Detecting user infection using wearable sensor data

Abstract

A method for reporting a risk of infection of a user, comprising: (i) receiving user cardiac data from a sensor of a wearable device worn by the user; (ii) determining a pulse rate variability phase acceleration value for the user for a first time period from the received user cardiac data by a pulse rate variability phase acceleration algorithm; (iii) determining a pulse rate variability phase deceleration value for the user for the first time period from the received user cardiac data by a pulse rate variability phase deceleration algorithm; (iv) determining a current infection risk of the user by using the pulse rate variability phase acceleration value of the user and the pulse rate variability phase deceleration value of the user for the first time period through a trained infection risk prediction algorithm; and (v) providing the determined current infection risk of the user via a user interface.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority under 35 U.S.C. 119(e) to U.S. Provisional Application No. 63 / 351,516, filed on June 13, 2022, the contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure generally relates to methods and systems for detecting, reporting, and / or treating an individual's infection detected via wearable device sensor data. Background Art

[0004] Detecting infection is an important aspect of personal healthcare. From an epidemiological perspective, early detection of infection can not only improve treatment, but also improve public health. However, detecting individual infections outside of a care facility is challenging, especially when the individual is not experiencing significant symptoms or is unaware that they may have been infected. While there are many ways for users to actively monitor their own health and detect infection, passive monitoring or detection systems that detect infection without requiring active input from the user would greatly improve early infection detection as well as public health surveillance, including but not limited to scenarios such as nosocomial infections due to spread by hospital staff or outbreaks on military aircraft carriers. Summary of the invention

[0005] Therefore, there is a need for methods and systems for detecting patient infection or infection risk using various data inputs, especially via passive monitoring. Various embodiments and implementations herein are directed to a method and system configured to detect and report patient infection or infection risk based on sensor data from a wearable device worn by a patient. A system receives user cardiac data from a sensor of a wearable device worn by the user. The system then determines the pulse rate of the user for a first time period based on the received user cardiac data. The system determines the following two based on the received user cardiac data: (i) determining the pulse rate variability phase acceleration value for the user for the first time period by a pulse rate variability phase acceleration algorithm; and (ii) determining the pulse rate variability phase deceleration value for the user for the first time period by a pulse rate variability phase deceleration algorithm. The trained infection risk prediction algorithm of the system uses the pulse rate variability phase acceleration value for the user for the first time period and the pulse rate variability phase deceleration value for the user to determine the current infection risk of the user. The determined current infection risk of the user is provided via a user interface. According to one embodiment, the medical professional prescribes antibiotics and / or antiviral treatment to treat the user's current infection in response to receiving the provided determined current infection risk of the user.

[0006] In general, in one aspect, a method for reporting a user's infection risk is provided. The method includes: (i) receiving user heart data from a sensor of a wearable device worn by the user; (ii) determining a pulse rate variability phase acceleration value of the user for a first time period based on the received user heart data by a pulse rate variability phase acceleration algorithm; (iii) determining a pulse rate variability phase deceleration value of the user for the first time period based on the received user heart data by a pulse rate variability phase deceleration algorithm; (iv) determining a current infection risk of the user using the pulse rate variability phase acceleration value and the pulse rate variability phase deceleration value of the user for the first time period by a trained infection risk prediction algorithm; and (v) providing the determined current infection risk of the user via a user interface.

[0007] According to one embodiment, the method further comprises prescribing, by a medical professional, antibiotics and / or antiviral treatment to treat the user's current infection in response to the provided determined current infection risk of the user.

[0008] According to one embodiment, the method further comprises: transmitting the received user heart data to a remote server, wherein the determining step is performed at the remote server.

[0009] According to one embodiment, the user interface comprises a web portal.

[0010] According to one embodiment, the method further comprises receiving, by a medical professional via the user interface, the determined current infection risk of the user.

[0011] According to one embodiment, the method is performed on the user at regular intervals by a remote service.

[0012] According to one embodiment: (i) the sensor is a heart rate sensor and the user heart data is heart rate sensor data, and / or (ii) the sensor is an electrocardiogram sensor and the user heart data is ECG data.

[0013] A method for treating an infection in a patient is also provided. The method includes: receiving, by a medical professional, a determined current infection of a patient from a user interface, wherein the current infection of the patient is determined by: (i) receiving user heart data from a sensor of a wearable device worn by the user; (ii) determining a pulse rate variability phase acceleration value of the user for a first time period based on the received user heart data by a pulse rate variability phase acceleration algorithm; (iii) determining a pulse rate variability phase deceleration value of the user for the first time period based on the received user heart data by a pulse rate variability phase deceleration algorithm; (iv) determining that the user is experiencing a current infection using the pulse rate variability phase acceleration value of the user for the first time period and the pulse rate variability phase deceleration value of the user by a trained infection risk prediction algorithm; and (v) providing a determination result that the patient is experiencing a current infection to the medical professional via the user interface. The method also includes: prescribing, by the medical professional, antibiotics and / or antiviral treatment to treat the current infection of the user in response to the provided current infection risk of the user.

