A hospital digital library retrieval and resource sharing system and method

Abstract

The present invention relates to a hospital digital library and resource sharing system and method, which comprises obtaining a first search result from the shared digital library and a third search result from the shared identity book list library of the shared digital library based on search content information; obtaining a second search result from the private identity book list library of the hospital library based on the search content information and search identity information; fusing the first search result, the second search result, and the third search result to obtain a user search result and the shared information; and sending the shared information to the shared identity book list library. The search results of the hospital digital library of the present invention simultaneously take into account the medical records of the hospital's patients, the knowledge structure of the hospital's medical staff, and the resources of other hospital digital libraries, and realize resource sharing between hospital digital libraries without infringing on copyright, privacy, etc.

Description

Technical Field

[0001] The present invention belongs to the technical field of information retrieval, and in particular relates to a hospital digital library and resource sharing system and method. Background Art

[0002] Digital libraries are a type of distributed software system. To meet the clinical and scientific research needs of medical staff, many hospitals have established hospital digital libraries (hospital libraries). However, current hospital digital library search results only consider search speed, accuracy, and comprehensiveness, without considering the hospital's patient diagnosis and treatment history or the knowledge structure of its medical staff. Furthermore, current hospital digital library search results do not consider resources from other hospital digital libraries. Furthermore, due to copyright and personal digital information protection (for medical staff and patients), hospital digital libraries do not share resources with each other. Summary of the Invention

[0003] In order to solve at least one of the technical problems raised by the present invention, the present invention proposes a hospital digital library search and resource sharing method, wherein the hospital library communicates with at least one shared digital library, and the method comprises:

[0004] Obtaining a first search result from the shared digital library according to the search content information and obtaining a third search result from a shared identity book list library of the shared digital library; the shared identity book list library is constructed based on shared information of an associated hospital library associated with the shared digital library, the shared information including the user search result;

[0005] Obtaining a second search result from a private identity book list library of the hospital library according to the search content information and the search identity information, wherein the private identity book list library is constructed based on medical staff-associated book data and case data; the medical staff-associated book data includes academic book data and academic book data;

[0006] The user search result is obtained by fusing the first search result, the second search result and the third search result and is shared to the shared digital library.

[0007] In order to solve at least one technical problem raised by the present invention, the present invention also proposes a hospital digital library retrieval and resource sharing system, which includes at least one processor; and a memory storing instructions, which, when executed by at least one processor, implements the steps of the aforementioned method.

[0008] In order to solve at least one technical problem raised by the present invention, the present invention also proposes a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of the aforementioned method when the computer program / instruction is executed by a processor.

[0009] In order to solve at least one technical problem proposed by the present invention, the present invention further proposes a computer program product, including a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0010] The beneficial effects of the present invention are: on the one hand, the search results of the hospital digital library of the present invention take into account the medical history of the patients in the hospital and the knowledge structure of the medical staff of the hospital. The hospital can benefit the medical staff and patients when improving the search performance and coverage of the hospital digital library; on the other hand, the search results of the hospital digital library of the present invention also take into account the resources of other hospital digital libraries, and the resources of the hospital digital libraries are shared with each other without infringing on copyright, privacy, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 Topological map of hospital library and shared digital library;

[0012] Figure 2 A flowchart of the hospital digital library search and resource sharing method;

[0013] Figure 3 The topological diagram is when the shared digital library is the superior digital library and the associated hospital library is the hospital library at the same level;

[0014] Figure 4 The topological diagram is when the shared digital library is the superior digital library and the associated hospital library is the superior digital library;

[0015] Figure 5 The topological diagram when the shared digital library is a superior digital library and several hospital libraries at the same level;

[0016] Figure 6 Schematic diagram of the ordered set of the first search results;

[0017] Figure 7 is a schematic diagram of the ordered set of the second search results;

[0018] Figure 8 This is a schematic diagram of the ordered set of the third search results;

[0019] Figure 9 A schematic diagram of an ordered set of search results for the first user;

