Device for machine learning-optimized prediction of drug response in oncology

Abstract

A device for predicting drug reactions in cancer patients through machine learning optimization, wherein the device comprises the following: a data acquisition unit that can capture patient-specific information from various sources, including genomic, proteomic, and clinical information; a feature extraction module that computationally processes the acquired data to determine relevant biomarkers for the drug response; an integrated machine learning processing unit that uses a range of algorithms optimized for pattern recognition in complex biological data based on historical treatment outcomes; an interface for prognostic output, intended to provide oncologists with clinically relevant interpretations and patient-specific treatment recommendations, including a feedback loop for continuous learning that contributes to refining prognostic models for new patients; and a communication subsystem configured for the secure transmission of generated patient data and treatment prognoses to medical professionals.

Description

Field of the invention

[0001] The present invention relates to a device for predicting drug response in cancer patients using machine learning optimization techniques. Background of the invention

[0002] More recently, advances in cancer biology and genomic research have enabled the development of targeted therapies that tailor treatment to each individual patient. However, available methods rely on generic treatment protocols that are still not adequately adapted to the unique molecular and clinical complexities of each patient's tumor. Diagnoses based on traditional tests such as histopathology and limited biomarkers often lead to suboptimal treatment decisions, with patients exposed to ineffective treatments or receiving them only due to side effects.

[0003] Precision medicine mechanisms, including genome sequencing technologies and pharmacogenomic testing, have been used for years to better tailor drug therapy to the genetic profile. However, these methods are limited by the accessibility and cost of an experienced data translator. Drug response prediction, another important aspect of precision medicine, has also been explored, and the incorporation of artificial intelligence (AI), particularly machine learning, into these processes has shown significant potential to assist in predictive models based on molecular profiling. However, data quality, transparency issues, and difficulties in using such techniques in current clinical workflows often pose major obstacles.

[0004] Given the complexity of cancer biology and the rather stochastic nature of individual patients' responses to specific treatments, one could argue that we truly need a solution that can account for all these heterogeneous strands of our data while still providing oncologists with clear guidance. Therefore, there is an urgent need in the field of applications today for optimized drug response predictions through the application of cutting-edge machine learning approaches that can improve cure outcomes while saving the lives of oncology patients.

[0005] Tremendous advances have been made in oncology in recent years, with multiple targeted therapies and a focus on personalized medicine based on our increasing understanding of cancer biology. The goal of these innovations is to tailor treatment plans according to each patient's specific tumor type, thus improving therapeutic responses. Yet, given the complexity of cancer and the data that must be considered in treatment planning, the implementation of personalized medicine has been challenging. Current strategies are largely based on histopathological assessments, clinical guidelines, and modest biomarker quantifications such as xEVs, which do not fully account for a patient's individual response to specific treatment approaches.

[0006] To date, oncology has used a one-size-fits-all approach: treatment protocols are developed based on studies conducted in the general population, rather than on individual patients. Such an approach often results in less effective treatment and exposes some patients to severe side effects that provide little therapeutic benefit. A common practice may be to administer multiple chemotherapeutic agents independently depending on tumor type and stage, without sufficient attention to how well a patient responds to that drug, whether in terms of their molecular profile or intratumoral heterogeneity. This results in a large number of patients not benefiting from treatments and experiencing unnecessary toxicities, underscoring the immediate need for more sophisticated prognostic tools.

[0007] Some existing solutions have been developed to improve drug response prediction in oncology. One notable technique involves leveraging genomic sequencing technologies to pinpoint precise mutations within specific tumor DNA. Using genomic profiling, oncologists can identify rearrangements in genetic sequences that are unique to cancer cells and select precise treatments that are more likely to work for specific disease subtypes. In fact, therapies that directly target these mutations—such as those targeting EGFR or ALK in lung cancer patients—can be highly effective. Genomic profiling is a remarkable development in itself, but it is not without its shortcomings. The high price of sequencing, as well as the demand for expertise in genetic / genomic data analysis to interpret such complex information, limits its use in clinical applications.Furthermore, genomic profiling is limited to a limited number of biomarkers and does not cover all facets of drug response, including epigenetic alterations or effects on the tumor microenvironment.