[0014] A system for reporting a user's predicted infection risk is also provided. The system includes: a wearable device worn by the user, the wearable device including a sensor configured to obtain user heart data; a trained infection risk prediction algorithm configured to determine the user's current infection risk based on the user's pulse rate variability phase acceleration value and the user's pulse rate variability phase deceleration value for a first time period; a processor configured to: (i) determine the user's pulse rate variability phase acceleration value for the first time period based on the received user heart data through the pulse rate variability phase acceleration algorithm; (ii) determine the user's pulse rate variability phase deceleration value for the first time period based on the received user heart data through the pulse rate variability phase deceleration algorithm; and (iii) determine the user's current infection risk using the user's pulse rate variability phase acceleration value and the user's pulse rate variability phase deceleration value for the first time period through the trained infection risk prediction algorithm; and a user interface configured to provide the determined user's current infection risk and a group-based assessment and ranking, so that personnel responsible for infection control can effectively monitor groups and respond to outbreaks.

[0015] It should be appreciated that all combinations of the foregoing concepts and the additional concepts discussed in more detail below (provided such concepts are not mutually inconsistent) are contemplated as part of the inventive subject matter disclosed herein. In particular, all combinations of the claimed subject matter appearing at the end of this disclosure are contemplated as part of the inventive subject matter disclosed herein. It should also be appreciated that terms explicitly used herein that may also appear in any disclosure incorporated by reference should be given the meaning most consistent with the specific concepts disclosed herein.

[0016] These and other aspects of the various embodiments will be apparent from and elucidated with reference to the embodiment(s) described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In the drawings, the same reference numerals generally refer to the same parts throughout the different views. The drawings illustrate features and modes of implementing various embodiments and should not be construed as limiting other possible embodiments within the scope of the appended claims. Moreover, the drawings are not necessarily drawn to scale, but emphasis is generally placed on illustrating the principles of the various embodiments.

[0018] Figure 1 is a flow chart of a method for reporting a user's predicted risk of infection according to one embodiment;

[0019] Figure 2 is a schematic representation of an infection detection system according to one embodiment;

[0020] Figure 3 is a flow chart of a method for reporting a user's predicted risk of infection according to one embodiment; and

[0021] Figure 4 is a flow chart of a method for training an infection prediction algorithm according to one embodiment. DETAILED DESCRIPTION

[0022] The present disclosure describes various embodiments of systems and methods configured to detect infection in an individual and / or determine the risk or likelihood that an individual is currently infected. More generally, the applicant has recognized and appreciated that it would be beneficial to provide a method or system for improving infection detection using heart rate data obtained from a sensor of a wearable device. Accordingly, an infection detection system receives user heart data from a sensor of a wearable device worn by a user. The system then determines the pulse rate of the user for a first time period based on the received user heart data. The system determines the following two based on the received user heart data: (i) determining a pulse rate variability phase acceleration value for the user for the first time period by a pulse rate variability phase acceleration algorithm; and (ii) determining a pulse rate variability phase deceleration value for the user for the first time period by a pulse rate variability phase deceleration algorithm. The system's trained infection risk prediction algorithm uses the pulse rate variability phase acceleration value for the user for the first time period and the pulse rate variability phase deceleration value for the user to determine the user's current infection risk. The determined current infection risk of the user is provided via a user interface. According to one embodiment, the medical professional prescribes antibiotics and / or antiviral treatment to treat the user's current infection in response to receiving the provided determined risk of the user's current infection.

[0023] According to one embodiment, in some non-limiting embodiments, the systems and methods described herein or otherwise contemplated can be implemented as elements of a commercial product for patient analysis or monitoring, such as, for example, a Philips telehealth product, connected care platform, HealthBand and / or HealthDot (available from Royal Philips of the Netherlands), or any suitable system.

[0024] According to another embodiment, in some non-limiting embodiments, the systems and methods described herein or otherwise contemplated can be implemented as elements for group monitoring. For example, a system for infection control in a hospital can utilize these systems and methods to monitor staff and / or patients in order to minimize the rate of nosocomial infections. As another example, a system such as the military or an institution of higher education can utilize these systems and methods, which are responsible for the public safety of their communities so they can monitor and respond to potential outbreaks. Many other embodiments are possible.

[0025] refer to Figure 1 , in one embodiment, Figure 1 1 is a flow chart of a method 100 for detecting and reporting an infection or likelihood of infection in an individual using an infection detection system. The methods described in conjunction with the accompanying drawings are provided as examples only and should be understood not to limit the scope of the present disclosure. The infection detection system can be any system described herein or otherwise contemplated. The infection detection system can be a single system or a plurality of different systems.

[0026] At step 110 of the method, an infection detection system is provided. Figure 2 The depicted embodiment of the infection detection system 200, for example, includes one or more of the following: a processor 220, a memory 230, a user interface 240, a storage device 260, a wearable device 270 having a sensor 280, which are interconnected via one or more system buses 212 and / or via one or more communication interfaces 250. It will be understood that Figure 2 In some respects, an abstraction is constituted, and the actual organization of the components of the system 200 may be different and more complex than that illustrated. In addition, the infection detection system 200 can be any system described or otherwise contemplated herein. Other elements and components of the infection detection system 200 are disclosed and / or otherwise contemplated herein. According to one embodiment, the infection detection system 200 includes a wearable device or other sensor device that communicates with a local or remote processor (e.g., a cloud-based processing system).

[0027] According to one embodiment, a user wears a wearable device including a sensor configured to obtain sensor data about the user. The wearable device may be the infection detection system 200, or the wearable device may be a component of the infection detection system 200. The user may be any individual capable of wearing a wearable device. According to one embodiment, the user is a patient. For example, a patient may be discharged from a hospital with a wearable device or a prescription, or otherwise be discharged with a wearable device or a prescription in order to purchase and / or use a wearable device. As another example, the user may be a member of the military or any other organization for which it is advantageous to monitor the health of the user. As yet another example, the user may be any individual interested in monitoring their health.

