System and method for maintaining data integrity in a health analysis platform by evaluating and correcting physiological measurements based on filtered healthcare data

Abstract

To maintain data integrity in a health analysis platform.SOLUTION: Systems and methods for maintaining data integrity in a computer health analysis platform are disclosed. For example, the method includes (i) filtering the existing healthcare data by first determining or extracting a first subset of data of the dataset such that the first subset focuses on a common health-related attribute, (ii) generating a reference measurement range from the extracted first subset, and (iii) determining, based on the reference measurement range, a unit of measurement of the clinical laboratory data that lacks the unit of measurement or exhibits a misdescription error. For example, the first set of data represents measurements of a physiological parameter of the entity. For example, the clinical study data is different from the first subset or the existing healthcare data used to determine the first subset. After the unit of measure is determined, a data structure representing the unit of measure is generated and stored.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present specification generally relates to a system and method for maintaining data integrity in a health analysis platform by evaluating and correcting physiological measurements of filtered healthcare data. [Background technology]

[0002] Generally, a user's health can be assessed by measuring one or more physiological characteristics of the user and comparing the measured physiological characteristics to health criteria. For example, the health criteria can correspond to or be derived from existing healthcare data, including historical data, previous laboratory test data, real-time data, and other existing healthcare data. Thus, having accurately measured existing healthcare data can improve the quality of the assessment. Summary of the Invention

[0003] An embodiment according to the present disclosure includes a system for maintaining data integrity in a computational health analysis platform, the system including at least one processor and a memory subsystem communicatively coupled to the at least one processor. The memory subsystem stores instructions that, when executed by the at least one processor, cause the at least one processor to perform operations including: (i) accessing one or more first data structures including a plurality of first datasets for a plurality of entities; (ii) determining a first subset of the first datasets based on one or more health-related attributes of the plurality of entities; (iii) generating reference measurement ranges for one or more physiological parameters of the plurality of entities in the first subset of the first datasets in one or more units of measurement; (iv) accessing one or more second data structures including one or more second datasets for one or more target entities; (v) determining, based on the reference measurement ranges, units of measurement for one or more measurements that are missing units of measurement or exhibit a misprint error related to the units of measurement; (vi) generating a third data structure representing the units of measurement; and (vii) storing the third data structure in a hardware storage device. Each of the first data sets represents measurements of one or more physiological parameters of a plurality of entities and one or more health-related attributes of a plurality of entities, and the machine transmitting the data structure determines that a system accessing the data structure is authorized to access the data structure. The one or more second data sets represent one or more measurements of one or more physiological parameters of one or more target entities, and the one or more target entities exhibit one or more health-related attributes. One or more measurements of the one or more target entities lack units of measurement or exhibit typographical errors related to the units of measurement.

[0004] An embodiment according to the present disclosure includes a method for maintaining data integrity in a computer health analysis platform, the method including: (i) accessing, by an electronic device, one or more first data structures including a plurality of first datasets for a plurality of entities; (ii) determining, by the electronic device, a first subset of the first datasets based on one or more health-related attributes of the plurality of entities; (iii) generating, by the electronic device, reference measurement ranges for one or more physiological parameters of the plurality of entities in the first subset of first datasets in one or more units of measurement; (iv) accessing, by the electronic device, one or more second data structures including one or more second datasets for one or more target entities; (v) determining, by the electronic device, units of measurement for the one or more measurements based on the reference measurement ranges; (vi) generating, by the electronic device, a third data structure representing the units of measurement; and (vii) storing, by the electronic device, the third data structure in hardware storage. Each of the first data sets represents measurements of one or more physiological parameters of a plurality of entities and one or more health-related attributes of a plurality of entities, and the machine transmitting the data structure determines that the system accessing them is authorized to access them. The one or more second data sets represent one or more measurements of one or more physiological parameters of one or more target entities, and the one or more target entities exhibit one or more health-related attributes. One or more measurements of the one or more target entities lack units of measurement or exhibit typographical errors related to the units of measurement.

[0005] Other embodiments of this aspect include corresponding computer systems, devices, and computer programs stored on one or more computer storage devices, each configured to perform the processes or actions described herein. One or more computer systems may be configured to perform particular processes or cause the system to perform a process through software, firmware, hardware, or a combination thereof installed on the system that causes the process to be performed during operation. Also, one or more computer programs may be configured to perform particular processes by including instructions that, when executed by a data processing device, cause the device to perform the process.

[0006] The details of one or more embodiments of the subject matter herein are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, drawings, and claims. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram of an exemplary data integrity maintenance system. [Figure 2] 1 is an example of a software or algorithm utilized by an electronic device. [Figure 3] FIG. 1 is a flowchart diagram of an exemplary process for determining units of measure for clinical test data. [Figure 4] FIG. 1 is a flowchart diagram of an exemplary process for determining units of measure for laboratory test data and correcting the laboratory test data. [Figure 5] FIG. 1 is a diagram of an exemplary computer system. [Figure 6] 1 is a frequency graph illustrating the relationship between frequency and units of measurement of physiological measurements. DETAILED DESCRIPTION OF THE INVENTION

[0008] Like reference numbers and designations in the various drawings indicate like elements.

[0009] Accurate laboratory data is important in various aspects of medical care. For example, in clinical settings, accurate laboratory test results are essential for diagnosing and monitoring disease because they provide important information about a patient's health status. For example, blood test data can contain important physiological parameters indicative of disease states or indicators such as diabetes, cardiovascular disease, and infectious diseases. Accurate laboratory data is also important in clinical trials, serving as reference data for evaluating the efficacy and safety of treatments. Furthermore, in research settings, accurate laboratory data forms the basis for accurate understanding, correct diagnoses, and the advancement of medical research. Inaccurate laboratory data can lead to misdiagnoses, ineffective medications, and misleading research results, ultimately compromising patient safety and hindering medical progress.

[0010] However, laboratory data may exhibit inconsistencies or errors in the data. For example, laboratory data that includes measurements (such as physiological parameters) may lack units of measurement or exhibit misprint errors.

[0011] Inconsistencies can frequently occur for a variety of reasons, including, for example, variations in test methodologies, specimens, units of measurement, in vitro diagnostic devices, different data entry standards, human error, sensor inaccuracies, inconsistencies in data formats, and more.

[0012] In particular, missing or mislabeled units of measurement for data entry (e.g., units of measurement are mislabeled or inaccurately documented) can lead to significant errors in data interpretation and analysis. For example, if a lab report incorrectly reports cholesterol levels in grams per liter (g / L) when they should be in milligrams per deciliter (mg / dL), this can lead to a misinterpretation of a patient's health status and potentially to inappropriate treatment decisions.

[0013] Therefore, when medical evaluations, treatment decisions, or medical research rely on such laboratory data that lack units of measurement or exhibit incorrect labeling errors, it can lead to misdiagnoses, ineffective treatments, and misleading research results, which can compromise patient safety and hinder medical progress.

