System and method for detecting and classifying pre-analysis errors in clinical laboratory diagnostics

Abstract

In some embodiments, a method of creating a machine learning based automated authentication system for clinical laboratory diagnostics includes obtaining a biomarker training dataset including biomarker values for a plurality of biomarkers. The method also includes training the auto-encoder with the biomarker training data set as both an input and a target of the auto-encoder to create a trained auto-encoder. The method further includes deploying the trained auto-encoder in a hospital or laboratory. Numerous other embodiments are provided.

Description

[0001] Cross Reference to Related Applications This application claims the benefit under 35 USC § 119(e) of U.S. Provisional Application No. 63 / 579,060, filed August 28, 2023. The entire contents of the patent application(s) cited above are hereby expressly incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to medical testing, and more particularly, to systems and methods for detecting and classifying pre-analytical errors. BACKGROUND

[0003] Clinical laboratories play a vital role in the diagnosis and treatment of disease by analyzing patient samples and providing accurate and reliable test results. However, the process of analyzing these samples is not without its challenges. One such challenge is detecting deviations from expected patient reference intervals, which can be caused by a variety of factors, including pre-analytical errors.

[0004] Pre-analytical errors refer to any mistakes or inconsistencies that occur even before a sample is analyzed. These errors can include issues with sample collection, handling, and storage, as well as inadequate labeling or documentation. Pre-analytical errors can have a significant impact on the accuracy and reliability of test results, necessitating that clinical laboratories detect and address them before any harm is caused.

[0005] To address this challenge, clinical laboratories have developed a method known as laboratory result auto-certification. Auto-certification involves using rule-based software algorithms to automatically review and certify the accuracy of laboratory test results before they are reported to patients and healthcare providers. This helps ensure that only accurate and reliable results are reported, reducing the risk of providing incorrect or misleading information.

[0006] Despite the benefits of auto-certification, there are still several challenges associated with this method. One such challenge is the cost and scarcity of qualified laboratory personnel, who are needed to manually review any results that do not pass the auto-certification criteria. These personnel are highly trained and experienced professionals, who are in high demand, making it difficult for clinical laboratories to maintain a sufficient workforce to meet the needs of their patients.

[0007] Furthermore, the manual review process can be both time-consuming and slow, which can further delay the availability of results to patients and healthcare providers. This delay can have serious consequences, particularly in emergency situations where timely diagnosis and treatment is critical.

[0008] Accordingly, there is a need for improved methods and apparatus for detecting and resolving pre-analytical errors in a clinical laboratory that are more efficient, accurate, and cost effective than current methods. SUMMARY

[0009] In some embodiments, a method of creating a machine learning based automated certification system for clinical laboratory diagnostics includes obtaining a biomarker training dataset, the biomarker training dataset including biomarker values for a plurality of biomarkers; training an autoencoder using the biomarker training dataset as both an input and a target for the autoencoder to create a trained autoencoder; and deploying the trained autoencoder in a hospital or laboratory.

[0010] In some embodiments, a method of creating a machine learning based automated certification system for clinical laboratory diagnostics includes obtaining a first list of biomarkers for automated certification; obtaining a first training dataset for the first list of biomarkers, the first training dataset including biomarker values for each biomarker on the first list of biomarkers; training a first autoencoder using the first training dataset as both an input and a target for the first autoencoder to create a first trained autoencoder; obtaining a second list of biomarkers for automated certification; obtaining a second training dataset for the second list of biomarkers, the second training dataset including biomarker values for each biomarker on the second list of biomarkers; training a second autoencoder using the second training dataset as both an input and a target for the second autoencoder to create a second trained autoencoder; and deploying the first trained autoencoder and the second trained autoencoder.

[0011] In some embodiments, a machine learning based automated certification system for clinical laboratory diagnostics includes a processor; a memory coupled to the processor, the memory including an autoencoder trained on a biomarker training dataset by using the biomarker training dataset as both an input and a target for the autoencoder; and computer program instructions stored in the memory that, when executed by the processor, cause the processor to receive one or more biomarkers measured for a patient; input the measured biomarkers to the autoencoder to generate output biomarkers; and determine whether the measured biomarkers are valid based on a comparison of the measured biomarkers input to the autoencoder to the output biomarkers of the autoencoder.

[0012] In some embodiments, a method of automatically authenticating a clinical laboratory diagnosis includes receiving one or more biomarkers measured for a patient; inputting the measured biomarkers into a self-encoder to generate output biomarkers, wherein the self-encoder is trained on a biomarker training dataset by using the biomarker training dataset as both input and target to the self-encoder; and determining whether the measured biomarkers are valid based on a comparison of the measured biomarkers input to the self-encoder to the output biomarkers of the self-encoder.

[0013] Other features and aspects of the present application will become more fully apparent from the following detailed description, the appended claims and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1A FIG. illustrates an example flow diagram of a method of training and deploying a machine learning based automated authentication system for clinical laboratory diagnoses according to embodiments presented herein.

[0015] Figure 1B FIG. illustrates an example computer in which methods according to one or more embodiments can be implemented. Figure 1A

[0016] Figure 2 FIG. illustrates a flow diagram of a method of creating a machine learning based automated authentication system for clinical laboratory diagnoses according to one or more embodiments.

[0017] Figure 3A FIG. illustrates an example machine learning model that can be trained using a biomarker training dataset according to embodiments presented herein.

[0018] Figure 3B FIG. illustrates an example automated authentication system employing a trained Figure 3A ML model according to embodiments presented herein.

[0019] Figure 4 FIG. illustrates a flow diagram of another method of creating a machine learning based automated authentication system for clinical laboratory diagnoses according to one or more embodiments.

[0020] Figure 5 FIG. illustrates a flow diagram of a method of automated authentication of clinical laboratory diagnoses according to one or more embodiments.

[0021] Figure 6A FIG. illustrates a first machine learning based automated authentication system provided according to one or more embodiments.

[0022] Figure 6B FIG. illustrates a second machine learning based automated authentication system provided according to one or more embodiments.​

[0023] Figure 7 An example graph illustrating the mean squared error between a measured biomarker and a predicted (reconstructed) biomarker generated by an ML model (in response to the measured biomarker) according to one or more embodiments is shown.

[0024] Figure 8A and Figure 8B The illustration shows the process before applying a clustering model according to one or more embodiments. Figure 8A ) and afterwards ( Figure 8B The potential spatial representation of the planar distribution of biomarkers measured.

[0025] Figure 9 The illustration shows a flowchart of a method for automatically authenticating measured biomarkers according to one or more embodiments.

[0026] Figure 10 The illustration shows a flowchart of a method for analyzing measured biomarkers subjected to IV contamination according to one or more embodiments. Detailed Implementation

[0027] Individuals with male or female identities are also included in this term, independent of the use of grammatical terms.