[0028] The infection detection system 200 may be provided in the form of software as a service (SAAS), where the wearable device is sold to the user together with the service, or the wearable device is sold to the user together with an offer to use or purchase the service. For example, a user may purchase a wearable device to monitor an aspect of their health (e.g., activity level) or provide other monitoring or notification services. The wearable device may be accompanied by the user's access to the infection detection system 200 (as a free or paid service).

[0029] A wearable device can be any device that can be worn or carried or otherwise communicate with a user so that the device's sensors can obtain sensor data about the user. A wearable device can be a ring, watch, necklace, or any other device. Examples of wearable devices include watch, watch, ring, Bracelets, etc. These devices typically monitor activity levels and / or provide notifications or other communication options, and are capable of monitoring vital signs, which typically include: photoplethysmography (PPG) results, skin temperature, acceleration, oxygen saturation, and other vital signs.

[0030] At step 120 of the method, the infection detection system 200 receives sensor data from the sensor 210 of the wearable device 270. According to one embodiment, the wearable device is an infection detection system, and thus the processor of the wearable device receives the sensor data from the sensor of the wearable device. According to another embodiment, the wearable device is a component of the infection detection system, and a local and / or remote processor (e.g., a remote server) of the infection detection system receives the sensor data transmitted from the sensor via wired and / or wireless communication.

[0031] According to one embodiment, the sensor data can be any data by which the possibility, risk, or presence of infection can be determined using input to the methods and systems described herein or otherwise contemplated. For example, the sensor data can be an electrocardiogram obtained from one or more electrode sensors. The sensor data can be heartbeat or heart rate data from a photoplethysmography (PPG) sensor or other cardiac sensor. Other sensors may also be suitable.

[0032] According to one embodiment, the infection detection system can continuously obtain or receive sensor data, or can obtain or receive sensor data periodically (e.g., at a predetermined request rate). For example, the sensor data can be immediately utilized, or the sensor data can be stored in a local or remote storage device for use in other steps of the method. Thus, the infection detection system can continuously or at a predetermined request rate receive sensor data from the sensor and / or storage device or request sensor data from the sensor and / or storage device. For example, at optional step 122 of the method, the sensor data and / or other data is transmitted to a remote server or other component of the infection detection system.

[0033] At step 130 of the method, a processor of the infection detection system analyzes the received sensor data to determine a pulse rate of the user for a first time period. The first time period may be any time period from which the system may obtain and analyze sufficient sensor data. For example, in a scenario where the infection detection system continuously analyzes data, the first time period may be one minute, less than one minute, or several / many minutes, and the first time period and subsequent time periods may or may not overlap. In a scenario where the infection detection system periodically analyzes data, the first time period may be any selected time period, for example, one minute, less than one minute, or several / many minutes. The pulse rate of the user for the first time period may be determined using any method, algorithm, or process for extracting or otherwise calculating or determining a pulse rate from the type of sensor data received.

[0034] According to one embodiment, before or after step 130, or at any other point in the method, the infection detection system may prepare the received sensor data for analysis. Figure 3 , Figure 3 is a flow chart of a method for infection detection or prediction. At 310, sensor data is obtained by a sensor of a wearable device, and the sensor data is transmitted to an infection detection system (which may be a wearable device and / or other components of the system) or received or otherwise utilized by the infection detection system to detect or predict infection. At 320 and 330, the received sensor data is prepared for analysis. For example, at 320, the sensor data is formatted for processing. Since the infection detection system can be configured to utilize sensor data from a variety of different wearable devices, the format of the sensor data may vary from wearable device to wearable device. Accordingly, the infection detection system can be configured, designed, programmed, or otherwise configured to process two or more different types of sensor data (e.g., sensor data from two or more different types or brands of wearable devices) to generate standardized sensor data used by downstream steps of the method. This may include: removing irrelevant sensor data, reformatting or otherwise reorganizing sensor data, and other processing steps.

[0035] At step 330, the received sensor data (which may or may not have required formatting or reformatting at 320) can be preprocessed so that downstream steps of the method can readily utilize the sensor data. Preprocessing can include any data analysis or modification required to change the sensor data into suitable input data. For example, preprocessing can include cleaning the data (e.g., removing noise or outliers (e.g., replacing the top and bottom 1% of the data with random uniform values ​​generated from neighboring data, among many other options)).

[0036] The formatted and processed sensor data may be utilized immediately, or may be stored in a local or remote storage device of the infection detection system (e.g., in a memory of a wearable device or other component of the system) for use in further steps of the method.

[0037] At step 140 of the method, a processor of the infection detection system analyzes the received sensor data using a pulse rate variability phase acceleration algorithm to determine a pulse rate variability phase acceleration value for the user for a first time period. A pulse rate variability phase acceleration algorithm is any algorithm configured to analyze the received sensor data and generate phase-corrected heart rate acceleration data. For example, the pulse rate variability phase acceleration algorithm averages noise that affects heart rate acceleration. According to one embodiment, information such as pulse rate and pulse rate variability can be obtained from sensor data including beat intervals. Since heart rate and heart rate variability can change with infection, heart rate and heart rate variability can be converted into pulse rate variability for system processing.

