Intelligent device with machine learning to optimize personalized cancer therapy based on genome data

Abstract

A machine learning-assisted intelligent device for optimizing personalized cancer treatment based on genomic data, the device comprising the following: a genome data input unit configured to receive and preprocess genome sequences from a variety of external data sources, the preprocessing including noise reduction, sequence alignment, and filtering of irrelevant genome segments; a mutation detection unit comprising a deep learning-based processing module, wherein the mutation detection unit is configured to compare the preprocessed genome sequences with a database of known cancer-related mutations and biomarkers and to create a mutation profile associated with a patient's cancer; an optimization unit for machine learning models comprising at least one neural network architecture, wherein the optimization unit is configured to receive the mutation profile, analyze the identified mutations, correlate the mutations with historical patient outcomes, and generate an initial treatment recommendation; a treatment recommendation engine configured to use reinforcement learning algorithms, wherein the engine continuously learns from real-time patient responses to previous treatments and dynamically adapts treatment strategies based on updated genomic data and evolving patient conditions, thus providing an optimized treatment plan; an outcome prediction module configured to analyze potential outcomes of the recommended treatments based on predictive analysis models, whereby the module generates a probability value for treatment success and presents a detailed explanation of the correlation between genomic markers and the expected efficacy of the treatment; a secure data transmission interface configured for bidirectional communication with hospital networks and genome research centers, with the interface complying with data protection standards to protect sensitive patient information; a display interface configured to visually represent the results of genomic analyses, treatment recommendations, and outcome predictions, wherein the display interface allows user interaction to adjust treatment parameters based on clinical inputs; and a power management unit configured to regulate energy consumption, wherein the power management unit includes a cooling system to maintain the operating temperature of the device during high-performance data processing tasks.

Description

Field of the invention:

[0001] The present invention relates to the personalized optimization of cancer treatments, particularly through machine learning and genomic data analysis. It comprises a machine learning-based IoT device for interpreting genome sequences, identifying cancer-causing mutations, and recommending personalized treatments. The patent addresses more than just collecting and classifying genomic data. It uses machine learning to predict how well a therapy will work, based on advanced algorithms, and also excludes options. The utility of the invention finds application in clinical, research, and personalized healthcare settings. Background of the invention:

[0002] The emergence of precision medicine (personalized medicine) offers compelling opportunities to individualize cancer therapy based on the unique characteristics of each patient's tumor. One of the biggest challenges is determining whether we can identify mutations and biomarkers in genomic data that are clearly associated with cancers. Such approaches typically rely on static algorithms or require manual intervention to match treatments to genetic profiles; therefore, the results can be far from ideal. Furthermore, analyzing large genomic data requires significant computational resources and expertise, which are not always easy to obtain. To this end, there is a critical need for a system that enables rapid genome sequence analysis, cancer-related mutation identification, and automated treatment plans using machine learning (ML).The goal of this invention is to solve this problem by enabling real-time genomic analysis using an AI-powered smart device to make predictions about treatment outcome and optimization strategies for cancer therapies for each patient during treatment.

[0003] In recent years, there have been explosive advances in personalized medicine, particularly in the field of oncology, thanks to molecular breakthroughs and machine learning technologies. This person-specific approach to developing cancer therapies has far-reaching implications for treatment efficacy and the reduction of harmful side effects. Previous standard systems for treating cancer, such as chemotherapy and radiotherapy, have many limitations, including a "one-size-fits-all" approach that results in varying outcomes from patient to patient. With the development of genome sequencing, we are now able to identify specific mutations and biomarkers responsible for different cancers. This aids in earlier diagnosis of patients and improves treatment strategies that may be associated with each individual mutation.

[0004] One of the most important current solutions for personalized cancer therapy is the use of next-generation sequencing (NGS) technologies to examine a patient's genomic data. Gen OMS has been working on developing devices that can sequence DNA at high throughput, allowing somatic mutations—the genetic changes that drive cancer progression in a person—to be found. These insights could help oncologists identify these mutations and target them with therapies. For example, targeted tyrosine kinase inhibitors (TKIs) have been developed to block overactive enzymes due to specific genetic mutations in certain cancers. Similarly, immunotherapy is a new treatment modality that harnesses the power of the immune system to attack cancer cells using tumor-specific antigen information from the genome.