[0028] The embodiments provided herein offer systems and methods for more efficiently, accurately, and cost-effectively detecting and identifying pre-analytical errors in clinical laboratories. In some embodiments, a deep neural network (DNN) architecture based on one or more autoencoders in an outlier detection pattern can be utilized within one or more machine learning-based automated certification systems. Other machine learning models can be employed for outlier detection (e.g., other dimensionality reduction algorithms). In one or more embodiments, the autoencoder model can be trained on a set of valid laboratory results (e.g., biomarkers) from clinical laboratory instruments, middleware, or laboratory information systems (LIS). The use of autoencoders can also be viewed as a dimensionality reduction method, whereby the autoencoder learns an internal representation of the data in a "latent space." Only important features relevant to the dataset (e.g., multiple biomarkers) are encoded in the latent space, while noise and outliers are suppressed. In the prediction (outlier detection) scenario, a reconstruction error (e.g., the biomarker reconstructed by the autoencoder from the measured biomarker) is computed. A high reconstruction error is an indication of one or more outliers (e.g., invalid samples). In some embodiments, once outliers are identified, a second step can be performed to attempt to cluster them using machine learning clustering methods targeting the root cause of the outliers.

[0029] In one or more embodiments, an auto-authentication system can be used to train a hospital or laboratory's auto-authentication system based on autoencoders or other machine learning based auto-authentication systems on a dataset from the hospital or laboratory. Such a dataset represents the target population of the hospital or laboratory (e.g., eliminating the cohort risk of the machine learning model).

[0030] In some embodiments, multiple models (e.g., multiple autoencoders) can be trained for different types of blood or other tests or biomarker panels. For example, different autoencoders can be trained for each of a lipid panel, a complete blood count (CBC) panel, a basic metabolic panel (BMP), a comprehensive metabolic panel (CMP), a thyroid panel, an immunoassay panel, a urine panel, a combination of multiple panels, etc.

[0031] In one or more embodiments, a general model (e.g., utilizing all available input biomarkers) and multiple specific models for different failure models (e.g., utilizing only a subset of relevant biomarkers for each failure model) can be trained (e.g., automatically). As an example, a failure model can be developed to detect IV fluid contamination or other error sources of interest.

[0032] Note that, in general, the systems and methods provided herein can be general purpose and can be generalized to any clinical laboratory. That is, in some embodiments, the ML models (e.g., autoencoders) can be trained on biomarkers for multiple tests and / or on general biomarker data (e.g., from multiple hospitals, laboratories, healthcare data providers, etc.).

[0033] In some embodiments, by creating individual models (e.g., ML models such as autoencoders trained on a particular set of biomarkers), fact-based model result reporting can be produced (e.g., for a particular biomarker pattern, the prevalence of disease states x, y, and pre-analytical error z has been observed). Traditionally, model results are reported as a presence / absence prediction with some confidence bound, such as the chance that a patient has colon cancer is 70%. The applicability of these confidence bounds depends heavily on a factor called "cohort risk," which is a theoretical measure of the difference between the model training population and the model prediction population (especially with respect to the mathematical relationship between the dependent variable and each independent variable). Due to the high variability of biological and ecological systems, cohort risk is often extremely high, resulting in poor models. Fact-based result reporting highlights the most relevant information so that practitioners can cross-reference additional information not directly captured in the biomarkers, such as symptoms.

[0034] As set forth above, in some embodiments provided herein, a two-step machine learning approach (e.g., error detection followed by error classification) can be employed. The two-step machine learning approach can allow for high-level analysis prior error detection and classification regarding possible root causes of errors. A general approach (e.g., training a model on a general biomarker dataset from multiple hospitals or laboratories) can be deployed in any clinical laboratory. Currently, in vitro diagnostics (IVD) and laboratory information system (LIS) manufacturers provide software solutions that allow laboratories to establish automated certification rules, but those rules must be developed, monitored, and updated by each laboratory individually. Designing, implementing, and validating such rules is both complex and time-consuming. The machine learning-based automated certification system and method described herein is customizable, easy to implement, and superior to rule-based approaches.

[0035] Reference is made below to Figures 1A-10 These and other embodiments of the application are described.

[0036] Figure 1A An example flowchart 100a illustrating a method of training and deploying a machine learning-based automated certification system for clinical laboratory diagnostics according to embodiments provided herein is shown. Reference is made to Figure 1A And as described in further detail below, the method of flowchart 100a includes obtaining a biomarker dataset 102 (e.g., from one or more hospitals, one or more laboratories, a healthcare data provider, etc.). The biomarker dataset 102 is then employed to generate a training dataset (a biomarker training dataset 104). In some embodiments, the biomarker dataset 102 is examined and biomarkers of interest are identified. For example, if automated certification is to be performed on a blood panel such as a lipid panel, a CBC panel, a BMP, a CMP, a thyroid panel, or an immunoassay panel, biomarkers related to one or more of these panels can be selected from the biomarker dataset 102 for inclusion in the biomarker training dataset 104. Multiple biomarker values (e.g., 3 or more, 10 or more, etc.) can be employed for each biomarker within the biomarker training dataset 104. Thereafter, the biomarker training dataset 104 can be employed to train a machine learning (ML) model 106. As described below, in some embodiments, the ML model 106 can include one or more autoencoders, although other ML models can be employed. In some embodiments, the entire biomarker dataset 102 can be used as the biomarker training dataset 104.

[0037] The trained ML model 106 can be used as a deployed ML model 108 within a clinical laboratory setting to predict whether test results for a patient sample are valid (e.g., automatically authenticate) or whether the results should be manually reviewed, whether the patient sample should be retested, and / or whether a new patient sample should be obtained. As further described below, predictive models such as clustering algorithms can be employed to facilitate identifying sources of errors associated with testing (e.g., identifying pre-analytical errors such as errors during sample collection, handling, and / or storage, as well as inadequate labeling or documentation).

[0038] Figure 1B FIGURE illustrates an example computer 120 in which methods in accordance with one or more embodiments can be implemented. Figure 1A Figure 1B The computer 120 includes a processor 122 coupled to a memory 124. The memory 124 can include the biomarker dataset 102, the biomarker training dataset 104, and the ML model 106. The memory 124 can also include one or more programs 126 for carrying out the methods described herein when executed by the processor 122, such as examining the biomarker dataset 102 for biomarkers of interest, creating the biomarker training dataset 104 based on the biomarker dataset 102, and the like. In some embodiments, the processor 122 executing one or more of the programs 126 can train the ML model 106 based on the biomarker training dataset 104 (e.g., generate a trained ML model 108 that can be deployed in a hospital, laboratory, etc. Figure 1A ). The memory 124 can include multiple memory units and / or multiple types of memory. In some embodiments, all or a portion of the memory 124 can be external and / or remote to the computer 120. Further, in some embodiments, multiple processors can be employed.

[0039] Figure 2 FIGURE illustrates a flowchart of a method 200 of creating a machine learning based automated authentication system for clinical laboratory diagnostics in accordance with one or more embodiments. Referring to Figure 2 ​The method 200 includes, in block 202, obtaining a biomarker training dataset including biomarker values for a plurality of biomarkers. In some embodiments, the biomarker training dataset can be obtained from a hospital, a laboratory, a healthcare data provider (such as Dandelion Health, Inc. of New York (NY), Prognos Health, Inc. of New York (NY), etc.), or created based on a biomarker dataset obtained from a hospital, a laboratory, a healthcare data provider, etc. For example, the biomarker training dataset can be formed from a subset of biomarkers in a larger biomarker dataset (e.g., biomarkers related to a CBC panel, BMP, CMP, lipid panel, immunoassay panel, urine panel, another biomarker panel, a combination of biomarker panels, etc. can be selected from the larger dataset and used to form the biomarker training dataset). Figure 1A and Figure 1B FIG. 1 illustrates an example biomarker training dataset 104 formed from a biomarker dataset 102.