[0014] Embodiments according to the present disclosure address the above data problem by at least [1] filtering existing healthcare data (e.g., historical data, prior laboratory data, real-time data, and other existing healthcare data) by first determining or extracting a first subset of data such that the first subset of data focuses on common health-related attributes; [2] generating a reference measurement range from the extracted first subset of data; and [3] determining units of measurement for laboratory test data that are missing units of measurement or exhibit misprint errors based on the reference measurement range.

[0015] For example, the data integrity maintenance system can filter such existing healthcare data by at least initially determining or extracting a first subset of data based on health-related attributes. For example, the first subset of time series data can be determined or extracted based on one or more health-related attributes that match the subject of the clinical test data to be evaluated (e.g., for accuracy of the measurement data). Health-related attributes can include disease indicators, medical conditions other than disease indicators, the same medication use, the same medical treatment, gender, etc. Determining or extracting this first subset of time series data can advantageously adjust the range of the physiological measurement data based on commonalities of the health-related attributes. Thus, by generating a reference measurement range based on these commonalities in health-related attributes between the existing healthcare data and the test data, a more accurate evaluation can be performed on the test data based on such reference measurement range. For example, the reference measurement range can be used to determine whether the clinical test data is missing or exhibits a misprint error in a measurement unit and to determine the correct measurement unit for the corresponding clinical test data.

[0016] After the first subset of data is determined or extracted based on the health-related attributes, a reference measurement range can be generated. For example, generating a reference measurement range includes: [1] comparing physiological parameters of the first subset of data with each other; and [2] converting different measurement units of the physiological parameters of the first subset into one or more measurement units. Filtering the first subset of data to have a reference measurement range in one or more measurement units ensures data uniformity and comparability. For example, when a reference measurement range is generated in one or more single units (e.g., multiple reference measurement ranges for cholesterol levels, such as mg / dL, g / L, etc.), such a reference measurement range not only ensures data uniformity but also simplifies calculations and reduces errors (e.g., when comparing the reference measurement range with different data or clinical test data).

[0017] After the reference measurement range is generated, the units of measurement for the clinical test data that exhibit missing or misprinted units of measurement errors can be determined based on the reference measurement range (i.e., adjusted based on health-related attributes). Furthermore, a determination of whether the clinical data exhibits missing or misprinted units of measurement errors can be made before determining the units of measurement. For example, a determination of whether the clinical test data exhibits missing or misprinted units of measurement errors can be made based on a comparison of the measurements of the clinical data that share a common health-related attribute with the reference measurement range. Once the units of measurement have been determined for the corresponding clinical test data that exhibit missing or misprinted units of measurement errors, an indicator or notification can be generated on the display of the user interface, as well as a frequency graph depicting the relationship between measurement frequency and units of measurement.

[0018] Furthermore, the data quality of the clinical data can be improved by preventing the processing of incomplete or erroneous clinical data. The data quality of the clinical data can be improved by incorporating the determined unit of measurement into missing units of measurement in the corresponding clinical data or by replacing each unit of measurement in the corresponding clinical data that exhibits a misprint error with the determined unit of measurement.

[0019] Furthermore, based on improved data quality, the accuracy of clinical data can lead to reliable health assessments, effective treatment decisions, and the promotion of medical research. For example, accurate clinical data can be used as reference data for clinical trials to evaluate the effectiveness and safety of treatments, for ongoing health monitoring and diagnosis to assess patient status, and for research purposes.

[0020] Furthermore, the embodiments described herein can also reduce the amount of computer resources consumed during computing of healthcare data. For example, when generating a health assessment, a computer system that encounters low-quality data may generate results with errors and / or inconsistencies that make it unusable. Thus, the computer system may reprocess the data multiple times (e.g., based on manual feedback from a user) until a satisfactory result is obtained. Such iterative operations may increase the amount of computing resources (e.g., CPU usage, computer memory resources, storage resources, etc.) consumed during the health assessment process. The embodiments described herein can be used to automatically identify and correct typographical errors and / or inconsistencies, thereby [1] reducing the likelihood that healthcare data will be reprocessed due to low quality and [2] reducing the consumption of computer resources.

[0021] 1 illustrates an exemplary data integrity maintenance system 100. In particular, the data integrity maintenance system 100 can: [1] filter existing healthcare data; [2] generate a reference measurement range from a first subset of the extracted data; and [3] determine units of measurement for laboratory test data that are missing units of measurement or exhibit misprint errors based on the reference measurement range.

[0022] Data integrity maintenance system 100 may include electronic device 120 and sensor device 110 communicatively coupled to each other (e.g., via one or more wired or wireless communication links 150). Generally, data integrity maintenance system 100 accesses a data structure (e.g., health-related data, such as existing healthcare data or lab test data, stored in a data store, such as database module 122, or otherwise accessible to electronic device 120, e.g., via a server) through a processing method according to embodiments described herein and determines the data integrity (e.g., data quality) of the data structure. Additionally, in some embodiments, data integrity maintenance system 100 acquires sensor data about a user using sensor device 110 and processes the sensor data using electronic device 120 to determine one or more biomarkers indicative of the user's medical condition.

[0023] Generally, electronic device 120 can include any number of devices configured to receive, process, and transmit data. Examples of electronic device 120 include client computing devices (e.g., desktop computers or notebook computers), server computing devices (e.g., server computers or cloud computing systems), mobile computing devices (e.g., cellular phones, smartphones, tablets, personal data assistants, notebook computers with network capabilities), wearable computing devices (e.g., smartphones or headsets), and other computing devices capable of receiving, processing, and transmitting data. In some embodiments, electronic device 120 can include computing devices that operate using one or more operating systems (e.g., Microsoft Windows, Apple macOS, Linux, Unix, Google Android, and Apple iOS, etc.) and one or more architectures (e.g., x86, PowerPC, and ARM, etc.).

[0024] The sensor device 110 includes one or more sensors 112 configured to obtain measurements related to a user's physiology, user behavior, and / or other characteristics of the user. For example, the sensor device 110 can include or correspond to a wearable device (e.g., a smart watch), a smartphone, a medical monitoring system, a clinical device, etc. By way of example, the sensor device can include one or more sensors 112 configured to obtain physiological parameters including vital signs such as glucose level, heart rate, blood pressure, respiratory rate, body temperature, etc. For example, the one or more sensors can be optical sensors (e.g., PPG), pulse pressure sensors (PP), pressure sensors, electrocardiograms (ECG), bioimpedance sensors, galvanic skin response sensors, intraocular pressure / contact sensors, accelerometers, gyroscopes, pressure sensors, acoustic sensors, electromechanical motion sensors, and / or electromagnetic sensors. Additionally, for example, if the sensor device takes the form of a laboratory device, it can also measure physiological parameters or perform blood tests such as analyzing blood glucose levels, cholesterol, and other biomarkers.

