2860results about "Therapies" patented technology

An example assistant system can use a multimodal multitask medical machine-learned model to perform image processing to answer natural language queries. A device can process speech data or other natural language inputs to obtain a query. The query can be processed alongside image data that provides context for the query. The example system can receive a query associated with a particular task domain; generate, based on the query, a query input that comprises query instruction data from a first modality and query context data from a second modality; generate a combined input comprising the query input and an exemplar input, wherein the exemplar input comprises exemplar instruction data from the first modality and an exemplar context placeholder in lieu of exemplar context data from the second modality; process the combined input with a multimodal machine-learned model to generate output data; and output a query response based on the output data.

The invention discloses a liver disease diagnosis and treatment strategy recommendation method based on a knowledge graph, a medium and equipment, and the method comprises the steps: collecting the basic information of a user, constructing a space-time correlated individual liver disease knowledge graph according to the basic information of the user, and enabling the knowledge graph to comprise a disease cause feature node, a pathology grading node, a complication early warning node and a treatment response node which are correlated with each other; inputting the individual hepatopathy knowledge graph into a hierarchical reinforcement learning model for joint reasoning, and outputting multiple groups of strategy options including an antiviral treatment scheme, a liver protection drug combination and a metabolic intervention measure; analyzing a topological propagation path of a complication early warning node through a complication prediction sub-model, and dynamically adjusting an initial candidate diagnosis and treatment strategy set; and updating the individual hepatopathy knowledge graph when obtaining the basic information of the new user. According to the method, multi-dimensional data fusion and dynamic strategy optimization of liver disease diagnosis and treatment are realized, and the accuracy and timeliness of a diagnosis and treatment scheme are remarkably improved.

The invention discloses a doctor-patient negotiation method and device fusing LLM and a multi-agent architecture, equipment and a medium, and relates to the technical field of doctor-patient negotiation. The doctor-patient negotiation method comprises the following steps: acquiring doctor-patient background information, and constructing a doctor agent, a patient agent and a coordination agent based on a large language model. The doctor agent generates a preliminary treatment regimen. The patient agent generates feedback to the preliminary treatment regimen. The coordination agent parses the feedback, generating a structured acceptance score and a semantic vector. The dynamic belief is updated based on the semantic vector doctor agent. And performing fuzzy utility evaluation on the current treatment scheme, and judging whether strategy adjustment is triggered or not. And the coordination agent calculates the adjustment amount of each issue based on the acceptance difference value, the patient yielding willingness coefficient and the maximum yielding amplitude, and obtains a new quotation scheme. And triggering a belief calibration and strategy reconstruction process at a preset stage node of multiple rounds of negotiation to obtain a new strategy. And when the negotiation round reaches a preset maximum round number, outputting a final treatment scheme.

The invention relates to the technical field of medical aid decision making, in particular to a system and method for generating an individualized nursing scheme based on the comprehensive evaluation condition of the elderly. According to the method, various physiological time sequence data of the old people in a specific life scene are collected and the periodic fluctuation characteristics of the data are extracted, so that the subtle change trend of the daily rhythm of the old people can be accurately identified, the dynamic influence of the chronic disease progress on the physiological state is revealed by further comparing the rhythm deviation degree and continuity in different time periods, and the accuracy of the physiological state of the old people is improved. Then, in combination with the trend change rate and directionality thereof, an interaction mode between physiological changes and chronic disease indexes is described in continuous observation, so that the measurement of the coupling adaptation degree between individual physiological responses and chronic disease conditions is achieved, through normalized amplitude difference and matching interval screening, individual physical ability and chronic disease adaptation labels are accurately given, and the accuracy of the physical ability and chronic disease adaptation labels is improved. And further comprehensively measuring the adaptation priority of each intervention element in the individual in a multi-dimensional scoring mode, and constructing a nursing intervention combination oriented to a specific crowd state.