[0005] While these advances are promising, current solutions suffer from several shortcomings. First, new sequencing technologies can produce a significant amount of genomic data. A single cancer genome may harbor thousands of mutations, most of which are innocent bystanders in the development and growth of specific types or subsets (when tracking different generations). Such sequence variations may not be amenable to processing with routine variant detection software, and the interpretation of some mutation classes is controversial, requiring sophisticated expert tools that can lead to biased results if the data is misused. Existing systems rely on static databases of known mutations, which may be incomplete or outdated relative to the rapidly growing knowledge base of cancer biology.In addition, many current genomic analysis solutions are not fully automated and require manual intervention in the result interpretation process, which can lead to human errors and treatment delays.

[0006] Another significant problem today is the lack of integration between genomic data analysis and treatment recommendation systems. When interpreting genomic data, oncologists often have to rely on their own knowledge and experience or manually search online for a suitable treatment option. This is a time-consuming process because it depends on the physician's expertise, leading to varying treatment effects. Traditional recommendation systems are static and do not change depending on the cancer progression, the patient's response to treatment, etc. This means that although pre-trained systems can suggest an initial treatment plan based on the trained knowledge, they cannot dynamically adapt to changes in the patient's health status or new information.

[0007] There are several attempts to overcome these limitations using machine learning and artificial intelligence (AI). Numerous research projects and commercial systems strive to integrate AI-driven models into all phases of genomic analysis in the hope of offering more accurate treatment recommendations in a timely manner. For example, some platforms use machine learning algorithms to predict whether a particular mutation might affect the effectiveness of a given cancer therapy. These systems train models based on large datasets of previous patient outcomes, allowing the system to become better and more accurate in its predictions over time, potentially offering more personalized and effective treatment strategies.

[0008] However, many of these machine learning-based solutions are still in their infancy and have significant limitations. A major problem is that most machine learning models in use today tend to require datasets based on supervised learning, which in this context equates to large and accurately labeled genomic patient data with the resulting treatment outcomes. While these datasets are useful, they typically contain a variety of cancer types that may not represent the full spectrum of cancer types and mutations. Furthermore, supervised learning models are generally designed to generalize from patterns they have seen in training data and therefore may not perform well on new or rare mutations that were never present during training.Patients with rare genetic profiles could receive a treatment recommendation based, in the best case, on the most common tumor.

[0009] The second point to note about current machine learning techniques in this field is that they are rarely adaptive. After deployment in the clinical setting, most of their machine learning models do not learn from new data (i.e., they do not adapt). However, cancer treatment is dynamic—patient responses to therapy change over time. A good answer would be for recommendations to improve with each technological advancement; a good answer would be to recommend the best modern therapy we can offer today and learn from real-time data to do better tomorrow. However, there is a wide range of systems that cannot support the integration of continuous learning or reinforcement learning methods suitable for handling adaptive decision-making in changing environments.

[0010] Furthermore, current AI-powered approaches to personalized cancer treatment recommendations are often opaque and uninterpretable. In this context, machine learning models, particularly deep neural networks (DNNs), often act as black boxes and are uninterpretable for clinicians who want to know why the model reached a particular prediction. This lack of explainability can lead to resistance from clinicians, who may be unwilling or unconfident to trust a system they do not truly understand and feel unable to confirm that it works as we say. Despite existing efforts to build transparent models, a large gap remains between accurate and explainable systems suitable for low-risk problems (e.g.,Digit classification) and higher-risk applications such as cancer treatment.

[0011] Cost and access are the major barriers to the use of these machine learning-based personalized cancer treatment approaches. Complex genomic analyses and machine learning models often require expensive high-performance computing resources, which are impractical for many resource-constrained healthcare environments. Furthermore, much of this IT infrastructure is written in-house, and the expertise required to develop, maintain, and validate such systems may not be available in many hospitals or clinics. Therefore, the widespread application of machine learning to optimize personalized cancer treatment is primarily limited to specialized research institutions or large medical centers with robust infrastructure.

[0012] In addition to the challenges posed by a lack of access, there are significant regulatory and ethical issues surrounding the implementation of machine learning in precision cancer medicine. In particular, for patient genomic data shared across other platforms or analyzed using cloud systems, there are some concerns related to the privacy and security of individual patient records. While compliance with regulatory frameworks such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States or the General Data Protection Regulation (GDPR) across Europe is of utmost importance, this creates complexities for the implementation of machine learning in clinical settings. Furthermore, the ethical aspects regarding the use of AI to decide who will live or die for their life-saving treatment are not yet clear (e.g.,Bias in test data; lack of accountability when a prediction kills people due to an incorrect recommendation).