[0025] Additionally, the sensor device 110 includes a communication module 116 configured to transmit and / or receive data from the electronic device 120. By way of example, the communication module 116 may include one or more receivers, transmitters, and / or transceivers. In some implementations, the communication module 116 may communicate with the electronic device 120 via one or more wireless links (e.g., serial links, Ethernet links, etc.) and / or radio links (e.g., Via links, Bluetooth links, etc.).

[0026] Generally, electronic device 120 is configured to receive sensor data (e.g., physiological parameter data such as clinical parameters) acquired by sensor device 110 and process the sensor data to determine one or more biomarkers indicative of a medical condition of the user. Furthermore, electronic device 120 is configured to present information regarding the biomarkers and any other information to the user and / or other users (e.g., healthcare providers).

[0027] In FIG. 1 , electronic device 120 is depicted as a single component. However, in practice, electronic device 120 may be implemented in one or more computing devices (e.g., each computing device includes at least one processor, such as a microprocessor or microcontroller). As one example, electronic device 120 may be a single computing device, such as a single smartphone. As another example, electronic device 120 may include multiple computing devices connected via a network (e.g., the Internet, a local area network, etc.), and the components of electronic device 120 may be maintained and operated on some or all of the computing devices. For example, electronic device 120 may include multiple computing devices, and the components of electronic device 120 may be distributed across one or more of these computing devices.

[0028] Additionally, electronic device 120 is illustrated as a separate component from sensor device 110. However, electronic device 120 can be a separate component from sensor device 110, but electronic device 120 can also include, be coupled to, or be adjacent to (e.g., within a housing for) sensor device 110. For example, electronic device 120 can be a wearable device that includes, is coupled to, or is adjacent to sensor device 110.

[0029] 1, electronic device 120 includes database module 122, communication module 124, processing module 126, and user interface module 128. The operational modules may be provided as one or more computer-executable software modules, hardware modules, or their respective computing devices. For example, one or more operational modules may be implemented as blocks of software code having instructions that cause one or more processors to perform the operations described herein. Additionally or alternatively, one or more of the operational modules may be implemented in electronic circuitry, such as, for example, a programmable logic circuit, a field programmable logic array (FPGA), or an application-specific integrated circuit (ASIC).

[0030] Communications module 124 is configured to transmit and / or receive data from sensor device 110. By way of example, communications module 124 may include one or more receivers, transmitters, and / or transceivers. In some embodiments, communications module 124 may communicate with sensor device 110 (e.g., via communications module 116) over one or more wired links (e.g., serial links, Ethernet links, etc.) and / or wireless links (e.g., Wi-Fi links, Bluetooth links, etc.).

[0031] The database module 122 maintains information related to the operation of the data integrity maintenance system 100 .

[0032] As an example, the database module 122 can store input data 122a that is used as input for determining one or more biomarkers indicative of a user's health. As an example, the input data 122a can include at least a portion of the sensor data generated by the sensor device 110.

[0033] As another example, the database module 122 can store output data 122b generated by the electronic device 120. As an example, the output data 122b can include one or more metrics or biomarkers generated by the electronic device 120 based on the input data 122a.

[0034] Additionally, the database module 122 may store processing rules 122c that specify how the data in the database module 122 may be processed to perform the operations described herein.

[0035] As an example, the processing rules 122c may include one or more rules that specify how the input data 122a is to be formatted, parsed, and processed to determine one or more corresponding metrics or biomarkers for the user.

[0036] As another example, processing rules 122c may include one or more rules that specify the conditions under which data is presented to a user (e.g., using user interface module 128) and how the data is presented.

[0037] As another example, processing rules 122c may include one or more rules that specify how data is stored for future retrieval and / or processing (eg, using database module 122).

[0038] Exemplary data processing techniques are described in further detail below.

[0039] The processing module 126 processes data stored or otherwise accessible on the electronic device 120. For example, the processing module 126 may be used to perform one or more of the operations described herein (e.g., by executing processing rules 122c on the input data 112a to generate output data 122b).

[0040] User interface module 128 is configured to present information to and / or receive input from a user. As an example, user interface module 128 may include one or more display devices (e.g., display screen, touch screen, etc.) configured to present a user interface (e.g., a graphical user interface, GUI) that allows a user to interact with electronic device 120 and / or sensor device 110. Exemplary interactions include viewing data, transmitting data from one component to another, and / or issuing commands to electronic device 120 and / or sensor device 110. Commands may include, for example, a user instructing one or more of electronic device 120 and / or sensor device 110 to perform a particular action or task. In some implementations, user interface module may also present information to the user audibly (e.g., using one or more speakers) and / or via haptic feedback (e.g., using one or more haptic generators, such as via generation).

[0041] In some implementations, software applications may be used to facilitate performance of the tasks described herein. By way of example, the applications may be installed on electronic device 120. Furthermore, a user may interact with the applications to input data and / or commands to electronic device 120 and to review data generated by electronic device 120.

[0042] 2 is an exemplary implementation 200 of software or algorithms utilized by a processor-based electronic device (e.g., electronic device 120 of data integrity maintenance system 100 of FIG. 1 , a computing device (which may also be a server) of system 500 of FIG. 5 ). In particular, the software or algorithm is utilized by the electronic device to: [1] filter existing healthcare data; [2] generate a reference measurement range from a first subset of the extracted data; and [3] determine units of measurement for laboratory test data that are missing units of measurement or exhibit misprint errors based on the reference measurement range.

[0043] Example embodiment 200 shows a data store 210 and unit of measure determination software 220 .

[0044] The data store 210 may include or correspond to a data store of an electronic device (which may also be a server). For example, the data store 210 may be the database module 122 of the electronic device 120 and one or more storage devices 530 of a computer system (which may also be a server) of the system 500. The data store 210 may be in data communication with the electronic device (which may also be a server).

[0045] The data store 210 may include one or more first data structures 212 and one or more second data structures 216. The first data structure 212 may include or correspond to existing healthcare data (e.g., historical data, prior clinical test data, real-time data, and other existing healthcare data). For example, such existing healthcare data may be used by the electronic device to generate reference measurement ranges used to [1] determine whether a second data set (e.g., clinical test data) in the second data structure 216 is missing a unit of measurement or exhibits a misprint error, and [2] determine the correct unit of measurement for the corresponding clinical test data. For example, the first data structure 212 may include a first data set related to an entity (e.g., an individual, a user, a patient, a subject, or the like). Each of the first data sets may represent measurements of one or more physiological parameters 213 of the entity and one or more health-related attributes 214 of the entity. For example, the one or more physiological parameters may represent clinical parameters collected continuously at regular intervals. For example, the one or more physiological parameters may represent one or more vital signs, such as glucose level, heart rate, blood pressure, respiratory rate, body temperature, etc. For example, the one or more health-related attributes may include at least one of a disease indicator, a medical condition other than a disease indicator, the same medication use, the same medical treatment, gender, etc.