[0008] At the same time, pharmacogenomics has emerged as a way to tailor drug therapy to genetic variations that affect drug metabolism. Pharmacogenomic testing uses information from a person's individual genetic profile to determine how drug response can be optimized and indicates whether therapy is likely to work, making it more beneficial or less optimal if widespread adverse effects would have occurred. However, the clinical utility of these pharmacogenomic tests may be limited due to variability in patient response and a small number of approved tests. Furthermore, most tests currently used for this purpose lack proper validation and do not account for all genetic, epigenetic, and environmental factors that could contribute to changes in outcomes.

[0009] Traditionally, drug response prediction in oncology has been performed manually by human experts based on patient data or experiments that take longer to produce accurate results. However, artificial intelligence (AI) and machine learning can be efficient tools to improve such predictions. In fact, when fed with large amounts of data from electronic health records (EHRs), genomics, and clinical trials, machine learning models can detect patterns that might not otherwise be obvious. These AI algorithms can combine information from a variety of data sources (radiological images, genome sequences, medical history) to create predictive models that lead to better treatment recommendations. Nevertheless, the use of AI in clinical oncology faces several obstacles.Too many existing models are essentially black boxes, offering only limited transparency into the mechanisms behind the predictions to be potentially credible to clinicians. Unfortunately, the accuracy of these models depends heavily on data quality and coverage. If insufficient or distorted data is provided, the algorithms lead to incorrect predictions, jeopardizing all purposes of drug efficacy audits using machine learning applications.

[0010] Another important innovation is the generation of PDXs and organoids from patients, which provide an even more physiological model for evaluating cancer response to therapies. Using these methods, researchers can evaluate the responses of human tumor tissue to drugs in a controlled state, either by implanting it into immunocompromised mice or culturing it in the open air. This may offer the opportunity to test certain therapeutic agents and their combinations that could serve as the basis for personalized treatment strategies. Despite their great potential, the use of PDXs and organoids is unfortunately limited by several limitations. The time-consuming and expensive process of developing such models limits their use in clinical practice.Furthermore, tumor heterogeneity makes inconsistent results from patient sample to patient sample a common obstacle in translating findings into standard treatment modalities.

[0011] Furthermore, by incorporating real-time health monitoring technologies into treatments—such as wearable devices—we can more accurately monitor patient response. These devices could continuously track physiological data, providing information on treatment tolerability and efficacy. The challenge is interpreting this data and incorporating it into clinical decision-making. While wearable devices can provide us with a wealth of information, this kind of massive dataset requires complex analysis. However, if real-time monitoring cannot be effectively integrated into the clinical workflow, it will be underutilized and potential benefits will not be realized.

[0012] Simply put, there have been great strides in predicting drug response in oncology over the years; however, current approaches suffer from numerous shortcomings. While histopathological evaluations and genomic profiling are important, traditional approaches often fail to capture the full complexity of cancer treatment. Artificial intelligence and machine learning are two of these new technologies that show promise, but barriers to actual clinical implementation exist, such as practical applicability due to a lack of transparency (no open box), reading datasets with poor fidelity or signal-to-noise ratios, and practical implementation. Furthermore, novel constructs such as PDXs and organoids—which are useful in research—are not standard components of clinical workflows due to their logistical limitations.One solution is to develop a modern, machine-learning-driven application that can combine and learn from the heterogeneity of patient data sources to improve accuracy in predicting oncological responses—thus becoming a powerful tool to redefine more personalized yet comprehensive treatment strategies for cancer patients. Summary of the invention

[0013] The invention describes a device that uses machine learning algorithms to improve predictions of prescription drug effects in cancer patients. The device includes a data acquisition unit, a feature extraction module, a machine learning processing unit for the prediction output interface, and a communication subsystem. Each of these elements is an integral part of the data acquisition, processing, and analysis that leads to actionable insights for healthcare providers.

[0014] The data acquisition unit is designed to capture patient-specific information, including genomic, proteomic, and clinical data from multiple sources, into a complete patient record. The input data is processed independently of downstream analysis, with an advanced feature extraction module performing valid drug response biomarker identification from the acquired dataset by applying sophisticated analytical tools, resulting in improved input quality.

