Method and system for annotation and classification of biomedical text having bacterial associations

A system for annotation and classification of biomedical text having bacterial associations, the system comprises:

• a user interface;
• one or more hardware processors;
• a memory in communication with the one or more hardware processors, wherein the one or more first hardware processors are configured to execute programmed instructions stored in the one or more first memories, to:
• identify a disease with known bacterial basis (DS);
• extract a sample having a microbiological content from each individual in a group of patients suffering from the identified disease (DS);
• obtain bacterial abundance data from the sample corresponding to the disease using an experimental technique, wherein the bacterial abundance data is used to construct a bacterial taxonomic abundance matrix consisting of abundance information of individual bacterial taxon across the group of patients;
• construct a first bacterial association network (NT1) using a statistical correlation to find relationships between the bacteria present in the bacterial taxonomic abundance matrix, wherein the first bacterial association network (NT1) comprises ‘m’ number of bacteria as nodes (N1, N2, . . . Nm) with their relationship as ‘e’ number of edges (E1, E2, . . . , En) and edge weights (EW1, EW2, . . . , EWn) as an association strength;
• formulate a plurality of search queries for each node in the first bacterial association network, wherein each of the plurality of search queries is searched in a biomedical search engine to obtain output tuples as a set of output lists containing a plurality of biomedical texts, wherein each text is identified by an ID;
• collate unique IDs from the set of output lists to form a list of unique IDs;
• obtain the biomedical text corresponding to each unique ID of the list of unique IDs to generate a biomedical text corpus ‘Cz’;
• calculate a set of domain features for each abstract present in the biomedical text corpus ‘Cz’ to generate a feature count matrix with one set of features for each abstracts;
• apply a first classifier to the feature count matrix to obtain a first list of biomedical texts corresponding to each unique ID, wherein the first list of biomedical texts comprising sentences with potential bacterial associations, wherein the sentences having potential bacterial associations is obtained using the first classifier and if a condition is satisfied in the set of features;
• utilize sentences having potential bacterial associations to create a first refined association network;
• apply a second classifier to the feature count matrix corresponding to the first list of biomedical text to obtain a readability for each text in the first list of biomedical text;
• estimate a threshold annotation time required to annotate each biomedical text based on its readability;
• identify sentences in the first list of biomedical text with probable bacterial associations;
• create a table of predicted sentences using the first classifier and calculated domain features for each identified sentences in the first list of biomedical text that contain the bacterial association along with the ID;
• record the list of predicted sentences corresponding to the bacterial associations to calculate corresponding count along with their unique IDs;
• send the first list of biomedical texts, the estimated threshold annotation time and the recorded list of predicted sentences corresponding to each unique ID, to a crowdsourcing annotation system for improved prediction of bacterial associations;
• and
• create a second refined association network utilizing the output of the crowdsourcing annotation system and the first refined association network.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• identifying a disease with known bacterial basis (DS);
• extracting a sample having a microbiological content from each individual in a group of patients suffering from the identified disease (DS);
• obtaining, bacterial abundance data from the samples corresponding to the disease using an experimental technique, wherein the bacterial abundance data is used to construct a bacterial taxonomic abundance matrix consisting of abundance information of individual bacterial taxon across the group of patients;
• constructing, via the one or more hardware processors, a first bacterial association network (NT1) using a statistical correlation to find relationships between the bacteria present in the bacterial taxonomic abundance matrix, wherein the first bacterial association network (NT1) comprises ‘m’ number of bacteria as nodes (N1, N2, . . . Nm) with their relationship as ‘e’ number of edges (E1, E2, . . . , En) and edge weights (EW1, EW2, . . . , EWn) as an association strength;
• formulating, via the one or more hardware processors, a plurality of search queries for each node in the first bacterial association network, wherein each of the plurality of search queries is searched in a biomedical search engine to obtain output tuples as a set of output lists containing a plurality of biomedical texts, wherein each text is identified by an ID;
• collating, via the one or more hardware processors, unique IDs from the set of output lists to form a list of unique IDs;
• obtaining, via the one or more hardware processors, the biomedical text corresponding to each unique ID of the list of unique IDs to generate a biomedical text corpus ‘Cz’;
• calculating, via the one or more hardware processors, a set of domain features for each abstract present in the biomedical text corpus ‘Cz’ to generate a feature count matrix with one set of features for each abstracts;
• applying, via the one or more hardware processors, a first classifier to the feature count matrix to obtain a first list of biomedical texts corresponding to each unique ID, wherein the first list of biomedical texts further comprising sentences with potential bacterial associations, wherein the sentences having potential bacterial associations is obtained using the first classifier and if a condition is satisfied in the set of features;
• utilizing, via the one or more hardware processors, sentences having potential bacterial associations to create a first refined association network;
• applying, via the one or more hardware processors, a second classifier to the feature count matrix corresponding to the first list of biomedical text to obtain a readability for each text in the first list of biomedical text;
• estimating, via the one or more hardware processors, a threshold annotation time required to annotate each biomedical text based on its readability;
• identifying, via the one or more hardware processors, sentences in the first list of biomedical text with probable bacterial associations;
• creating, via the one or more hardware processors, a table of predicted sentences using the first classifier and calculated domain features for each identified sentences in the first list of biomedical text that contain the bacterial association along with the ID;
• recording, via the one or more hardware processors, the list of predicted sentences corresponding to the bacterial associations to calculate corresponding count along with their unique IDs;
• sending, via the one or more hardware processors, the first list of biomedical texts, the estimated threshold annotation time and the recorded list of predicted sentences corresponding to each unique ID, to a crowdsourcing annotation system for improved prediction of bacterial associations;
• and
• creating, via the one or more hardware processors, a second refined association network utilizing the output of the crowdsourcing annotation system and the first refined association network.