[0046] The second data structure 216 can include or correspond to clinical test data that is subject to evaluation for data integrity (e.g., whether measurements are missing units of measurement or exhibit misprint errors). For example, the second data structure 216 can include second data sets related to the target entities. For example, each of the second data sets represents measurements of one or more physiological parameters 217 of the target entities and one or more health-related attributes 218 of the target entities.

[0047] In some implementations, first data structure 212 and second data structure 216 may be stored in different data stores. For example, first data structure 212 may be stored on a separate server (e.g., a computing device (which may also be a server) of system 500), while second data structure 216 may be stored in a data store on an electronic device (e.g., assuming in this example that the electronic device does not take the form of a server).

[0048] Additionally, at least a portion of the data filtering or unit detection can be implemented as respective software programs that can be executed on an electronic device. The software programs can be stored in a memory (e.g., database module 122 in FIG. 1 , memory 520, storage device 530 in FIG. 5 ), and can include machine-readable instructions that, when executed by a processor, cause the processor-based electronic device to carry out the instructions of the software program. As shown, unit of measurement determination software 220 can include data filtering tool 230 and / or unit determination tool 240. In some embodiments, unit of measurement determination software 220 can include one or more tools. In some embodiments, some of the tools can be combined, some of the tools can be divided into more tools, or a combination thereof. In some embodiments, unit of measurement determination software 220 can run on a server (e.g., a computing device (which can also be a server) of system 500), or on both an electronic device and a server.

[0049] In some implementations, the data filtering tool 230 may take the form of software separate from the unit of measure determination software 220 and execute on a server, while the unit determination tool 240 may take the form of unit of measure determination software 220 and execute on an electronic device in data communication with the server. Other variations are possible in which the data filtering tool 230 and the reference measurement range generation tool 232 are separate software executed on an electronic device, a server, or a combination thereof.

[0050] The data filtering tool 230 includes a reference measurement range generation tool 232. For example, the data filtering tool 230 can initially filter such existing healthcare data by at least initially determining or extracting a first subset of data based on one or more health-related attributes 214 that match one or more health-related attributes 218 of the target entity. After the first subset of data is determined or extracted based on the health-related attributes 214 and 218, the reference measurement range generation tool 232 can be used to generate a reference measurement range. For example, generating the reference measurement range includes [1] comparing physiological parameters of the first subset of data to each other, and [2] converting different units of measurement of the physiological parameters of the first subset into one or more units of measurement. A more detailed process is described in example processes 300 and 400 of FIGS. 3 and 4.

[0051] The unit determination tool 240 can determine the units of measurement for the second data set (e.g., clinical data) in the second data structure 216 that are missing or exhibit misprint errors based on the reference measurement range. Additionally, a determination can be made whether the second data set in the second data structure 216 is missing or exhibits misprint errors for the units of measurement prior to determining the units of measurement. More detailed processes are described in example processes 300 and 400 of Figures 3 and 4.

[0052] Example Process 3 is a flowchart diagram of an exemplary process 300 for determining units of measure for clinical test data. In particular, the exemplary process 300 includes: [1] filtering existing healthcare data (e.g., historical data, prior clinical test data, real-time data, and other existing healthcare data) by first determining or extracting a first subset of data such that the first subset of data focuses on common health-related attributes; [2] generating a reference measurement range from the first subset of data; and [3] determining units of measure for clinical test data that are missing units of measure or exhibit misprint errors based on the reference measurement range. Process 300 can be implemented by processor-based systems, such as data integrity maintenance system 100 and system 500, and in combination with exemplary embodiment 200, as described herein.

[0053] At 302, one or more first data structures including the first datasets are accessed. For example, a processor-based electronic device (e.g., electronic device 120 of data integrity maintenance system 100 or a computing device of system 500) can be used to access the one or more first data structures. For example, the one or more first data structures can correspond to existing healthcare data (e.g., historical data, prior clinical test data, real-time data, other existing healthcare data, etc.) stored in a data store (e.g., data store 210). For example, the data store can correspond to database module 122 of electronic device 120 or a storage device of a computing device (e.g., one or more storage devices 530 of the computing device in FIG. 5 including a server). The one or more first data structures can include multiple first datasets related to multiple entities (e.g., individuals, users, patients, subjects, etc.) and one or more health-related attributes of the multiple entities. The first datasets can represent measurements of one or more physiological parameters of the multiple entities. For example, the one or more physiological parameters may represent one or more vital signs, such as glucose level, heart rate, blood pressure, respiratory rate, body temperature, and the like.

[0054] At 304, a first subset of the first dataset is determined based on one or more health-related attributes. For example, the electronic device can extract or determine the first subset of the first dataset based on one or more health-related attributes. For example, the one or more health-related attributes can include at least one of a disease indicator, a medical condition other than the disease indicator, use of the same medication, the same medical treatment, gender, etc. For example, the first subset can represent measurements of one or more physiological parameters of multiple entities that share a common health-related attribute. For example, the one or more physiological parameters of the first subset can represent outcome variables measuring the effect of the same medication or the same medical treatment. For example, by way of illustration, the entities corresponding to the first subset can correspond to a population group that is taking or taking the same medication or has received or undergone the same medical treatment. For example, the multiple entities in the first subset can correspond to a population that exhibits the same disease indicator.

[0055] At 306, one or more reference measurement ranges are generated. For example, the electronic device may generate one or more reference measurement ranges by comparing values ​​(e.g., measurements) of one or more physiological parameters of a first subset of the first data set to one another and converting different units of measurement of the one or more physiological parameters of the first subset to one or more units of measurement. For example, by way of illustration, if the physiological parameter corresponds to a blood glucose level and the health-related attribute is a particular medication taken by the patient (corresponding to the first subset), there may be many different values ​​(e.g., measurements) within the first subset having many different units of measurement, such as mmol / L (millimoles per liter) or mg / dL (milligrams per deciliter). The electronic device may convert (or standardize) those different values ​​having different units of measurement to one or more single units of measurement to generate the reference measurement range. For example, for blood glucose levels, all values ​​may be standardized to mmol / L or mg / dL, and a reference measurement range may be generated in mmol / L, mg / dL, or both.

[0056] At 308, one or more second data structures are accessed. For example, the one or more second data structures may be accessed using an electronic device. For example, the one or more second data structures include one or more second data sets related to one or more target entities (e.g., target patients, target users, target subjects, etc.). For example, the one or more second data sets may correspond to test data or target healthcare data that are to be evaluated for data integrity.

[0057] For example, the one or more second datasets may represent measurements of one or more physiological parameters of target entities that exhibit or share one or more health-related attributes with the health-related attributes of the first subset of the first dataset, from which the reference measurement range is generated. For example, by way of illustration, the target entities corresponding to the one or more second datasets may correspond to a population that takes or consumes the same medication or receives or has received the same medical treatment. For example, the physiological parameters of the one or more target entities may correspond to blood glucose levels, and the health-related attribute may be a particular medication that the target entities take (where the particular medication is the same medication that the entities in the first subset of the first dataset are taking or have taken).