[0038] The pulse rate variability phase acceleration values ​​for the user for the first time period may be utilized immediately, or may be stored in a local or remote storage device of the infection detection system (e.g., stored in a memory of a wearable device or other component of the system) for use in further steps of the method.

[0039] At step 150 of the method, a processor of the infection detection system analyzes the received sensor data using a pulse rate variability phase deceleration algorithm to determine a pulse rate variability phase deceleration value for the user for a first time period. A pulse rate variability phase deceleration algorithm is any algorithm configured to analyze the received sensor data and generate phase-corrected heart rate deceleration data. For example, the pulse rate variability phase deceleration algorithm averages noise that affects heart rate deceleration. According to one embodiment, information such as pulse rate and pulse rate variability can be obtained from sensor data including beat-to-beat intervals. Since heart rate and heart rate variability can change with infection, heart rate and heart rate variability can be converted into pulse rate variability for system processing.

[0040] The pulse rate variability phase deceleration values ​​for the user for the first time period may be utilized immediately, or may be stored in a local or remote storage device of the infection detection system (e.g., stored in a memory of a wearable device or other component of the system) for use in further steps of the method.

[0041] According to one embodiment, the pulse rate variability phase acceleration algorithm and / or the pulse rate variability phase deceleration algorithm can be an algorithm derived from, based on, or otherwise derived from the following algorithm: the algorithm described in "Heart Rate Variability (HRV) Signal Analysis: Clinical Applications" edited by Kamath et al. (2012). However, other pulse rate variability phase acceleration and / or deceleration algorithms are also possible.

[0042] At step 160 of the method, the trained infection risk prediction algorithm of the infection detection system uses at least the pulse rate variability phase acceleration value of the user for the first time period and the pulse rate variability phase deceleration value of the user as input to generate a determination result about the current infection and / or the risk or possibility of the current infection. The infection risk prediction algorithm of the infection detection system can be any algorithm configured to use these inputs and generate a determination result about the current infection and / or the risk or possibility of the current infection. For example, the algorithm can be any kind of machine learning algorithm and other types of algorithms. The algorithm can be trained using known mechanisms for training machine learning algorithms.

[0043] According to one embodiment, the infection detection system utilizes a trained infection risk prediction algorithm to determine the user's current infection and / or the risk or likelihood of current infection according to the following process. However, this is provided only as a non-limiting example, and other methods may be utilized to determine the user's current infection and / or the risk or likelihood of current infection.

[0044] According to this embodiment, the infection detection system receives sensor data (e.g., beat-to-beat intervals (IBIs)) from a sensor for a first time period (e.g., an entire sleep occurrence period (e.g., eight hours)). The infection detection system breaks the sleep occurrence period IBI into 5-minute windows or any other size windows. The size of the window can be a predetermined size, or can be a learned size.

[0045] For each window, the infection detection system uses the sensor data to calculate the pulse rate variability phase acceleration value and the pulse rate variability phase deceleration value. According to one embodiment, in order to generate the pulse rate variability phase acceleration value (and similarly generate the pulse rate variability phase deceleration value), the system creates a buffer called an anchor to store all heartbeats whose heartbeat values ​​increase (decrease). This buffer stacks all heartbeats that cause an increase in heart rate horizontally. The size of the buffer is N×L, where N is the number of heartbeats that cause an increase (decrease) in heart rate and L is the window length, which means that the system will store all IBI values ​​for L / 2 heartbeats before the selected heartbeat of the Nth row and all values ​​for L / 2 heartbeats after the selected heartbeat of the Nth row. According to one embodiment, L=4, so the system stores two IBI values ​​before the current heartbeat, the current IBI value, and one IBI value after the current heartbeat.

[0046] According to one embodiment, for each heartbeat in the window, if the value of the IBI increases (or, during the analysis by the deceleration algorithm, the value of the IBI decreases), the system adds the IBI of the window to the anchor buffer along with the IBI of the previous heartbeat and the IBI of the subsequent heartbeat. For optional filtering, if the IBI changes by more than 5%, the system can reject the heartbeat and not enter it into the anchor buffer.

[0047] According to one embodiment, if there are at least 20 heartbeats stored in the anchor buffer (i.e., N ≥ 20), the system calculates the average of all anchor heartbeats by calculating the mean across rows. This will cause the anchor buffer to now have a size of 1×4. According to one embodiment, the resulting phase-corrected acceleration and deceleration for the window period (5 minutes in this non-limiting example) is given by the average difference of the heartbeat pairs before and after the reference heartbeat according to the following formula:

[0048] Output value = (average value [2] + average value [3] - average value [0] - average value [1] / 4) (Formula 1)

[0049] According to one embodiment, the infection detection system aggregates all window values ​​into a single daily statistical value, including but not limited to minimum, mean, maximum, standard deviation, quantile, and the like.

[0050] According to one embodiment, the infection detection system aggregates all information across multiple times (e.g., across 10 days, according to one non-limiting example), stacks that information into a vector, and inputs that information into a machine learning algorithm to generate an infection prediction score or determine an outcome.

[0051] The infection prediction score or determination result can be a binary score indicating infection (e.g., yes / no, 1 / 0), infection risk (e.g., low risk, medium risk, high risk), and / or infection prediction result (e.g., percentage likelihood of infection), or any other indication of a user's infection, risk of infection, or likelihood of infection.