[0040] In some embodiments, the biomarker training dataset can include biomarkers determined for at least one of a plurality of hospitals and a plurality of laboratories. In other embodiments, the biomarker training dataset can include only biomarkers determined for a particular hospital (e.g., biomarkers determined for at least one of an emergency room, an inpatient clinic, an outpatient clinic, a doctor’s office, and / or a laboratory affiliated with the particular hospital).

[0041] In instances where the biomarker training dataset includes hospital or laboratory data, in some embodiments, the biomarkers present within the biomarker training dataset (e.g., validated biomarkers such as biomarkers validated by a rule-based approach) can be obtained from a laboratory information system. In other embodiments, the biomarker training dataset can include unvalidated biomarkers (e.g., from middleware).

[0042] In one or more embodiments, obtaining the biomarker training dataset can include obtaining a list of biomarkers to be automatically authenticated (e.g., from a hospital or laboratory), and creating the biomarker training dataset based on the list of biomarkers. For example, a hospital or laboratory can want to automatically authenticate biomarkers related to one or more blood panels. The hospital or laboratory can provide a list of specific biomarkers that it plans to automatically authenticate. Based on the provided list of biomarkers, a biomarker training dataset can be generated from a more general biomarker dataset. For example, a biomarker dataset can be obtained from the hospital or laboratory, and a biomarker training dataset can be created based on the biomarker dataset and the list of biomarkers. Alternatively, the biomarker dataset can be provided by another hospital or laboratory, multiple hospitals or laboratories, etc.

[0043] In some embodiments, the list of biomarkers can include a list of biomarkers in a biomarker panel, such as biomarkers from at least one of a lipid panel, a CBC panel, a BMP, a CMP, a thyroid panel, a urine panel, and an immunoassay panel. In another example, the biomarker panel can include routine diagnostic tests with common biomarkers (e.g., biomarkers from a lipid panel, a CBC panel, and a CMP) measured during an annual physical, a routine health check, etc.

[0044] In some embodiments, the biomarker training dataset can include 3 or more biomarker values for each biomarker in the training dataset. Other numbers of biomarker values can be employed (e.g., 4, 5, 10, 20, 50, or more).

[0045] After the biomarker training dataset (e.g., biomarker training dataset 104 of Figure 1A and Figure 1B has been obtained, in block 204, the method 200 includes training the autoencoder using the biomarker training dataset as both input and target for the autoencoder (thereby creating a trained autoencoder). It will be appreciated that other ML-based models can be employed, such as another dimensionality reduction algorithm (e.g., principal component analysis, isometric feature mapping (isomap), t-distributed stochastic neighbor embedding, linear discriminant analysis, uniform manifold approximation and projection, etc.).

[0046] Figure 3A FIG. illustrates an example machine learning model, referred to as an initial ML model 300, that can be trained using a biomarker training dataset (e.g., biomarker training dataset 104 of Figure 1A and Figure 1B is provided herein. In the example of FIG. 3, the initial ML model 300 is a dimensionality reduction algorithm, such as an autoencoder. It will be appreciated that other ML-based models can be employed, such as another dimensionality reduction algorithm (e.g., principal component analysis, isometric feature mapping (isomap), t-distributed stochastic neighbor embedding, linear discriminant analysis, uniform manifold approximation and projection, etc.). Figure 3AIn example embodiments, the initial ML model 300 is an autoencoder that maps biomarkers within the biomarker training dataset to a reduced dimension latent space representation (as described further below). Any suitable autoencoder can be employed, such as a vanilla autoencoder, an undercomplete autoencoder, a denoising autoencoder, a sparse autoencoder, a contractive autoencoder, a convolutional autoencoder, a recurrent autoencoder, a variational autoencoder, and the like. Other dimensionality reduction algorithms can be employed.

[0047] In some embodiments, the latent space representation can encode data across different scales. For example, a normalizing flow, an autoregressive model, a variational autoencoder (VAE), and a deep energy-based model are all examples of deep generative learning. Further, the latent space can be constrained to a known parametric distribution (e.g., Gaussian or Gaussian mixture) or a non-parametric distribution such as with a vector-quantized variational autoencoder (VQ-VAE). It will be appreciated that other ML models can be employed.

[0048] With reference to Figure 3A , the ML model 300 includes an encoder 302 configured to map input biomarkers to a latent space L and a decoder 304 configured to map the latent space L to output biomarkers (e.g., reconstructed biomarkers). The biomarker training dataset can be input to the encoder 302 to generate latent features in the latent space L A (e.g., in a low-dimensional space). The features of the latent space L A may be partitioned into groups of features, such as W, X, Y, and Z as shown in Figure 3A . Each of the features can correspond to a particular attribute of the attributes of the biomarkers of the biomarker training dataset input to the encoder 302. In some embodiments, at least one of the features (e.g., feature Z) can be reserved for intrinsic properties of the biomarker dataset in which variation cannot be controlled by setting.

[0049] During training, the decoder 304 can use the features of the latent space L A to reconstruct the biomarkers input to the encoder 302 (e.g., as similar as possible to the input biomarkers). The reconstruction loss module 306 can compare the input biomarkers to the output (reconstructed) biomarkers to determine whether the input biomarkers and the output biomarkers are close to each other. For example, the reconstruction loss module 306 can analyze the attributes of the input biomarkers and the output biomarkers to determine whether the latent space L A is correct and whether the encoder model (encoder 302) and the decoder model (decoder 304) are properly trained.

[0050] Once the ML model has been trained using both the biomarker training dataset as input and target (e.g., ...), Figure 2 In some embodiments, the autoencoder (as in the example), method 200 includes: in block 206, deploying the trained autoencoder in a hospital or laboratory. In some embodiments, the trained autoencoder may be deployed in the same hospital or laboratory that supplies the biomarker dataset for generating the biomarker training dataset.

[0051] Figure 3B The illustration shows the training process according to the embodiments provided herein. Figure 3A Example of an automated authentication system 350 using ML model 300. (Reference) Figure 3B 300 trained ML models (e.g., Figure 3B The autoencoder in the embodiments can be employed for automated authentication by inputting the measured biomarker 352 into the trained model 300 to generate the output (reconstructed) biomarker 354. The error calculation module 356 can be employed to determine the amount of reconstruction error between the measured biomarker 352 input into the trained ML model 300 and the output (reconstructed) biomarker 354 output from the trained ML model 300. In some embodiments, the reconstruction error can represent one or more mean squared error values ​​of the biomarker. A high reconstruction error can identify an invalid patient sample, while a low reconstruction error can indicate a valid patient sample.

[0052] As further described below, in some embodiments, if a sample is determined to be invalid, a classification model can be used to predict the source of error associated with the patient sample. Figure 3B In the embodiments, the latent space is represented (e.g., latent variables L) A This can be used in conjunction with clustering model 358 to classify the latent spatial features of outliers into clusters associated with pre-analysis errors (e.g., clinical condition clusters, pollution clusters, or any other relevant clusters). Clustering model 358 can include one or more clustering algorithms. For example, clustering algorithms such as k-means clustering or other dimensionality reduction and / or grouping algorithms can be employed. Other example clustering or grouping algorithms that can be employed include t-distributed random neighborhood embedding, isometric feature map embedding, locally linear embedding, spectral embedding, multidimensional scaling, latent Dirichlet assignment, etc.