[0058] For example, one or more second data sets relating to one or more target entities may be subject to an evaluation for data completeness. For example, measurements of physiological parameters of the target entities may be missing or exhibit misprint errors, and the corresponding data (one or more second data sets, such as clinical data) may be subject to an evaluation for data completeness. For example, a determination of whether measurements are missing or have misprint errors may be made as part of the unit of measure determination step of 310.

[0059] At 310, a unit of measurement for one or more measurements of a physiological parameter of the target entity that exhibits a missing or misprinted unit of measurement error is determined. For example, the unit of measurement can be determined based on a reference measurement range.

[0060] For example, determining a unit of measurement may include: [1] first comparing one or more values ​​of one or more measurements of a physiological parameter of the target entity to a reference measurement range; [2] determining, based on the comparison, that one or more measurements exhibit a misprint error or lack a unit of measurement; and [3] determining an appropriate unit of measurement for the one or more measurements.

[0061] In some embodiments, a score can be generated based on a comparison of one or more measurements to a reference measurement range. Such a score can then be compared to a threshold score to determine that one or more measurements are missing a unit of measurement or exhibit a misprint error. For example, the measurements of the clinical test data target generator can be compared to a reference measurement range for each unit of measurement, and each comparison (with the reference measurement range for each reference unit) can generate a score. Once the score is generated, the score can be compared to a threshold score, and the electronic device can determine whether one or more measurements are missing a unit of measurement or exhibit a misprint error.

[0062] In some embodiments, determining that one or more measurements exhibit a misprint error may include determining a frequency of one or more measurements that fall within a reference measurement range and determining the misprint error based on the frequency. For example, by way of example, if the physiological parameter is blood glucose level and the reference range is 70-100 mg / dL, the blood glucose levels of the target entity may form frequency clusters within this reference range or within thresholds outside this reference range (e.g., as depicted by frequency of one or more measurements 602 in FIG. 6 ). In this manner, values ​​that fall outside the reference range or thresholds (e.g., ±20% of the maximum or minimum value of the reference range) may be considered data points that exhibit a misprint error.

[0063] In some embodiments, a machine learning classifier can be used to determine the units of measurement for one or more measurements that are missing a unit of measurement or that exhibit a misprint error related to the unit of measurement. For example, the machine learning classifier can be trained based on one or more of z-score probabilities and frequency features extracted from one or more first data structures (e.g., existing healthcare data, such as historical data), where the frequency features include unit frequencies by test, unit frequencies by site, and unit frequencies by subject. For example, frequency features are listed in Table 1 below. For example, a logistic regression model can be used to predict a binary result indicating the likelihood that one or more measurements are measured in one or more respective units of measurement based on the reference measurement range and the z-score probabilities derived from the frequency features. For example, the probability result from the classifier can serve as another threshold for detecting misprinted units, which helps reduce the false positive rate. JPEG2026009821000002.jpg78167 Table 1: Characteristics of machine learning classifiers used for unit determination or prediction

[0064] At 312, a third data structure representing the determined units of measure is generated. For example, the electronic device may format the data into a standardized data format, such as JavaScript Object Notation (JSON) format or Extensible Markup Language (XML) format. The standardized data format may be any other suitable data format suitable for storage in a data store or hardware storage device.

[0065] At 314, the third data structure is stored in a hardware storage device. For example, the data structure may be stored in a data store (e.g., data store 210, database module 122 of electronic device 120, one or more storage devices 530, etc.).

[0066] In some implementations, instead of or in addition to storing the data structure in a hardware storage device, the data structure may be output to a user interface (e.g., using user interface module 128). For example, if the data structure is to be output for user display without being stored, the data structure may be generated in another format suitable for display in step 312. For example, if the data structure is saved and then output for user display, the data structure may be converted from the saved format to the appropriate display format and then output.

[0067] In some embodiments, the third data structure may be provided or transmitted to a computational health analysis platform.

[0068] 4 is a flowchart diagram of an exemplary process 400 for determining units of measure for laboratory test data and correcting the laboratory test data. In particular, the exemplary process 400 [1] determines units of measure for laboratory test data that are missing units of measure or exhibit a misprint error, and [2] corrects one or more second data sets (in a second data structure). Process 400 can be implemented by processor-based systems such as data integrity maintenance system 100 and system 500, and in combination with the exemplary embodiment 200 and exemplary process 300 as described in this disclosure.

[0069] One or more second data structures are accessed at 402. The technique used in step 402 may be the same as the technique used in step 308 of Figure 3, so the technique will not be repeated here.

[0070] At 404, units of measurement for one or more measurements of the physiological parameter of the target entity that lack units of measurement or exhibit misprint errors are determined. For example, the units of measurement may be determined based on a reference measurement range. The technique used in step 404 may be the same as the technique used in step 310 of FIG. 3, and therefore the technique will not be repeated here.

[0071] A third data structure representing the determined units of measure is generated in step 406. The technique used in step 406 may be the same as the technique used in step 312 of Figure 3, so the technique will not be repeated here.

[0072] At 408, the units of measure are output (e.g., using user interface module 128) for display on a user interface. For example, a graph representing a first subset of the time series data can be generated for display on the user interface. For example, an indication of whether one or more measurements exhibit a misprint error or are missing a unit of measure is generated on the display of the user interface. For example, a frequency graph representing the frequency of one or more measurements versus multiple units of measure is generated on the display of the user interface.

[0073] For example, FIG. 6 illustrates a frequency graph 600 depicting the relationship between the frequency of one or more measurements 602 and multiple units of measurement 604. For example, the x-axis represents multiple units of measurement 605, and the y-axis represents the actual measurements of a physiological parameter. For example, the shaded area 606 represents multiple reference measurement ranges in multiple units. Furthermore, for example, the first column 608 indicates a set of points or measurements lacking a unit of measurement. For example, embodiments described herein, including process 300 and process 400, determine what the unit of measurement should be for those measurements in the first column 608 lacking a unit of measurement based on the reference measurement range for each unit of measurement as indicated by the respective shaded areas in shaded area 606. On the other hand, if data point [1] is not within the main shaded area (e.g., the area with the highest frequency) [2] to which the most data points belong but is within a different shaded area within the same unit of measurement category on the x-axis, these data points may indicate a misprint error. In such cases, for example, the units of measure corresponding to the different shaded area (outside the main shaded area) capturing these data points may be the appropriate units of measure for these data points.

[0074] In some embodiments, if one or more measurements exhibit a misprint error or are missing units of measure, the data points corresponding to the one or more measurements may be subject to review and flagged as records or data points requiring review or verification.

[0075] At 410, one or more second datasets in the second data structure are corrected. For example, the second datasets can be corrected based on the determined units of measurement. For example, correction can include [1] incorporating the determined units of measurement into one or more measurements that lack a unit of measurement, or [2] replacing the unit of measurement of each of one or more measurements that exhibit a misprint error with the determined units of measurement. This correction can improve the data quality of the one or more second datasets by preventing the processing of incomplete or erroneous second datasets. In some embodiments, such correction can occur after flagged records or data points requiring review or verification have been reviewed or verified.