[0052] According to one embodiment, the infection prediction score or determination result can be used immediately, or the infection prediction score or determination result can be stored in a local or remote storage device of the infection detection system (for example, in the memory of a wearable device or other component of the system) for use in other steps of the method.

[0053] At step 170 of the method, the determined infection, infection score, infection risk and / or infection prediction result are provided via the user interface of the infection detection system. The system can display or otherwise provide information to a user (e.g., a wearer of a wearable device, a care provider or other individual) via a user interface. The display may include information about the patient, input data for the patient or any other information. Infection information may be communicated to another device via wired and / or wireless communication. For example, the system may communicate infection information and other information to a mobile phone, a computer, a laptop, a wearable device and / or any other device configured to allow display and / or otherwise communicate infection information and other information. The user interface can be any device or system that allows transmission and / or reception of information, and may include a display, a mouse and / or a keyboard for receiving user commands. According to one embodiment, the user interface is a portal website that a user or other individual accesses to obtain infection information and / or other information.

[0054] According to one embodiment, the user interface may include an alarm system. For example, if the infection detection system detects an infection, or detects that the risk or likelihood of an infection reaches or exceeds a predetermined threshold, the system or user interface may be configured to provide or otherwise generate an alarm. A moderate risk or >50% likelihood of infection may trigger the system or user interface to generate an alarm. The alarm may be, for example, a noise, a tactile response, a text message, or any other mechanism that informs a user or other individual of an alarm message.

[0055] At optional step 180 of the method, a medical professional or other professional user or individual receives, via a user interface, an infection, infection risk, or infection likelihood determined for a wearer of the wearable device. The medical professional or other professional user or individual can be anyone who uses the provided information for management or care decisions. For example, the individual can be the wearer's physician. As another example, the individual can be a manager of an organization or other group that monitors one or more wearers for potential infection.

[0056] According to one embodiment, a medical professional or other professional user or individual can receive a determined infection, risk of infection, or likelihood of infection via a user interface using any mechanism. For example, an individual can receive an alert or other information indicating a determined infection, risk of infection, or likelihood of infection. Alternatively, a user can log in to a web portal to receive information or otherwise access the information. As yet another option, a user can install an application on their smartphone or other device to access the information, and the application can also include a notification or alert mechanism.

[0057] At optional step 190 of the method, a medical professional or other professional user or individual has received the information and is able to act on the information. For example, a medical professional may receive and review an infection, risk of infection, or likelihood of infection determined for a wearer of the wearable device. After reviewing the information, the medical professional is able to make a treatment or therapy decision. Because the infection detection system is identifying an infection or risk of infection, a treatment or therapy is designed or selected to treat the infection or prevent infection. For example, treatment may include antibiotics to treat infections originating from bacteria, treatment may include antivirals to treat infections originating from viruses, and / or treatment may include antifungals to treat infections originating from fungi, treatment may include antiparasitics to treat infections originating from parasites, and treatment may also include other possible treatments.

[0058] The selected antibiotic, antiviral, antifungal or antiparasitic agent can be selected to treat an infection broadly, or can be more narrowly selected to treat a very specific bacteria, virus, fungus or parasite. The selected antibiotic, antiviral, antifungal or antiparasitic agent can also be based on information about the wearer of the wearable device, including but not limited to recent travel history, exposure history, medical history, demographic information and / or any other information. Since the infection detection system can store or otherwise access any or all of this information, a medical professional can obtain all of this information and thereby obtain information for the selected treatment or treatment. The selected treatment or treatment can be communicated directly or indirectly (e.g., by electronic communication, prescription, telephone, voicemail, text message or any other mechanism) to the wearer of the wearable device.

[0059] refer to Figure 2 , Figure 2 is a schematic representation of an infection detection system 200. System 200 may be any system described herein or otherwise contemplated, and may include any components described herein or otherwise contemplated. It will be understood that Figure 2An abstraction is constituted in some respects, and the actual organization of the components of system 200 may be different and more complex than that illustrated.

[0060] According to one embodiment, the system 200 includes a processor 220 that can run instructions stored in the memory 230 or the storage device 260 or otherwise process data to, for example, perform one or more steps of the method. The processor 220 may include one or more modules. The processor 220 may take any suitable form, including but not limited to a microprocessor, a microcontroller, multiple microcontrollers, a circuit, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a single processor, or multiple processors.

[0061] Memory 230 can take any suitable form, including non-volatile memory and / or RAM. Memory 230 can include various memories, for example, L1, L2 or L3 cache memory or system memory. As such, memory 230 can include static random access memory (SRAM), dynamic RAM (DRAM), flash memory, read-only memory (ROM) or other similar memory devices. The memory can store operating systems, etc. The processor uses RAM to temporarily store data. According to one embodiment, the operating system may include code that controls the operation of one or more components of system 200 when run by the processor. It is apparent that in an embodiment where the processor implements one or more functions described herein in hardware, software described in other embodiments as corresponding to such functions may be omitted.

[0062] The user interface 240 may include one or more devices for realizing communication with the user. The user interface can be any device or system that allows transmission and / or reception of information, and may include a display, a mouse and / or a keyboard for receiving user commands. In certain embodiments, the user interface 240 may include a command line interface or a graphical user interface, which may be presented to a remote terminal via a communication interface 250. The user interface may be located at one place with one or more other components of the system, or may be located at a position away from the system and communicated via a wired and / or wireless communication network.

