Koninklijke Philips Corporation N.v.

Microsoft Technology Licensing, LLC

Ashequl Qadir

Oladimeji Feyisetan Farri

Sheikh Sadid Al Hasan

Junyi Liu

Vivek Varma Datla

Aaditya Prakash

Kathy Mi Young Lee

Michael Gamon

Ahmed Hassan Awadallah

Patrick Pantel

Minnan Xu

Tilak Raj Arora

Claire Yunzhu Zhao

Reza Ghaeini

Adi Prakash

Kathy Lee

Siyuan Zhao

A system and method for training a text report identification machine learning model and an image identification machine learning model, including: initially training a text report machine learning model, using a labeled set of text reports including text pre-processing the text report and extracting features from the pre-processed text report, wherein the extracted features are input into the text report machine learning model; initially training an image machine learning model, using a labeled set of images; applying the initially trained text report machine learning model to a first set of unlabeled text reports with associated images to label the associated images; selecting a first portion of labeled associated images; re-training the image machine learning model using the selected first portion of labeled associated images; applying the initially trained image machine learning model to a first set of unlabeled images with associated text reports to label the associated text reports; selecting a first portion of labeled associated text reports; and re-training the text report machine learning model using the selected first portion of labeled associated text reports.

Co-training framework to mutually improve concept extraction from clinical notes and medical image classification

Techniques are described for training and/or utilizing sub-agent machine learning models to generate candidate dialog responses. In various implementations, a user-facing dialog agent (), or another component on its behalf, selects one of the candidate responses which is closest to user defined global priority objectives (). Global priority objectives can include values () for a variety of dialog features such as emotion, confusion, objective-relatedness, personality, verbosity, etc. In various implementations, each machine learning model includes an encoder portion and a decoder portion. Each encoder portion and decoder portion can be a recurrent neural network (RNN) model, such as a RNN model that includes at least one memory layer, such as a long short-term memory (LSTM) layer.

System for multi-perspective discourse within a dialog

A system and method for unsupervised training of a text report identification machine learning model, including: labeling a first set of unlabeled text reports using a seed dictionary to identify concepts in the unlabeled text reports; inputting images associated with the first set of seed-labeled text reports into an auto-encoder to obtain an encoded first set of images; calculating a set of first correlation matrices as a dot product of the first encoded images with their associated text report features; determining a distance between the set of first correlation matrices and a filter bank value associated with the auto-encoder; identifying a first set of validated images as the images in the first set of images that have a distance less than a threshold value; and training the text report machine learning model using the labeled text reports associated with the set of first validated images.

Method to improve instance selection in bootstrapping framework for concept extraction from text documents

A system () for automated question answering, including: semantic space () generated from a corpus of questions and answers; a user interface () configured to receive a question; and a processor () comprising: (i) a question decomposition engine () configured to decompose the question into a domain, a keyword, and a focus word; (ii) a question similarity generator () configured to identify one or more questions in a semantic space using the decomposed question; (iii) an answer extraction and ranking engine () configured to: extract, from the semantic space, answers associated with the one or more identified questions; and identify one or more of the extracted answers as a best answer; and (iv) an answer tuning engine () configured to fine-tune the identified best answer using one or more of the domain, keyword, and focus word; wherein the fine-tuned answer is provided to the user via the user interface.

Multi domain real-time question answering system

Techniques are described herein for training and applying memory neural networks, such as 'condensed' memory neural networks ('C-MemNN') and/or &#x201c;average&#x201d; memory neural networks (&#x201c;A-MemNN&#x201d;). In various embodiments, the memory neural networks may be iteratively trained using training data in the form of free form clinical notes and clinical reference documents. In various embodiments, during each iteration of the training, a so-called &#x201c;condensed&#x201d; memory state may be generated and used as part of the next iteration. Once trained, a free form clinical note associated with a patient may be applied as input across the memory neural network to predict one or more diagnoses or outcomes of the patient.

Condensed memory networks

The described embodiments relate to systems, methods, and apparatus for providing a multimodal deep memory network () capable of generating patient diagnoses (). The multimodal deep memory network can employ different neural networks, such as a recurrent neural network and a convolution neural network, for creating embeddings () from medical images () and electronic health records (). Connections between the input embeddings () and diagnoses embeddings () can be based on an amount of attention that was given to the images and electronic health records when creating a particular diagnosis. For instance, the amount of attention can be characterized by data () that is generated based on sensors that monitor eye movements of clinicians observing the medical images and electronic health records. Resulting patient diagnoses can be provided according to a predetermined classification of weights, or a compilation of words that are generated over multiple iterations of the multimodal deep memory network.

Systems, methods, and apparatus for diagnostic inferencing with a multimodal deep memory network

Techniques described herein relate to semi-supervised training and application of stacked autoencoders and other classifiers for predictive and other purposes. In various embodiments, a semi-supervised model () may be trained for sentence classification, and may combine what is referred to herein as a 'residual stacked de-noising autoencoder' ('RSDA') (), which may be unsupervised, with a supervised classifier () such as a classification neural network (e.g., a multilayer perceptron, or &#x201c;MLP&#x201d;). In various embodiments, the RSDA may be a stacked denoising autoencoder that may or may not include one or more residual connections. If present, the residual connections may help the RSDA &#x201c;remember&#x201d; forgotten information across multiple layers. In various embodiments, the semi-supervised model may be trained with unlabeled data (for the RSDA) and labeled data (for the classifier) simultaneously.

Semi-supervised classification with stacked autoencoder

Methods and systems for generating a question from free text. The system is trained on a corpus of data and receives a tuple consisting of a paragraph (free text), a focused fact, and a question type. The system implements a language model to find the most optimal combination of words to return a question for the paragraph about the focused fact.

Question generation

This disclosure describes techniques and architectures that involve a latent activity model for workplace emails. Such a model is based, at least in part, on a concept that communications, such as email at a workplace, are purposeful and organized by activities. An activity is a set of interrelated actions and events around a common goal, involving a particular group of people, set of resources, and time framework, for example. The latent activity model involves a probabilistic inference in graphical models that jointly captures the interplay between latent activities and the email contexts governed by the emails. Such contexts may be email recipients, subject and body of the email, and so on.

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Melrose, MA, United States of America

Ashequl Qadir