Patent Publication Number: US-2021169740-A1

Title: Medical transport container monitoring using machine learning

Description:
CLAIM OF PRIORITY 
     This patent application claims the benefit of priority, under 35 U.S.C. Section 119(e), to Josh Janzen U.S. Patent Application Ser. No. 62/945,410, entitled “MEDICAL TRANSPORT CONTAINER MONITORING USING MACHINE LEARNING,” filed on Dec. 11, 2019 (Attorney Docket No. 4394.019PRV) each of which is hereby incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     Medical transport containers are used when moving temperature sensitive materials, such as blood samples, organs for transplant, or the like. The container is required to maintain a specified temperature or range, which may vary depending on the material being transported. The amount of time the container must maintain the temperature or range varies depending on the distance the material, sample, specimen, or the like is required to travel. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document. 
         FIG. 1  illustrates a medical transport container system, in accordance with some embodiments. 
         FIG. 2  illustrates components and sensors of a medical transport container, in accordance with some embodiments. 
         FIG. 3 . illustrates an example graphical user interface of a medical transport container, according to some embodiments. 
         FIG. 4  illustrates a flowchart for a method of monitoring a medical transport container, in accordance with some embodiments. 
         FIG. 5  illustrates training and use of a machine-learning system, according to some example embodiments. 
         FIG. 6  is a block diagram illustrating an example of a machine upon which one or more embodiments may be implemented, according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Medical transport containers are used to transport a variety of organic materials or specimens, such as vaccines, blood samples, tissue samples, or organs for transplant. Transport may occur from one hospital to another, from a hospital or clinic to an offside lab, or the like. In the case of transporting organs for transplant, there may be cases when an organ must be transported a substantial distance, including from one state to another, or across multiple states or countries. 
     In such examples, temperature may be regulated within the container to maintain viability of the transported material. A temperature setpoint or range may vary depending on the material being transported. Often, items being transported may be heat sensitive (e.g., such as certain vaccines) but must not be allowed to freeze. In this example, a temperature range of, for example, forty-five-degrees to fifty-nine-degrees Fahrenheit may be maintained. Some medical transport containers use passive refrigeration systems, which may require frequent replenishing of a cooling source (e.g., an ice pack) to keep the interior of a container within a desired temperature range. Passive containers may also require constant monitoring of their inner temperature to make sure the cooling source being used is maintaining the temperature in a desired range. 
     A container utilizing an active refrigeration system may be more efficient at keeping the interior of a medical transport container within a desired temperature range for an extended period of time, such as, for example, forty-eight-hours. An active refrigeration system uses multiple components (e.g., a compressor, a drive, a capillary, a refrigerant drier, a fan, a controller, temperature sensors, an accelerometer, a condenser, an evaporator, a battery, a battery charger, or the like) to maintain the desired temperature range within the container. 
     The components making up an active refrigeration system operate properly when they maintain the desired temperature range for the duration of the transport. A component failure in the system while transporting material may cause the material being transported to be damaged, degraded, or otherwise become unusable. 
     Disclosed herein are systems and methods that can monitor a medical transport container, specifically, by using a processor to receive data from a sensor, or from a plurality of sensors, corresponding to one or more components of the medical transport container. The plurality of sensors may include two or more of: a compressor current sensor, an accelerometer, a drive signal, an outside air temperature sensor, a fan current sensor, an internal temperature sensor, a high side refrigerant temperature sensor, a high side refrigerant pressure sensor, a low side refrigerant pressure sensor, a battery sensor, a battery charger sensor, or the like. 
     The data for the component may be classified using a classification model generated using a machine learning technique. Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. Machine learning generates a model that may learn from existing data and make predictions about new data. 
     Two common types of problems in machine learning are classification problems and regression problems. Classification problems, also referred to as categorization problems, aim at classifying items into one of several category values (e.g., is this object an apple or an orange, or is this component normal or damaged?). Regression algorithms aim at quantifying some items (for example, by providing a value that is a real number). 
     In some embodiments, example machine-learning techniques provide a score (e.g., a number from 1 to 100) to quantify the operational status of a component. The machine-learning techniques may use training data or testing data to identify correlations among identified features that affect the outcome, including a prediction. 
     The machine-learning techniques may use features for analyzing the data to generate assessments. A feature may be an individual measurable property of a phenomenon being observed (e.g., a temperature inside a medical transport container, an amount of charge on a battery, a refrigerant level, a degree of tilt, or the like). The concept of a feature may be related to that of an explanatory variable used in statistical techniques such as linear regression. Features may be of different types, such as numeric features, strings, or graphs. 
     The machine-learning techniques may use the training data or testing data to find correlations among the identified features that affect the outcome or assessment. With the training data or the testing data and the identified features, the machine-learning model may be trained. The machine-learning model may appraise the value of the features as they correlate to the training data. The result of the training is a trained machine-learning model. When the machine-learning model is used to perform an assessment, new data (e.g., new sensor data) is provided as an input to the trained machine-learning model, and the machine-learning model generates the assessment as output. 