The invention provides a neurology clinical nursing potential safety hazard analysis method and device, and relates to the technical field of neurology clinical nursing, and the method comprises the steps: inputting a risk feature matrix generated based on multi-source heterogeneous nursing data into a rule engine and graph neural network model, and outputting a preliminary screening risk event set; taking the primary screening risk event set and the risk feature matrix as input, and constructing a causal conduction map through a causal discovery algorithm; performing risk conduction quantitative integration on each risk event in the primarily screened risk event set based on a causal conduction map to generate a quantitative risk list; based on the dynamic risk priority number and the conduction path chain, reversely tracing to a root cause node along a directed conduction edge, and generating a targeted intervention strategy packet bound with the root cause node; the targeted intervention strategy package is pushed to the nursing responsible person terminal, the strategy execution effect and the risk evolution data are recorded, and a closed-loop disposal database is generated, so that the dynamics and the effectiveness of potential safety hazard analysis of clinical nursing of the neurology department are improved.

The invention discloses an Alzheimer's disease long-term prediction and dynamic intervention method based on deep learning. Accurate dynamic intervention from group statistics to individual dynamics is realized through time sequence alignment, multi-scale time sequence feature extraction, dynamic risk assessment and personalized intervention. Through time sequence alignment and multi-scale time sequence feature extraction, a time-sensitive personalized intervention scheme can be generated. And carrying out continuous time modeling by adopting a cubic Hermite interpolation method and a neural control differential equation, and predicting the long-term risk. By establishing a double-track interaction model, a pathological track and a functional track of a user are analyzed, so that time-varying association among multi-modal data can be dynamically captured. A personalized intervention strategy is provided through reinforcement learning, the intervention strategy is dynamically adjusted in combination with risk reduction amplitude, intervention measure compliance and physiological index change, the effect of short-term behavior change and long-term prediction is balanced, and the Alzheimer's disease is further promoted to be converted from passive treatment to active intervention.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

The invention relates to the technical field of medical rehabilitation, and particularly discloses an anorectal postoperative rehabilitation nursing evaluation method and system based on a sensor. According to the scheme, the system comprises a multi-source physiological information sensing module, a data preprocessing and feature extraction module, a multi-modal data fusion analysis module, a dynamic risk assessment and early warning module and a man-machine interaction and decision support module, multi-dimensional physiological signals are continuously collected, a rehabilitation state index is generated through fusion, the complication risk is assessed in real time, and the rehabilitation effect is improved. Early warning and accurate nursing decision support are realized, and comprehensiveness and timeliness of rehabilitation evaluation are improved.

The invention discloses a data-driven self-adaptive cognitive competence training method and a data-driven self-adaptive cognitive competence training device. The method comprises the following steps: collecting initial cognitive competence data of a user; establishing a mapping relationship between the training tasks and the cognitive ability dimensions, and associating a plurality of predefined training tasks to the corresponding cognitive ability dimensions respectively; based on the user cognitive competence vector, selecting a training task matched with the current cognitive competence level of the user from a training task library through an adaptive recommendation algorithm; providing the training task matched with the current cognitive ability level of the user for the user to execute; and updating the user cognitive ability vector according to the behavior feedback data. According to the technical scheme, the training task is finely matched through the multi-dimensional cognitive vector, the difficulty is adjusted in real time, and the load is prevented from being too high or too low; and by utilizing behavior data closed-loop updating, a capability increasing curve can be accurately quantified, task sorting and rhythm are dynamically optimized, and a learning motivation is stimulated.

The invention provides an oral health monitoring method and system based on three-dimensional reconstruction and multi-feature constraint, and relates to the technical field of medical data processing and oral monitoring, and the method comprises the steps: obtaining a multi-angle high-definition image and depth data in an oral cavity; performing semantic segmentation on the preprocessed multi-angle high-definition image, and identifying a tooth structure and a key region in an oral cavity; constructing a three-dimensional oral cavity model by using an edge-guided depth reconstruction method; a timestamp is introduced, the depth data, the tooth structure in the oral cavity and the key area are mapped to a three-dimensional oral cavity model, on the basis of the three-dimensional space dimension, the time dimension and the key point distribution dimension are added, and a multi-dimensional dynamic oral cavity model is constructed; based on the multi-dimensional dynamic oral cavity model, analyzing the evolution trend of the key region through a space-time dynamic prediction network, predicting future evolution risks, and obtaining a risk assessment result; and generating a health management suggestion according to a risk assessment result, and feeding back the health management suggestion to the multi-dimensional dynamic oral cavity model to form a closed-loop feedback mechanism.

