System and method for early diagnostics and prognostics of mild cognitive impairment using hybrid machine learning

Abstract

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing hybrid machine learning. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A platform may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

What is claimed is: 
     
         1 . A system for diagnostics and prognostics of mild cognitive impairment using hybrid machine learning, comprising:
 a computer system comprising a memory and a processor;   a deep learning engine, comprising a first plurality of programming instructions stored in the memory and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, causes the computer system to:
 receive a request for a diagnosis or prognosis related to a target patient; 
 use a trained deep learning model to predict a first diagnosis or prognosis of the target patient; and 
 send the first prediction to a data fusion engine; and 
   a machine learning engine, comprising a second plurality of programming instructions stored in the memory and operating on the processor, wherein the second plurality of programming instructions, when operating on the processor, causes the computer system to:
 receive the request for a diagnosis or prognosis related to the target patient; 
 use a trained machine learning model to predict a second diagnosis or prognosis of the target patient; and 
 send the second prediction to the data fusion engine; and 
   the data fusion engine, comprising a third plurality of programming instructions stored in the memory and operating on the processor, wherein the third plurality of programming instructions, when operating on the processor, causes the computer system to:
 receive the first prediction from the deep learning engine; 
 receive the second prediction from the machine learning engine; 
 process the first prediction and the second prediction using an ensemble learning method, wherein the ensemble learning method is used to predict a final diagnosis or prognosis of the target patient; and 
 output the target patient's final prediction. 
   
     
     
         2 . The system of  claim 1 , wherein the deep learning model and the machine learning model are trained using a plurality of patient data, the plurality of patient data comprising medical imaging data, medical non-imaging data, and a combination of both. 
     
     
         3 . The system of  claim 1 , further comprising an image processing engine, comprising a fourth plurality of programming instructions stored in the memory and operating on the processor, wherein the fourth plurality of programming instructions, when operating on the processor, causes the computer system to:
 receive medical imaging data;   preprocess the medical imaging data; and   send the preprocessed medical imaging data to the deep learning engine and the machine learning engine.   
     
     
         4 . The system of  claim 1 , further comprising a data processing pipeline, comprising a fifth plurality of programming instructions stored in the memory and operating on the processor, wherein the fifth plurality of programming instructions, when operating on the processor, causes the computer system to:
 receive medical non-imaging data;   preprocess the medical non-imaging data; and   send the preprocessed medical non-imaging data to the deep learning engine and the machine learning engine.   
     
     
         5 . The system of  claim 1 , wherein the deep learning predictive diagnosis and prognosis model and the machine learning diagnosis and prognosis model are trained by an incomplete multi-modality transfer learning algorithm. 
     
     
         6 . The system of  claim 5 , wherein the incomplete multi-modality transfer learning algorithm comprises multitask and transfer learning. 
     
     
         7 . The system of  claim 5 , wherein the machine learning diagnosis and prognosis model is further trained using a feature selection algorithm. 
     
     
         8 . The system of  claim 7 , wherein the feature selection algorithm is a particle swarm optimization algorithm. 
     
     
         9 . The system of  claim 1 , wherein the ensemble learning method transfers knowledge between the deep learning algorithm and the machine learning algorithm and uses the transferred knowledge to predict the final diagnosis or prognosis of the target patient. 
     
     
         10 . The system of  claim 1 , wherein medical imaging data is selected from the group of MRI, FDG-PET, amyloid-PET, FLAIR, DTI, fMRI, Florbetapir-PET, and any combination thereof. 
     
     
         11 . A method for diagnostics and prognostics of mild cognitive impairment using hybrid machine learning, comprising the steps of:
 receiving a request for a diagnosis or prognosis related to a target patient;   using a trained deep learning model to predict a first diagnosis or prognosis of the target patient;   sending the first prediction to a data fusion engine;   receiving the request for a diagnosis or prognosis related to the target patient;   using a trained machine learning model to predict a second diagnosis or prognosis of the target patient; and   sending the second prediction to the data fusion engine;   receiving the first prediction from the deep learning engine;   receiving the second prediction from the machine learning engine;   processing the first prediction and the second prediction using an ensemble learning method, wherein the ensemble learning method is used to predict a final diagnosis or prognosis of the target patient; and   outputting the target patient's final prediction.   
     
     
         12 . The method of  claim 11 , wherein the deep learning model and the machine learning model are trained using a plurality of patient data, the plurality of patient data comprising medical imaging data, medical non-imaging data, and a combination of both. 
     
     
         13 . The method of  claim 11 , further comprising the steps of:
 receiving medical imaging data;   preprocessing the medical imaging data; and   sending the preprocessed medical imaging data to the deep learning engine and the machine learning engine.   
     
     
         14 . The method of  claim 11 , further comprising the steps of:
 receiving medical non-imaging data;   preprocessing the medical non-imaging data; and   sending the preprocessed medical non-imaging data to the deep learning engine and the machine learning engine.   
     
     
         15 . The method of  claim 11 , wherein the deep learning predictive diagnosis and prognosis model and the machine learning diagnosis and prognosis model are trained by an incomplete multi-modality transfer learning algorithm. 
     
     
         16 . The method of  claim 15 , wherein the incomplete multi-modality transfer learning algorithm comprises multitask and transfer learning. 
     
     
         17 . The method of  claim 15 , wherein the machine learning diagnosis and prognosis model is further trained using a feature selection algorithm. 
     
     
         18 . The method of  claim 17 , wherein the feature selection algorithm is a particle swarm optimization algorithm. 
     
     
         19 . The method of  claim 11 , wherein the ensemble learning method transfers knowledge between the deep learning algorithm and the machine learning algorithm and uses the transferred knowledge to predict the final diagnosis or prognosis of the target patient. 
     
     
         20 . The method of  claim 11 , wherein medical imaging data is selected from the group of MRI, FDG-PET, amyloid-PET, FLAIR, DTI, fMRI, Florbetapir-PET, and any combination thereof.