Methods and systems are provided for detecting a mental health condition. Structured and unstructured information is analyzed using natural language processing to extract information including clinical data values and medical concepts pertaining to a user. Reference medical information is evaluated using natural language processing to correlate medical data with mental health conditions. A classification for a mental health condition of the user is determined using a machine learning model and based on the extracted information and correlations, wherein the extracted information includes blood analysis for the user. The user is assigned to a segment of users based on the extracted information. A treatment for the mental health condition of the user is indicated based on the classification and the assigned segment of users.

Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

A disease prediction method, apparatus, and computer program are provided. A disease prediction method according to several embodiments of the present disclosure can comprise the steps of: constructing a disease prediction model by learning learning data including ribosome data and disease information for learning, acquiring test ribosome data of an examinee; and predicting disease information about the examinee form the test ribosome data by using the disease prediction model. The disease prediction model can accurately predict disease information about the examinee by detecting and learning the characteristics of ribosome data, which vary according to disease information.

A disease prediction method, apparatus, and computer program are provided. A disease prediction method according to several embodiments of the present disclosure can comprise the steps of: constructing a disease prediction model by learning learning data including ribosome data and disease information for learning, acquiring test ribosome data of an examinee; and predicting disease information about the examinee form the test ribosome data by using the disease prediction model. The disease prediction model can accurately predict disease information about the examinee by detecting and learning the characteristics of ribosome data, which vary according to disease information.

The need for an accurate, early, and precise estimation of expected delivery date (EDD) for the pregnant subject is vital. A system and method for predicting a day/date of delivery for a pregnant subject using one or more microbiome samples collected from the pregnant subject is provided. The disclosure relates to applying machine learning techniques on the microbiome characterization data corresponding to the biological sample(s) collected from the pregnant subject. The method further comprises using the predicted EDD to suitably plan and take required medical treatment or precautions or medical advice for the pregnant subject to prevent any pregnancy and/or delivery related complications and to manage the delivery appropriately. The disclosure also provides compositions of the microbiome data which can potentially influence the delivery date, or the method provides exemplary compositions of the microbiome data which plays vital role in estimating the EDD of the pregnant subject.

A system for smart pooling includes a computing device configured to obtain a feature datum, identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set, and identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum, and determine an enhanced well count.

A system for smart pooling includes a computing device configured to obtain a feature datum, identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set, and identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum, and determine an enhanced well count.

The present disclosure provides a method for automatically collecting and matching laboratory data, including: obtaining a creation time of experimental data, determining target experimental data corresponding to a target time in accordance with the creation time, segmenting the target experimental data into a plurality data blocks, generating a data block index table, including at least one data block identifier, according to the data blocks, selecting a target matching mode from a plurality of predetermined matching modes according to the data block index table, obtaining the data block identifier upon determining the target experimental data in a storage node is loaded, and extracting data content in the target experimental data corresponding to the data block identifier by the target matching mode. This method may greatly reduce the number of string matching and may reduce the complexity of the algorithm.

The present disclosure provides a method for automatically collecting and matching laboratory data, including: obtaining a creation time of experimental data, determining target experimental data corresponding to a target time in accordance with the creation time, segmenting the target experimental data into a plurality data blocks, generating a data block index table, including at least one data block identifier, according to the data blocks, selecting a target matching mode from a plurality of predetermined matching modes according to the data block index table, obtaining the data block identifier upon determining the target experimental data in a storage node is loaded, and extracting data content in the target experimental data corresponding to the data block identifier by the target matching mode. This method may greatly reduce the number of string matching and may reduce the complexity of the algorithm.