[0015] The machine learning processing unit includes a suite of algorithms designed to analyze complex biological data and refine predictions using historical treatment outcomes. These insights are displayed in a user-friendly manner when reviewing the prediction results, providing an actionable treatment recommendation. The communication subsystem also ensures secure data transmission and complies with applicable data protection laws.

[0016] The system also includes a feedback loop to refine the prediction algorithms and continuously improve learning based on new patient data and by collecting clinical accuracy assessments. The full integration of these diverse techniques can effectively improve the precision and predictability of drug response predictions, thus leading to optimized personalized treatment options in oncology.

[0017] The primary objective of the invention is to develop a machine learning-based system that can optimize drug response prediction in cancer patients, enabling personalized treatment strategies that promote therapeutic benefits while reducing adverse effects. The device is intended to overcome the limitations of such common oncology practices, which generally follow standard treatment protocols based on common characteristics and are not individualized for each patient regarding molecular or clinical characteristics. The invention aims to develop a holistic patient profile by integrating various sources, including genomic data, proteomic data, and clinical information, enabling oncologists to make informed decisions.

[0018] The invention further offers methods for predicting drug responses with a higher degree of accuracy and statistical significance through the use of sophisticated algorithms capable of analyzing complex biological networks. The device is designed to synthesize predictions from multiple models using an ensemble learning approach, providing a well-founded assessment of available treatment options precisely tailored to the individual patient's needs. This goal is in line with the move toward individualized medicine, in which treatment regimens are flexibly adapted to different responses and changing patient status.

[0019] The invention also aims to enable real-time feedback mechanisms for structural health diagnostics and prognosis, allowing the performance of predictive algorithms to be repeatedly tested iteratively with newly acquired data. By adding a feedback loop to the human side of such devices, where physicians enter treatment results via an RFID-tagged display strip that must be scanned to authorize use of the device, this iterative improvement can leverage machine learning and other AI. This feedback loop will lead to both better patient management through a collaborative approach and improve the accuracy of predicting drug responses over time.

[0020] One aspect of the invention specifically aims to enable secure and efficient handling of patient data from medical records while meeting legal protection requirements by providing communication subsystems with at least two different security modes. One goal of the invention is to bridge this gap between research and clinical practice and to provide oncologists with a user-friendly functional front-end presentation interface. The goal of the device is to bridge this gap and make clinicians less dependent on their technical experts, enabling clinicians in all healthcare organizations to fully utilize predictive analytics technologies. This goal, in turn, enables the predictable use of machine learning-based predictive models to improve the care of cancer patients. Short description of the drawing Fig. Figure 1 shows a block diagram of a device for predicting drug responses in cancer patients using machine learning as optimization. Detailed description

[0021] With reference to Fig.Figure 1 shows a block diagram of a device 100 for predicting drug responses in cancer patients through machine learning optimization, the device 100 comprising: a data acquisition unit 102 capable of acquiring patient-specific information from various sources, including genomic, proteomic, and clinical information; a feature extraction module 104 that computationally processes acquired data to determine relevant biomarkers for drug response; an integrated machine learning processing unit 106 employing a set of algorithms optimized for pattern recognition in complex biological data based on historical treatment outcomes;a prognosis output interface 108 created to provide oncologists with clinically relevant interpretations and patient-specific treatment recommendations, including a continuous learning feedback loop that supports the refinement of prognostic models for new patients; and a communications subsystem 110 that enables secure transmission of the generated patient data and treatment prognostics to medical professionals.

[0022] In one embodiment, the device 100 includes a biomarker database that links certain genetic adaptations to drug efficacy, thus enabling the feature extraction module to use predefined relationships to improve prediction accuracy.

[0023] In one embodiment, the feature extraction module performs multivariate analysis to determine which patient characteristics are most informative, thereby refining its input data used by the machine learning processing unit.

[0024] In one embodiment, the prognosis output interface is configured to generate a visualization dashboard that includes patient-specific prognosis analyses, treatment recommendations, and the likelihood of side effects.