[0053] In some embodiments, it may be desirable for hospitals or laboratories to employ automated authentication models based on multiple machine learning trained on different sets of biomarkers. For example, separate autoencoders may be trained for different combinations of CBC groups, BMP, CMP, lipid groups, immunoassay groups, biomarker groups, etc.

[0054] Figure 4 FIGURE illustrates a flow diagram of another method 400 of creating a machine learning based automated authentication system for clinical laboratory diagnoses, in accordance with one or more embodiments. Referring to FIGURE Figure 4 , the method 400 includes, in block 402, obtaining a first biomarker list for automated authentication, in block 404, obtaining a first training data set for the first biomarker list, the first training data set including biomarker values for each biomarker on the first biomarker list, and in block 406, training a first autoencoder with the first training data set as both input and target to the first autoencoder to create a first trained autoencoder. The method 400 further includes, in block 408, obtaining a second biomarker list for automated authentication, in block 410, obtaining a second training data set for the second biomarker list, the second training data set including biomarker values for each biomarker on the second biomarker list, and in block 412, training a second autoencoder with the second training data set as both input and target to the second autoencoder to create a second trained autoencoder. Thereafter, the method 400 includes, in block 414, deploying the first trained autoencoder and the second trained autoencoder.

[0055] In some embodiments, the first biomarker list and the second biomarker list can be obtained from a hospital or laboratory (e.g., a hospital or laboratory that plans to use the trained autoencoders). The first training data set and the second training data set can be created from one or more biomarker data sets obtained from the hospital or laboratory that plans to use the trained autoencoders, or from a different source such as another hospital or laboratory, a healthcare data provider, etc.

[0056] In one or more embodiments, the first biomarker list can include a list of biomarkers in a first biomarker panel, and the second biomarker list can include a list of biomarkers in a second biomarker panel. For example, the first and second lists can include different sets of biomarkers from at least one of a lipid panel, a CBC panel, a BMP, a CMP, a thyroid panel, a urine panel, an immunoassay panel, etc.

[0057] Figure 5 FIGURE illustrates a flow diagram of a method 500 of automating clinical laboratory diagnoses, in accordance with one or more embodiments. Referring to FIGURE Figure 5The method 500 includes, in block 502, receiving one or more biomarkers measured for a patient. For example, a patient sample can be collected from a patient at a hospital, an ambulatory care center, an outpatient clinic, a no-appointment-lab, etc. A test can then be performed to measure one or more biomarkers of the patient sample, such as a biomarker of a CBC panel, a BMP, a CMP, a thyroid panel, a urine panel, an immunoassay panel, another biomarker panel, etc. Thereafter, the method 500 includes, in block 504, inputting the measured biomarkers to the autoencoder to generate output biomarkers, wherein the autoencoder is trained on a biomarker training dataset by using the biomarker training dataset as both an input and a target to the autoencoder. As described above, in some embodiments, the biomarker training set used to train the autoencoder can be from the hospital or lab in which the autoencoder is deployed. In other embodiments, the biomarker training set can be from one or more different hospitals and / or labs.

[0058] The method 500 further includes, in block 506, determining whether the measured biomarkers are valid based on a comparison of the measured biomarkers input to the autoencoder to the output biomarkers of the autoencoder. In some embodiments, determining whether the measured biomarkers are valid can include determining a reconstruction error (e.g., at least one mean squared error value or another error metric based on the measured biomarkers of the patient sample and the output (reconstructed) biomarkers of the autoencoder in response to the measured biomarkers) between the measured biomarkers and the output biomarkers.

[0059] Because the autoencoder is trained using the same biomarkers as both an input and a target to the autoencoder, if the biomarkers measured from the patient sample input to the autoencoder are similar to the biomarkers in the biomarker training dataset, then the autoencoder will result in a small reconstruction error. Moreover, if the biomarker training dataset used to train the autoencoder includes validated biomarkers (e.g., from one or more laboratory information systems), then a low reconstruction error between the biomarkers measured from the patient sample and the (reconstructed) biomarkers output from the autoencoder in response to the measured biomarkers indicates that the measured biomarkers are valid. Likewise, a high reconstruction error between the biomarkers measured from the patient sample and the biomarkers output from the autoencoder in response to the measured biomarkers indicates that the measured biomarkers are invalid (e.g., can need to be manually reviewed, the patient sample re-taken, etc.). In this way, a trained autoencoder can be used for automatic authentication of patient biomarkers.

[0060] Figure 6AThe illustration shows a first machine learning-based automated authentication system 600a provided according to one or more embodiments. Reference Figure 6A A first machine learning-based automated authentication system 600a can communicate with a first analyzer 602a configured to determine one or more biomarkers (e.g., creatinine, BUN, BUN / creatinine ratio, chloride, red blood cell width (RDW), sodium, albumin, hematocrit, platelet count, red blood cell or white blood cell count, neutrophils, lymphocytes, eosinophils, etc.) from a patient's blood sample. The resulting biomarkers are fed to a processor 604 and processed by a deployed ML model 606 (e.g., a trained autoencoder, such as...) stored in a memory 608. Figure 3B The trained ML model 300 takes the measured biomarker as input and provides the predicted (reconstructed) biomarker as output.

[0061] Error calculation module 610 (in Figure 6A A tool labeled "error calculator" can be used to determine the amount of reconstruction error (e.g., the mean squared error of the biomarker) between the measured biomarker input to the ML model 606 and the reconstructed biomarker output from the ML model 606. A high reconstruction error can identify an invalid patient sample, while a low reconstruction error can indicate that the patient sample is valid. Figure 7 An example graph illustrating the mean squared error between a measured biomarker according to one or more embodiments and a predicted (reconstructed) biomarker generated by ML model 606 (in response to the measured biomarker). Figure 7 As shown, the mean squared error values ​​of most normal biomarkers are below a predefined threshold (e.g., Figure 7 The mean squared error is approximately 0.013, although any suitable threshold can be used. Most anomalous biomarkers have a mean squared error value higher than the predefined threshold.

[0062] Error calculation module 610 (within memory 608) may include computer program instructions that, when executed by processor 604, cause processor 604 to determine the reconstruction error between the measured biomarker and the output (reconstructed) biomarker of the trained ML model 606 (e.g., determining at least one mean square error value based on the measured biomarker and the output biomarker of the trained autoencoder).

[0063] Clustering model 612 can be used to predict the sources of error for the measured biomarkers in patients. For example, in some embodiments, the latent spatial representation of the measured biomarkers (e.g., Figure 3B The latent variable L in A) can be used with clustering model 612 (e.g., with Figure 3B Similar to clustering model 358, clustering model 612 classifies the latent spatial features of outliers into clusters associated with pre-analysis errors (e.g., clinical condition clusters, contamination clusters, and / or any other relevant clusters). Clustering model 612 may include one or more clustering algorithms (as described above with respect to clustering model 358).