[0063] The communication interface 250 may include one or more devices for implementing communication with other hardware devices. For example, the communication interface 250 may include a network interface card (NIC) configured to communicate according to the Ethernet protocol. In addition, the communication interface 250 may implement a TCP / IP stack for communication according to the TCP / IP protocol. Various alternative or additional hardware or configurations for the communication interface 250 will be apparent.

[0064] The storage device 260 may include one or more machine-readable storage media, such as a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium, an optical storage medium, a flash memory device, or a similar storage medium. In various embodiments, the storage device 260 may store instructions executed by the processor 220 or data that the processor 220 can operate on. For example, the storage device 260 may store an operating system 261 for controlling various operations of the system 200.

[0065] It will be apparent that various information described as being stored in storage device 260 may be stored in memory 230 in addition or in lieu thereof. In this regard, memory 230 may also be considered to constitute a storage device, and storage device 260 may be considered to be a memory. Various other arrangements will be apparent. Additionally, both memory 230 and storage device 260 may be considered to be non-transitory machine-readable media. The term "non-transitory" as used herein will be understood to exclude transient signals, but rather to include all forms of storage devices (including volatile memory and non-volatile memory).

[0066] Although system 200 is shown as including one such component for each described component, in various embodiments, various components may be repeated. For example, processor 220 may include multiple microprocessors that are configured to independently run the methods described herein, or are configured to perform the steps or subroutines of the methods described herein, so that the multiple processors collaborate to implement the functions described herein. In addition, in the case where one or more components of system 200 are implemented in a cloud computing system, various hardware components may belong to separate physical systems. For example, processor 220 may include a first processor in a first server and a second processor in a second server. Many other variations and configurations are possible.

[0067] According to one embodiment, the system 200 includes a wearable device 270 having one or more sensors 280 configured to obtain sensor signals or sensor data about a patient (i.e., heartbeat data about the patient). The wearable device can be any device capable of including a sensor and obtaining sensor data, including but not limited to rings, watches, bracelets, necklaces, clothing, and other devices. Figure 2 2. System 200 is shown as one component in FIG. 2, but system 200 may also be multiple components of wearable device 270. Sensor 280 may be any sensor capable of generating sensor data described herein or otherwise contemplated.

[0068] According to one embodiment, the storage device 260 of the system 200 may store one or more algorithms, one or more modules, and / or one or more instructions to perform one or more functions or one or more steps of the methods described herein or otherwise contemplated. For example, the system may include pulse rate variability phase acceleration algorithm instructions 262, pulse rate variability phase deceleration algorithm instructions 263, trained infection risk prediction algorithm 264, and reporting instructions 265, etc.

[0069] According to one embodiment, pulse rate variability phase acceleration algorithm instructions 262 direct the system to analyze the received sensor data to determine a pulse rate variability phase acceleration value for the user for a first time period. A pulse rate variability phase acceleration algorithm is any algorithm configured to analyze the received sensor data and generate phase-corrected heart rate acceleration data. For example, the pulse rate variability phase acceleration algorithm averages the noise that affects heart rate acceleration. The pulse rate variability phase acceleration value for the user for the first time period may be used immediately, or the pulse rate variability phase acceleration value for the user for the first time period may be stored in a local or remote storage device of the infection detection system (e.g., stored in a memory of a wearable device or other component of the system) for use in additional steps of the method.

[0070] According to one embodiment, pulse rate variability phase deceleration algorithm instructions 263 direct the system to analyze the received sensor data to determine a pulse rate variability phase deceleration value for the user for a first time period. A pulse rate variability phase deceleration algorithm is any algorithm configured to analyze the received sensor data and generate phase-corrected heart rate deceleration data. For example, the pulse rate variability phase deceleration algorithm averages the noise that affects the heart rate deceleration. The pulse rate variability phase deceleration value for the user for the first time period may be used immediately, or the pulse rate variability phase deceleration value for the user for the first time period may be stored in a local or remote storage device of the infection detection system (e.g., stored in a memory of a wearable device or other component of the system) for use in additional steps of the method.

[0071] According to one embodiment, the trained infection risk prediction algorithm 264 guides the system to use at least the pulse rate variability phase acceleration value of the user for the first time period and the pulse rate variability phase deceleration value of the user as input to generate a determination result about the current infection and / or the risk or possibility of the current infection. The infection risk prediction algorithm of the infection detection system can be any algorithm configured to use these inputs and generate a determination result about the current infection and / or the risk or possibility of the current infection. For example, the algorithm can be any kind of machine learning algorithm and other types of algorithms. The algorithm can be trained using known mechanisms for training machine learning algorithms.

[0072] According to one embodiment, the report instruction 265 guides the system to provide the determined infection, infection score, infection risk and / or infection prediction results to the user via the user interface of the infection determination system. The system can display or otherwise provide infection information to the user (e.g., a care provider or the wearer of the wearable device) via the user interface. The infection information may include the determined infection, infection score, infection risk and / or infection prediction results and other information (e.g., information about the wearer of the wearable device). Information can be communicated to another device via wired and / or wireless communication. For example, the system can communicate information to a mobile phone, a computer, a laptop, a wearable device and / or any other device configured to allow display and / or otherwise communicate infection information and other information. The user interface can be any device or system that allows transmission and / or reception of information, and may include a display, a mouse and / or a keyboard for receiving user commands.