     In an example, the system may output a score from the classification model for the data received by the sensors and determine whether the score has traversed a threshold value. For example, to determine whether the score is within a range. The classification may indicate whether the temperature within the container is within a range, such as an operational temperature range for a particular material being transported. 
     In an example, responsive to determining that the score has traversed the threshold value, the system may output an indication related to an operational status of the component. The operational status may indicate that the component is operating within an established tolerance. The operational status may also indicate that the component is operating outside an established tolerance. For example, the operational status may include a status corresponding to the state of a battery. This may include information indicating that the battery has reached a level of charge capable of powering an active refrigeration system for a specified period of time. Said period of time may be a travel time required to transport a particular type of material to a destination. For example, if a particular material requires it remain at a temperature between forty-degrees and fifty-degrees Fahrenheit and is estimated to take seventy-two-hours to reach a destination, the battery may be charged until capable of maintaining that temperature range for at least the entire seventy-two-hour duration. In another example, the duration required may be longer than the estimated travel time so as to provide a buffer should an unexpected delay occur. 
     The operational status may include information indicating that the battery is draining faster than a specified rate. This may be determined by a machine learning technique utilizing data from multiple components. For example, the status of the battery and the time between charges (e.g., how much time has elapsed since the battery charger was last used) may be used to determine that the battery is draining faster than an established tolerance. An alert may be provided based on the identified issue, such as advising that the battery may need recharging or recommending that the battery be replaced. 
     The operational status may include a status of a refrigerant level. For example, the indication may include a suggestion to fill refrigerant of the medical transport container at a next servicing in response to an evaluation (e.g., using machine learning) of the status of the refrigerant level. This may be based on the amount of time the system remains in a desired temperature range. In another example, the system may use data from multiple components to make a prediction or a recommendation regarding refrigerant. For example, the system may determine, based on a refrigerant level, and the amount of time has elapsed since the refrigerant was last filled, that there may be a leak in the refrigeration or coolant system of the medical transport container. 
     Outputting an indication may include illuminating an LED, transmitting a message to a graphical user interface, sending a message to a cloud-based server, an email address, or the like. In another example, the indication may include sounding an audible alarm. The alarm may sound, for example, when an accelerometer connected to the container detects that the container has been tilted greater than a specified number of degrees (e.g., greater than thirty-five-degrees), tilted over a threshold number of degrees a number of times (e.g., over twenty-five-degrees five times), tilted a total time over a specified angle over a time period (e.g., the container spent more than two minutes tilted at over twenty-five-degrees during a ten minute interval), or the like. The angle of tilt can be selected based on a refrigerant system located within the container where the angle is at or near a position where there is a likelihood of trapping oil within the system or otherwise preventing oil from returning to the compressor, which can cause the compressor to burn out. In another example when the accelerometer detects a tilt greater than a specified number of degrees, the compressor may stop, be disengaged, shut off, or otherwise rendered non-operational. 
       FIG. 1  illustrates a medical transport container system, in accordance with some embodiments. The system includes a medical transport container  100 , which may include a processor  116 , a graphical user interface  118 , a plurality of sensors  110 , a light emitting diode (LED)  112 , a speaker  114 , active refrigeration system components  120 , or a transceiver  122 . The transceiver  122  may include one or more antennas, for example a Wi-Fi antenna, a BLUETOOTH® antenna, an NFC (near field communication) reader or transmitter, an RFID (radio frequency identification) antenna, a cellular antenna (e.g., 3G, 4G, 5G, etc.), or the like. The transceiver  122  may communicate with a server, such as server  106 , which may include an email server to send data, or a remote server. The transceiver  122  may use one or more communication protocols to send data to various devices such as user device  102 , which may include a mobile device, a nearby device, a remote server, or the like. 
     The transceiver  122  may be powered or passive. The transceiver  122  may communicate intermittently (e.g., when there is data to send, such as when a buffer is full, or according to a periodic schedule) or via a continuous connection. A machine learned model such as described for  FIG. 5  below may be sent to the medical transport container. In an example, the machine learned model may be received via the transceiver  122  and stored in memory such as described for  FIG. 6  below. 
     The medical transport container  100  may include circuitry to connect to a user device  102  (e.g., a smartphone, a tablet, a notebook computer, a desktop computer, or the like), or to a network  104 . The network  104  may include a local area network (LAN), a wide area network (WAN), the internet, or the like. The network  104  may connect to a server  106 , which may be a machine described in  FIG. 6  below. The server may contain a database  108  in which information regarding the sensors  110 , status of the components included in the active refrigeration system components  120 , or messages that may be sent to the graphical user interface  118  may be stored. 
     The active refrigeration system components  120 , may include: a compressor, a condenser, a drive, an evaporator, a capillary, a drier, a fan, a controller, a battery, refrigerant, or the like, as described further below with respect to  FIG. 2 . One or more of the components  120  may be connected to respective one or more sensors of the plurality of sensors  110 , which may collect data from the components  120 . The collected data may be classified using a classification model generated using a machine learning technique such as one of the techniques described in  FIG. 5  below. 