[0020] Figure 10A schematic diagram of an ordered set of search results for the second user;

[0021] Figure 11 Sorting matching results to indicate intent;

[0022] Figure 12 This is a schematic diagram of the correction set;

[0023] Figure 13 Schematic diagram of the ordered set of search results for the second user (after correction);

[0024] Figure 14 Sorting matching results to indicate intent;

[0025] Figure 15 This is a schematic diagram of the search results of related books with negative cases deleted. DETAILED DESCRIPTION

[0026] Digital libraries are distributed software systems. To meet the clinical and scientific research needs of medical staff, many hospitals have established hospital digital libraries. However, current hospital digital library search results only consider search speed, accuracy, and comprehensiveness, without considering the hospital's patient diagnosis and treatment history or the knowledge structure of the hospital's medical staff. Furthermore, current hospital digital library search results do not consider resources from other hospital digital libraries, and due to copyright and privacy concerns, hospital digital libraries do not share resources with each other.

[0027] Hospital libraries that do not consider resource sharing

[0028] These embodiments provide a hospital library search method, wherein the hospital library communicates with at least one superior digital library, the method comprising: obtaining a first search result from the superior digital library based on search content information; obtaining a second search result from an identity book list library of the hospital library based on the search content information and search identity information, the identity book list library being constructed based on medical staff-associated book data and case data; the medical staff-associated book data including academic book data and academic book data; and merging the first search result and the second search result to obtain the user search result. Based on the above embodiments, a digital library platform is provided that is based on the current hospital resource situation and takes clinical needs into consideration. Since the search results take into account the diagnosis and treatment cases of patients in this hospital and the knowledge structure of the medical staff of this hospital, the hospital can benefit the hospital, medical staff and patients when improving the search performance and coverage of the hospital digital library. The hospital does not have to blindly pursue search efficiency, search accuracy and search comprehensiveness, and does not need to waste medical funds (if a database that does not match the knowledge structure of the medical staff of this hospital is purchased, it is equivalent to the investment of the hospital in the library not being reflected in patient services). The search results obtained by medical staff are more targeted and available (reducing information redundancy, such as the search results obtained by nurses, will not include certain foreign books or books with higher professional depth; the search results obtained by doctors will not include books that record poor treatment plans.), and patients can obtain more advanced treatment services. It can be seen from this that the hospital libraries of these embodiments cannot share data with the superior digital libraries, and between hospital libraries. The superior digital libraries of different hospital libraries may be different, so there are copyright issues when sharing resources. The case data of different hospitals must be saved separately, so there are privacy issues when sharing resources.

[0029] Therefore, it is necessary to provide a hospital digital library retrieval and resource sharing method that can take into account the hospital's patient treatment history and the knowledge structure of the hospital's medical staff, while also enabling the hospital's digital libraries to share copyright and privacy compliant resources with each other.

[0030] Hospital libraries considering resource sharing

[0031] In some embodiments, a hospital digital library search and resource sharing method is provided, such as Figure 1 、 Figure 2 As shown, the hospital library 1 communicates with at least one shared digital library, and the method includes:

[0032] S1: obtaining a first search result from the shared digital library according to the search content information and obtaining a third search result from a shared identity book list library of the shared digital library; the shared identity book list library is constructed based on shared information of an associated hospital library associated with the shared digital library, the shared information including the user search result;

[0033] S2: Obtaining a second search result from the private identity book list library of the hospital library according to the search content information and the search identity information, wherein the private identity book list library is constructed based on medical staff-related book data and case data; the medical staff-related book data includes academic book data and academic book data;

[0034] S3: The first search result, the second search result and the third search result are merged to obtain the user search result and shared to the shared digital library.

[0035] "Retrieval content information" refers to the information input by the user into the retrieval system. The modalities of retrieval content information include written (such as keywords) or spoken natural language signals (such as voice), pictures and video signals, etc. It can be a single-modal retrieval or a cross-modal retrieval. The latter is generally used in multimedia digital libraries.