[0013] Existing personalized cancer treatment solutions have made progress but are fraught with challenges, including the avalanche of genomic data for each patient, a lack of integration between analytics and recommendation systems, machine learning models that fail to evolve with a changing disease state or medication regimen, barriers to transparency in predicting outcomes based on complex genotypic information, and cost / accessibility constraints that make this untenable. Machine learning has the potential to be a game-changer in addressing these problems, but existing approaches have not fully delivered on this promise. There is a clear need for an advanced, adaptive, and transparent system that can deliver personalized treatment recommendations in real time based on genomic data while continuously learning from patient outcomes.There is hope that a newly developed machine-learning intelligent device for optimal treatment selection in cancer can overcome all these problems with an approach that can combine genomic analysis and real-time decision-making on a single accessible platform. Summary of the invention:

[0014] The product is a smart machine learning device designed to personalize cancer treatment through genomic profiling. The system combines multiple subsystems, including a genomic data input subsystem, mutation detection for gene selection, and machine learning model optimization, before integrating them into the treatment recommendation engine, which then makes a decision based on an outcome prediction module. The system also uses a powerful machine learning engine that continuously learns from previous treatment results and evolves its recommendations in real time, providing personalized treatment for each patient.

[0015] The smartphone can process large genomic datasets, identify causative mutations for specific cancer types, and assess the functional impact of these mutations on treatment efficacy. The device features a treatment optimization engine based on reinforcement learning, allowing it to self-improve in real time based on patient feedback. This would allow oncologists in clinical practice to identify the most appropriate therapy, previously used at a point where treatment failure and the risk of resistance to therapy existed. This could potentially offer added value as a predictive marker or for sequential monitoring of patient survival prior to treatment.

[0016] The primary purpose of the present invention is to integrate machine learning into a smart device and apply it to personalized cancer therapy using genomic data for optimal treatment. By streamlining the analysis of patient-specific genome sequences to identify key cancer mutations and therapeutic options based on individualized genetic information, this invention aims to serve as a tool for interpreting how these variations can be addressed in the development of targeted medical interventions with personalized treatment plans. By leveraging machine learning models, it aims to counteract more precise, data-driven treatment strategies than those offered today, which are based on static databases or manual interpretation by oncologists.

[0017] Another goal of the invention is to enable treatment recommendations that can be dynamically adapted in real time to patient outcomes and ongoing genomic analyses. This algorithm, developed by Ypsomed, also continuously learns from new data, refining its ability to predict blood glucose levels and create individualized treatment plans over time. Because adaptability is so important, the Ruffalo Noel Levitz scientists opted for an adaptive learning approach that allows their models to change over time and keep pace with ongoing medical advances in oncology as these approaches continue to mature.

[0018] This includes an automated platform that harmonizes genomic and treatment analyses. By integrating these functions into a single system, the device eliminates the need for separate tools or laborious manual work to analyze genomic data and determine treatment regimens. This streamlined approach enables more efficient and precise personalized cancer care, eliminates barriers to timely intervention decisions, and minimizes the risk of error.

[0019] The invention also aims to make sophisticated genomic analyses and machine-learning-driven personalized cancer treatments more accessible. The device is small, lightweight, and user-friendly, allowing it to be used in many clinical settings, such as smaller hospitals or resource-poor settings. The invention requires fewer extensive computing resources or specialized expertise, making personalized cancer treatments more accessible.

[0020] Another goal of the invention is to enable more transparent and understandable treatment recommendations through machine learning. Each potential treatment recommendation is supported by the device with a transparent rationale that shows you which genomic indicators and data influenced that decision. And that really builds physician confidence, because a doctor can say, "Wait, I understand why you chose this therapy, because in my opinion, it's based on what we know at the genomic level... 1; clip here)...", thus giving them a rationale for their actions.