The invention provides a dynamic myopia management lens prescription customization method and system, and relates to the technical field of lens design and customization. According to the dynamic myopia management lens prescription customization technology provided by the invention, ophthalmic basic data of a patient is acquired; then periodically follow-up the patient to obtain ophthalmology measurement follow-up data of the patient; the joint evaluation model dynamically updates a myopia risk evaluation grade of the patient based on ophthalmology basic data and ophthalmology measurement follow-up visit data; and finally, dynamically updating the lens prescription based on the myopia risk assessment level of each time by the joint assessment model. According to the technical scheme, the combined evaluation model can perform dynamic lens prescription adjustment according to follow-up visit ocular axis progress and choroid change conditions, dynamic myopia management lens prescription customization, whole-course tracking of a myopia cycle and individualized prevention and control management are realized, and thus the effectiveness and continuity of myopia prevention and control are improved.

The invention discloses a mild cognitive impairment brain rehabilitation training device and system in the technical field of cognitive nerve rehabilitation, and the device comprises a multi-modal data fusion module, a personalized training task generation module, a multi-sensory stimulation presentation module and a real-time adaptive regulation and control module. And the personalized training task generation module is used for dynamically generating a personalized narrative training task fusing the user interest theme and the cognitive training target by utilizing a reinforcement learning algorithm based on the user cognitive weak domain evaluation result and the user personal interest and life narrative content obtained through natural language processing. Clinical data of a patient and real-time electroencephalogram and behavior data are fused, a narrative training task deeply fused with life experience and interest of the patient is dynamically generated, the difficulty of the training task is automatically adjusted according to real-time physiological indexes, meanwhile, seamless connection with a medical system is achieved through digital biomarkers, and the training efficiency is improved. And the precision and compliance of rehabilitation training are obviously improved.

The invention relates to the technical field of treatment scheme optimization, and provides an emergency treatment critical patient first-aid path optimization method and system based on big data, and the method comprises the steps: building a standardized event sequence through extracting historical diagnosis and treatment data of a hospital information system, and analyzing the time sequence correlation between events through a graph structure mining technology, key diagnosis and treatment nodes and high and low frequency paths are identified, and a layered first-aid path state transition network is constructed based on the key diagnosis and treatment nodes and high and low frequency paths. When a real-time patient is processed, the system matches an electronic health record of the patient with a network core state, dynamically plans a subsequent standard transfer path, and avoids a low-frequency risk path marked as a taboo, so that an emergency path optimization scheme of risk avoidance and path dynamic optimization is realized, and the treatment efficiency of emergency critical patients is improved. The treatment effect is improved, and the bad prognosis risk is reduced.

The invention relates to the technical field of medical prediction, in particular to a Web-based big data driven stroke risk prediction method, which comprises the following steps: S1, multi-scale data acquisition and alignment: acquiring physiological data, behavior data and health state data of a user; s2, causal path identification: generating a causal feature sub-graph with confidence rating; s3, feature fusion modeling: performing response modeling on fluctuation features in the real-time physiological data, performing trend feature extraction on behavior data, and dynamically allocating fusion weights of the fluctuation features and the trend features to generate unified risk characterization features; s4, risk evolution simulation: simulating the influence of different intervention strategies on the stroke risk, and generating a risk evolution trajectory; and S5, intervention scheme generation: generating a personalized intervention scheme. According to the invention, the accuracy of risk prediction is improved, and the change of the health condition of the patient can be responded in real time.

The invention discloses an aquaculture comprehensive guarantee method and system based on multi-modal data acquisition, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting aquaculture multi-modal data and an aquaculture image of an aquaculture region, carrying out the aquaculture feature capture of the aquaculture image, and outputting a visual feature group; inputting the visual feature group into a MobileNet lightweight model, and outputting a health state label and a confidence score; based on the health state label and the confidence score, outputting disease type data through a residual network structure optimized by transfer learning; constructing a graph neural network according to the disease type data, and generating a dynamic regulation and control scheme; and the control center executes the dynamic regulation and control scheme, carries out regulation and control effect detection and target comparison on the aquaculture area of the dynamic regulation and control scheme, judges whether the regulation and control effect is effective or not, and generates a feedback regulation instruction. According to the invention, through multi-modal data fusion and intelligent decision closed loop, dynamic and accurate guarantee of aquatic product health management is realized.