     The medical transport container  100  may connect to the user device  102  in multiple ways. Such as, for example, through a universal serial bus (USB) cable, wireless local area networking (e.g. Wi-Fi), or any similar wireless technology (e.g. BLUETOOTH®). In an example, the medical transport container  100  may connect to the network  104  directly or through the user device  102 . 
       FIG. 2  illustrates the components and sensors of the active refrigeration system of the medical transport container  100 , in accordance with some embodiments. In an example, the medical transport container  100  may include active refrigeration components, for example: a compressor  200 , a drive  202 , a capillary  204 , a drier  206 , a fan  208 , a condenser  214 , an evaporator  216 , a battery,  218 , a battery charger  220 , refrigerant  210 , or the like. The compressor  202  may be a variable speed drive, which can control the speed of the fan  208 , compressor  200 , or the like, and may reduce the power consumption of the components  200 - 220 . 
     The sensors may include: a compressor current sensor  224 , an accelerometer  226 , a drive signal  228 , an outside air temperature sensor  230 , a fan current sensor  232 , an internal temperature sensor  234 , a high side refrigerant temperature sensor  236 , a high side refrigerant pressure sensor  238 , a low side refrigerant pressure sensor  240 , a battery sensor  242 , a battery charger sensor  244 , a low side refrigerant temperature sensor  246 , or the like. 
     The drive signal  228  may be used to control the speed of the drive  202 , which may be a part of a feedback control to maintain a desired threshold value. The drive signal  228  may be connected directly to the drive  202 , or the processor  222 . 
     The compressor current sensor  224  can be a current sensor configured to measure current drawn by the compressor  200  during operation thereof. Similarly, the fan current sensor  234  can be a current sensor configured to measure current drawn by the fan  208  during operation thereof. Each of the compressor current sensor  224  and the fan current sensor  234  can be in communication with the controller  222 . 
     The battery sensor  242  may determine the amount of charge currently on the battery  218  (e.g. whether the battery  218  is fully charged). The battery charger sensor  244 , may keep track of an amount of time required to charge the battery  218 . Each of the battery sensor  242  and the battery charge sensor  244  can be in communication with the controller  222  and may be used to recommend that that the battery  218  be replaced. 
     The high side refrigerant pressure sensor  238  can be a refrigerant pressure sensor connected to the discharge side of the compressor or between the compressor and the condenser coil. Similarly, the high side refrigerant temperature sensor  236  can be a temperature sensor connected to the discharge side of the compressor or between the compressor and the condenser coil. In some examples, the high side refrigerant pressure and temperature sensors can be positioned downstream of the condenser coil and upstream of an expansion device (capillary tube). In other examples, a refrigerant pressure and temperature sensor can be included downstream of the condenser and upstream of the capillary tube in addition to the high side refrigerant pressure sensor  238  and the high side refrigerant temperature sensor  236 . 
     The low side refrigerant pressure sensor  240  can be a refrigerant pressure sensor connected to the inlet side of the evaporator. Similarly, the low side refrigerant temperature sensor  246  can be a temperature sensor connected to the inlet side of the evaporator. In some examples, a refrigerant pressure sensor and a refrigerant temperature sensor can be included downstream of the evaporator and upstream of the compressor in addition to the low side refrigerant pressure sensor  240  and the low side refrigerant temperature sensor  246 . 
     A controller  222  may be connected to the active refrigeration components  120  or the sensors  110 , and may be configured to control aspects of the components  120 , for example changing a temperature, causing the battery charger  220  to charge the battery  218 , activating or changing speed of the fan  208 , etc. In an example, the controller  222  may control aspects of the sensors  110 , for example by causing sensor data to be collected or sent to a processor of the medical transport container  100  or to be transmitted to a remote device. 
     In some examples, the accelerometer  226  may output information indicating that the medical transport container  100  is tilting at an angle known to cause oil to not return completely or partially to the compressor  200 . In such a case, the controller  222  can produce an alert, deactivate the compressor  200 , and/or increase a speed of the compressor  200  via the drive  202  to increase refrigerant velocity and therefore oil velocity to improve oil return to the compressor  200 . 
     In some examples, the accelerometer  226  may output information indicating that the medical transport container  100  is tilting, but not at an angle which would otherwise cause a problem. However, one or more of the high side refrigerant pressure sensor  236 , the high side temperature sensor,  238 , the low side pressure sensor  240 , and the low side temperature sensor  246  together, the compressor current sensor  224 , the ambient air temperature sensor  230 , and the temperature sensor  234  (which may be a payload temperature sensor) may output information to the controller  222  that can be analyzed to indicate that thermal performance of the refrigerant system  120  is lower than expected. In such a case, it may be determined that a level of refrigerant  210 , which when combined with the tilt, may indicate a problem, such as that oil flow to the compressor is limited at unexpected angles of tilt and manifests immediately or over time through performance degradation of the thermal performance of the system  120 . A determination of the problem may occur using a machine learned model that outputs an indication of the problem based on the tilt and refrigerant p inputs. The machine learned model may be run on the medical transport container  100  (e.g., using memory and a processor of the medical transport container  100 ) or remotely, for example when the input data is sent to a remote device. The results of the machine learned model can be used to update the angle of tilt at which the alarm is produced by the controller  222  and/or the angle of tilt at which the controller  222  deactivates the compressor  200 . In examples where a variable speed drive is used to control a speed of the compressor  200 , the results of the machine learned model can be used to update a minimum speed of the compressor  200  at various tilts. 
