Abstract: A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.

Abstract: A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.

Abstract: A model-assisted selection system for predicting a date of an event relating to a patient may include at least one processor configured to obtain a medical record including a plurality of unstructured documents and obtain a model for predicting the date of the event. The at least one processor may further be configured to input the medical record into the model and assign, for each of the plurality of unstructured documents, a label from the model among a pre-event label, a mid-event label, a post-event label, and a non-event label. The at least one processor may also be configured to predict a start date of the event based on the labels of the plurality of unstructured documents and output the predicted start date.

Abstract: A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.