The invention provides a medical decision-oriented multi-level knowledge graph construction and semantic reasoning method, and relates to the technical field of knowledge graphs, and the method comprises the steps: carrying out semantic segmentation and standardization processing on medical text corpora, extracting medical entities and attributes thereof, and constructing an incidence matrix; a reasoning path is mined based on recursion deep search, and an optimal path is selected by using an attention mechanism; and performing decision verification and optimization in combination with medical rules. According to the method, the accuracy and reliability of medical decision making can be improved, efficient mining and application of complex medical knowledge are achieved, and effective support is provided for clinical diagnosis and treatment.

The invention discloses a health care service data management method based on machine learning, and relates to the technical field of data management. The method comprises the following steps: collecting health care service data in real time, constructing a health care knowledge graph, and carrying out feature dynamic alignment on the health care knowledge graph by adopting agency attention and multi-scale contrast learning to obtain a dynamic alignment feature vector; based on the dynamic alignment feature vectors, training CatBoost, XGBoost and a random forest model through Bayesian optimization, and obtaining a health prediction model through stacking generalization fusion of a logistic regression model; inputting the dynamic alignment feature vector into a health prediction model, and outputting to obtain a prediction result; corresponding service measures are executed according to the prediction result, new data are collected again after the service measures are executed, the health care knowledge graph is updated, and therefore management of health care service data is achieved.

A method for providing on demand verifiability of a medical metric for a patient using a distributed ledger is disclosed. The method may include receiving, by a node that is part of a network of nodes with access to the distributed ledger, a request to verify the medical metric of the patient, wherein the request comprises an identification of the patient. The method may include querying, using the identification of the patient, the distributed ledger to determine the medical metric of the patient, wherein the distributed ledger comprises at least one block including medical information pertaining to the patient, and the medical information is endorsed by a medical personnel associated with causing the medical information to be stored in the block. The method may include providing the medical metric to the computing device to cause a computing device to perform a responsive action based on the medical metric.

The invention relates to the technical field of physiological information processing, and discloses a personalized burn rehabilitation path planning method and system, and the method comprises the steps: obtaining historical rehabilitation data and real-time evaluation data, constructing a state time sequence, extracting a score fluctuation vector and an emotion change vector, and carrying out the window adjustment processing of a fluctuation feature vector set, a rhythm factor and a training factor are calculated in each window range, so that path analysis has the capability of synchronously evaluating rhythm change and a training state, the phenomenon of excessive path adjustment caused by short-time abnormal fluctuation is avoided, a rhythm feature sequence is input into a whale optimization algorithm to execute path updating processing, and the path updating efficiency is improved. The method ensures that the path generation process has the dynamic adjustment capability, enhances the adaptability of the algorithm to the recovery rhythm change, performs grading processing on each stage according to the stage sequence set, can effectively output the rehabilitation path matrix, and realizes the unified expression of the multi-stage rhythm and the training grade.

The invention discloses an intelligent analysis system and method for rehabilitation treatment of lumbar intervertebral disc protrusion, and relates to the technical field of medical informationization, and the system comprises a multi-modal data collection module which is used for obtaining multi-modal medical data of a patient; the feature adaptive optimization module is used for screening a rehabilitation prediction feature set and dynamically optimizing a feature weight; the rehabilitation prediction analysis module is used for generating a rehabilitation process prediction result; the training scheme generation module is used for generating a rehabilitation training scheme; the feedback data acquisition module is used for monitoring feedback data in the training process; the rehabilitation evaluation and adjustment module is used for evaluating a rehabilitation process state and dynamically adjusting a rehabilitation training scheme; and the incremental learning updating module is used for updating the feature correlation prediction model. Through mutual cooperation and mutual promotion of the functional modules, the precision, intelligence and standardization level of lumbar intervertebral disc protrusion rehabilitation treatment is improved, and powerful technical support is provided for improvement of the rehabilitation treatment effect.