     One or more of the sensors  224 - 246  may be used to collect data from one or more of the components  200 - 220 . The collected data may be classified, using a classification model generated using a machine learning technique. From the classification generated from the classification model, a score may be output. A processor (e.g., of the medical transport container  100  or a remote device) may determine whether the score has traversed a threshold value or whether the threshold value is within a particular range. Responsive to determining whether the threshold value is within a range, the classification model may output an indication related to an operational status of a component (e.g., one of  200 - 220 ). The operational status may indicate that the component (e.g., one of  200 - 220 ) is operating within an established tolerance. In another example, the operational status may indicate that the component is operating outside an established tolerance. 
     In an example, the data collected from the compressor  200  may include a level of liquid refrigerant  210  within an accumulator of the compressor  200 . It may be efficient to keep the amount of refrigerant  210  in the accumulator of the compressor  200  under a desired level (e.g., half filled). Thus, the system may determine that the compressor  200  is operating within an established tolerance when the amount of refrigerant  210  in the accumulator of the compressor  200  is the desired level. 
     In another example, data from multiple sensors  110  may be combined to determine the operational status of one or more components  200 - 220 . For example, the compressor  200  may only be activated, in an example, when the evaporator  216 , the condenser  214 , or the drive  202  are each operating within respective threshold value sets. In another example, the speed of the compressor  200  may be based on an ambient temperature as measured by the outside air temperature sensor  230 , and controlled by a temperature controller (e.g., controller  222 ). Similarly, the speed of the fan  208 , may be based on the temperature of the condenser  214 , which may be determined using on the internal temperature sensor  234 . In an example, there may be multiple internal temperature sensors  234  which monitor the temperature of specific components  200 - 220 , or the temperature inside the medical transport container  100 . Likewise, the outside air temperature sensor  230  may be affixed to an outer portion of the medical transport container  100  to measure the ambient air temperature surrounding the container  100  or may be located to measure the temperature of the air at the intake of the fan  208 . 
     Based on determining the operational status of a component  200 - 220 , the system may output an indication. The indication may be output through a user interface. The user interface may be a graphical user interface located on the medical transport container  100  (such as shown and described in  FIG. 3  below) or may be the user interface of a user device such as  102  in  FIG. 1  above. The indication may be in the form of a message sent to an email address or to a cloud-based server, such as the server  106  as described in  FIG. 1 . In another example, the indication may be an audible alarm emitted from a speaker  114  on the medical transport container  100 . This example may include an alarm sounding when an accelerometer  226  detects that the medical transport container  100  is tilting greater than a certain number of degrees. When the accelerometer  226  detects that the medical transport container  100  is tilting greater than a certain number of degrees (e.g., fifteen-degrees), the compressor  200  may be disengaged, shut off, or otherwise rendered non-operational until the medical transport container  100  is no longer tilting beyond a threshold angle. 
     In an example, a machine learning technique may output, based on a certain number of tilts of the container  100  over a period of time (e.g., over a particular angle for a particular number of minutes), or an amount of time that the container  100  has been tilted over a particular angle (e.g., within a timeframe), a notification or indication regarding a status of the container  100 . For example, the output may indicate that the container  100  has not operated optimally or within a specified tolerance and that the contents may be spoiled. 
     The indication may include illuminating an LED  112  located on the container  100 . For example, when all components  200 - 220  are operating within an established tolerance, the LED  112  may be illuminated with a specific color (e.g., green). In another example, when one or more components  200 - 220  are operating outside an established tolerance, the LED  112  may be illuminated with a different color (e.g., red). 
     While  FIG. 2  lists a number of components  120  and sensors  110 , the number and type of components  200 - 220  and sensors  224 - 248  are not intended to be limited to those listed. One or more of each of the components  200 - 220  or the sensors  224 - 248  may be included in or on the container  100 , and one or more of the components  200 - 220  or sensors  224 - 248  may be replaced, swapped out, augmented, or the like with another similar sensor or component which may perform a similar function. For example, a gyroscope, an inertial measurement unit (IMU), a piezoelectric vibration sensor, or other similar sensor may be used along with, or in place of, the accelerometer  126 . In another example, the container  100  may include more than one internal temperature sensor  234  in order to measure the temperature inside the container  100  or a component  200 - 220  along with the outside air temperature sensor  230  to determine the temperature of the environment outside the container  100 . 
       FIG. 3  illustrates a graphical user interface of a medical transport container, according to some embodiments. In the example of  FIG. 3 , a graphical user interface  302  may be mounted, connected, or otherwise coupled to a medical device container  300 . In an example, the graphical user interface  302  may be an LCD screen or other similar screen. In another example, the graphical user interface  302 , may generated on a display, including optionally a touchscreen. 