[0021] In a state-of-the-art environment, the security and privacy of patient genomic information can become a concern in the area of ​​data analysis / optimization. This invention seeks to achieve precisely this goal. Secure data transmission protocols and compliance with legal regulations regarding patient data privacy ensure that confidential information is protected while enabling the continuous flow of structured, clinical trial-related research across various complex healthcare systems. These goals are set in the context of a potentially groundbreaking invention for personalized cancer treatment—a comprehensive, adaptive, and secure solution for improving performance in all aspects of patient needs "when it matters"! SHORT DESCRIPTION OF THE FIGURE

[0022] These and other features, aspects, and advantages of the present invention will become more readily understood when the following detailed description is read in conjunction with the accompanying drawings, in which like characters represent like parts throughout. Fig. Figure 1 shows a block diagram of a smart device using machine learning for personalized optimization of cancer treatment using genomic data.

[0023] In Fig. A block diagram of a smart device with machine learning for personalized optimization of cancer treatment using genomic data is shown. The system 100 includes: a genomic data input unit 102 configured to receive and preprocess genomic sequences from a variety of external data sources, wherein the preprocessing includes noise reduction, sequence alignment, and filtering of irrelevant genome segments; a mutation detection unit 104 comprising a deep learning-based processing module, wherein the mutation detection unit is configured to compare the preprocessed genomic sequences with a database of known cancer-related mutations and biomarkers and create a mutation profile associated with a patient's cancer;a machine learning model optimization unit 106 comprising at least one neural network architecture 106(a), wherein the optimization unit is configured to receive the mutation profile, analyze the identified mutations, correlate the mutations with historical patient outcomes, and generate an initial treatment recommendation; a treatment recommendation engine 108 configured to use reinforcement learning algorithms, wherein the engine continuously learns from real-time patient responses to previous treatments, and wherein the engine dynamically adapts treatment strategies based on updated genomic data and evolving patient conditions, providing an optimized treatment plan;an outcome prediction module 110 configured to analyze potential outcomes of the recommended treatments based on predictive analysis models, wherein the module generates a probability value for treatment success and presents a detailed explanation of the correlation between genomic markers and expected treatment efficacy; a secure data transmission interface 112 configured to enable bidirectional communication with hospital networks and genomic research centers, wherein the interface adheres to data protection standards to protect sensitive patient information; a display interface 114 configured to visually display results of genomic analyses, treatment recommendations, and outcome predictions, wherein the display interface enables user interaction to adjust treatment parameters based on clinical inputs;and a power management unit 116 configured to regulate power consumption, wherein the power management unit includes a cooling system to maintain the operating temperature of the device during high-performance computing tasks.;

[0024] In one embodiment, the mutation detection unit 104 is further configured to use convolutional neural networks (CNNs) for pattern recognition within the genome sequences, where the CNNs are trained to distinguish between driver mutations that contribute to cancer development and passenger mutations that do not, thereby improving the accuracy of mutation identification.

[0025] In one embodiment, the machine learning optimization unit 106 further comprises a transfer learning module, wherein the transfer learning module uses pre-trained models on large, diverse genomic datasets to improve the ability of the device to identify rare mutations and predict treatment responses in patients with rare cancer subtypes.

[0026] In one embodiment, the treatment recommendation engine 108 further comprises an external clinical trial integration unit, the unit configured to access and analyze data from ongoing clinical trials and compare the study data with the patient's genomic profile to suggest possible experimental therapies or participation in relevant clinical trials.

[0027] In one embodiment, the outcome prediction module 110 further includes a confidence adjustment sub-module, the sub-module configured to dynamically adjust the confidence levels of treatment recommendations based on new patient outcome data, wherein the confidence levels are influenced by real-time feedback from clinical observations and treatment outcomes.

[0028] In one embodiment, the genomic data input unit 102 is configured to integrate data from multiple sequencing platforms, including whole genome sequencing (WGS), whole genome sequencing (WES), and RNA sequencing, where the input unit automatically standardizes the data format and normalizes variations between sequencing methods prior to processing.

[0029] In one embodiment, the secure data transmission interface 112 comprises quantum cryptography-based encryption protocols, wherein the transmission of genomic data and treatment recommendations between the device and external servers is secured by quantum key distribution (QKD), ensuring that patient data is protected from unauthorized access during transmission.

[0030] In one embodiment, the device further comprises an on-device storage unit, wherein the storage unit comprises a solid-state drive (SSD) with error correction coding (ECC) capabilities configured to store patient-specific genomic data, mutation profiles, and treatment histories, enabling offline analysis and decision-making in environments with limited network connectivity.