The invention relates to the technical field of artificial intelligence and biological characteristic analysis, in particular to an AI-based personalized postpartum rehabilitation medical beauty evaluation system and method. The method comprises the following steps: generating a standardized action sequence according to a rehabilitation stage and a historical record of a user and guiding execution; collecting dynamic biological characteristic data in real time; aligning data and distributing dynamic weights by using a multi-modal fusion model guided by time sequence semantics, constructing a three-dimensional response graph by combining a tension response function, a collaborative exponential function and a stable fluctuation function, and generating individual embedded vectors through a graph neural network; identifying a potential high-risk time period through map potential energy offset analysis and disturbance sensitivity factors; integrating a user state, a historical response and an intervention knowledge graph, recommending a dynamically adaptive intervention scheme by using a multi-channel attention network, and continuously optimizing through a feedback closed loop; precise evaluation and personalized intervention recommendation of the postpartum rehabilitation process are realized through multi-modal fusion and dynamic modeling, and the rehabilitation efficiency and safety are improved.

The invention provides an IBS micro-ecological transplantation intelligent prediction method and system based on multi-omics driving, and relates to the technical field of biomedicine. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect metagenome, metabolome, host genome and clinical phenotype group data of a target patient; inputting the data into a flora-metabolite combined network analysis model to construct an interaction network and extracting features; generating an incidence matrix based on the features and the host genome data and calculating indexes; generating indexes through a dynamic response algorithm in combination with the clinical phenotypic data and the indexes; and outputting a curative effect prediction result by using a transfer learning framework combined with modeling. The system comprises a data acquisition module, a network analysis module, a correlation calculation module, a dynamic response module and a joint modeling module. According to the method, multiple omics data are integrated, the flora and host relation is accurately mined, intelligent prediction of the micro-ecological transplantation curative effect is achieved, powerful support is provided for IBS personalized treatment, and meanwhile data processing and safety guarantee measures are taken.

A wound triaging and recommendation system for wound triaging and recommendation for treatments is disclosed. A wound triaging and recommendation system includes a text and voice based conversational artificial intelligence (AI) subsystem determines data associated with at least one of: genetic predisposition, the one or more diseases influencing a wound, and a state of the wound. A wound image analytics subsystem that determines severity and risk category of wound using a machine learning algorithm. An AI based text and image analytics subsystem extracts key clinical parameters and changes in key clinical parameters over time using optical character recognition techniques. A patient treatment recommendation subsystem triages the wound to identify the severity of wound by qualifying risk score for the wound, to provide wound prognostics for wound healing, and to provide treatment recommendations associated with personalized therapeutic routes for wound healing to the patient.

The invention discloses an environmental health risk assessment system, electronic equipment and a nonvolatile storage medium. The system comprises an environment image acquisition module which is used for acquiring an environment image of a target area; the image feature extraction module is used for performing feature extraction on the environment image to obtain a first feature corresponding to the streetscape image and a second feature corresponding to the remote sensing image; the environment variable prediction module is used for fusing the first feature and the second feature, and determining a parameter value of an environment variable corresponding to the target area according to a fused feature obtained after fusion; and the comprehensive index determination module is used for determining a comprehensive environment index corresponding to the target crowd object in the environment of the target area according to the parameter value of the environment variable. According to the method and the device, the technical problem that the environmental health risk cannot be effectively assessed due to the fact that a method for measuring and analyzing the environmental variables mainly depends on field monitoring, the efficiency is low and the data coverage is narrow in the prior art is solved.

The invention discloses a personalized tinnitus treatment method based on artificial intelligence, and particularly relates to the technical field of artificial intelligence, and the method comprises the steps: S1, user data collection: collecting user data, and transmitting the collected data to an edge computing node; s2, user data preprocessing: carrying out data preprocessing; s3, sending a medical diagnosis request: collecting medical data, and sending the medical data and the medical diagnosis request to a control center; s4, comprehensive analysis and scheme making, wherein comprehensive analysis is carried out, and a personalized tinnitus treatment scheme is made; s5, decision instruction transmission and scheme execution; and S6, dynamic optimization and feedback: collecting treatment feedback data in real time, obtaining a treatment effect evaluation index, and dynamically adjusting a treatment scheme. By collecting user data and medical data in real time, constructing an artificial intelligence model to judge the tinnitus probability and tinnitus subtype, formulating a treatment scheme, judging the treatment effect and dynamically adjusting according to the treatment effect, the treatment effect of the user is effectively improved.