     The graphical user interface  302 , may include a temperature indication  304  to display a temperature captured by the internal temperature sensor  234  or the outside air temperature sensor  230 . The graphical user interface  302  may further include a battery indicator  306 . In this example, the battery indicator  306  may display the extent to which the battery  218  is charged. In another example the battery indicator  306  may indicate that the battery  218  is in the process of charging. 
     The graphical user interface  300 , may include a message display area  308 , which may display text indications. For example, the system may display messages regarding the operational status of a component, such as the components  200 - 220  described in  FIG. 2  above. For example, the message display area  308  may display a message such as “refrigerant low, refill at next servicing” or other similar status alerts. In another example, the display area  308  may, based on a machine learning technique, determine that based on a current charge on the battery  218 , how fast the battery  218  is discharging, an ambient temperature, and the time remaining to the destination, the battery  218  will not hold enough charge to power the container  100  until delivery, and should be charged or replaced during transit. 
     In another example, the graphical user interface  302  may include a menu which may be accessed through a touchscreen or through a button coupled to the graphical user interface  302 , which may allow for checking the operational status of a component of the system. For example, the temperature inside the container  100 , the refrigerant level, an amount of time until the battery is drained, or the like. 
       FIG. 4  illustrates a flowchart for a method  400  of monitoring a medical transport container, in accordance with some embodiments. Operation  402  may include receiving data from a sensor of a plurality of sensors of a medical transport container. The sensor may correspond to a component or components (such as components  200 - 220  as described in  FIG. 2 ) of the medical transport container. 
     Operation  404  may include classifying data for the component using a classification model generated using a machine learning technique. Operation  406  may include outputting a score from the classification model of operation  404  for the data. For example, the system may output a score for the amount of charge remaining on the battery or the discharge rate. The score may be based on, for example, the material being transported, or an amount of time required to reach a destination. In another example, the method  400  may include outputting a score corresponding to a remaining useful life for the battery  218 . This may be based on the amount of time between charges, or a level of charge required to power an active refrigeration system for a specified period of time. The specified period of time may include a travel time required to transport a particular type of material to a destination. In another example, the specified period of time may be the time remaining to a next maintenance servicing of the medical transport container. In another example, the period of time may be longer than an estimated travel time to a destination in order to account for a delay in transporting the material. 
     Outputting a score from the classification model may include outputting a score corresponding to a refrigerant level. Such a score may be based on the amount of refrigerant  210  compared with a prior level, such as the level after a last maintenance servicing, or a last filling of the refrigerant  210 . The score for the refrigerant level may also be based on an amount of time since the refrigerant was last filled. 
     Operation  408  may include determining whether a score has traversed a threshold value. Such a determination may include whether the score has fallen below a threshold value, such as, for example, whether a score corresponding to the level of refrigerant is below a threshold value and as a result, determine that the refrigerant must be refilled. The determination may also include whether the score has exceeded a threshold value, for example, a determination that a score for the amount of charge on the battery has exceeded a threshold value and as a result, determine that the battery is sufficiently charged. Or, for example, determination the amount of time required to charge the battery  218  has fallen below a threshold value, and as a result determine that replacing the battery  218  is recommended. 
     Responsive to determining whether the score has traversed a threshold value at operation  408 , operation  410  may include an indication related to an operational status of the component. Outputting the indication may include illuminating an LED, transmitting a message to a graphical user interface such as described in  FIG. 3  above, sending a message to a cloud-based server, an email address, or the like, or sounding an audible alarm. 
     The indication may include a message, such as, to refill the refrigerant, or to charge the battery. The message may indicate that the battery is unable to sufficiently hold a charge and should be replaced. In another example, the message may indicate that the refrigerant level is reducing too fast, and that there may be a leak in the refrigeration system. 
     The indication may include sounding an alarm. An alarm may occur when an accelerometer connected to the container detects that the container has been tilted greater than an established number of degrees (e.g., greater than thirty-five-degrees). In another example, the alarm may sound when the accelerometer, gyroscope, IMU, or similar sensor detects that the container has been titled greater than an established number of degrees for more than an established time period. For example, the alarm may not sound when a tilt for a short period of time (e.g., 1-10 seconds) is detected, suggesting that the container may have been tipped, but immediately returned to a level position. When however, the system detects the container has been tilting for an extended period of time (e.g., greater than 10 seconds), the alarm may sound. Likewise, in such a situation when the container has tilted for an extended period of time, another indication may be sent such as sending a message to a GUI as described above. 
     In an example, first sensor data may be used to generate a thermal operating efficiency for the medical transport container. Second sensor data may indicate a tilt of the medical transport container, such as with respect to a direction of gravitational force (e.g., angle information may be output from the sensor). The thermal operating efficiency and the tilt may be input to a machine learned model. The model may be used to determine whether to activate a tilt threshold alarm for the medical transport container as an example of operation  406 . In an example, when the tilt threshold alarm is activated the compressor  200  may be disengaged. In another example, the indication of operation  410  may be output. The tilt threshold alarm may be activated for the medical transport container based on an output of the machine learned model. 
