313 results about "Prognosis prediction" patented technology

The invention discloses a brain tumor multi-mode large model construction method, device and equipment and a storage medium, and is applied to the technical field of brain tumor imagines.The method comprises the steps that pixel-concept level alignment is conducted on a multi-mode MRI image and a pathological text; constructing a multi-modal feature fusion network for fusing image features and text features by adopting an attention mechanism of pathology perception and combining medical semantic information; training the multi-modal feature fusion network to generate an analysis report and a segmentation result; according to the technical scheme of multi-task cooperation, cross-modal pathological semantic accurate alignment, pathological knowledge graph injection and lightweight and continuous optimization parallelization, full-process coverage of brain tumor accurate segmentation, analysis report generation and prognosis prediction is achieved, the problems that a traditional model lacks pathological semantic support and is insufficient in clinical adaptability are solved, and the clinical adaptability of the traditional model is improved. And the deployment feasibility and the dynamic optimization capability are also considered.

The invention discloses a cerebral apoplexy risk and prognosis prediction system and method, and relates to the field of intelligent medical treatment, and the system comprises a data processing and knowledge construction layer which is used for extracting, cleaning and constructing a space-time multi-modal knowledge graph and structured clinical features from multi-source heterogeneous medical data; the feature engineering and fusion layer is used for deeply fusing dynamic semantic information in the space-time multi-modal knowledge graph and the structured clinical features through a graph embedding and attention mechanism to generate a fusion feature vector for a cerebral apoplexy prediction task; and the prediction model and output layer is used for performing cerebral apoplexy risk and prognosis prediction based on the fusion feature vector to obtain a prediction result, and generating a decision result for assisting a doctor in understanding the model through an interpretable mechanism. The method provided by the invention can improve the accuracy of stroke recurrence, bleeding transformation or function prognosis prediction, and provides a new way for accurate stroke management.

The invention provides a laryngeal cancer multi-mode prognosis prediction method and a laryngeal cancer multi-mode prognosis prediction system fusing a CT (Computed Tomography) image and a ViT model. Relates to the technical field of biomedical images. The method comprises the following steps: acquiring and preprocessing multi-modal data of a laryngocarcinoma patient; carrying out lightweight compression, redundant information screening and robustness training on the ViT model to obtain an optimized ViT model; extracting depth features of the CT image data based on the optimized ViT model, and performing multi-stage fusion on the depth features and clinical and genome data to construct a prognosis prediction model; and performing risk stratification on the patient according to a prognosis prediction result predicted by the prognosis prediction model, and outputting treatment guidance suggestions based on the risk stratification. Through ViT model optimization, multi-modal data fusion and clinical adaptation design, precise prediction and personalized treatment guidance of laryngocarcinoma prognosis are realized, and the problems of insufficient image degradation processing, low model deployment efficiency and the like in existing laryngocarcinoma prognosis prediction are solved.

The invention discloses a tumor prognosis prediction method and a tumor prognosis prediction system, which are used for constructing a multi-modal fusion model based on image-pathology to improve the prognosis prediction efficiency of tumors, especially pancreatic cancer, and providing reference information for clinical decision-making. According to the technical scheme, the method comprises the following steps: S1, preprocessing an original tumor enhanced CT image, segmenting a tumor region, extracting radiomics features and depth image features of the tumor region, and establishing a CT image feature set; s2, after feature preprocessing is carried out on the tumor clinical data, clinical features with statistical significance are screened out, and a clinical feature set is established; s3, carrying out Hamp; e, preprocessing the pathological image, segmenting a tissue region, extracting spatial relation features, and generating a pathological spatial feature set; and S4, based on a feature interaction method, carrying out multi-modal fusion on the CT image features, the clinical features and the pathological spatial features, inputting a full-connection neural network, constructing a tumor survival risk prediction model, and outputting a tumor survival risk probability through the tumor survival risk prediction model.