[0064] In one or more embodiments, memory 608 may include one or more programs 614 having computer program instructions that, when executed by processor 604, cause processor 604 to employ clustering model 612 to predict the cause of a difference between a measured biomarker input to ML model 606 (e.g., a trained autoencoder) and the output biomarker obtained by ML model 606. For example, program 614 may use clustering model 612 to determine the classification of the representation of the measured biomarker (such as the latent spatial representation of the measured biomarker generated by ML model 606). In some embodiments, clustering model 612 may cluster the latent spatial representation of the measured biomarker into one of a plurality of clusters that classify (e.g., predict) sources of reconstruction error between the measured biomarker and the output (reconstructed) biomarker. Example clusters include clinical condition clusters, contamination clusters, and / or any other relevant clusters that identify sources of error.

[0065] Figure 8A and Figure 8B The illustration shows the application of clustering model 612 according to one or more embodiments. Figure 6A )Before( Figure 8A ) and afterwards ( Figure 8B The latent spatial representation of biomarkers measured (e.g., Figure 3B The planar distribution of features (W, X, Y, and Z). For example... Figure 8A and Figure 8B As shown, clustering model 612 spatially groups 800 latent features ( Figure 8A ) is divided into multiple clusters: 802, 804, 806, and 808. Figure 8B (More generally, clusters are separated in the feature space.) Each cluster can represent a specific source of pre-analysis error (e.g., clinical condition, contamination, erroneous samples, etc.). For example, such clusters can be determined by training a clustering model on a training dataset that includes known pre-analysis errors.

[0066] Therefore, in some embodiments, the program(s) 614 stored in memory 608 may include computer program instructions that cause processor 604 to employ a clustering algorithm to cluster the representation of the measured biomarker into one of a plurality of clusters, which can be used to classify (e.g., predict the cause) differences (e.g., reconstruction errors) between the measured biomarker and the output biomarker of a trained ML model in response to the measured biomarker. For example, after using clustering model 612, the prediction of the source of error for the measured biomarker can be determined by identifying the cluster(s) in which the potential representation of the measured biomarker exists.

[0067] Figure 6B The illustration shows a second machine learning-based automated authentication system 600b provided according to one or more embodiments. Reference Figure 6B The second machine learning-based automated authentication system 600b can communicate with a first analyzer 602a (and / or a second analyzer 602b or other analyzers not shown) configured to determine one or more biomarkers of a patient sample. The resulting biomarkers are transmitted to a processor 604 and processed by a first ML model 606 stored in memory 608. For example, the first ML model 606 may be a first autoencoder trained on a biomarker dataset of a first blood cohort. The first ML model 606 can be used to automatically authenticate biomarkers measured by the first analyzer 602a (or another analyzer), such as by generating output (reconstructed) biomarkers in response to the measured biomarkers input to the first ML model 606. For example, one or more programs 614 executed by processor 604 may receive biomarkers from first analyzer 602a (e.g., directly, from medical personnel, or via a central computer system), use first ML model 606 to generate reconstructed biomarkers based on the biomarkers received from first analyzer 602a, determine the validity of the biomarkers (e.g., using error calculation module 610 as described above), predict the cause if the biomarkers are invalid (e.g., using clustering model 612 as described above), and / or make recommendations.

[0068] Multiple uniquely trained ML models 606a-606n (e.g., uniquely trained autoencoders) can be used to automatically identify different biomarker groups (e.g., using error calculation model 610) and identify sources of error within such biomarker groups (e.g., using clustering model 612 or other clustering models).

[0069] Figure 9 A flowchart illustrating a method 900 for automatically authenticating measured biomarkers according to one or more embodiments is shown. (Reference)Figure 9 In block 902, the method 900 includes obtaining a reconstruction error between the measured biomarker and an output (reconstructed) biomarker generated by the ML model in response to the measured biomarker. For example, the measured biomarker can be input to a trained autoencoder (such as a generally trained or a specifically trained autoencoder) to generate an output (reconstructed) biomarker. An amount of reconstruction error between the input biomarker and the output biomarker can then be determined.

[0070] In block 904, the method 900 includes determining whether the reconstruction error is high (e.g., above a predetermined threshold). If the reconstruction error is not high, the sample is determined to be valid (block 906); otherwise, in block 908, clustering is performed based on the measured biomarker. For example, a clustering algorithm (as described above) can be used to cluster a reduced dimension representation of the measured biomarker (such as a latent space representation generated by the autoencoder).

[0071] The method 900 includes determining, in block 910, whether the clustering results in a distinct pattern (e.g., whether at least one defined cluster is produced). If no distinct pattern is produced, no determination is made about the source of error in the sample (block 912); otherwise, if a distinct pattern is observed after clustering, a determination can be made as to which cluster(s) the representation of the measured biomarker exists, such as a first outlier class (e.g., cluster) representing sample error due to a clinical condition (block 914), an n-th outlier class representing sample contamination (block 916), and / or the like. Any number of outlier classes can be employed.

[0072] Figure 10 FIG. 1 illustrates a flowchart of a method 1000 of analyzing measured biomarkers subject to IV contamination, in accordance with one or more embodiments. Reference is made to FIG. 1 and FIG. 2 for purposes of explanation. Figure 10 In block 1002, the method 1000 includes obtaining a reconstruction error between the measured biomarker and an output (reconstructed) biomarker generated by the ML model in response to the measured biomarker. For example, the measured biomarker can be input to a trained autoencoder (such as a generally trained or a specifically trained autoencoder) to generate an output (reconstructed) biomarker. An amount of reconstruction error between the input biomarker and the output biomarker can then be determined.

[0073] In block 1004, the method 1000 includes determining whether the reconstruction error is high (e.g., above a predetermined threshold). If the reconstruction error is not high, the sample is determined to be valid (block 1006); otherwise, in block 1008, clustering is performed based on the measured biomarkers. For example, a clustering algorithm (as described above) can be used to cluster a reduced dimension representation of the measured biomarkers, such as a latent space representation generated by an autoencoder.

[0074] The method 1000 includes determining whether the clustering resulted in a distinct pattern (e.g., whether at least one defined cluster was produced), in block 1010. If no distinct pattern was produced, no determination is made about the source of error in the sample (block 1012); otherwise, if a distinct pattern is observed after clustering, a determination can be made about which cluster(s) the representation of the measured biomarkers exists in. In some embodiments, a first outlier class (e.g., cluster) can represent a first level or type of IV contamination (block 1014), a second outlier class can represent a second level or type of IV contamination (block 1016), and a third outlier class can represent a third level or type of IV contamination (block 1018). Any number and / or type of outlier classes can be employed (e.g., clustering for additional levels of IV contamination, additional types of IV contamination, specific biomarkers and / or biomarker relationships as outliers, etc.).

[0075] In some embodiments, the clustering (e.g., outlier classes) can be developed to identify diseases and / or other clinical conditions, such as acute kidney injury (AKI), myocardial infarction, ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), unstable angina, acute coronary syndrome, heart failure, atrial fibrillation, acute liver failure, etc.

[0076] In some embodiments, the reduced dimension model and clustering model can be initially trained to identify only a few clinical conditions. The reduced dimension model and / or clustering model can be retrained to identify additional clinical conditions as training data becomes available (e.g., from use of the trained model in a real-world, clinical setting). Additionally, the reduced dimension model and / or clustering model deployed in a hospital or laboratory can initially be trained with training data from one or more different hospitals or laboratories (or healthcare data providers). Thereafter, the reduced dimension model and / or clustering model can be retrained on data from the location in which the model is deployed (as training data becomes available).