     In an example, the thermal operating efficiency may be determined using data from a payload sensor or a refrigerant pressure sensor. In another example, when the tilt threshold alarm is activated, a speed curve of a compressor may be adjusted. The speed curve may be a speed setting of the compressor based on a condensing temperature (e.g. a high-side temperature or low-side temperature), condensing pressure (e.g. a high-side pressure or low-side pressure), ambient temperature, payload temperature, or the like. 
     In another example, the alarm may sound when the temperature inside the container rises above or falls below a specified temperature. In another example, the alarm may sound when the charge on the battery  218  falls below a certain level. An alarm may sound in response to a battery charge status, or an alarm may sound in response to an indication that the battery  218  is draining too fast and will not sufficiently power the container in a time required to reach a destination. This may be used in conjunction with an indication (e.g., a message) sent to a GUI on the container  100  as described for  FIG. 3  above or sent to a user device  102  as described above for  FIG. 1 . 
     In another example, the indication may include illuminating the LED  112 . The illumination may be a positive indication (e.g., illuminating the LED  112  green) or a negative indication (e.g., illuminating the LED  112  red). For example, when the LED  112  is green, it may indicate that all components are operating within a normal range as established by the threshold value at operation  408 . Or, when the LED  112  is red, it may illuminate indicating that one or more component is operating outside a range established by the threshold value at operation  408 . In an example a message regarding the operational status of the component may be sent to the graphical user interface  302 , or to an email address, the user device  102  (e.g. in an application, as a text alert, or the like), a cloud-based server, or the like. In another example, there may be multiple sets of LEDs which indicate the operational status for individual components  200 - 220  (e.g., a set of LEDs corresponding to the operational status of the battery  218 , a set of LEDs corresponding to the inner temperature of the container  100 , or the like). 
     In another example LED  112  may be illuminated upon activation, or in conjunction with a tilt sensor alarm. Activation of the tilt sensor alarm may cause an indication (e.g., a message) to be sent to an email address, cloud-based server, or the like. Activation of the tilt threshold alarm may cause an audible, alarm to sound. 
       FIG. 5  shows an example machine learning module  500  according to some examples of the present disclosure. Machine learning module  500  utilizes a training module  510  and a prediction module  520 . Training module  510  feeds training feature data information  530  into feature determination module  550 . Feature data  530  may be labelled or unlabeled. Feature determination module  550  determines one or more features  560  from this information. Features  560  are a subset of the information input and is information determined to be predictive of a desired result. The machine learning technique  570  produces a model  580  based upon the features  560  and in some examples, the model  580  is refined based upon feedback associated with those features. 
     In the prediction module  520 , feature data  590  may be input to the feature determination module  595 . Feature determination module  595  may determine the same set of features or a different set of features as feature determination module  550 . In some examples, feature determination module  595  and  550  are the same module. Feature determination module  595  produces features  597 , which are input into the model  580  to generate a result  599 . The training module  510  may operate in an offline manner to train the score model  580 . The prediction module  520 , however, may be designed to operate in an online manner. It should be noted that the score model  580  may be periodically updated via additional training and/or user feedback. 
     The machine learning technique  570  may be selected from among many different potential supervised or unsupervised machine learning techniques. Examples of supervised learning techniques include artificial neural networks, Bayesian networks, instance-based learning, support vector machines, decision trees (e.g., Iterative Dichotomiser 3, C4.5, Classification and Regression Tree (CART), Chi-squared Automatic Interaction Detector (CHAID), and the like), random forests, linear classifiers, quadratic classifiers, k-nearest neighbor, linear regression, and hidden Markov models. Examples of unsupervised learning algorithms include expectation-maximization algorithms, vector quantization, and information bottleneck method. 
     In some examples, the machine learning module  500  may be used to predict a leak in the refrigeration system. In these examples, the feature data  530  and  590  may the level of refrigerant and the amount of time since the refrigerant was last refilled. Further, the feature data  530  may include refrigerant levels in particular components of the system, such as the compressor. The result  599  comprises a prediction that there is a leak in the system and may include a prediction as to where in the system the leak is and may further include a recommendation regarding a component which will need to be repaired or replaced. 
     Similarly, in some examples, the machine learning module  500  may be used to predict the need to replace a battery. In these examples, the feature data  530  and  590  may include current amount of charge on the battery, an amount of time since the battery was last fully charged, or a discharge rate. The result  599  may comprise a prediction that the battery is no longer holding an adequate charge or is discharging too quickly or may include a recommendation that the battery be replaced. 
       FIG. 6  illustrates a block diagram of an example machine  600  upon which any one or more of the techniques (e.g., methodologies) discussed herein may perform. In alternative embodiments, the machine  600  may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine  600  may operate in the capacity of a server machine, a client machine, or both in server-client network environments. In an example, the machine  600  may act as a peer machine in peer-to-peer (P2P) (or other distributed) network environment. The machine  600  may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a smart phone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify commands to be taken by that machine. Machine  600  may implement the GUIs of  FIG. 3  and implement the process of  FIG. 4  and any process described herein. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations. 