[0036] "Search results" refer to the search results that meet the search requirements obtained by the user after the search tool or search system executes the search process, including titles, abstracts, image thumbnails, etc.

[0037] "Search identity information" includes medical and nursing role information, medical and nursing academic qualifications, work experience information (such as clinical research papers published or supervised during work, academic conferences attended), and other registration information that can distinguish the user's knowledge structure; medical and nursing role information includes doctor identity information and nurse identity information. The hospital library search system can collect search identity information through the user registration form.

[0038] “Academic book data” refers to a book dataset or book catalog dataset that represents the knowledge structure of medical personnel during their study period, obtained through computer search or manually compiled based on the academic information of medical personnel, including but not limited to textbook data, degree theses and their citation data. For example, the textbook data of Doctor B who graduated from University A with a master's degree in clinical medicine includes Physiology (Y edition of X Publishing House), Internal Medicine (Y edition of X Publishing House), Surgery (Y edition of X Publishing House), Preventive Medicine (Y edition of X Publishing House), Neurology (Y edition of X Publishing House), Biochemistry and Molecular Biology (Y edition of X Publishing House), Pharmacology (Y edition of X Publishing House), Pathology (Y edition of X Publishing House), Pathophysiology (Y edition of X Publishing House), Diagnostics (Y edition of X Publishing House), Medical Microbiology (Y edition of X Publishing House), Traditional Chinese Medicine (Y edition of X Publishing House), Obstetrics and Gynecology (Y edition of X Publishing House), Histology and Embryology (Y edition of X Publishing House), Pediatrics (Y edition of X Publishing House), Advanced Medical Mathematics (Y edition of X Publishing House), and Doctor-Patient Communication ” (Y edition of X Publishing House), Organic Chemistry (Y edition of X Publishing House), Medical Biology (Y edition of X Publishing House), Basic Chemistry (Y edition of X Publishing House), Medical Genetics (Y edition of X Publishing House), Medical Psychology (Y edition of X Publishing House), Anesthesiology (Y edition of X Publishing House), Internal Medicine (Y edition of X Publishing House), Surgery (Y edition of X Publishing House), Preventive Medicine (Y edition of X Publishing House), Neurology (Y edition of X Publishing House), Forensic Medicine (Y edition of X Publishing House), Medical Statistics (Y edition of X Publishing House), Ophthalmology (Y edition of X Publishing House), Clinical Epidemiology and Evidence-Based Medicine (Y edition of X Publishing House), Human Parasitology (Y edition of X Publishing House), Dermatology (Y edition of X Publishing House), Systemic Anatomy (Y edition of X Publishing House), and Pathophysiology (Y edition of X Publishing House).

[0039] "Academic book data" refers to a book dataset representing the knowledge structure of medical personnel during their working life, obtained through computer search or manually compiled based on their academic information. This academic book data includes post-work paper data and its citation data, academic conference data, etc.

[0040] The "academic book data" and "academic book data" of the present invention are obtained using publicly known computer technologies, such as data crawling (such as DFS, BFS crawlers, etc.), data screening, text description supplementation, data annotation, etc., to establish a data set that can be used in a digital library.

[0041] "Medical staff-related book data" refers to a dataset that reflects the relationship between "retrieval identity information" and "book data" by merging "educational book data" and "academic book data" after data preprocessing. This dataset represents the overall knowledge structure of medical staff. The data preprocessing method includes one or more steps such as data cleaning, data standardization, data normalization, category coding, and feature selection.

[0042] "Case data" includes but is not limited to digital biochemical test reports, discharge records, imaging test reports, case records, emergency medical records, admission records, case surveys, case study reports, etc. The "case data" of the present invention can be directly obtained from the digital hospital system or obtained using machine learning technology. The digital hospital system includes but is not limited to hospital information systems (HIS), telemedicine systems (Tele medicine), online business processing systems (OLTP), clinical information systems (CIS), online analytical processing systems (OLAP) Internet systems (Intranet / Internet), etc. Healthcare information technology refers to information and communication technologies specifically used to handle or process medical or health data, such as ICT technology for medical simulation or medical data mining based on patient-specific data (such as electronic medical records). There are no reports in the prior art on the combination of hospital digital libraries and healthcare information technology. In order to solve the technical problems of the present invention, multiple embodiments of the present application combine digital libraries and healthcare information technology.