[0076] In some embodiments, the modified second data set or second data structure may be provided or transmitted to a computational health analysis platform.

[0077] Exemplary Computer System FIG. 5 illustrates an exemplary computer system according to embodiments of the present disclosure. System 500 may be used for any of the operations described with respect to the various embodiments discussed herein. System 500 may be included in, used by, in communication with, or corresponding to electronic device 120. Furthermore, system 500 may be included in, used by, or in communication with sensor device 110. System 500 may include one or more processors 510, memory 520, one or more storage devices 530, and one or more input / output (I / O) devices 560 controllable via one or more I / O interfaces 540. The various components 510, 520, 530, 540, or 560 may be interconnected via at least one system bus 550, which may enable data to be transferred between the various modules and components of system 500.

[0078] The processor 510 may be configured to process instructions for execution within the system 500. The processor 510 may include a single-threaded processor, a multi-threaded processor, or both. The processor 510 may include a single-threaded processor, a multi-threaded processor, or both. The processor 510 may be configured to process instructions stored in the memory 520 or the storage device 530. The processor 510 may include hardware-based processors, each including one or more cores. The processor 510 may include a general-purpose processor, a special-purpose processor, or both.

[0079] Memory 520 may store information within system 500. In some implementations, memory 520 includes one or more computer-readable media. Memory 520 may include any number of volatile memory units, any number of non-volatile memory units, or both volatile and non-volatile memory units. Memory 520 may include read-only memory, random access memory, or both. In some examples, memory 520 may be employed as active or physical memory by one or more executing software modules.

[0080] Storage device 530 may be configured to provide mass storage (e.g., persistent) for system 500. In some implementations, storage device 530 may include one or more computer-readable media. For example, storage device 530 may include a floppy disk device, a hard disk device, an optical disk device, or a tape device. Storage device 530 may include read-only memory, random access memory, or both. Storage device 530 may include one or more of an internal hard disk drive, an external hard disk drive, or a removable drive.

[0081] Either or both of memory 520 or storage device 530 may include one or more computer-readable storage media (CRSM). The CRSM may include one or more of electronic storage media, magnetic storage media, optical storage media, magneto-optical storage media, quantum storage media, mechanical computer storage media, etc. The CRSM may provide storage of computer-readable instructions describing data structures, processes, applications, programs, other modules, or other data for operation of system 500. In some embodiments, the CRSM may include a data store that provides storage of computer-readable instructions or other information in a non-transitory format. The CRSM may be incorporated into system 500 or may be external to system 500. The CRSM may include read-only memory, random-access memory, or both. The one or more CRSMs suitable for embodying computer program instructions and data may include any type of non-volatile memory, including, but not limited to, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. In some examples, processor 510 and memory 520 may be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs).

[0082] System 500 may include one or more I / O devices 560. I / O device(s) 560 may include one or more input devices, such as a keyboard, mouse, pen, game controller, touch input device, audio input device (e.g., microphone), gesture input device, haptic input device, image or video capture device (e.g., camera), or other device. In some examples, I / O device(s) 560 may also include one or more output devices, such as a display, LED, audio output device (e.g., speaker), printer, haptic output device, etc. I / O device(s) 560 may be physically incorporated into one or more computing devices of system 500 or may be external with respect to one or more computing devices of system 500.

[0083] The system 500 may include one or more I / O interfaces 540 to enable components or modules of the system 500 to control, interface with, or otherwise communicate with I / O devices 560. The I / O interfaces 540 may enable the transfer of information within, outside of, or between components of the system 500 through serial, parallel, or other types of communication. For example, the I / O interfaces 540 may conform to a version of the RS-232 standard for serial ports or a version of the IEEE 1284 standard for parallel ports. As another example, the I / O interfaces 540 may be configured to provide connections via Universal Serial Bus (USB) or Ethernet. In some examples, the I / O interfaces 540 may be configured to provide serial connections conforming to a version of the IEEE 1394 standard.

[0084] I / O interface 540 may also include one or more network interfaces that enable communication between computing devices within system 500 or between system 500 and other networked computing systems. A network interface may include one or more network interface controllers (NICs) or other types of transceiver devices configured to send and receive communications over one or more networks using any network protocol.

[0085] The computing devices of system 500 can communicate with each other or with other computing devices using one or more networks. Such networks can include public networks such as the Internet, private networks such as institutional or personal intranets, or any combination of private and public networks. The networks can include any type of wired or wireless network, including, but not limited to, local area networks (LANs), wide area networks (WANs), wireless WANs (WWANs), wireless LANs (WLANs), mobile communication networks (e.g., 3G, 4G, Edge, etc.), etc. In some implementations, communications between computing devices can be encrypted or otherwise secured. For example, communications can employ one or more public encryption keys, ciphers, digital certificates, or other authentication information supported by security protocols such as the Secure Sockets Layer (SSL) or any version of the Transport Layer Security (TLS) protocol.

[0086] System 500 may include any number of computing devices of any type. Computing devices may include, but are not limited to, personal computers, smartphones, tablet computers, wearable computers, implant computers, mobile gaming devices, e-readers, automotive computers, desktop computers, laptop computers, notebook computers, gaming consoles, home entertainment devices, network devices, server computers, mainframe computers, distributed computing devices (e.g., cloud computing devices), microcomputers, systems-on-chips (SoCs), systems-in-packages (SiPs), etc. Examples herein may describe computing devices as physical devices, although implementations are not limited thereto. In some examples, a computing device may include one or more of a virtual computing environment, hypervisor, emulation, or virtual machine running on one or more physical computing devices. In some examples, two or more computing devices may comprise a cluster, cloud, farm, or other grouping of multiple devices that cooperate to provide load balancing, failover support, parallel processing capabilities, shared storage resources, shared network capabilities, or other aspects.

[0087] The term "configured" is used herein in connection with systems and computer program components. A system of one or more computers configured to perform a particular operation or actions means that the system has software, firmware, hardware, or components installed on these computing systems that, when in operation, cause the system to perform the operation or actions. One or more computer programs configured to perform a particular operation or actions means that they contain instructions that, when executed by a data processing device, cause the device to perform the operation or actions.

[0088] Embodiments and functional operations of the subject matter described herein can be implemented in digital electronic circuitry, in tactilely embodied computer software or firmware, in computer hardware including the structures disclosed herein and their structural equivalents, or in any combination of one or more of these. Embodiments of the subject matter described herein can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory storage medium for execution by or to control the operation of a data processing apparatus. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random access memory or serial access memory device, or a combination of one or more of these. Alternatively, or in addition, the program instructions can be encoded in an artificially generated propagated signal, such as a mechanically generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to a suitable receiver for execution by the data processing apparatus.