The invention discloses a lung cancer lifetime prediction system based on prognosis factor multi-data fusion, and belongs to the technical field of lung cancer prognosis prediction, and the system comprises a multi-source data collection module which is used for collecting prognosis multi-source data of a patient; the multi-source data processing module is used for carrying out preprocessing and feature extraction on the prognosis multi-source data of the patient; the multi-source data fusion module is used for carrying out cross-modal alignment and fine-grained fusion on the extracted multi-modal feature vectors, and capturing a dependency relationship between modals based on a hierarchical attention mechanism to form patient prognosis fusion data; and the survival analysis and prediction module is used for analyzing the prognosis fusion data of the patient according to the lung cancer lifetime prediction model, automatically predicting the lifetime of the patient and displaying the lifetime in a visual form. The problems that existing lung cancer lifetime prediction is low in accuracy and cannot provide support for personalized treatment are solved. The lung cancer lifetime prediction accuracy can be improved, and support can be provided for personalized treatment.

The invention discloses a disease prognosis evaluation and dynamic prediction method based on time sequence analysis, which comprises the following steps: acquiring disease prognosis medical data of a patient to construct a medical data time sequence set, extracting medical data time sequence characteristics and constructing a prognosis evaluation model, updating the prognosis evaluation model, and performing dynamic prediction. Inputting the to-be-evaluated patient disease prognosis medical data into the updated prognosis evaluation model to obtain a disease prognosis evaluation result, constructing a prognosis prediction model, inputting the to-be-evaluated patient disease prognosis medical data into the prognosis prediction model to obtain a disease prognosis prediction result, and updating the disease prognosis prediction result. And generating evaluation interpretation, prediction interpretation, a disease prognosis evaluation chart and a disease prediction trend chart. According to the method, the efficiency and accuracy of disease prognosis evaluation and prediction can be improved, timely and effective decision support is provided for clinic, the requirements of personalized medical treatment are met, and more accurate treatment schemes and rehabilitation guidance are provided for patients.

The invention discloses a multi-mode head and neck tumor segmentation and survival prognosis prediction method. The method comprises the following steps: constructing a DE-LS-UNet model; training the DE-LS-UNet model to obtain a trained DE-LS-UNet model, and inputting the new PET image and the CT image into the trained DE-LS-UNet model to obtain a predicted tumor segmentation mask; extracting multi-modal radiomics features according to the predicted tumor segmentation mask, and fusing clinical features through the multi-modal radiomics features to generate multi-source features of the patient; inputting the multi-source features of the patient into the survival analysis model for training to obtain a trained survival analysis model, and inputting the features of the patient into the trained survival analysis model to obtain a prognosis prediction result. According to the method, a feature extraction and fusion mechanism is provided, and a survival analysis model strategy is combined, so that the stability and prediction performance of survival prognosis modeling can be enhanced while the image segmentation precision is improved.

The invention provides a glycolipid metabolism related gene-based endometrial cancer prognosis prediction model construction method, which comprises the following steps of 1, acquiring data containing gene expression and clinical information, and preprocessing the data; 2, differential expression and prognosis gene screening; 3, constructing a prognosis model; 4, analyzing model gene enrichment; 5, evaluating the immunocompetence of the two GLRG related dangerous groups; and step 6, statistical analysis. According to the technical scheme, more accurate and reliable prognosis evaluation is provided for endometrial cancer by comprehensively analyzing multi-dimensional information such as gene expression, immune characteristics, mutation characteristics and drug sensitivity.

A hybrid liver cancer prognosis model, prognosis system and medium, the model takes independent influence factors as input and outputs hybrid liver cancer postoperative risk scores, the hybrid liver cancer postoperative risk scores are used for dividing risk levels, and the risk levels are used for hybrid liver cancer prognosis evaluation. And / or is used for postoperative adjuvant chemotherapy decision-making of mixed liver cancer. The hybrid liver cancer prognosis model is jointly constructed by combining the traditional clinical pathological characteristics and immune microenvironment indexes, the key effect of the tumor immune microenvironment in CHC prognosis is indicated, and the constructed hybrid liver cancer prognosis model has good CHC prognosis prediction performance; in addition, decisions of postoperative adjuvant chemotherapy can be provided for patients with different risk levels, precise treatment of the CHC patients is achieved, and long-term survival prognosis of the CHC patients is improved.

The invention discloses a medical aid decision-making system based on a multi-modal large model, and relates to the technical field of medical artificial intelligence, and the system comprises a multi-modal data fusion module, a treatment scheme coding and management module, a prognosis prediction model module, a scheme simulation and deduction module, and a visual comparison module. The comprehensive state vector of a patient and the action vector of a candidate treatment scheme are jointly calculated, multiple long-term prognosis indexes after the scheme is executed are directly simulated, the model converts the treatment scheme into a computable variable by learning rules in historical treatment data, probabilistic prediction is carried out on a future result, and the prediction accuracy is improved. This enables a doctor to clearly see the risk brought by different selections and the long-term influence of treatment changes before making a decision, thereby converting the decision mode from experience-based inference to future simulation-based anticipation.

