly complex datasets, this implementation provides a blueprint for organizations seeking to harness their data assets through machine learning while maintaining the flexibility to adopt emerging AI methodologies such as federated learning and reinforcement learning for treatment optimization in the future. 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