Abstract: A method of medical data auto collection segmentation and analysis, includes collecting, from a plurality of sources, unstructured medical data in a plurality of formats, recognizing a medical name entity of each piece of the unstructured medical data, using a medical dictionary, and performing semantic text segmentation on each piece of the unstructured medical data so that each piece of the unstructured medical data is partitioned into groups sharing a same topic. The method further includes generating, as structured medical data, each piece of the unstructured medical data of which the medical name entity is recognized, each piece of the unstructured medical data being partitioned into the groups, and indexing the structured medical data into elastic search clusters.

Abstract: A deep state space generative model is augmented with intervention prediction. The state space model provides a principled way to capture the interactions among observations, interventions, critical event occurrences, true states, and associated uncertainty. The state space model can include a discrete-time hazard rate model that provides flexible fitting of general survival time distributions. The state space model can output a joint prediction of event risk, observation and intervention trajectories based on patterns in temporal progressions, and correlations between past measurements and interventions.

Abstract: A method and apparatus include receiving, by a device, medical information associated with a user. Inquiry information is determined based on the medical information associated with the user and a reinforcement learning model. The inquiry information is provided to permit response information to be received. The response information is received based on providing the inquiry information. Diagnosis information is determined based on the medical information and the response information using a machine learning model. The diagnosis information is provided to a set of devices via a network.

Abstract: A method for processing unstructured Chinese-language medical text includes identifying a medical entity in the unstructured Chinese-language medical text using an attention-based named-entity recognition (NER) model, structuring the identified medical entity using a multiple-dimensional entity understanding framework, normalizing the structured medical entity using a medical knowledge graph, and outputting the normalized medical entity.

Abstract: A method of medical data auto collection segmentation and analysis, includes collecting, from a plurality of sources, unstructured medical data in a plurality of formats, recognizing a medical name entity of each piece of the unstructured medical data, using a medical dictionary, and performing semantic text segmentation on each piece of the unstructured medical data so that each piece of the unstructured medical data is partitioned into groups sharing a same topic. The method further includes generating, as structured medical data, each piece of the unstructured medical data of which the medical name entity is recognized, each piece of the unstructured medical data being partitioned into the groups, and indexing the structured medical data into elastic search clusters.

Abstract: A method and apparatus include receiving information associated with a bug bite. Information that identifies the bug bite is determined using a model, based on receiving the information associated with the bug bite. Information that identifies the bug bite is provided, based on determining the information that identifies the bug bite.

Abstract: A method of performing a heart abnormalities analysis, includes learning text information from an electronic medical record (EMR) and/or an electronic health record (EHR) of a user, learning signal information from electrocardiography (ECG) signal data of the user, merging the learned text information and the learned signal information to generate one or more representations of the text information and the signal information that are merged, and performing the heart abnormalities analysis on the generated one or more representations.

Abstract: Method and apparatus for vertebral artery dissection risk analysis using hemodynamic variable based four dimensional magnetic resonance flow imaging, comprising obtaining four-dimensional phase-contrast magnetic resonance imaging data, performing pre-processing of the four-dimensional phase-contrast magnetic resonance imaging data, obtaining at least one blood hemodynamic marker from the four-dimensional phase-contrast magnetic resonance imaging data, classifying the at least one blood hemodynamic marker as a hemodynamic predictor of vertebral artery dissection, and creating a comprehensive risk evaluation of vertebral artery dissection using the hemodynamic predictor.

Abstract: A method of determining a risk probability of vertebral artery dissection (VAD) in a patient, including receiving medical image information of the patient and clinical report information of the patient; extracting at least one biomarker corresponding to a vertebral artery segment included in the medical image information; extracting patient history information from the clinical report information; and determining the risk probability of VAD using a deep learning classification model based on the extracted at least one biomarker and the extracted patient history information.

Abstract: An encoder for encoding an input binary value of a binary input data sequence by generating an output current of an output current signal is provided. The encoder includes a control module and a switchable resistive element. The switchable resistive element is configured to either be in a first state or in a different second state depending on a first input voltage at a first point in time and depending on a second input voltage at a later second point in time. The control module is configured to apply the first input voltage to the switchable resistive element at the first point in time so that the first input voltage depends on the input binary value.

Abstract: A method and apparatus are provided that includes, in a same framework, storing an artificial intelligence (AI) model, loading the AI model into a serving platform, loading and testing a test unit against the AI model loaded into the serving platform, and collecting reports from results of storing the AI model, loading the AI model into the serving platform and testing the test unit.

Abstract: A method and apparatus are provided that includes iteratively sampling candidates from medical records and evaluating whether ones of the candidates better explain a member from the medical records. The iterations replace the member with the candidates and depending on whether the candidates better explain the member from the medical records may be weighted in a next iteration.