The invention discloses a semi-supervised prognosis prediction system based on irregular sampling medical data pre-training, and the system comprises a clinical electronic medical record data collection and preprocessing module which automatically collects original clinical electronic medical record EHR data from a medical database; and the pre-training module is used for receiving the patient feature vector sequence output by the clinical electronic medical record data acquisition and preprocessing module and carrying out feature representation learning on the preprocessed clinical time sequence data by utilizing a combined multi-task self-supervised learning mechanism. And the classifier fine tuning and pseudo-label iterative optimization module is used for training the pre-trained model through fine tuning of samples with labels and carrying out iterative optimization through guidance of pseudo-labels to obtain a prediction result. And the application display module is used for displaying and outputting a post-hospital-admission vital sign sequence and a prediction result. According to the method, irregular sampling and missing data are effectively processed, information loss is avoided, and the prediction capability of the model and the performance of the model under the conditions of data imbalance and label scarcity are improved.

The present invention pertains to the field of predictive medicine in which diagnostic assays, prognostic assays, and monitoring therapy can be used for prognostic (predictive) purposes to thereby treat an individual having cancer. Accordingly, one aspect of the present invention relates to methods of treating a subject having cancer by determining the amount and / or activity level of ZNFX1 in the context of a biological sample (e.g., blood, serum, cells, or tissue) to thereby determine whether an individual afflicted with a cancer is likely to respond to anti-cancer therapy, whether in an original or recurrent cancer.

The invention relates to a training method of a respiratory tract infection disease progress and prognosis prediction model. The training method comprises the following steps: extracting mRNA from peripheral blood of a target patient, and carrying out transcriptome sequencing to obtain a sequencing result; based on the ferroptosis related gene set, comparing ferroptosis score differences of two groups of patients with community-acquired pneumonia and sepsis, and screening corresponding ferroptosis related genes with statistical significance from a sequencing result; screening out genes meeting preset conditions from the ferroptosis related genes based on LASSO regression; and establishing an RTI clinical outcome prediction model through logistic regression by taking whether the patient is sepsis or not as an outcome dichotomy variable and taking the screened gene expression quantity as a prediction variable. According to the invention, after the prediction model is subjected to machine learning screening such as LASSO and the like, the core feature with the highest prediction value is reserved, so that the risk of over-fitting of the model on training data is reduced.

The invention discloses a cancer prognosis prediction method and system based on multi-omics fusion. The method comprises the following steps: acquiring multiple groups of omics data; preprocessing the plurality of groups of omics data to obtain a plurality of groups of first omics data features; inputting the plurality of groups of first omics data features into a multi-head attention-based graph convolutional network feature extraction model to obtain a plurality of groups of second omics features; inputting the plurality of groups of second group of characteristics into an attention mechanism fusion module based on biological priori knowledge guidance to obtain fusion characteristics; and inputting the fusion features into a prognostic scoring model to obtain prognostic scores. In addition, potential gene targets related to cancer prognosis are obtained by adopting an omics data feature importance evaluation method. The prognosis prediction conclusion obtained by the method is high in accuracy and high in interpretability.

The invention relates to the technical field of hepatocellular carcinoma, in particular to a hepatocellular carcinoma prognosis model construction method based on immunogen cell death related genes and application, and an accurate prognosis prediction model is constructed by integrating the immunogen cell death related genes (IRGs) and molecular characteristics of hepatocellular carcinoma. A training set and a verification set provided by TCGA and ICGC databases are utilized, so that the model can perform effective sample analysis under a large-scale data background. Through consistency clustering analysis, the optimal clustering number is determined, the samples are orderly divided into different molecular subtypes, and it is ensured that the samples in each subtype have similar molecular characteristics. According to the invention, the capability of capturing liver cancer heterogeneity on the molecular level of the model is increased, so that the accuracy of prognosis prediction is improved.