A computer-implemented method comprising receiving, via one or more processors, an input query associated with a patient; obtaining, via one or more processors, a healthcare record associated with the patient; analyzing, via the one or more processors and using a trained machine learning model, the healthcare record associated with the patient to extract data elements relevant to the input query; and generating, via the one or more processors and using the trained machine learning model, a first mapping of the extracted data elements relevant to the query.

The invention provides a mental and psychological auxiliary inquiry system based on multi-modal AI, and belongs to the technical field of artificial intelligence and medical crossing, and the mental and psychological auxiliary inquiry system comprises a multi-modal data acquisition module which is used for collecting and analyzing multi-modal data of a patient in the inquiry process in real time based on the artificial intelligence technology, the multi-modal data is related to voice emotion, micro-expression, semantics and face emotion; the abnormal node labeling module is used for establishing a double-standard knowledge graph to analyze the multi-modal data, labeling abnormal data nodes of the patient, and generating a visual inquiry path in combination with contribution weights of the multi-modal data automatically optimized based on a dynamic weight adjustment mechanism; and the scheme generation module is used for performing comparative analysis on the visual inquiry path and a historical case database to generate a personalized treatment scheme. And real-time and objective auxiliary decision support is provided for doctors.

The invention discloses an oral cavity data monitoring and early warning method and system based on deep learning, and the method comprises the steps: solving a problem that the focus recognition is inaccurate because the image collection process of an oral cavity endoscope of a patient is affected by various environment and equipment parameters; according to the method, synchronous binding of collected images and equipment parameters and intelligent image preprocessing are proposed, historical time sequence images and variation trend features are combined, lesion space and time features are extracted through combination of a lightweight convolutional neural network and a long-short term memory network, feature weighted reasoning is realized by using a context awareness attention fusion network, and the focus space and time features are extracted. According to the method, the model parameters and the risk threshold value can be dynamically adjusted, the focus recognition accuracy and the early warning individuation are improved, and clinical grading treatment and intelligent health management are effectively assisted.

The invention discloses a motion state monitoring and feedback system and method based on electroencephalogram signals, in the system, an electrode array is arranged corresponding to a forehead part and used for collecting the electroencephalogram signals, a motion sensor is used for collecting motion data signals, and a vibration motor is used for sending out vibration feedback; the electroencephalogram collection hair band sends the electroencephalogram signals and the motion data signals to the mobile terminal, and the mobile terminal sends the electroencephalogram signals and the motion data signals to the cloud processing platform; the cloud processing platform is used for predicting input electroencephalogram signals and motion data signals by adopting a physical and mental state prediction model based on a deep neural network to obtain physical and mental state indexes; triggering a self-adaptive feedback intervention strategy according to the physical and mental state indexes, and controlling a vibration motor to send vibration feedback in a preset mode. According to the technical scheme of the invention, the recognition accuracy of the physical and mental state characteristics of the movement of the user is improved, and the wearing comfort and convenience of the signal acquisition device are improved.

Methods and systems disclosed herein can improve inference of gene expression using cell-free DNA fragments. In an aspect, the present disclosure provides a computer-implemented method for inferring gene expression, the method comprising: obtaining a biological sample from a subject; extracting cell-free deoxyribonucleic acid (cfDNA) from the biological sample, wherein the cfDNA comprises a plurality of cfDNA fragments; performing a sequencing assay on the plurality of cfDNA fragments to generate a plurality of cfDNA sequencing fragments; computer processing the plurality of cfDNA sequencing fragments; and calculating, based at least in part on the computer processing, a gene expression score for a gene in a plurality of genes, wherein the gene expression score indicates a probability of expression or non-expression of the gene in the plurality of genes.