     Examples, as described herein, may include, or may operate on, logic or a number of components, modules, or mechanisms (hereinafter “modules”). Modules are tangible entities (e.g., hardware) capable of performing specified operations and may be configured or arranged in a certain manner. In an example, circuits may be arranged (e.g., internally or with respect to external entities such as other circuits) in a specified manner as a module. In an example, the whole or part of one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware processors may be configured by firmware or software (e.g., instructions, an application portion, or an application) as a module that operates to perform specified operations. In an example, the software may reside on a machine readable medium. In an example, the software, when executed by the underlying hardware of the module, causes the hardware to perform the specified operations. 
     Accordingly, the term “module” is understood to encompass a tangible entity, be that an entity that is physically constructed, specifically configured (e.g., hardwired), or temporarily (e.g., transitorily) configured (e.g., programmed) to operate in a specified manner or to perform part or all of any operation described herein. Considering examples in which modules are temporarily configured, each of the modules need not be instantiated at any one moment in time. For example, where the modules comprise a general-purpose hardware processor configured using software, the general-purpose hardware processor may be configured as respective different modules at different times. Software may accordingly configure a hardware processor, for example, to constitute a particular module at one instance of time and to constitute a different module at a different instance of time. 
     Machine (e.g., computer system)  600  may include a hardware processor  602  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory  604  and a static memory  606 , some or all of which may communicate with each other via an interlink (e.g., bus)  608 . The machine  600  may further include a display unit  610 , an alphanumeric input device  612  (e.g., a keyboard), and a user interface (UI) navigation device  614  (e.g., a mouse). In an example, the display unit  610 , input device  612  and UI navigation device  614  may be a touch screen display. The machine  600  may additionally include a storage device (e.g., drive unit)  616 , a signal generation device  618  (e.g., a speaker), a network interface device  620 , and one or more sensors  630 , such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor (e.g. sensors  224 - 248  in  FIG. 2 ). The machine  600  may include an output controller  628 , such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.). 
     The storage device  616  may include a machine readable medium  622  on which is stored one or more sets of data structures or instructions  624  (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions  624  may also reside, completely or at least partially, within the main memory  2804 , within static memory  606 , or within the hardware processor  602  during execution thereof by the machine  600 . In an example, one or any combination of the hardware processor  602 , the main memory  604 , the static memory  606 , or the storage device  616  may constitute machine readable media. 
     The system may, using its processing circuitry and instructions executed by at least one non-transitory machine-readable media, implement any of the methods or phases, such as those described, for example, for  FIGS. 1-5  above, or any other methods or phases described herein. 
     While the machine readable medium  622  is illustrated as a single medium, the term “machine readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) configured to store the one or more instructions  624 . 
     The term “machine readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine  2800  and that cause the machine  2800  to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine-readable medium examples may include solid-state memories, and optical and magnetic media. Specific examples of machine-readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; Random Access Memory (RAM); Solid State Drives (SSD); and CD-ROM and DVD-ROM disks. In some examples, machine readable media may include non-transitory machine-readable media. In some examples, machine readable media may include machine readable media that is not a transitory propagating signal. 
     The instructions  624  may further be transmitted or received over a communications network  626  using a transmission medium via the network interface device  620 . The Machine  600  may communicate with one or more other machines utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®, IEEE 802.16 family of standards known as WiMax®), IEEE 802.15.4 family of standards, a Long Term Evolution (LTE) family of standards, a Universal Mobile Telecommunications System (UMTS) family of standards, peer-to-peer (P2P) networks, among others. In an example, the network interface device  2820  may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network  2826 . In an example, the network interface device  2820  may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. In some examples, the network interface device  2820  may wirelessly communicate using Multiple User MIMO techniques. 
     Example 1 is a machine-readable storage medium having instructions stored thereon, which, when executed by processing circuitry, cause the processing circuitry to: receive data from a sensor of a plurality of sensors of a medical transport container, the sensor corresponding to a component of the medical transport container; classify the data for the component using a classification model generated using a machine learning technique; output a score from the classification model for the data; determine whether the score has traversed a threshold value; and responsive to determining that the score has traversed the threshold value, output an indication related to an operational status of the component. 
     In Example 2, the subject matter of Example 1 includes, wherein the plurality of sensors include two or more of: a compressor current sensor, an accelerometer, a drive signal, an outside air temperature sensor, a fan current sensor, an internal temperature sensor, a high side refrigerant temperature sensor, a high side refrigerant pressure sensor, a low side refrigerant pressure sensor, a battery sensor, or a battery charger sensor. 
     In Example 3, the subject matter of Example 2 includes, wherein when a tilt outside an established tolerance is detected using data from the accelerometer, a compressor is disengaged. 
     In Example 4, the subject matter of Examples 1-3 includes, wherein to determine whether the score has traversed a threshold value includes determining whether the score is within a range. 
     In Example 5, the subject matter of Examples 1-4 includes, wherein the operational status indicates that the component is operating within an established tolerance. 
     In Example 6, the subject matter of Examples 1-5 includes, wherein the operational status indicates that the component is operating outside an established tolerance. 
     In Example 7, the subject matter of Examples 1-6 includes, wherein outputting the indication includes illuminating an LED of the medical transport container. 
