FIELD

The subject matter described herein relates generally to data processing and more specifically to the machine learning based generation of an ontology for structural and functional mapping.

BACKGROUND

Functional neuroimaging has been a longtime mainstay of human neuroscience. For example, functional neuroimaging may include applying one or more neuroimaging techniques to measure an aspect of brain function, with the goal of understanding the relationship between the activity across brain structures and mental functions. Examples of neuroimaging techniques include positron emission tomography (PET), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), and single-photon emission computed tomography (SPECT).

The neuroimaging imaging technique may be applied while a subject is performing a task such as, for example, being exposed to a visual stimulation. The neuroimaging technique may be applied in order to measure localized fluctuations in cerebral blood flow, electrical current, and/or magnetic fields that indicate activities in certain regions of the brain during the performance of the task. A link between the mental functions associated with the task and the brain structures responsible for the mental functions may be identified based on the regions shown to be active during the performance of the task. For example, activities in the occipital lobe of the brain when the subject is exposed to a visual stimulation may indicate a link between the occipital lobe and visual perception.

SUMMARY

In one aspect, there is provided a method for generating an ontology for structural and functional mapping. The method may include: applying, to a corpus of data, a first machine learning technique to identify one or more candidate domains of an ontology mapping brain structure to mental function, the corpus of data including textual data describing a plurality of mental functions and spatial data corresponding to a plurality of brain structures, and the ontology including a plurality of domains each of which (1) corresponding to a neural circuiting including one or more brain structures and including (2) one or more mental function terms associated with the one or more brain structures; applying a second machine technique to optimize a quantity of domains included in the ontology and/or a quantity of mental function terms included in each of the plurality of domains; and applying the ontology to process an electronic medical record.

In some variations, one or more features disclosed herein including the following features can optionally be included in any feasible combination. The first machine learning technique may include an unsupervised machine learning technique. The second machine learning technique may include a supervised machine learning technique.

In some variations, the first machine learning technique may include a k-means clustering algorithm configured to cluster the plurality of brain structures include in the corpus of data based at least on a co-occurrence value between each of the plurality of brain structures and each of the plurality of mental function terms. The co-occurrence value may correspond to a frequency at which a brain structure and a mental function term appear in a same article in the corpus of data. The co-occurrence value may be further weighted based on a pointwise mutual information (PMI) corresponding to a probability that the brain structure and the mental function term appear in the same article.

In some variations, the second machine learning technique may include a forward inference model trained to predict an occurrence of a brain structure based on an occurrence various quantities of mental function term. The second machine learning technique may further includes a reverse inference model trained to predict the occurrence of the various quantities of mental function terms based on the occurrence of a brain structure.

In some variations, an optimal quantity of domains in the ontology and/or an optimal quantity of mental function terms included in each of the plurality of domains may be selected to maximize a performance of the forward inference model and/or a performance of the reverse inference model. The performance of the forward inference model and/or the performance of the reverse inference model may include an average area under the receiver operating characteristic curve (ROC-AUC).

In some variations, the forward inference model and/or the reverse inference model may include a multilayer neural network classifier.

In some variations, the method may further include applying a natural language processing (NLP) technique to preprocess the corpus of data prior to applying the first machine learning technique, the preprocessing includes one or more of a case-folding, a removal of stop words and punctuation, and a lemmatization.

In some variations, the electronic medical record may be processed by at least determining, based at least on the ontology, one or more phenotypes associated with the electronic medical record and (2) predicting, based at least on the one or more phenotypes, a clinical outcome for a patient associated with the electronic medical record.

In some variations, the one or more phenotypes for the electronic medical record may be determined by at least determining, for each of the plurality of domains of the ontology, a rating corresponding to a proportion of mental function terms associated with the domain that is present in the electronic medical record. The one or more phenotypes may correspond to one or more highest rated domains and/or one or more domains having an above-threshold rating.

In some variations, the clinical outcome may include a duration of hospital stay, a quantity of office visits, a quantity of emergency room visits, healthcare cost, prescriptions, refills, comorbid conditions, and/or the like.

In some variations, the plurality of domains may include emotion, retrieval, language, arousal, and movement.

In another aspect, there is provided a system for generating an ontology for structural and functional mapping. The system may include at least one data processor and at least one memory storing instructions. When executed by the at least one data processor, the instructions may cause operations include: In some variations, one or more features disclosed herein including the following features can optionally be included in any feasible combination. The first machine learning technique may include an unsupervised machine learning technique. The second machine learning technique may include a supervised machine learning technique.

In some variations, the first machine learning technique may include a k-means clustering algorithm configured to cluster the plurality of brain structures include in the corpus of data based at least on a co-occurrence value between each of the plurality of brain structures and each of the plurality of mental function terms. The co-occurrence value may correspond to a frequency at which a brain structure and a mental function term appear in a same article in the corpus of data. The co-occurrence value may be further weighted based on a pointwise mutual information (PMI) corresponding to a probability that the brain structure and the mental function term appear in the same article.

In some variations, the second machine learning technique may include a forward inference model trained to predict an occurrence of a brain structure based on an occurrence various quantities of mental function term. The second machine learning technique may further includes a reverse inference model trained to predict the occurrence of the various quantities of mental function terms based on the occurrence of a brain structure.

In some variations, the electronic medical record may be processed by at least determining, based at least on the ontology, one or more phenotypes associated with the electronic medical record and (2) predicting, based at least on the one or more phenotypes, a clinical outcome for a patient associated with the electronic medical record.

In another aspect, there is provided a computer program product including a non-transitory computer-readable medium that stores instructions. When executed by at least one data processor, the instructions may cause operations that include: applying, to a corpus of data, a first machine learning technique to identify one or more candidate domains of an ontology mapping brain structure to mental function, the corpus of data including textual data describing a plurality of mental functions and spatial data corresponding to a plurality of brain structures, and the ontology including a plurality of domains each of which (1) corresponding to a neural circuiting including one or more brain structures and including (2) one or more mental function terms associated with the one or more brain structures; applying a second machine technique to optimize a quantity of domains included in the ontology and/or a quantity of mental function terms included in each of the plurality of domains; and applying the ontology to process an electronic medical record.

Implementations of the current subject matter can include, but are not limited to, methods consistent with the descriptions provided herein as well as articles that comprise a tangibly embodied machine-readable medium operable to cause one or more machines (e.g., computers, etc.) to result in operations implementing one or more of the described features. Similarly, computer systems are also described that may include one or more processors and one or more memories coupled to the one or more processors. A memory, which can include a non-transitory computer-readable or machine-readable storage medium, may include, encode, store, or the like one or more programs that cause one or more processors to perform one or more of the operations described herein. Computer implemented methods consistent with one or more implementations of the current subject matter can be implemented by one or more data processors residing in a single computing system or multiple computing systems. Such multiple computing systems can be connected and can exchange data and/or commands or other instructions or the like via one or more connections, including but not limited to a connection over a network (e.g. the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), via a direct connection between one or more of the multiple computing systems, etc.

The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. While certain features of the currently disclosed subject matter are described for illustrative purposes in relation to generating an ontology for cerebral structural and functional mapping, it should be readily understood that such features are not intended to be limiting. The claims that follow this disclosure are intended to define the scope of the protected subject matter.