The present invention relates to clinical diagnostics including diagnosis, prognosis, prediction, risk assessment and / or risk stratification of preterm birth (PTB) and subsequent treatment in a pregnant subject, and corresponding methods and products. The invention provides decision tools to help clinicians choosing the most appropriate management for the pregnant women. In particular, the present invention relates to a method for the diagnosis, prognosis, prediction, risk assessment and / or risk stratification of preterm birth (PTB) in a pregnant subject, the method comprising determining a level of one or more biomarkers in a sample that has been isolated from said pregnant subject, wherein the one or more biomarkers comprise at least one of matrix metallopeptidase 9 (MMP9) or fragment(s) thereof and Pappalysin-2 (PAPP-A2) or fragment(s) thereof, wherein the level of the one or more biomarkers in the sample is indicative of the presence or absence of a subsequent PTB.

The invention relates to the technical field of neurotrauma prognosis image analysis, and discloses a craniocerebral trauma prognosis prediction analysis system based on a three-dimensional model. According to the system, multi-scale segmentation and topology construction are carried out on a craniocerebral three-dimensional image of a patient, the morphological evolution rate of a trauma area is tracked, and key signal events in the trauma evolution process are accurately recognized in combination with an edema signal change curve. The system further quantitatively analyzes dynamic deviations associated with the integrity of normal brain tissue fiber bundles when a signal event occurs, thereby generating a lesion propagation path and mapping it to functional network nodes of a standard brain map, ultimately identifying a prognostic key brain network. According to the technical scheme, key event capture and path foresight prediction in the dynamic propagation process of the secondary injury after the craniocerebral trauma are realized, and the accuracy of prognosis evaluation is improved.

The invention discloses a tumor prognosis prediction system based on a multi-sequence nuclear magnetic imaging, and relates to the technical field of tumor prognosis prediction. According to the invention, through the single-mode tumor and peritumoral feature multi-graph fusion module, the whole tumor is taken as a global feature, the peritumoral region is subjected to local feature and global guidance local feature fusion is carried out, so that introduction of excessive redundant features can be avoided, and heterogeneity features of the tumor and the peritumoral region can be fully utilized, so that the interpretability of the model is improved; according to the method, multi-modal spatial features are divided into two-order neighbor nodes through a multi-modal feature spatial topology slice fusion module, a public learnable feature vector is introduced, a multi-modal spatial topology relation is learned, the multi-modal features are utilized more effectively, and the accuracy of model prediction is improved; through the interval consistency control module and the KL divergence and interval consistency joint loss function, the consistency of the multi-sequence nuclear magnetic image feature expression ability is controlled, overfitting of the model can be avoided, and the generalization ability of the model is improved.

The invention discloses a colorectal cancer metabolism-related prognosis model construction method based on multiple omics. By integrating multiple omics data, the defect is overcome, a new view angle and method are provided, the accuracy and practicability of the prognosis model are effectively improved, and the blank in the prior art is filled. Meanwhile, the method provided by the invention is not only suitable for prognosis prediction of colorectal cancer patients, but also can be popularized and applied to other types of cancers. As the metabolism-related gene plays an important role in various cancers, the technical scheme provided by the invention has relatively high universality and popularization value, and can provide reference for prognosis prediction and personalized treatment of other cancers. Finally, the prognosis model provided by the invention can be used as a clinical decision support tool to help doctors to make more scientific and accurate decisions in diagnosis and treatment processes. Through early recognition of high-risk patients and implementation of targeted intervention measures, prognosis of the patients can be significantly improved, and the risk of relapse and metastasis can be reduced.

The invention discloses a chronic disease prognosis method, a chronic disease prognosis model, a chronic disease prognosis device, computer equipment and a storage medium. The chronic disease prognosis method comprises the following steps: acquiring a medical image and medical data of a to-be-prognosed person; in the medical image, deep learning features and radiomics features are extracted; determining shared features and unique features of the deep learning features and the radiomics features in the deep learning features and the radiomics features; performing fusion processing based on the shared features and the unique features to obtain target fusion features; based on the target fusion features and the medical data, constructing a multi-omics graph; and performing prognosis treatment based on the multiple omics graphs to obtain a prognosis result of the to-be-prognosed person. Through the target fusion features and data of different modalities such as medical data, the constructed multi-omics graph can enhance the modeling ability of the model for complex pathological information on the basis of improving expression complementarity among different modalities, so that the accuracy and robustness of a prognosis prediction result are remarkably improved.

