A computing system (10; 50; 100), comprising: at least one processor; a machine-learned health model (120, 140; 300; 500; 600) comprising: a sound model (302; 502; 602), wherein the sound model comprises a machine-learned audio classification model that is trained to receive data descriptive of a patient audio recording and, in response to receipt of the patient audio recording, output sound description data, wherein the sound description data comprises an embedding provided by a hidden layer of the machine-learned audio classification model; and a diagnostic model (304; 504; 604), wherein the diagnostic model is trained to receive the sound description data, and in response to receipt of the sound description data, output a diagnostic score; and at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising: obtaining the patient audio recording; inputting data descriptive of the patient audio recording into the sound model; receiving, as an output of the sound model, the sound description data; inputting the sound description data into the diagnostic model; and receiving, as an output of the diagnostic model, the diagnostic score. at least one processor; a machine-learned health model (120, 140; 300; 500; 600) comprising: a sound model (302; 502; 602), wherein the sound model comprises a machine-learned audio classification model that is trained to receive data descriptive of a patient audio recording and, in response to receipt of the patient audio recording, output sound description data, wherein the sound description data comprises an embedding provided by a hidden layer of the machine-learned audio classification model; and a diagnostic model (304; 504; 604), wherein the diagnostic model is trained to receive the sound description data, and in response to receipt of the sound description data, output a diagnostic score; and a sound model (302; 502; 602), wherein the sound model comprises a machine-learned audio classification model that is trained to receive data descriptive of a patient audio recording and, in response to receipt of the patient audio recording, output sound description data, wherein the sound description data comprises an embedding provided by a hidden layer of the machine-learned audio classification model; and a diagnostic model (304; 504; 604), wherein the diagnostic model is trained to receive the sound description data, and in response to receipt of the sound description data, output a diagnostic score; and at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising: obtaining the patient audio recording; inputting data descriptive of the patient audio recording into the sound model; receiving, as an output of the sound model, the sound description data; inputting the sound description data into the diagnostic model; and receiving, as an output of the diagnostic model, the diagnostic score. obtaining the patient audio recording; inputting data descriptive of the patient audio recording into the sound model; receiving, as an output of the sound model, the sound description data; inputting the sound description data into the diagnostic model; and receiving, as an output of the diagnostic model, the diagnostic score. <EOS>