[0025] In one embodiment, the device further comprises a user feedback module for collecting and evaluating feedback from the clinician regarding the accuracy of treatment recommendations to enable continuous improvement of the predictive capability by the machine learning processing unit.

[0026] In one embodiment, the data acquisition unit is compatible with a wearable health monitoring device to enable real-time prediction of drug response based on the acquired data.

[0027] In one embodiment, the feature extraction module includes a dimensionality reduction component that reduces the input data by removing noise elements while retaining the essential information required for accurate predictions.

[0028] In oncology, a drug prediction device optimized through machine learning is being used. It is divided into a data acquisition unit, a processing unit, and a communication subsystem to ensure adequate airflow during operation, allowing efficient cooling. On the outside of the device is an intuitive user interface for interaction with clinical staff, where, for example, patient data can be entered and predictive analytics can be accessed.

[0029] The data acquisition unit collects a variety of patient-specific raw data in plain text from various sources. This information includes, for example, genomic data from next-generation sequencing studies, proteomic measurements from mass spectrometry profiles or downstream molecular interaction assessments, and clinical descriptions detailing the patient's treatment history and response. For example, a patient diagnosed with lung cancer could have their tumor genetically tested to detect mutations related to the EGFR gene, along with information about their previous chemotherapy treatments. The device is capable of creating a complete, multi-data-source, four-dimensionally compressed clinical picture of each individual patient.

[0030] This module is important for data preprocessing, a feature extraction module. It uses innovative, proprietary algorithms to identify and extract relevant biomarkers associated with drug response. A simple implementation could be the analysis of genomic data to identify mutations associated with resistance or sensitivity to known therapies. Multivariate analysis methods are then used for various patient attributes to determine the relationships between different fields and their meaningful correlation. In the future, key indicators for building predictive models will be generated by filtering out noise or irrelevant data.

[0031] With these processed inputs, the machine learning processing unit generates predictions about drug effects. Each of these units can be configured to use ensemble methods, combining the predictions of multiple models. For example, while one model examines genomic data, another does the same with medical records. The machine learning model is trained on the results of previous treatments and, through this training, learns to recognize complex relationships between genetic alterations in combination with factors about patients who have used which type of treatment, thus providing tailored patient-level predictions.

[0032] The prognosis output interface is a way to organize this information in a clear and useful way. The result is a simple dashboard that provides key information, along with a harm-balance prediction regarding the chance of therapeutic benefit, treatment recommendations, and potential side effects of various therapies. For example, an oncologist might review the dashboard and determine that for a patient with a particular genomic profile (like ours—we have our DNA tested), a particular targeted therapy has a fairly high chance of success, while also having one or two potential side effects that need to be monitored.

[0033] In addition, the communications subsystem is responsible for the secure transmission and storage of patient data. Using sophisticated encryption protocols, information is protected during transmission and is fully HIPAA-compliant. It ensures easy integration with leading electronic health record systems (EHRs), allowing oncologists to access the device's insights within their existing workflow.

[0034] The device also features a user feedback component that captures how physicians rate the accuracy of treatment suggestions and analyzes this data. This enables "perpetual learning" as the system continuously learns and evolves, improving its predictive capabilities in the Machine Learning Processing Unit (MLPU) and supporting an approach based on collaboration between physicians and their devices.

[0035] The device can continuously collect physiological data that provides information about treatment tolerability and efficacy because it integrates real-time health monitoring technologies, such as wearable devices. For example, a patient receiving chemotherapy can be equipped with such a device that tracks their physiological responses during treatment, allowing oncologists to adjust the dose or modify therapy in real time based on live feedback.

[0036] The invention generally relates to the technical field of oncology and computational biology, particularly to machine learning approaches integrated with clinical data analytics for personalized medicine. Developed using advances in genomics, data science, and medical informatics, they together form a powerful system that integrates personalized patient profiles to deliver best-in-class treatment recommendations. This innovation is extremely useful in the era of precision medicine in cancer, as a better understanding of the interactions between genetic mutations and clinical parameters through drug efficacy can enable developers to achieve better treatment outcomes for patients.Using cutting-edge data analytics and predictive modeling technologies, the invention works to advance the field of precision medicine in oncology, with the goal of changing the way patients choose cancer treatments.