[0031] In one embodiment, the neural network architecture 106(a) of the machine learning optimization unit 106 is further configured to employ unsupervised learning techniques, which techniques enable the device to identify new genetic patterns and potential new biomarkers without prior labeling, expanding the utility of the device in researching and discovering emerging cancer mutations.

[0032] In one embodiment, the power management unit 116 further includes an energy-efficient processing framework, wherein the framework uses dynamic voltage and frequency scaling (DVFS) to optimize device power consumption during genomic data analysis, thereby reducing energy waste during low computational load times.

[0033] This article presents a machine learning-enabled smart device for individualized cancer precision therapy based on genomic data. It can process high-throughput sequencing reads of the genome, identify associated mutations, and identify resulting drugs for personalized cancer treatment using cutting-edge machine learning algorithms. The system consists of multiple technological components, including genomic data ingestion, a mutation detection unit, machine learning model optimization units, a treatment recommendation engine, an outcome prediction module, and a secure transmission interface. These software components work together in an integrated manner to provide a comprehensive, fully automated digital decision support system for oncologists, enabling the generation of personalized treatment recommendations for cancer patients.

[0034] Genomic data input unit: This unit is responsible for accessing and preprocessing genomic data from various sources. These can originate from whole genome (WGS), exome (WES), or RNA platforms. The quality and reliability of the subsequent analysis depend primarily on the preprocessing phase. This involves first reducing background noise as efficiently and selectively as possible. Then, the sequences are aligned to the reference genome or an improved, newly constructed genome. Irrelevant DNA regions (non-coding segments) and segments with mutations that do not significantly contribute to cancer development are filtered out. The data itself is then normalized across sequencing methods, enabling consistent analysis across multiple platforms. Fig. : After preprocessing, the genomic data is sent to a mutation detection unit.

[0035] It uses convolutional neural networks (CNNs) to detect cancer-specific mutations in the genome sequences of sequencing data from a mutation detection unit based on deep learning. CNNs are well-suited for an image-oriented task that requires detecting complex patterns in a large set of images. The CNN in this device was trained on a huge database of cancer mutations and biomarkers, allowing it to distinguish between driver mutations—those that directly contribute to development / progression—and the rest, which play no role in the sequence.

[0036] These mutations are then used to create a mutation profile that can form the basis for personalized treatment recommendations.

[0037] At the heart of the entire process is a machine learning optimization engine, which takes the mutation profile generated by mutation detection engines and correlates it with historical outcomes under different treatments. The engine uses a deep neural network (DNN) architecture and has been trained to detect patterns across thousands of cancer cases, treatment regimens, and outcomes. Transfer learning is used to refine this DNN over time, leveraging parameters from pre-trained models trained on larger / more diverse datasets. This strategy has the advantage of improving both the ability to detect rare mutations and predict treatment responses in patients affected by unusual cancer subtypes.Transfer learning offers the machine more flexibility in its application, extending its usability to ALL types of cancer, not just the most common ones, e.g., breast cancer... colon..., and it allows the detection of rare or less well-studied cases

[19] .

[0038] The optimization unit contains a machine learning model that uses supervised and unsupervised techniques. On the one hand, supervised learning detects new genes and predicts patient outcomes using labeled training data (Supplementary Tables 1-6). On the other hand, this unsupervised approach discovers numerous new genetic patterns that could serve as attractive new biomarkers for both common DMDs and DRGs using unlabeled support vectors. This knowledge was integrated into the system using a hybrid learning approach that learns from feedback, strengthening its long-term memory with new developments in cancer genomics and eliminating the need for constant manual updates.

[0039] Using reinforcement learning, the treatment recommendation engine then iteratively updates its treatment suggestions based on feedback on the online results available for patients at that moment. This type of algorithm learns over time by incorporating feedback, and as more data comes in about patients' response to a treatment method, it can adjust its advice for similar cases. The accuracy of this information is especially important in cancer treatments, and a particular patient's response can change over time. For example, if a patient initially responds positively to a targeted therapy but later becomes resistant, this framework can modify its suggestion to test other treatment options.Based on a patient's mutation profiles and medical history, a reinforcement learning model evaluates each possible treatment, such as chemotherapy, immunotherapy, or targeted therapies, by simulating their potential efficacy. There's also an external integration unit that incorporates data from ongoing clinical trials, allowing the recommendation engine to suggest experimental therapies as alternatives when traditional options have been exhausted.