     In Example 8, the subject matter of Examples 1-7 includes, wherein outputting the indication includes transmitting a message to a graphical user interface. 
     In Example 9, the subject matter of Examples 1-8 includes, wherein outputting the indication includes sending the indication to an email address or to a cloud-based server. 
     In Example 10, the subject matter of Examples 1-9 includes, wherein outputting the indication includes sounding an audible alarm. 
     In Example 11, the subject matter of Example 10 includes, wherein the alarm sounds when an accelerometer detects a tilt outside an established tolerance. 
     In Example 12, the subject matter of Examples 1-11 includes, wherein the operational status includes a status corresponding to a state of a battery. 
     In Example 13, the subject matter of Example 12 includes, wherein the operational status includes an information indicating that the battery has reached a level of charge capable of powering an active refrigeration system for a specified period of time. 
     In Example 14, the subject matter of Examples 12-13 includes, wherein the operational status includes an information indicating that the battery is draining faster than a specified rate. 
     In Example 15, the subject matter of Example 14 includes, wherein the indication further includes a recommendation to recharge or replace the battery. 
     In Example 16, the subject matter of Examples 1-15 includes, wherein the operational status includes a status of a refrigerant level. 
     In Example 17, the subject matter of Example 16 includes, wherein the indication includes a suggestion to fill refrigerant of the medical transport container at next servicing in response to the status of the refrigerant level. 
     In Example 18, the subject matter of Examples 16-17 includes, wherein the operational status indicates a leak in a cooling system of the medical transport container. 
     Example 19 is a method for monitoring a medical transport container, the method comprising: receiving data from a sensor of a plurality of sensors of the medical transport container, the sensor corresponding to a component of the medical transport container; classifying the data for the component using a classification model generated using a machine learning technique; outputting a score from the classification model for the data; determining whether the score has traversed a threshold value; and responsive to determining that the score has traversed a threshold value, outputting an indication related to an operational status of the component. 
     Example 20 is a system for monitoring a medical transport container, the system comprising: an active refrigeration system; a processor; memory including instructions stored thereon which, when executed by the processor, cause the processor to: receive data from a sensor of plurality of sensors of the medical transport container, the sensor corresponding to a component the medical transport container; classify the data for the component using a classification model generated using a machine learning technique; output a score from the classification model for the data; determine whether the score has traversed a threshold value; and responsive to determining that the score has traversed the threshold value, output an indication related to the operational status of the component. 
     In Example 21, the subject matter of Example 20 includes, wherein the plurality of sensors include two or more of: a compressor current sensor, an accelerometer, a drive signal, an outside air temperature sensor, a fan current sensor, an internal temperature sensor, a high side refrigerant temperature sensor, a high side refrigerant pressure sensor, a low side refrigerant pressure sensor, a battery sensor, or a battery charger sensor. 
     Example 22 is a machine-readable storage medium having instructions stored thereon, which, when executed by processing circuitry, cause the processing circuitry to: receive data from a first sensor of a plurality of sensors of a medical transport container, the sensor corresponding to a component of the medical transport container; generate, using the received data, a thermal operating efficiency for the medical transport container; receive data from a second sensor of the plurality of sensors indicating a tilt of the medical transport container with respect to a direction of gravitational force; input the thermal operating efficiency and the tilt to a machine learned model to determine whether to activate a tilt threshold alarm for the medical transport container; and activate the tilt threshold alarm for the medical transport container based on an output of the machine learned model. 
     In Example 23, the subject matter of Example 22 includes, wherein the processing circuitry is further caused to: disengage a compressor when the tilt threshold alarm is activated. 
     In Example 24, the subject matter of Examples 22-23 includes, wherein the first sensor includes a payload temperature sensor or a refrigerant pressure sensor. 
     In Example 25, the subject matter of Examples 22-24 includes, wherein the processing circuitry is further caused to: adjust a speed curve of a compressor when the tilt threshold alarm is activated. 
     In Example 26, the subject matter of Examples 22-25 includes, wherein the second sensor includes an accelerometer. 
     In Example 27, the subject matter of Examples 22-26 includes, wherein to activate the tilt threshold alarm, the processing circuitry is further caused to illuminate an LED of the medical transport container. 
     In Example 28, the subject matter of Examples 22-27 includes, wherein to activate the tilt threshold alarm, the processing circuitry is further caused to transmit a message to a graphical user interface. 
     In Example 29, the subject matter of Examples 22-28 includes, wherein to activate the tilt threshold alarm, the processing circuitry is further caused to send the indication to an email address or to a cloud-based server. 
     In Example 30, the subject matter of Examples 22-29 includes, wherein to activate the tilt threshold alarm, the processing circuitry is further caused to sound an audible alarm. 
     Example 31 is at least one machine-readable medium including instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations to implement of any of Examples 1-30. 
     Example 32 is an apparatus comprising means to implement of any of Examples 1-30. 
     Example 33 is a system to implement of any of Examples 1-30. 
     Example 34 is a method to implement of any of Examples 1-30. 
     The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments may be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is to allow the reader to quickly ascertain the nature of the technical disclosure and is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment. The scope of the embodiments should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.