[0037] According to one embodiment, the present invention comprises a device for implementing machine learning optimization in predicting drug responses in cancer patients. Implementation utilizes genomic data from next-generation sequencing (NGS), proteomic information generated by mass spectrometry, or any medical record reflecting the patient's medical history, demographics, and previous treatment outcomes. Implementation involves a breast cancer patient submitting their tumor (or a variant thereof) for analysis to examine mutations in the HER2 gene and possible prior chemotherapy history. Using data from these various streams, the device can build a comprehensive picture of what's actually going on with you.

[0038] After retrieving data from various sources, this information is processed by the feature extraction module. Using advanced algorithms, this module identifies significant and related biomarkers of drug activity. One implementation would be an analysis of genomic data that looks for specific mutations associated with resistance or sensitivity to a particular oncology therapy. This involves using multivariate analysis techniques to examine various patient characteristics, determine the relationships between them, and—equally important—identify which data are actually not that useful, allowing us to later use this information in prediction.

[0039] Finally, these optimized inputs are fed into a machine learning processing unit to produce drug response estimates. It features a suite of machine learning algorithms specifically designed for analyzing biological data, and you can enable the ensemble methods option. This means the implementation uses a combination of decision trees, support vector machines, and neural networks, which the sensors can display, to learn all kinds of data patterns. In this case, the machine learning model is trained on historical treatment outcomes and learns how different genetic alterations interact with different patient characteristics to produce different responses, producing fine-tuned predictions unique to each individual patient.

[0040] The predictive output interface is specifically designed to present these insights in a concise and actionable way. It creates interactive outcome dashboards, including treatment recommendations with expected efficacy and potential side effects for each therapy. In another implementation, an oncologist viewing the dashboard might note that this targeted therapy indeed has a good chance of being effective, due in part to a patient's unique genome, but also mention that there are specific side effects that need to be monitored. The interface supports the clinical decision-making process and also establishes a feedback loop for medical staff to send outcome reports to the device (thus improving predictive accuracy over time with repeated use).

[0041] The communications subsystem is required to prevent unauthorized access through wireless transmission and streaming of all data, as well as recommendations for additional treatments. This subsystem provides integration with currently deployed electronic health record (EHR) systems, giving oncologists access to the device's insights without having to change their work environment. Upon implementation, clinicians could receive immediate notifications of potential drug interactions or be kept up-to-date on changes to dosage and management protocols, as the latest clinical data would be readily available to them.

[0042] In one embodiment, the data acquisition unit comprises a Biomarker™ database. The database is a collection of predefined genetic variations already known to be associated with drug efficacy, allowing the feature extraction module to use these epistasis relationships as prior knowledge for analysis. For example, in the case of a patient with a KRAS gene mutation (left), the device could search its database for similar mutations that match targeted therapies and inform physicians whether such a drug is likely to be effective or not—or whether an alternative treatment should be considered. This feature not only increases the accuracy of predictions but also facilitates treatment selection.

[0043] Furthermore, in some embodiments, multivariate analysis techniques can also be integrated into feature extraction modules. This means the module will be able to consider a multitude of parameters simultaneously when deciding the importance of each biomarker. It will be able to analyze not only genetic data (one of the most common forms of multifactorial input) itself, but also determine how different combinations interact with one or more additional sets, such as protein expression, and clinical factors such as patient age, genetics, or other comorbidities, to determine drug response. All of these capabilities now enable a much more sophisticated approach to understanding the factors contributing to the efficacy of this treatment and, thus, better predictions.

[0044] According to one embodiment, the machine learning processing unit uses ensemble learning. This technique refers to the combination of multiple machine learning models, each of which learns different features from data. One of these models might work with genomic data, while the other looks at clinical records. Combining these different predictions improves operational robustness and accuracy and reduces potential errors that could occur when using only one model. Thanks to the multi-layered approach, the system can be adapted to the complexity of cancer biology and patient-to-patient differences, making drug response prediction more robust.