[0043] "Fusion" refers to data fusion, which means using a computer to automatically analyze and synthesize the first search results, second search results and third search results obtained under certain criteria to determine user search results that are more in line with the user's knowledge structure.

[0044] Based on the above embodiments, on the one hand, the effect of hospital libraries that do not consider resource sharing is taken into account, which will not be repeated here. On the other hand, the shared identity book list library is constructed based on the shared information of the associated hospital library associated with the shared digital library, and the shared information includes the user search results of other users (non-hospital), and although the non-hospital user search results are generated based on the copyright data and privacy data of non-hospital, the search results themselves do not touch upon copyright privacy compliance issues. Therefore, these embodiments can take into account the diagnosis and treatment history of patients in this hospital and the knowledge structure of medical staff in this hospital, while also enabling the hospital digital library to share copyright privacy compliance resources with each other.

[0045] In some other embodiments of the present invention, a hospital digital library search and resource sharing method is also involved, combining Figure 3 、 Figure 4As shown, the shared digital library is the superior digital library 2, and the associated hospital library is the hospital library 3 of the same level as the hospital library or the superior digital library 4 of the hospital library of the same level.

[0046] like Figure 3 , the same-level hospital library 3 of the hospital library 1 is directly associated with the shared digital library 2 (superior digital library 2). Figure 4 The hospital library 3 of the same level as the hospital library 1 is associated with the shared digital library 2 (superior digital library 2) through the superior digital library 4 of the hospital library 3 of the same level.

[0047] "Superior digital library" refers to a digital library whose retrieval system can perform combined retrieval on a large number of databases and display unified retrieval results. Taking university digital libraries as an example, they usually purchase a large number of shared databases (such as MedSci, Pubmed, CNKI, China Biomedical Literature Database (CBM), China Science Citation Database (CSCD), Wanfang Data, Duxiu Academic Search, VIP Information, Super Star Digital Library, TANet, PubChem, JSTOR, IEEE Xplore, EBSCOhost, ScienceDirect, SpringerLink, Web of Science, NSSD, etc.) or build several school databases (such as library collections). In order to improve retrieval efficiency, its retrieval system can perform combined retrieval on a large number of databases and display unified retrieval results. In the present invention, the retrieval system of the superior digital library is a known technology, such as the retrieval system of Chinese patent CN118170816A that screens database weights based on user search history data to obtain a target database combination. Compared with the university digital library, the affiliated hospital digital library is a lower-level digital library. The affiliated hospital digital library can obtain search results by accessing the university digital library interface instead of accessing the shared database interface, thus avoiding duplicate construction of digital libraries.

[0048] In these embodiments, Figure 3In the topology shown, although hospital library 1 and hospital library 3 at the same level have different parent digital libraries, they are both equipped with independent private identity book list libraries 5. For example, Dr. X is a user of hospital library 1, and Dr. Y is a user of a library 2 at a hospital at the same level. If Dr. Y performs a remote consultation with Dr. X's hospital, Dr. Y's user search results at the library 2 at the same level can be shared with the parent digital library 2 of Dr. X's hospital library 1. Thereafter, when Dr. X conducts a query in hospital library 1, his user search results will take into account Dr. Y's knowledge structure. Currently, medical resources are unevenly distributed between hospitals, especially between hospitals in developed and underdeveloped regions. Therefore, it is common for doctors in developed regions to participate in consultations and training sessions with doctors in underdeveloped regions remotely. To accommodate the changes in knowledge structure brought about by the influence of doctors from other hospitals on doctors in the hospital, the hospital library search results in these embodiments can take into account the knowledge increments and knowledge focus of doctors outside the hospital, and these knowledge increments and knowledge focus can be learned and mastered by doctors in the hospital.