[0089] The term "data processing apparatus" means data processing hardware and includes any type of apparatus, device, and machine for processing data, including, by way of example, a programmable processor, a computer, or multiple processors or computers. An apparatus may also be or include special-purpose logic circuitry, such as an FPGA (field-programmable gate array) or an ASIC (application-specific integrated circuit). An apparatus may optionally include, in addition to hardware, code that creates an execution environment for a computer program, such as code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or one or more of these computing systems.

[0090] A computer program may also be referred to or written as a program, software, software application, app, module, software module, script, or code, and may be written in any form of programming language, including compiled or interpreted, or declarative or procedural, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A program may, but need not, correspond to a file in a file system. A program may be stored as part of a file that holds other programs or data, e.g., one or more scripts stored in a markup language document, a single file dedicated to the program in question, or multiple cooperating files, e.g., files that store one or more modules, subprograms, or portions of code. A computer program can be deployed to run on one computer, on multiple computers located at a single site, or on multiple computers distributed across multiple sites and interconnected by a data communications network.

[0091] As used herein, the term "database" is used broadly to refer to any collection of data: the data need not be structured in any particular way, or even at all, and may be stored on storage devices in one or more locations. Thus, for example, an index database may contain multiple data collections, each of which may be organized and accessed in a different way.

[0092] The processes and logic flows described herein may be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows may also be performed by special purpose logic circuitry, such as an FPGA or an ASIC, or a combination of special purpose logic circuitry and one or more programmable computers.

[0093] A computer suitable for executing a computer program can be based on a general-purpose or special-purpose microprocessor, or both, or any other type of central processing unit. Generally, the central processing unit receives instructions and data from a read-only memory or a random-access memory, or both. The essential elements of a computer are a central processing unit for executing instructions and one or more memory devices for storing instructions and data. The central processing unit and memory may be supplemented by, or incorporated in, special-purpose logic circuitry. Generally, a computer also includes one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, operatively coupled to receive data from, or transfer data to, one or both. However, a computer need not have such devices. Furthermore, a computer may be incorporated in other devices, such as a mobile phone, a personal digital assistant (PDA), a portable audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage mechanism, such as a universal serial bus (USB) flash drive.

[0094] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, by way of example, semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0095] The term "memory subsystem" may include one or more memories, each of which may be a computer-readable medium. A memory subsystem may include a memory hardware unit (e.g., a hard drive or disk) that stores data or instructions in software form. Alternatively or in addition, a memory subsystem may include data or instructions that are hardwired to a processing circuit.

[0096] To provide for user interaction, embodiments of the subject matter described herein can be implemented on a computer having a display device, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user, and a keyboard and pointing device, or a mouse or trackball, through which the user can provide input to the computer. Other types of devices can also be used to provide for user interaction. For example, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and input from the user can be received in any form, including acoustic input, voice input, or tactile input. Furthermore, a computer can interact with a user by sending documents to and receiving documents from a device used by the user, for example, by sending a web page to a web browser on the user's device in response to a request received from the web browser. A computer can also interact with a user by sending text messages or other types of messages to a personal device, such as a smartphone running a messaging application, and receiving a response message from the user.

[0097] Embodiments of the subject matter described herein can be implemented in a computing system that includes a back-end component, e.g., a data server, or a middleware component, e.g., an application server, or a front-end component, e.g., a client computer having a graphical user interface, a web browser, or an app through which a user can interact with embodiments of the subject matter described herein, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include local area networks (LANs) and wide area networks (WANs), e.g., the Internet.

[0098] A computing system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server sends data, such as HTML pages, to a user device for the purpose of displaying the data to and receiving user input from a user interacting with the user device acting as a client. Data generated at the user device, e.g., a result of a user interaction, can be received from the device by the server.

[0099] Although many specific embodiment details are described herein, these should not be construed as limitations on the scope of the invention or the claims, but rather as descriptions of features that may be specific to particular embodiments of a particular invention. Certain features that are described herein in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable subcombination. Furthermore, while features may be described above as acting in particular combinations, and even initially claimed as such, one or more features from a claimed combination may, in some cases, be removed from the combination, and the claimed combination may be directed to subcombinations or variations of the subcombinations.

[0100] Similarly, while operations are depicted in the figures and claimed in a particular order, this should not be understood as requiring such operations to be performed in the particular order shown, or sequentially, or that all of the operations shown be performed, to achieve desirable results. In certain situations, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems may generally be integrated together in a single software product or packaged in multiple software products.

[0101] Specific embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. As an example, the processes depicted in the accompanying figures do not necessarily require the particular order shown or sequential order to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. [Explanation of symbols]

[0102] 100 Data Integrity Maintenance System 110 Sensor device 112 Sensors 116 Communication Module 120 Electronic Devices 122 Database Module 122a Input data 122b output data 122c Processing Rules 124 Communication Module 126 Processing Module 128 User Interface Module

Claims

1. 1. A system for maintaining data integrity in a computational health analysis platform, comprising: at least one processor; a memory subsystem communicatively coupled to the at least one processor; Equipped with the memory subsystem stores instructions that, when executed by the at least one processor, cause the at least one processor to perform operations; The operation is accessing one or more first data structures, the one or more first data structures comprising a plurality of first datasets relating to a plurality of entities, each of the first datasets representing measurements of one or more physiological parameters of the plurality of entities; a machine transmitting the data structure comprising the plurality of first datasets determines that a system accessing the one or more first data structures is authorized to access the data structure; and accessing one or more first data structures, the one or more first data structures further comprising one or more health-related attributes of the plurality of entities; determining a first subset of the first dataset based on the one or more health-related attributes of the plurality of entities; generating a reference measurement range for one or more physiological parameters of a plurality of entities of a first subset of the first data set in one or more units of measurement; accessing one or more second data structures, the one or more second data structures including one or more second datasets relating to one or more target entities, the one or more second datasets representing one or more measurements of one or more physiological parameters of the one or more target entities, the one or more target entities exhibiting the one or more health-related attributes, and the one or more measurements of the one or more target entities exhibiting missing units of measurement or misprint errors relating to the units of measurement; determining a unit of measurement for the one or more measurements that lack the unit of measurement or exhibit a misprint error related to the unit of measurement based on the reference measurement range; generating a third data structure representing the units of measure; storing the third data structure on a hardware storage device; A method comprising:

2. The system of claim 1 , wherein the operation further comprises providing the third data structure to the computer health analysis platform.

3. The system of claim 1 , wherein the one or more health-related attributes include at least one of a disease indicator, a medical condition other than a disease indicator, same medication use, same medical treatment, or gender.

4. The system of claim 1 , wherein the one or more physiological parameters represent clinical parameters collected continuously at regular intervals.

5. The system of claim 1 , wherein the one or more physiological parameters represent one or more vital signs.

6. The system of claim 5 , wherein the one or more vital signs include at least one of glucose level, heart rate, blood pressure, respiratory rate, or body temperature.

7. The system of claim 1 , wherein the first subset of entities corresponds to a population group exhibiting the same disease indicator.