[0045] According to one implementation, the predictive output interface works by generating a visualization dashboard that displays predictive analytics in a natural form. For example, in the future, a physician could have an interactive virtual video dashboard providing up-to-date calculations of success rates for various treatments and the likelihood of side effects. If the device is 75% certain that a particular chemotherapy will work for that patient, a visualization could appear alongside historical side effect data, showing oncologists which treatment decisions are more informed.

[0046] According to the exemplary framework, security is of utmost importance, with the communications subsystem emphasizing the implementation of encryption protocols for secure data processing. This prevents unauthorized access to confidential patient information, such as genomic data and treatment history, during transmission or storage. Whenever a patient sends their treatment data to an external oncologist or submits it for entry into a clinical trial database, these processes are always secured by robust encryption protocols, helping to protect the data from misuse—and ultimately, ensuring all possible safeguards to maintain confidentiality and trust between patients and providers.

[0047] According to one embodiment, a user feedback module is configured to receive and analyze feedback from clinicians regarding the accuracy of treatment recommendations. Because the device allows healthcare providers to report results and recommend adjustments based on their clinical experience, it is constantly learning—these feedback loops feed into predictive algorithms. One implementation: Several oncologists from different practices frequently visit the same message board, and most of them have found that a particular drug they were told would work fairly consistently fails to produce the desired effect in practice. If there is a mechanism for recording such information, a feedback module can prompt further investigation (either of data or algorithms) needed to refine future predictions.

[0048] According to one embodiment, the data acquisition unit is configured for communication with wearable health monitoring devices. This integration enables the collection of real-time data on the patient's health metrics (e.g., heart rate, activity level, vital signs). For example, a patient with colon cancer receiving chemotherapy could be equipped with a monitor that evaluates their physiological responses during chemotherapy sessions. Such real-time data can be invaluable for understanding how the patient is coping and allows oncologists to reduce the dose or preemptively adjust treatment based on live feedback.

[0049] In one implementation, the feature extraction module includes a dimensionality reduction subsystem. The goal of this technique is to standardize the input data so that we can filter out unnecessary features without losing important information necessary for accurate prediction. For example, if the device is processing a dataset with hundreds of genetic markers (between 20 and 100 that strongly correlate with drug response), this can reduce dimensionality while maintaining relevant context. This improves computational performance and facilitates the interpretation of the result by identifying more relevant variables. REFERENCES 100 A device for predicting drug response in cancer patients through machine learning optimization. 102 Data acquisition unit 104 Feature extraction module 106 Machine Learning Processing Unit 108 Prediction output interface 110 Communication subsystem

Claims

[1] A device for predicting drug responses in cancer patients through machine learning optimization, the device comprising: a data acquisition unit capable of capturing patient-specific information from various sources, including genomic, proteomic, and clinical information; a feature extraction module that computationally processes the acquired data to determine relevant biomarkers for drug response; an integrated machine learning processing unit that uses a set of algorithms optimized for pattern recognition in complex biological data based on historical treatment outcomes; a prognostic output interface to provide oncologists with clinically relevant interpretations and patient-specific treatment recommendations, including a feedback loop for continuous learning to refine prognostic models for new patients; and a communications subsystem configured to securely transmit generated patient data and treatment prognosis to healthcare professionals. [2] The device of claim 1, wherein the device comprises a biomarker database linking certain genetic adaptations to drug efficacy, thus enabling the feature extraction module to use predetermined relationships to improve prediction accuracy. [3] The device of claim 1, wherein the feature extraction module performs multivariate analysis to determine which patient characteristics are most informative and thereby refines its input data used by the machine learning processing unit. [4] The device of claim 1, wherein the prognostic output interface is configured to generate a visualization dashboard including patient-specific prognostic analysis, treatment recommendations, and the likelihood of side effects. [5] The device according to claim 1 further comprises a user feedback module for collecting and evaluating feedback from the clinician regarding the accuracy of treatment recommendations to enable continuous improvement of the predictive capability by the machine learning processing unit. [6] The device of claim 1, wherein the data acquisition unit is compatible with a portable health monitoring device for real-time prediction of drug response using acquired data. [7] The apparatus of claim 1, wherein the feature extraction module comprises a dimensionality reduction component that reduces the input data by removing noise elements and retains the essential information required for accurate predictions.

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