[0040] In addition to the recommendation engine, another Horizon model performs outcome predictions and estimates the chance that a patient will actually succeed with the treatment. This module uses a multivariable regression model that, in addition to genomics, considers several other patient-specific factors, including patient age, general pre-treatment health, and response interval. The system uses these parameters as factors to inform the prediction process, helping to provide a comprehensive view of how a treatment might affect an individual patient. Predictions are displayed along with a confidence level, allowing clinicians to understand how certain or uncertain a recommendation is compared to others.Where these levels lie in the confidence level is indicated by the confidence adjustment sub-module, which allows position translation of gray guide scales based on lower values.

[0041] The machine learning models used in the system are fast and accurate, enabling near-instantaneous processing of massive genomic datasets. The parallel processing architecture, featuring field-programmable gate arrays (FPGAs) and GPUs, accelerates computation for mutation detection, treatment recommendation, and outcome prediction. This parallel processing capability enables the device to handle high-throughput genomic data production and support critical patient care decisions in clinical settings.

[0042] All sensitive patient data is protected during data transmission using quantum cryptography protocols via the device's secure data transfer interface. When this feature is disabled, the Health Insurance Portability and Accountability Act (HIPAA) or controls that protect against unauthorized access to your health data for illegal activities are not enforced. The system can communicate bidirectionally with hospital networks, research centers, and cloud-based genome databases via a data transfer interface to continuously learn about the latest genomics and treatment information.

[0043] On the hardware side, the device comes with a solid-state drive (SSD) with Error Correction Coding (E CC) for secure and more reliable data storage over the long term. This allows patient-specific genomic data, mutation profiles, and treatment histories to be stored on the system for offline analysis in situations where network connectivity is limited. The device can be used for high-performance tasks without running out of power, as DVFS works to optimally manage power consumption. Furthermore, the accompanying cooling system with heat sinks and airflow modulators ensures a stable operating temperature by default and prevents performance throttling due to your hardware overheating.

[0044] The user interface is designed to be intuitive and user-friendly, featuring a high-resolution, real-time visualization display. The display presents genomic analysis results, treatment recommendations, and outcome predictions in a visually intuitive way, allowing physicians to quickly process the data and provide immediate decision support. The voice-activated control system allows you to work as hands-free as possible in a busy clinical environment.

[0045] We have developed an intelligent machine-learning device that delivers personalized cancer treatment optimization in a comprehensive, fully integrated solution. The device leverages advanced genomic data analysis and adaptive machine-learning algorithms to deliver personalized, real-time treatment recommendations based on each patient's unique genetic profile. These features, including the system's ability to incrementally learn over time as new patient data becomes available and securely transmit information for monitoring key events in real time, as well as its powerful processing architecture, make it a critical resource for advancing scientific knowledge among practicing oncologists who strive daily to optimize care in a challenging environment of rapidly evolving cancer treatment options.

[0046] Somewhat more specialized than the refrigerator is this structure, which houses a highly interactive, machine-learning-based smart device aimed at individually optimized cancer therapy. The first component is the genomic data input unit, which receives genetic data from various sources such as DNA sequencing machines, patient records, cloud databases, etc. The data is preprocessed (by filtering out noise and irrelevant information) before being sent to the mutation detection relation.

[0047] It is powered by a sophisticated data processing module that uses deep learning algorithms to scan the genome sequences for cancer mutations. It works by taking the input genome sequences and comparing them with a reference dataset containing all known, verified cancer mutations / biomarkers. If an interesting set of mutations is detected, the system creates a mutation profile, which it then submits to our machine learning optimization unit.

[0048] At the heart of the device is the machine learning optimization unit. This is a neural network model trained on thousands of cancer cases, their treatment outcomes, and genetic characteristics. It operates based on the mutations found in the patient's genomic profile and helps predict whether a treatment would benefit (or not) from this model. The device uses adaptive learning techniques to continuously improve its predictive capabilities as data increases. Screening criteria are necessary to ensure the system only offers treatment recommendations based on each patient's individual genetic analysis and apparent cancer profile.