8. the first subset of entities corresponds to a population group that takes the same medication or undergoes the same medical treatment; the one or more physiological parameters of the first subset represent outcome variables measuring the effect of the same drug or the same medical procedure; The system of claim 1 .

9. generating the reference measurement range, comparing the measurements of one or more physiological parameters of the first subset of the first data set with each other; converting different units of measurement of the one or more physiological parameters of the first subset into the one or more units of measurement; The system of claim 1 , comprising:

10. determining the unit of measure of the one or more measurements that lack the unit of measure or exhibit a misprint error relating to the unit of measure; comparing one or more of the one or more measurements to a reference measurement range; determining, based on said comparison, that said one or more measurements exhibit said misprint error or lack said unit of measurement; The system of claim 9 , comprising:

11. Determining that the one or more measurements indicate the misprint error includes: determining a frequency of the one or more values ​​of the one or more measurements falling within the reference measurement range; determining the misprint errors based on the frequency; and The system of claim 10, comprising:

12. determining the units of measure generating a score based on a comparison of the one or more values ​​of the one or more measurements to the reference measurement range; The system of claim 10, comprising:

13. the one or more measurements indicate the misprint error; determining the unit of measurement includes determining that the one or more measurements indicate the misprint error based on comparing the score to a threshold score. The system of claim 12.

14. The system of claim 9 , wherein the action further comprises outputting the units of measure to a display of a user interface.

15. 15. The system of claim 14, wherein the operations further include generating an indication on a display of the user interface that one or more measurements exhibit the misprint error or are missing the unit of measurement.

16. 16. The system of claim 15, wherein the operations further comprise generating a frequency graph depicting the frequency of the one or more measurements versus a number of units of measurement.

17. determining the unit of measurement of the one or more measurements that lack the unit of measurement or exhibit a misprint error related to the unit of measurement; utilizing a machine learning classifier to determine said units of measure; Including, the machine learning classifier is trained based on one or more of z-score probability and frequency features; The frequency features include unit frequency by study, unit frequency by site, and unit frequency by subject. The system of claim 1 .

18. utilizing the machine learning classifier to determine the units of measure; utilizing a logistic regression model to predict a binary outcome indicative of the likelihood that the one or more measurements will be measured in the one or more respective units of measurement based on the reference measurement range and the z-score probabilities derived from the frequency features; 20. The system of claim 17, comprising:

19. The operation further comprises: modifying the one or more second data sets based on the third data structure; The system of claim 1 , comprising:

20. 20. The system of claim 19, wherein correcting the one or more second data sets improves data integrity by preventing processing of incomplete or erroneous second data sets, and the unit of measurement (i) is incorporated into the one or more measurements that lack the unit of measurement, or (ii) replaces the respective unit of measurement for the one or more measurements that exhibit the misprint error.

21. 1. A method comprising: accessing, by an electronic device, one or more first data structures, the one or more first data structures including a plurality of first data sets relating to a plurality of entities, each of the first data sets representing measurements of one or more physiological parameters of the plurality of entities, and one or more health-related attributes of the plurality of entities; determining, by the electronic device, a first subset of the first data set based on the one or more health-related attributes of the plurality of entities; generating, by the electronic device, reference measurement ranges for the physiological parameters of a plurality of entities of a first subset of the first data set in one or more units of measurement; accessing, by the electronic device, one or more second data structures, the one or more second data structures including one or more second data sets relating to one or more target entities, the one or more second data sets representing one or more measurements of one or more physiological parameters of the one or more target entities, the one or more target entities exhibiting the one or more health-related attributes, and the one or more measurements lacking units of measurement or exhibiting a typographical error relating to the units of measurement; determining, by the electronic device, the units of measurement of the one or more measurements that lack the units of measurement or that exhibit a misprint error relating to the units of measurement; generating, by the electronic device, a third data structure representing the units of measure; storing, by the electronic device, the third data structure on a hardware storage device; A method comprising:

22. providing, by the electronic device, the third data structure to a computational health analysis platform; 22. The method of claim 21 further comprising:

23. 22. The method of claim 21, wherein the one or more health-related attributes comprise at least one of a disease indicator, a medical condition other than a disease indicator, same medication use, same medical treatment, or gender.

24. 22. The method of claim 21, wherein the one or more physiological parameters represent one or more vital signs.

25. the first subset of entities corresponds to a population group taking the same medication or receiving the same medical treatment; the one or more physiological parameters of the first subset represent outcome variables measuring the effect of the same drug or the same medical procedure; 22. The method of claim 21.

26. generating the reference measurement range, comparing the measurements of one or more physiological parameters of the first subset of the first data set with each other; converting different units of measurement of the one or more physiological parameters of the first subset into the one or more units of measurement; 22. The method of claim 21, comprising:

27. determining the unit of measure of the one or more measurements that lack the unit of measure or exhibit a misprint error relating to the unit of measure; comparing one or more of the one or more measurements to a reference measurement range; determining, based on said comparison, that said one or more measurements exhibit said misprint error or lack said unit of measurement; 22. The method of claim 21, comprising:

28. determining that the one or more measurements indicate a misprint error; determining a frequency of the one or more measurements falling within the reference measurement range; determining the misprint errors based on the frequency; and 28. The method of claim 27, comprising:

29. determining the units of measure generating a score based on a comparison of the one or more values ​​of the one or more measurements to the reference measurement range; 28. The method of claim 27, comprising:

30. said one or more measurements lacking said unit of measurement; determining the units of measure determining that the one or more measurements are missing the unit of measurement based on comparing the score to a threshold score; 30. The method of claim 29, comprising:

31. the one or more measurements exhibit a misprint error, determining the units of measure determining that the one or more measurements indicate the misprint error based on comparing the score to a threshold score; 30. The method of claim 29, comprising:

32. 28. The method of claim 27, further comprising outputting, by the electronic device, the units of measure on a display of a user interface.

33. 33. The method of claim 32, further comprising generating, by the electronic device, an indication on a display of the user interface that one or more measurements exhibit the misprint error or are missing the unit of measurement.

34. 34. The method of claim 33, further comprising generating, by the electronic device, a frequency graph representing a relationship between the frequency of the one or more measurements and a plurality of units of measurement.

35. determining the unit of measurement of the one or more measurements that lack the unit of measurement or that exhibit the misprint error with respect to the unit of measurement; utilizing a machine learning classifier to determine said units of measure; Including, the machine learning classifier is trained based on one or more of z-score probability and frequency features; The frequency features include unit frequency by study, unit frequency by site, and unit frequency by subject.

22. The method of claim 21.

36. utilizing the machine learning classifier to determine the units of measure; utilizing a logistic regression model to predict a binary outcome indicative of the likelihood that the one or more measurements will be measured in the one or more respective units of measurement based on the reference measurement range and the z-score probabilities derived from the frequency features; 36. The system of claim 35, comprising:

37. One or more non-transitory computer readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the method of any of claims 21 to 36.

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