[0077] In some embodiments, a dimensionality reduction model and a clustering model can be trained to assist in microscopic analysis of patient samples. For example, the dimensionality reduction model and the clustering model can be trained to identify, quantify, label, or otherwise facilitate analysis of one or more features of cells, shaped elements, bacteria, parasites, and the like, such as white blood cell differential, red blood cell morphology, body fluid differential, microscopic urine analysis, Gram stain, parasite smear, pathology smear, and the like.

[0078] While some embodiments have been described herein with respect to autoencoders, it will be appreciated that other dimensionality reduction models can be employed, such as normalized flows, autoregressive models, deep energy-based models, generative adversarial networks (GANs), other deep generative learning models, and the like.

[0079] Non-limiting illustrative embodiments Illustrative Embodiment 1. A method of creating a machine learning based automated certification system for clinical laboratory diagnostics, comprising: obtaining a biomarker training data set, the biomarker training data set comprising biomarker values for a plurality of biomarkers; training an autoencoder using the biomarker training data set as both input and target for the autoencoder to create a trained autoencoder; and deploying the trained autoencoder in a hospital or laboratory.

[0080] Illustrative Embodiment 2. The method of illustrative embodiment 1, wherein obtaining the biomarker training data set comprises obtaining a list of biomarkers to be automatically certified from the hospital or laboratory and creating the biomarker training data set based on the list of biomarkers.

[0081] Illustrative Embodiment 3. The method of one of the preceding embodiments, wherein creating the biomarker training data set comprises obtaining a biomarker data set from the hospital or laboratory and creating the biomarker training data set based on the biomarker data set and the list of biomarkers.

[0082] Illustrative Embodiment 4. The method of one of the preceding embodiments, wherein the biomarker training data set comprises at least 3 biomarker values for each biomarker on the list of biomarkers.

[0083] Illustrative Embodiment 5. The method of one of the preceding embodiments, wherein the list of biomarkers comprises a list of biomarkers in a panel of biomarkers.

[0084] Illustrative Embodiment 6. The method of one of the preceding embodiments, wherein the panel of biomarkers includes biomarkers from at least one of a lipid panel, a complete blood count (CBC) panel, a basic metabolic panel (BMP), a comprehensive metabolic panel (CMP), a thyroid panel, a urine panel, and an immunoassay panel.

[0085] Illustrative Embodiment 7. The method of one of the preceding embodiments, wherein the panel of biomarkers includes routine diagnostic tests.

[0086] Illustrative Embodiment 8. The method of one of the preceding embodiments, wherein the routine diagnostic tests include biomarkers from a lipid panel, a complete blood count (CBC) panel, and a comprehensive metabolic panel (CMP).

[0087] Illustrative Embodiment 9. The method of one of the preceding embodiments, further comprising deploying an error calculation module in a hospital or laboratory, the error calculation module configured to determine a reconstruction error between measured biomarkers input to the trained autoencoder and reconstructed biomarkers output from the autoencoder in response to the measured biomarkers.

[0088] Illustrative Embodiment 10. The method of one of the preceding embodiments, further comprising employing the deployed autoencoder on biomarkers measured for a patient sample.

[0089] Illustrative Embodiment 11. The method of one of the preceding embodiments, wherein employing the deployed autoencoder includes: obtaining a patient sample from a patient; performing at least one test on the patient sample to generate measured biomarkers from the patient sample; inputting the measured biomarkers to the autoencoder to generate output biomarkers; and determining whether the measured biomarkers are valid based on a comparison of the measured biomarkers to the output biomarkers.

[0090] Illustrative Embodiment 12. The method of one of the preceding embodiments, wherein the measured biomarkers include at least one of a lipid panel, a complete blood count (CBC) panel, a basic metabolic panel (BMP), a comprehensive metabolic panel (CMP), a thyroid panel, a urine panel, and an immunoassay panel.

[0091] Illustrative Embodiment 13. The method of one of the preceding embodiments, wherein the comparison includes determining a reconstruction error between the measured biomarkers and the output biomarkers.

[0092] Illustrative Embodiment 14. The method of one of the preceding embodiments, wherein determining the reconstruction error includes determining at least one mean squared error value.

[0093] Illustrative Embodiment 15. The method of one of the preceding embodiments, further comprising determining a cause of the difference between the measured biomarker and the output biomarker.

[0094] Illustrative Embodiment 16. The method of one of the preceding embodiments, wherein determining a cause of the difference between the measured biomarker and the output biomarker comprises determining a classification of a representation of the measured biomarker.

[0095] Illustrative Embodiment 17. The method of one of the preceding embodiments, wherein the representation of the measured biomarker comprises a latent space representation of the measured biomarker generated by a self-encoder.

[0096] Illustrative Embodiment 18. The method of one of the preceding embodiments, wherein determining a classification of a representation of the measured biomarker comprises: clustering the representation of the measured biomarker into one of a plurality of clusters using a clustering algorithm; and classifying the difference between the measured biomarker and the output biomarker based on the plurality of clusters.

[0097] Illustrative Embodiment 19. The method of one of the preceding embodiments, wherein the plurality of clusters comprises a clinical condition cluster and a contamination cluster.

[0098] Illustrative Embodiment 20. A method of creating a machine learning based automated certification system for clinical laboratory diagnostics, comprising: obtaining a first list of biomarkers for automated certification; obtaining a first training data set for the first list of biomarkers, the first training data set comprising biomarker values for each biomarker on the first list of biomarkers; training a first self-encoder using the first training data set as both input and target for the first self-encoder to create a first trained self-encoder; obtaining a second list of biomarkers for automated certification; obtaining a second training data set for the second list of biomarkers, the second training data set comprising biomarker values for each biomarker on the second list of biomarkers; training a second self-encoder using the second training data set as both input and target for the second self-encoder to create a second trained self-encoder; and deploying the first trained self-encoder and the second trained self-encoder.

[0099] Illustrative Embodiment 21. The method of illustrative embodiment 20, wherein: obtaining the first biomarker list and the second biomarker list comprises obtaining the first biomarker list and the second biomarker list from a hospital or laboratory; obtaining the first training data set and the second training data set comprises obtaining at least one biomarker data set from the hospital or laboratory and creating the first training data set and the second training data set based on the at least one biomarker data set; and deploying the first trained autoencoder and the second trained autoencoder comprises deploying the first trained autoencoder and the second trained autoencoder in the hospital or laboratory.

[0100] Illustrative Embodiment 22. The method of one of the preceding embodiments, wherein the first biomarker list comprises a list of biomarkers in a first biomarker panel and the second biomarker list comprises a list of biomarkers in a second biomarker panel.

[0101] Illustrative Embodiment 23. The method of one of the preceding embodiments, wherein the first biomarker panel and the second biomarker panel comprise different sets of biomarkers from at least one of a lipid panel, a complete blood count (CBC) panel, a basic metabolic panel (BMP), a comprehensive metabolic panel (CMP), a thyroid panel, a urine panel, and an immunoassay panel.