[0073] refer to Figure 4 , in one embodiment, Figure 4 4 is a flow chart of a method 400 for training an infection prediction algorithm 264 of an infection detection system 200. At step 410 of the method, the system receives a training data set including heartbeat data and infection status data for a plurality of individuals. The training data can include any information or data required to train the infection prediction algorithm, including labeled heartbeat data and infection status data that indicate the absence or presence of infection. The training data can be stored in one or more databases and / or the training data can be received from one or more databases. The database can be a local and / or remote database. For example, the infection detection system can include a database of training data.

[0074] According to one embodiment, the infection detection system may include a data preprocessor or similar component or algorithm configured to process the received training data. For example, the data preprocessor analyzes the training data to remove noise, bias, errors, and other potential problems. The data preprocessor may also analyze the input data to remove low-quality data. Many other forms of data preprocessing or data point identification and / or extraction are possible.

[0075] At step 420 of the method, the system processes the received information to extract localized features about the heartbeat and infection status data. The heartbeat and infection status features can be any features that will be used to train an infection prediction algorithm (e.g., any heartbeat and infection status features that can or will be used by a trained algorithm to perform infection analysis on future heartbeat data). Feature extraction can be accomplished by various embodiments for feature recognition, extraction, and / or processing, including any method for extracting features from a data set. The result of the feature processing step or module of the ultrasound system is a collection of heartbeat and infection status features for multiple wearers of the wearable device, which therefore comprises a training data set that can be used to train a classifier.

[0076] At step 430 of the method, the system trains a machine learning algorithm, which will be an algorithm used in analyzing heartbeat information as described or otherwise contemplated. The extracted features are used to train the machine learning algorithm according to known methods for training machine learning algorithms. According to one embodiment, the algorithm is trained using a processed training data set as described or otherwise contemplated herein to generate determined infections, infection scores, infection risks, and / or infection prediction results. The generated information or report can include any information described or otherwise contemplated herein, including but not limited to determined infections, infection scores, infection risks, and / or infection prediction results for the wearer of the wearable device.

[0077] At step 440 of the method, the trained infection prediction algorithm is stored for future use. According to one embodiment, the trained model may be stored in a local or remote storage device.

[0078] According to one embodiment, the infection detection system is configured to process thousands or millions of data points in the input data, which are used to train the classifier and to process and analyze the received multiple heartbeat and infection status features. For example, using automated processes such as feature recognition and extraction and subsequent training to generate a functional and trained classifier requires processing millions of data points from the input data and the generated features. This may require millions or billions of calculations to generate novel trained classifiers from these millions of data points and millions or billions of calculations. As a result, each trained classifier is novel and unique based on the input data and parameters of the machine learning algorithm, and thus improves the functionality of the infection detection system. Therefore, generating a functional and trained classifier includes a process with a large number of calculations and analyses, which cannot be completed by the human brain in a lifetime or several lifetimes.

[0079] Additionally, the infection detection system can be configured to continuously receive heartbeat and infection status features, perform analysis, and provide periodic or continuous updates to the wearer of the wearable device via reports provided to the user. This requires continuous analysis of thousands or millions of data points to optimize the reports, which in turn requires a large amount of computation and analysis that the human brain cannot accomplish in a lifetime.

[0080] By providing improved infection prediction or detection, this novel infection detection system has a tremendous positive impact on patient care compared to prior art systems. As just one example, by providing a system that can improve user analysis by detecting infection at an early stage, the system can facilitate early treatment decisions and improve survival prognosis, thereby saving lives.

[0081] All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and / or ordinary meanings of the defined terms.

[0082] Unless explicitly stated to the contrary, the terms "a" and "an" as used in this specification and the claims should be understood to mean "at least one."

[0083] The phrase "and / or" as used in this specification and claims should be understood to mean "either or both" of the elements so combined, i.e., elements that are present in combination in some cases and separately in other cases. Multiple elements listed with "and / or" should be interpreted in the same manner, i.e., "one or more" of the elements so combined. In addition to the elements specifically identified with the "and / or" clause, other elements may optionally be present, whether related or unrelated to those elements specifically identified.

[0084] "Or" used in the specification and claims herein should be understood to have the same meaning as "and / or" defined above. For example, when separating items in a list, "or" or "and / or" should be interpreted as inclusive, that is, including at least one, but also including more than one element of a plurality of elements or element lists and (optionally) additional unlisted items. Only terms that clearly indicate the opposite (e.g., "only one of" or "exactly one of" or "consisting of" used in the claims) will refer to including exactly one element in a plurality of elements or element lists. In general, when there is an exclusive term (e.g., "any one of", "one of", "any one of" or "exactly one of") in front of the term "or" used herein, the term "or" should only be interpreted as representing an exclusive alternative (i.e., "one or the other but not both").

[0085] The phrase "at least one" used herein in the specification and claims referring to a list of one or more elements should be understood to mean at least one element selected from any one or more elements in the list of elements, but not necessarily including at least one element of each element specifically listed in the list of elements, and not excluding any combination of elements in the list of elements. This definition also allows for optional elements other than the elements specifically identified in the list of elements to which the phrase "at least one" refers, whether related or unrelated to those elements specifically identified.

[0086] It should also be understood that in any method claimed herein that includes more than one step or action, the order of the steps or actions of the method is not necessarily limited to the order in which the steps or actions of the method are described unless explicitly stated to the contrary.