The invention provides a method and a system for predicting early gastric cancer prognosis by a circulating marker, and relates to the technical field of auxiliary diagnosis. The method comprises the following steps: performing multi-omics detection on a blood sample based on a preset sampling time sequence to obtain a multi-dimensional time sequence characteristic data set containing three groups of heterogeneous data of circulating tumor DNA, exosomes and protein markers; calculating a change slope and a fluctuation variance of the heterogeneous data in adjacent time sequence intervals, constructing a dynamic variation feature matrix in combination with a standard attenuation weighting factor, and deeply mining spatial cross-correlation and sequence dependence features of the matrix to generate a multi-modal fusion feature fingerprint; and performing regression operation on the feature fingerprints by using an integrated learning stack model to obtain a dynamic prognosis risk score, and further retrieving a risk hierarchical mapping table to generate a prognosis evaluation result containing a survival curve. According to the method, multi-modal heterogeneous data can be effectively fused, the biological dynamic characteristics in the tumor postoperative recovery phase are captured, and the accuracy and timeliness of early gastric cancer prognosis prediction are remarkably improved.

The invention relates to the technical field of biology, and provides a genetic marker for children nephrotic syndrome genetic risk assessment. The invention relates to genetic detection and application of hormone sensitive nephrotic syndrome (pSSNS) of children. Nine risk sites, including new sites of 1q23.1, 1p36.13, 5p13.2, 10q21.3, 10q24.1 and the like, highly related to diseases are found by integrating whole genome association research (GWAS) data, combining Meta analysis and a conjugate false discovery rate (conjFDR) method and taking genetic information of IgA nephropathy as assistance. Research results show that genes near the loci have differential expression in pSSNS and IgAN patients, which prompts that the genes play an important role in the occurrence and development of diseases. The invention provides a molecular detection method based on the risk site, which can be used for risk assessment, auxiliary diagnosis and prognosis prediction of children's nephropathy. Meanwhile, the invention provides potential application values of the loci and related genes thereof in individualized medication and targeted therapy.

The invention discloses a periodontitis dynamic prognosis prediction model construction method and system based on survival analysis, and the method comprises the steps: collecting and preprocessing periodontitis patient data, and constructing a structured multi-modal clinical data set; the method comprises the following steps: randomly dividing a multi-modal clinical data set into a training set and a test set according to patient IDs, only on the training set, carrying out single-factor Cox proportional risk regression analysis on all patient level data and tooth level data together, and screening indexes significantly related to periodontitis progress risks; taking periodontitis progress time as a dependent variable, and using the training set and the screened indexes to construct a time-dependent Cox periodontitis dynamic prognosis prediction model; and introducing an SHAP algorithm to analyze a prediction result of the time-dependent Cox periodontitis dynamic prognosis prediction model. The method can be used for realizing dynamic, accurate and explainable prediction of the periodontitis progress risk.

The invention relates to the technical field of tumor marker detection, in particular to a breast cancer lung metastasis related biomarker screening system which comprises a differential genetic factor mining module, a function association module, a verification screening module, an interference elimination module and an optimization integration module. According to the method, the candidate genetic factors highly associated with lung metastasis are extracted, and the expression fluctuation characteristics are combined to carry out difference analysis, so that the preliminary screening accuracy is improved, pathological information and pathway annotation are integrated to construct an action network, pathway weights are analyzed, and functional expressions of the candidate factors are quantitatively evaluated; an independent sample is called to verify transcription consistency and clinical relevance, interference factors with poor repeatability are eliminated, non-tumor tissue high-expression interference items are rejected in combination with background transcription characteristics, the specificity and adaptability of screening results are improved, and finally a marker set with metastasis mechanism indicating significance and prognosis prediction value is formed through comprehensive evaluation. And clinical transformation support is provided for identification of lung metastasis of breast cancer.

The invention relates to the technical field, in particular to a cerebral hemorrhage intelligent decision support system based on deep adaptive feature fusion, which comprises a multi-modal data standardization module, a cross-modal feature fusion module, a focus segmentation calculation module, a hemorrhage type identification module and an illness state grading output module. According to the method, deep association between fusion features and bleeding types is mined through an attention mechanism, related feature indexes are converted into standardized scores by referring to clinical common scoring standards, model attention weight distribution is optimized by combining actual prognosis result deviation, the suitability of illness state grading and the clinical scoring standards is improved, and the probability of illness state grading is lowered. An accurate grading result and prognosis prediction reference are provided for clinical treatment decision and rehabilitation intervention; effective data are screened from multiple types of brain images according to image quality related indexes, clinical text key information extraction and association labeling are combined, data integrity and consistency verification is carried out at the same time, and the standardization degree of multi-source data is improved.