[0049] The treatment recommendation engine builds on the results of the ML model optimization unit. It uses reinforcement learning algorithms to optimally decide between a range of treatment options, such as chemotherapy, radiation, targeted therapies, or immunotherapies, depending on which are likely to be most effective for a given patient's specific mutation profile. The accuracy of this engine's recommendations is also improved by using external data from clinical trials and previous patient outcomes.

[0050] Outcome Prediction: Predictions of the exponential potential success of recommended treatments. Numerous predictive analytics and machine learning models are applied to predict treatment outcomes based on relevant patient history, genomic information, and treatment characteristics. The output of this module is a list of predictions, presented to the oncologist in an intuitive, easy-to-use format, accompanied by evidence explaining how each recommendation was arrived at and certainty levels.

[0051] The device also features a secure data transfer interface for connecting to hospital networks, cloud databases, and genome research centers. The system meets strict privacy and security standards while protecting sensitive genomic data.

[0052] Furthermore, the device is small and portable, and can be easily adapted to clinical scenarios. The company notes that the device features a screen that can instantly display genomic data and treatment recommendations. Control is via integrated manual overrides. The hardware architecture consists of high-performance processors, memory units, and data storage systems to process large data sets quickly and accurately.

[0053] The system has a built-in battery and can be manually started or stopped as needed by the intensive care unit or other departments. To ensure the device functions even in extreme high-performance environments, a cooling mechanism is integrated to prevent overheating during computationally intensive tasks.

[0054] The design of the intelligent device for personalized cancer treatment optimization with machine learning includes several required structural components. The device features a genomic data input port that can be connected to external DNA sequencing devices or repositories. This is an input port on the genomic data processing board, where incoming raw gene sequences are processed for further analysis. It is connected to a dedicated neural processing unit on the board that executes machine learning models and mutation analysis.

[0055] Within the compartment is a mutation detection unit that performs high-throughput data analysis and real-time scanning of genetic information for mutated genes. It features a collection of FPGA-based accelerators to improve performance in genome comparison and mutation detection.

[0056] The device contains a set of GPUs specifically tailored to run machine learning routines, serving as the compute core. These GPUs run the deep learning and reinforcement algorithms that power the treatment optimization engine. These are supported by storage units and SSDs that store the patient's genomic data, their mutation profiles, and their previous response to treatment.

[0057] The device's control system is connected to a hospital-based treatment database and automatically becomes part of the recommended treatments. The device features a secure circuit that protects the motor in an armored compartment from potential data corruption or access. An external display interface (Strawberry) connects the outcome prediction module to a clinical view of treatment recommendations, predictions, and associated reliability.

[0058] The device features an internal cooling system with heatsinks and airflow control to keep the internal components at a suitable operating temperature. The outer casing is constructed of high-quality materials, allowing the weight difference to be kept to a minimum without compromising performance.

[0059] A front-panel touchscreen interface allows physicians to access the system to adjust treatment parameters and review recommendations. This facilitates the implementation of wireless connectivity for remote updates and integration with electronic medical record (EMR) systems to transfer data between the device and the healthcare infrastructure.

[0060] We present a smart machine learning device designed for genome-based, personalized cancer treatment optimization, integrating it into a complete solution that provides seamless clinical decision support in oncology through cutting-edge precision medicine. By using sophisticated machine learning models combined with genomics, the device can significantly improve outcomes for cancer patients.

[0061] The invention relates to personalized medicine technology, particularly oncology but not limited to it. It involves optimizing the selection of individualized treatments for cancer in chicks through genetic data analysis. The invention is primarily based on machine learning and artificial intelligence to analyze genome sequences and identify all cancer mutations suitable for treatment at the genome level, including individualized treatment strategies. Using real-time data on patient recovery and details of clinical trials, the system also determines dynamic treatment recommendations. The invention is aimed at clinical applications where the pool of cell-free nucleic acids can be analyzed for genomic and pan-genomic precision oncology for personalized cancer interventions based on an individual's unique genetic characteristics. REFERENCES 100 A smart device equipped with machine learning to optimize personalized cancer therapy using genomic data. 102 Genomic Data Entry Unit 104 Mutation detection unit 106 Optimization Unit for Machine Learning Models 106a Neural network architecture 108 Treatment Recommendation Engine 110 Result prediction module 112 Secure data transmission interface 114 Display interface 116 A power management unit