[0102] Illustrative Embodiment 24. An automated authentication system for machine learning based clinical laboratory diagnostics, comprising: a processor; a memory coupled to the processor, the memory comprising an autoencoder trained on a biomarker training data set by using the biomarker training data set as both an input and a target to the autoencoder; and computer program instructions stored in the memory, which when executed by the processor, cause the processor to: receive one or more biomarkers measured for a patient; input the measured biomarkers to the autoencoder to generate output biomarkers; and determine whether the measured biomarkers are valid based on a comparison of the measured biomarkers input to the autoencoder to the output biomarkers of the autoencoder.

[0103] Illustrative Embodiment 25. The automated authentication system of illustrative embodiment 24, wherein the biomarker training set comprises a biomarker data set from a hospital or laboratory.

[0104] Illustrative Embodiment 26. The automated authentication system of one of the preceding embodiments, wherein the autoencoder is deployed at a hospital or laboratory that provides the biomarker data set.

[0105] Illustrative Embodiment 27. The automatic authentication system of one of the preceding embodiments, wherein the autoencoder is deployed at a hospital or laboratory different from a hospital or laboratory that provided the biomarker dataset.

[0106] Illustrative Embodiment 28. The automatic authentication system of one of the preceding embodiments, wherein the biomarker training set includes biomarkers in a biomarker panel.

[0107] Illustrative Embodiment 29. The automatic authentication system of one of the preceding embodiments, wherein the biomarker panel includes biomarkers from at least one of a lipid panel, a complete blood count (CBC) panel, a basic metabolic panel (BMP), a comprehensive metabolic panel (CMP), a thyroid panel, a urine panel, and an immunoassay panel.

[0108] Illustrative Embodiment 30. The automatic authentication system of one of the preceding embodiments, wherein the biomarker training set includes biomarkers in a routine diagnostic test.

[0109] Illustrative Embodiment 31. The automatic authentication system of one of the preceding embodiments, wherein the routine diagnostic test includes biomarkers from a lipid panel, a complete blood count (CBC) panel, and a comprehensive metabolic panel (CMP).

[0110] Illustrative Embodiment 32. The automatic authentication system of one of the preceding embodiments, further comprising computer program instructions stored in the memory that, when executed by the processor, cause the processor to determine a reconstruction error between the measured biomarkers and the output biomarkers.

[0111] Illustrative Embodiment 33. The automatic authentication system of one of the preceding embodiments, further comprising computer program instructions stored in the memory that, when executed by the processor, cause the processor to determine at least one mean squared error based on the measured biomarkers and the output biomarkers of the autoencoder.

[0112] Illustrative Embodiment 34. The automatic authentication system of one of the preceding embodiments, further comprising computer program instructions stored in the memory that, when executed by the processor, cause the processor to predict a cause of a difference between the measured biomarkers and the output biomarkers of the autoencoder.

[0113] Illustrative Embodiment 35. The automatic authentication system of one of the preceding embodiments, further comprising computer program instructions stored in the memory that, when executed by the processor, cause the processor to determine a classification of a representation of the measured biomarkers.

[0114] Illustrative Embodiment 36. The automatic authentication system of one of the preceding embodiments, wherein the representation of the measured biomarker comprises a latent space representation of the measured biomarker, wherein the latent space representation is generated by the autoencoder.

[0115] Illustrative Embodiment 37. The automatic authentication system of one of the preceding embodiments, further comprising computer program instructions stored in the memory that, when executed by the processor, cause the processor to employ a clustering algorithm to cluster the representation of the measured biomarker into one of a plurality of clusters; and classify a difference between the measured biomarker and the output biomarker based on the plurality of clusters.

[0116] Illustrative Embodiment 38. The automatic authentication system of one of the preceding embodiments, wherein the plurality of clusters comprises a clinical condition cluster and a contamination cluster.

[0117] Illustrative Embodiment 39. The automatic authentication system of one of the preceding embodiments, further comprising computer program instructions stored in the memory that, when executed by the processor, cause the processor to recommend manual handling of a test used to obtain the one or more biomarkers measured for the patient based on a difference between the measured biomarker and the output biomarker of the autoencoder.

[0118] Illustrative Embodiment 40. A method of automatically authenticating a clinical laboratory diagnosis, comprising: receiving one or more biomarkers measured for a patient; inputting the measured biomarker to an autoencoder to generate an output biomarker, wherein the autoencoder is trained on a biomarker training dataset by using the biomarker training dataset as both an input and a target to the autoencoder; and determining whether the measured biomarker is valid based on a comparison of the measured biomarker input to the autoencoder to the output biomarker of the autoencoder.

[0119] Illustrative Embodiment 41. The method of Illustrative Embodiment 40, wherein the biomarker training set comprises a biomarker dataset from a hospital or laboratory, and wherein the autoencoder is deployed at the hospital or laboratory that provided the biomarker dataset.

[0120] Illustrative Embodiment 42. The method of one of the preceding embodiments, wherein the biomarker training set comprises a biomarker dataset from a first hospital or laboratory, and wherein the autoencoder is deployed at a second hospital or laboratory different from the first hospital or laboratory that provided the biomarker dataset.

[0121] Illustrative Embodiment 43. The method of one of the preceding embodiments, wherein the autoencoder is a variational autoencoder.

[0122] The foregoing description merely explains illustrative embodiments of the application. Modifications to the above disclosed apparatus and method of operation that fall within the scope of the application will be readily apparent to those having ordinary skill in the art.

[0123] Accordingly, while the application has been disclosed in connection with example embodiments, it will be understood that other embodiments can fall within the spirit and scope of the application, as defined by the following claims.

Claims

1. A method for creating a machine learning-based automated authentication system for clinical laboratory diagnostics, comprising: Obtain the biomarker training dataset, which includes biomarker values ​​for multiple biomarkers; The autoencoder is trained by using a biomarker training dataset as both input and target data. as well as Deploy trained autoencoders in hospitals or laboratories.

2. The method of claim 1, wherein obtaining the biomarker training dataset comprises obtaining a list of biomarkers to be automatically certified from a hospital or laboratory, and creating the biomarker training dataset based on the list of biomarkers.

3. The method of claim 2, wherein creating the biomarker training dataset comprises obtaining a biomarker dataset from a hospital or laboratory, and creating the biomarker training dataset based on the biomarker dataset and the biomarker list.

4. The method of claim 3, wherein the biomarker training dataset comprises at least three biomarker values ​​for each biomarker in the biomarker list.

5. The method of claim 2, wherein the biomarker list comprises a list of biomarkers in a biomarker group.

6. The method of claim 5, wherein the biomarker group comprises biomarkers from at least one of the lipid group, the complete blood cell count (CBC) group, the basal metabolic group (BMP) group, the comprehensive metabolic group (CMP) group, the thyroid group, the urine group, and the immunoassay group.

7. The method of claim 5, wherein the biomarker group comprises routine diagnostic tests.

8. The method of claim 7, wherein the routine diagnostic test comprises biomarkers from the lipid group, the complete blood count (CBC) group, and the comprehensive metabolic group (CMP).

9. The method of claim 1, further comprising deploying an error calculation module in a hospital or laboratory, the error calculation module being configured to determine a reconstruction error between a measured biomarker input to a trained autoencoder and a reconstructed biomarker output from the autoencoder in response to the measured biomarker.

10. The method of claim 1, further comprising employing the deployed autoencoder on the biomarkers measured for the patient sample.