[0087] In the claims and the description above, all transitional phrases (e.g., "including," "comprising," "carrying," "having," "containing," "involving," "holding," "with," etc.) should be construed as open-ended, i.e., meaning including, but not limited to. Only the transitional phrases "consisting of" and "consisting essentially of" should be closed or semi-closed transitional phrases, respectively.

[0088] Although several inventive embodiments have been described and illustrated herein, a person of ordinary skill in the art will readily appreciate various other units and / or structures for performing functions and / or obtaining results and / or one or more of the advantages described herein. Each of such changes and / or modifications is considered to be within the scope of the inventive embodiments described herein. More generally, it will be readily understood by those skilled in the art that all parameters, dimensions, materials, and configurations described herein are intended to be exemplary, and that actual parameters, dimensions, materials, and / or configurations will depend on one or more specific applications using the teachings of the present invention. Those skilled in the art will recognize or be able to use experimental means not exceeding conventional means to determine many equivalents of the specific inventive embodiments described herein. Therefore, it should be understood that the foregoing embodiments are presented only as examples, and within the scope of the claims and their equivalents, embodiments of the present invention may be practiced in a manner different from that specifically described and claimed. The inventive embodiments of the present disclosure relate to each individual feature, system, article, material, kit, and / or method described herein. In addition, if such features, systems, articles, materials, kits, and / or methods are not mutually contradictory, any combination of two or more such features, systems, articles, materials, kits, and / or methods can be included within the scope of the invention of the present disclosure.

Claims

1. A method for reporting infection risk of a user, comprising: receiving user heart data from a sensor of a wearable device worn by the user; Determine a pulse rate variability phase acceleration value for the user in a first time period according to the received user heart data by using a pulse rate variability phase acceleration algorithm; Determining a pulse rate variability phase deceleration value for the user in the first time period according to the received user heart data by a pulse rate variability phase deceleration algorithm; determining the current infection risk of the user by using the pulse rate variability phase acceleration value of the user and the pulse rate variability phase deceleration value of the user for the first time period through a trained infection risk prediction algorithm; and The determined current risk of infection for the user is provided via the user interface.

2. The method according to claim 1, further comprising the steps of: Antibiotics and / or antiviral treatment are prescribed by a medical professional to treat the user's current infection in response to the provided determined risk of current infection for the user.

3. The method according to claim 1, further comprising the steps of: The received user cardiac data is transmitted to a remote server, wherein the determining step is performed at the remote server.

4. The method according to claim 3, wherein: The user interface includes a web portal.

5. The method according to claim 1, further comprising the steps of: The determined current risk of infection for the user is received by a medical professional via the user interface.

6. The method according to claim 1, wherein: The method is performed on the user at regular intervals via a remote service.

7. The method according to claim 1, wherein: (i) the sensor is a heart rate sensor and the user heart data is heart rate sensor data, and / or (ii) the sensor is an electrocardiogram sensor and the user heart data is ECG data.

8. A method for treating an infection in a patient, comprising: Receiving, by a medical professional from a user interface, a determined current infection of a patient, wherein the current infection of the patient is determined by: receiving user heart data from a sensor of a wearable device worn by the user; Determine a pulse rate variability phase acceleration value for the user in a first time period according to the received user heart data by using a pulse rate variability phase acceleration algorithm; Determining a pulse rate variability phase deceleration value for the user in the first time period according to the received user heart data by a pulse rate variability phase deceleration algorithm; determining that the user is experiencing a current infection using the user's pulse rate variability phase acceleration value and the user's pulse rate variability phase deceleration value for the first time period through a trained infection risk prediction algorithm; and providing, via the user interface, to the medical professional a determination that the patient is experiencing a current infection; and Antibiotics and / or antiviral treatment are prescribed by the medical professional to treat the user's current infection in response to the provided user's current infection risk.

9. The method according to claim 8, wherein: (i) the sensor is a heart rate sensor and the user heart data is heart rate sensor data, and / or (ii) the sensor is an electrocardiogram sensor and the user heart data is ECG data.

10. The method according to claim 8, wherein: The user interface includes a web portal.

11. A system for reporting a user's predicted infection risk, comprising: a wearable device worn by the user, the wearable device comprising a sensor configured to obtain cardiac data of the user; a trained infection risk prediction algorithm configured to determine a current infection risk of the user based on a pulse rate variability phase acceleration value of the user and a pulse rate variability phase deceleration value of the user for a first time period; A processor configured to: (i) determine a pulse rate variability phase acceleration value of the user for the first time period based on the received user heart data by a pulse rate variability phase acceleration algorithm; (ii) determining a pulse rate variability phase deceleration value of the user for the first time period based on the received user heart data by a pulse rate variability phase deceleration algorithm; and (iii) determining a current infection risk of the user by using the trained infection risk prediction algorithm for the user's pulse rate variability phase acceleration value and the user's pulse rate variability phase deceleration value for the first time period; as well as A user interface is configured to provide the determined current risk of infection for the user.

12. The system according to claim 11, wherein: The user interface includes a web portal.

13. The system according to claim 11, wherein: The received user heart data is transmitted to a remote server.

14. The system according to claim 11, wherein: The processor performs the claimed steps on the user at regular intervals.

15. The system according to claim 11, wherein: (i) the sensor is a heart rate sensor and the user heart data is heart rate sensor data, and / or (ii) the sensor is an electrocardiogram sensor and the user heart data is ECG data.

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