Claims

[1] A machine learning-assisted smart device for optimizing personalized cancer treatment based on genomic data, the device comprising: a genome data input unit configured to receive and preprocess genome sequences from a plurality of external data sources, wherein the preprocessing includes noise reduction, sequence alignment, and filtering of irrelevant genome segments; a mutation detection unit comprising a deep learning-based processing module, wherein the mutation detection unit is configured to compare the preprocessed genome sequences with a database of known cancer-related mutations and biomarkers and to create a mutation profile associated with a patient's cancer; a machine learning model optimization unit comprising at least one neural network architecture, the optimization unit configured to receive the mutation profile, analyze the identified mutations, correlate the mutations with historical patient outcomes, and generate an initial treatment recommendation; a treatment recommendation engine configured to use reinforcement learning algorithms, wherein the engine continuously learns from real-time patient responses to previous treatments, and wherein the engine dynamically adapts treatment strategies based on updated genomic data and evolving patient conditions, thus providing an optimized treatment plan; an outcome prediction module configured to analyze potential outcomes of the recommended treatments based on predictive analysis models, generating a probability value for treatment success and presenting a detailed explanation of the correlation between genomic markers and the expected efficacy of the treatment; a secure data transmission interface configured for bidirectional communication with hospital networks and genomic research centers, whereby the interface complies with data protection standards to protect sensitive patient information; a display interface configured to visually present results of genomic analyses, treatment recommendations, and outcome predictions, wherein the display interface enables user interaction to adjust treatment parameters based on clinical inputs; and a power management unit configured to regulate power consumption, wherein the power management unit includes a cooling system for maintaining the operating temperature of the device during high-performance computing tasks. [2] The machine learning-enhanced smart device of claim 1, wherein the mutation detection unit is further configured to use convolutional neural networks (CNNs) for pattern recognition within the genome sequences, the CNNs being trained to distinguish between driver mutations that contribute to cancer development and passenger mutations that do not, thereby improving the accuracy of mutation identification. [3] The machine learning-enhanced smart device of claim 1, wherein the machine learning model optimization unit further comprises a transfer learning module, the transfer learning module using pre-trained models on large, diverse genomic datasets to improve the ability of the device to identify rare mutations and predict treatment responses in patients with rare cancer subtypes. [4] The machine learning-enhanced smart device of claim 1, wherein the treatment recommendation engine further comprises an external clinical trial integration unit, the unit configured to access and analyze data from ongoing clinical trials and compare the trial data with the patient's genomic profile to suggest possible experimental therapies or participation in relevant clinical trials. [5] The machine learning-enhanced smart device of claim 1, wherein the outcome prediction module further comprises a reliability adjustment sub-module, the sub-module configured to dynamically adjust the reliability levels of treatment recommendations based on new patient outcome data, the reliability levels being influenced by real-time feedback from clinical observations and treatment outcomes. [6] The machine learning-enhanced smart device according to claim 1, wherein the genomic data input unit is configured to integrate data from multiple sequencing platforms, including whole genome sequencing (WGS), whole exome sequencing (WES), and RNA sequencing, the input unit automatically standardizing the data format and normalizing deviations between sequencing methods before processing. [7] The machine learning-enhanced smart device of claim 1, wherein the secure data transmission interface comprises encryption protocols based on quantum cryptography, wherein the transmission of genomic data and treatment recommendations between the device and external servers is secured by quantum key distribution (QKD), thereby ensuring that patient data is protected from unauthorized access during transmission. [8] The machine learning-enhanced smart device of claim 1, wherein the device further comprises an on-device storage unit, wherein the storage unit comprises a solid-state drive (SSD) with error correction coding (ECC) capabilities configured to store patient-specific genomic data, mutation profiles, and treatment histories, thereby enabling offline analysis and decision-making in environments with limited network connectivity. [9] The machine learning-enhanced smart device of claim 1, wherein the neural network architecture of the machine learning model optimization unit is further configured to employ unsupervised learning techniques, which techniques enable the device to identify new genetic patterns and potential new biomarkers without prior labeling, thereby expanding the utility of the device in researching and discovering emerging cancer mutations. [10] The machine learning-enhanced smart device of claim 1, wherein the power management unit further comprises an energy-efficient processing framework, the framework using dynamic voltage and frequency scaling (DVFS) to optimize power consumption of the device during genomic data analysis to reduce power waste during off-peak computing times.

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