11. The method of claim 10, wherein employing the deployed autoencoder comprises: Obtain patient samples from patients; Perform at least one test on the patient sample to generate the measured biomarker from the patient sample; The measured biomarkers are input into an autoencoder to generate output biomarkers; as well as The effectiveness of the measured biomarker is determined by comparing it with the output biomarker.

12. The method of claim 11, wherein the biomarker measured comprises at least one of the lipid group, complete blood cell count (CBC) group, basal metabolic group (BMP) group, comprehensive metabolic group (CMP) group, thyroid group, urine group, and immunoassay group.

13. The method of claim 11, wherein the comparison includes determining the reconstruction error between the measured biomarker and the output biomarker.

14. The method of claim 13, wherein determining the reconstruction error includes determining at least one mean square error value.

15. The method of claim 11, further comprising determining the cause of the difference between the measured biomarker and the output biomarker.

16. The method of claim 15, wherein determining the cause of the difference between the measured biomarker and the output biomarker includes determining the classification of the representation of the measured biomarker.

17. The method of claim 16, wherein the representation of the measured biomarker comprises a latent spatial representation of the measured biomarker generated by an autoencoder.

18. The method of claim 16, wherein determining the classification of the representation of the measured biomarker comprises: Clustering algorithms are used to cluster the representations of the measured biomarkers into one of multiple clusters; as well as The differences between the measured biomarkers and the output biomarkers are classified based on the multiple clusters mentioned above.

19. The method of claim 18, wherein the plurality of clusters includes clinical condition clusters and contamination clusters.

20. A method for creating a machine learning-based automated authentication system for clinical laboratory diagnostics, comprising: Obtain a list of primary biomarkers for automated authentication; Obtain a first training dataset for a first biomarker list, the first training dataset including the biomarker value of each biomarker on the first biomarker list; The first training dataset is used as both the input and target of the first autoencoder to train the first autoencoder, thereby creating the first trained autoencoder; Obtain a list of second biomarkers for automated authentication; Obtain a second training dataset for the second biomarker list, the second training dataset including the biomarker value of each biomarker on the second biomarker list; The second training dataset is used as both the input and target of the second autoencoder to train the second autoencoder, thereby creating the second trained autoencoder; as well as Deploy the first trained autoencoder and the second trained autoencoder.

21. The method of claim 20, wherein: Obtaining the list of primary biomarkers and the list of secondary biomarkers includes obtaining the list of primary biomarkers and the list of secondary biomarkers from the hospital or laboratory; Obtaining the first training dataset and the second training dataset includes obtaining at least one biomarker dataset from a hospital or laboratory, and creating the first training dataset and the second training dataset based on the at least one biomarker dataset; as well as Deploying the first trained autoencoder and the second trained autoencoder includes deploying the first trained autoencoder and the second trained autoencoder in a hospital or laboratory.

22. The method of claim 21, wherein the first biomarker list comprises a list of biomarkers in a first biomarker group, and the second biomarker list comprises a list of biomarkers in a second biomarker group.

23. The method of claim 22, wherein the first biomarker group and the second biomarker group comprise a different set of biomarkers from at least one of the lipid group, the complete blood cell count (CBC) group, the basal metabolic group (BMP) group, the comprehensive metabolic group (CMP) group, the thyroid group, the urine group, and the immunoassay group.

24. A machine learning-based automated authentication system for clinical laboratory diagnostics, comprising: processor; The memory coupled to the processor includes an autoencoder trained on the biomarker training dataset by using the biomarker training dataset as both input and target of the autoencoder. as well as Computer program instructions stored in memory, when executed by a processor, cause the processor to: Receive one or more biomarkers measured for the patient; The measured biomarkers are input into an autoencoder to generate output biomarkers; as well as The validity of a measured biomarker is determined by comparing the biomarker input to the autoencoder with the biomarker output from the autoencoder.

25. The automated authentication system of claim 24, wherein the biomarker training set comprises a biomarker dataset from a hospital or laboratory.

26. The automated authentication system of claim 25, wherein the autoencoder is deployed at a hospital or laboratory that provides a biomarker dataset.

27. The automated authentication system of claim 25, wherein the autoencoder is deployed at a different hospital or laboratory than the hospital or laboratory providing the biomarker dataset.

28. The automated authentication system of claim 24, wherein the biomarker training set comprises biomarkers from a biomarker group.

29. The automated authentication system of claim 28, wherein the biomarker group comprises biomarkers from at least one of the lipid group, the complete blood cell count (CBC) group, the basal metabolic group (BMP) group, the comprehensive metabolic group (CMP) group, the thyroid group, the urine group, and the immunoassay group.

30. The automated authentication system of claim 24, wherein the biomarker training set comprises biomarkers from routine diagnostic tests.

31. The automated authentication system of claim 30, wherein the routine diagnostic tests include biomarkers from the lipid group, the complete blood count (CBC) group, and the comprehensive metabolic group (CMP).

32. The automated authentication system of claim 24, further comprising computer program instructions stored in a memory, which, when executed by a processor, cause the processor to determine a reconstruction error between the measured biomarker and the output biomarker.

33. The automatic authentication system of claim 32, further comprising computer program instructions stored in a memory, which, when executed by a processor, cause the processor to determine at least one mean square error based on the measured biomarker and the output biomarker of the autoencoder.

34. The automated authentication system of claim 24, further comprising computer program instructions stored in a memory, which, when executed by a processor, cause the processor to predict the cause of the difference between the measured biomarker and the output biomarker of the autoencoder.

35. The automated authentication system of claim 34, further comprising computer program instructions stored in a memory, which, when executed by a processor, cause the processor to determine the classification of the representation of the measured biomarker.

36. The automated authentication system of claim 35, wherein the representation of the measured biomarker includes a latent spatial representation of the measured biomarker, wherein the latent spatial representation is generated by an autoencoder.

37. The automatic authentication system of claim 36, further comprising computer program instructions stored in a memory, which, when executed by a processor, cause the processor to... Clustering algorithms are used to cluster the representations of the measured biomarkers into one of several clusters; and The differences between the measured biomarkers and the output biomarkers are classified based on the multiple clusters mentioned above.

38. The automated authentication system of claim 37, wherein the plurality of clusters includes a clinical condition cluster and a contamination cluster.

39. The automated authentication system of claim 24, further comprising computer program instructions stored in memory, which, when executed by a processor, cause the processor to recommend manual processing of a test to obtain the one or more biomarkers measured for the patient, based on the difference between the measured biomarker and the output biomarker of the autoencoder.

40. A method for automatically certifying clinical laboratory diagnostics, comprising: Receive one or more biomarkers measured for the patient; The measured biomarkers are input into an autoencoder to generate output biomarkers, wherein the autoencoder is trained on a biomarker training dataset using both the biomarker training dataset as input and target. as well as The validity of a measured biomarker is determined by comparing the biomarker input to the autoencoder with the biomarker output from the autoencoder.

41. The method of claim 40, wherein the biomarker training set comprises a biomarker dataset from a hospital or laboratory, and wherein the autoencoder is deployed at the hospital or laboratory providing the biomarker dataset.

42. The method of claim 40, wherein the biomarker training set comprises a biomarker dataset from a first hospital or laboratory, and wherein the autoencoder is deployed at a second hospital or laboratory different from the first hospital or laboratory providing the biomarker dataset.

43. The method of claim 40, wherein the autoencoder is a variational autoencoder.

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