The invention provides a pancreatic cancer multi-modal hierarchical fusion prognosis prediction system and method.The system comprises a data input module, a preprocessing module, a multi-modal feature fusion module and a risk prediction module, and the data input module is used for receiving and managing medical image data, clinical table data and text report data of a patient; the preprocessing module is used for performing standardization processing and feature extraction on input medical image data, clinical table data and text report data; the multi-modal feature fusion module is used for fusing the image features and carrying out hierarchical fusion on the multi-modal features; and the risk prediction module is used for calculating a prognosis prediction result based on the multi-modal characteristics and providing support for clinical decision-making of pancreatic cancer. According to the method, the risk stratification of the patient can be accurately carried out, and a powerful basis is provided for clinically formulating a personalized treatment scheme.

The invention discloses an acute myelogenous leukemia prognosis prediction method and device based on multi-omics fusion, and the method comprises the steps: collecting and preprocessing gene mutation data and gene expression data to construct a data set; constructing an acute myelogenous leukemia prognosis prediction model, and training by using the data set; inputting the preprocessed gene mutation data and gene expression data as genomics data and transcriptomics data into the trained prediction model to obtain a risk score of prognosis prediction; wherein the two shared encoders in the model share part of parameters, two modal features extracted by the two shared encoders are subjected to CLIP-based feature alignment, and features extracted by the private encoder and the shared encoder of each modal are subjected to feature decoupling. According to the method, complementarity and synergy of genomics and transcriptomics data are fully mined through a layered feature decoupling and dynamic fusion mechanism, and the prognosis prediction accuracy of the acute myelogenous leukemia patient is improved.

The invention discloses a nasopharyngeal carcinoma prognosis prediction method based on MRI habitat and habitat space interaction characteristics. The method comprises the following steps: selecting a batch of nasopharyngeal carcinoma image data and prognosis data information corresponding to the nasopharyngeal carcinoma image data; the method comprises the steps of preprocessing data, extracting gray features of a nasopharyngeal carcinoma focus area as input of a K-Means algorithm, deconstructing a tumor area into habitat subareas with different biological characteristics, constructing a multi-area space interaction matrix based on the habitat subareas, extracting multi-area space interaction features, and taking obtained features and prognosis information as training data; a nasopharyngeal carcinoma prognosis prediction model based on multi-region space interaction features is established, training data is used as feature input of a machine learning classifier algorithm to serve as a learning sample for training, a corresponding nasopharyngeal carcinoma prognosis prediction model is obtained, the death risk of nasopharyngeal carcinoma patients can be predicted, and the patients are divided into high and low risk groups. The method does not need hypothesis, and is small in model scale, high in speed and high in accuracy.

The invention discloses a construction method and application of a neurological function prognosis prediction model after cardio-pulmonary resuscitation, and the method comprises the steps: obtaining clinical data of a patient reaching spontaneous circulation recovery after cardio-pulmonary resuscitation, and screening a clinical patient meeting a discharge standard; taking brain function classification as dependent variables, respectively adopting an LASSO regression analysis method and a Boruta feature screening method to screen feature variables, and selecting common feature variables; constructing a plurality of machine learning models based on the common feature variables, and drawing an ROC curve and a decision curve corresponding to each machine learning model; carrying out discrimination evaluation on the machine learning model by adopting AUC, evaluating clinical benefits of the machine learning model by adopting a decision curve, and screening an optimal prediction model; and explaining the optimal prediction model and the common feature variables by adopting an SHAP tool. The prediction model constructed by the invention can conveniently and quickly predict the prognosis condition of the neurological function within 24 hours after sudden cardiac arrest and resuscitation.

Disclosed is an approach that uses artificial intelligence to make predictions regarding patient outcomes, and more specifically, to machine-learning models for inpatient prognosis prediction. A machine-learning classifier may be trained for predicting likelihoods of patients dying a number of days following inpatient admission. A training dataset may comprise, for subjects in a cohort, numerical and categorical values based on a set of tests, as well as demographic or biometric and / or historical values. The machine-learning classifier is trained so as to subsequently output likelihood of patients dying within the number of days following an admission at a healthcare facility.