Abstract: There is included an apparatus and system including an intra-heartbeat (HB) extraction code configured to extract intra-HB features from electrocardiography (ECG) signals, and an inter-HB extraction code configured to extract inter-HB features from the ECG signals, and at least one attention mechanism code configured to control at least one of the intra-HB extraction code and inter-HB extraction code based on at least one attention mechanism.

Abstract: Systems and methods are disclosed for question generation to infer the most probable cause from the observable outcome and known Noisy-OR causal relations. In embodiments, the outcomes are sorted by indices according to an order including but not limited to the outcomes' natural frequency order, expert-labeled order, machine-learning derived order, etc. According to their assigned indices, in embodiments, observed outcomes with lower indices are assigned for exact inference while observed outcomes with higher indices are assigned for variational inference. In embodiments, results of exact inference and variational inference are combined to predict the most probable cause. The unique combination of exact inference and variational inference according to outcome indices makes the probable cause inferring process faster.

Abstract: Presented are relation inference methods and systems that use deep learning techniques for data mining documents to discover a relation between terms of interest in a given field covering a specific topic. For example, in the healthcare domain, various embodiments of the present disclosure provide for a relation inference system that mines large-scale medical documents in a free-text database to extract symptom and disease terms and generates relation information that aids in disease diagnosis. In embodiments, this is accomplished by training and using an RNN, such as an LSTM, a Gated Recurrent Unit (GRU), etc., that takes advantage of a term dictionary to examine co-occurrences of terms of interest within documents to discover correlations between the terms. The correlation may then be used to predict statistically most probable terms (e.g., a disease) related to a given search term (e.g., a symptom).

Abstract: Presented are systems and methods that estimate the strength of a relationship between elements gathered from online and/or offline information sources by estimating the trustworthiness both of the gathered data and the information sources from which the data originates. In one exemplary application, the relatedness between co-occurring symptom and disease terms collected from information sources, such as health-related online databases, is iteratively evaluated based on the trustworthiness of symptom-disease pairings and the trustworthiness of the information sources themselves. In various embodiments of the present disclosure, an objective function is used to extract a knowledge base that aids in identifying a potential relationship between a set of given symptoms provided by a user of an online healthcare service and co-occurring disease terms, such that a likely disease may be inferred from the set of symptoms.

Abstract: Image sensors may include phase detection pixels that are used to determine the distance between the image sensor and objects in a scene. To account for variations in the angular response of phase detection pixels across a pixel array, calibration may be performed. During calibration, an image sensor may take images of targets at known distances from the sensor. The known distances may be used to determine scaling factors that account for differences between the initially calculated distances between the sensor and the targets and the actual distances between the sensor and the targets. The scaling factors may then be stored on the image sensor for future reference. During subsequent use of the image sensor, the image sensor may then obtain the appropriate scaling factor in real time for more accurate phase and distance determinations.

Abstract: An electronic memristive device that has a complementary analog reconfigurable memristive bidirectional resistive switch. The device has a memristive layer sequence having a BFTO/BFO/BFTO three-ply layer and two electrodes. Titanium traps are arranged in the BFTO interfaces. As a result of mobile acid vacancies, the potential barriers at the interfaces of the electrodes with respect to the memristive layer sequence are in flexible form. By applying voltage pulses, the acid vacancies can be shifted from the interface with respect to the first electrode to the interface with respect to the second electrode, with raising of the potential barrier at one electrode bringing about complementary lowering of the potential barrier of the other electrode. The method for operating the device proposes adapted writing processes that use the overlaying of writing pulse sequences to achieve stipulation of a state pair of complementary resistor states.

Abstract: Image sensors may include phase detection pixels that are used to determine the distance between the image sensor and objects in a scene. To account for variations in the angular response of phase detection pixels across a pixel array, calibration may be performed. During calibration, an image sensor may take images of targets at known distances from the sensor. The known distances may be used to determine scaling factors that account for differences between the initially calculated distances between the sensor and the targets and the actual distances between the sensor and the targets. The scaling factors may then be stored on the image sensor for future reference. During subsequent use of the image sensor, the image sensor may then obtain the appropriate scaling factor in real time for more accurate phase and distance determinations.

Abstract: The invention relates to an electronic memristive device that has a complementary analogue reconfigurable memristive bidirectional resistive switch. The device has a memristive layer sequence having a BFTO/BFO/BFTO three-ply layer and two electrodes. Titanium traps are arranged in the BFTO interfaces. As a result of mobile acid vacancies, the potential barriers at the interfaces of the electrodes with respect to the memristive layer sequence are in flexible form. By applying voltage pulses, the acid vacancies can be shifted from the interface with respect to the first electrode to the interface with respect to the second electrode, with raising of the potential barrier at one electrode bringing about complementary lowering of the potential barrier of the other electrode. The method according to the invention for operating the device proposes adapted writing processes that use the overlaying of writing pulse sequences to achieve stipulation of a state pair of complementary resistor states.