[0049] Another advantage of considering the search results of users from other hospitals in a hospital library is that it increases the impact of case data on user search results. Some small or new hospitals may have less accumulated case data for certain diseases. By sharing this data, these hospitals can invite doctors with rich experience in treating these diseases to participate in consultations and training. This allows these doctors' user search results to take into account the influence of a large amount of case data for the disease, indirectly increasing the impact of case data on the hospital's user search results.

[0050] The information acquisition capabilities of county-level hospitals and provincial and ministerial hospitals are different. Only effective information acquisition can truly transform learning into benefits for patients. For example, county-level hospitals cannot understand some English documents. Even for provincial and ministerial hospitals, the amount of knowledge they have is different due to differences in doctors' academic qualifications, academic achievements, and experience. These embodiments of the present invention take into account the effects of hospital libraries that do not consider resource sharing, and enable the hospital digital libraries to share copyright and privacy compliant resources with each other. It can also save the cost of building digital libraries for small hospitals. For example, large hospitals share more user search results with small hospitals.

[0051] In some other embodiments of the present invention, a hospital digital library search and resource sharing method is also involved, combining Figure 5 As shown, the shared digital library is a superior digital library 2 and several hospital libraries 3 at the same level. Several hospital libraries 3 at the same level form the associated hospital library. The first search result is obtained from the superior digital library 2, and the third search result is obtained from the hospital libraries 3 at the same level.

[0052] In these embodiments, each hospital library at the same level includes a shared library, which together constitute a shared identity book list library. The decentralized setting of the shared identity book list library enables resource sharing between hospital digital libraries in a wider network.

[0053] In some other embodiments of the present invention, a hospital digital library search and resource sharing method is also provided. The method for constructing the private identity book list library based on the medical staff-associated book data and the case data includes the following steps:

[0054] 1. Construct a medical staff-related book dataset based on the medical staff-related book data;

[0055] 2. Construct a case-related book dataset based on the case data;

[0056] 3. The medical staff-related book dataset and the case-related book dataset are merged to obtain the private identity book list library.

[0057] These embodiments are described below in conjunction with Table 1.

[0058] Table 1 Data and data set element attributes

[0059]

[0060]

[0061] Furthermore, “constructing a medical staff-related book dataset based on medical staff-related book data” includes the following steps:

[0062] 1.1 Obtain the hospital's educational and academic data;

[0063] The educational background data includes the educational background information (school, major, year of admission, etc.) of all current and historical medical staff of the hospital, and multiple educational backgrounds of each medical staff are entered as multiple pieces of educational background information; for example, neurosurgeon XXX studied clinical medicine (undergraduate) at University A in 1996 and neurosurgery (master) at University B in 2002, which is recorded as two pieces of educational background information: {XXX-1, University A, clinical medicine, 1996}, {XXX-2, University B, neurosurgery, 2002}.

[0064] The academic data includes the academic information of all current and historical medical staff of the hospital (published papers, published books, conference papers, etc.), and the same academic resume of different medical staff is recorded as multiple academic information; for example, Doctor XXX and Doctor YYY jointly published Paper S in 2010, which is recorded as two pieces of academic information: {XXX, Paper S}, {YYY, Paper S}.

[0065] 1.2 Based on the medical personnel's deduplicated academic information, obtain academic book data representing the medical personnel's knowledge structure during their studies by computer search or manually organize; based on the medical personnel's deduplicated academic information, obtain academic book data representing the medical personnel's knowledge structure during their academic research by computer search or manually organize;

[0066] For example, continuing with the previous example, XXX neurosurgeon studied clinical medicine (undergraduate) at A University in 1996, and YYY cardiac surgeon has the same first degree as XXX neurosurgeon. The search system only needs to use books related to "1996 A University clinical medicine (undergraduate)" (deduplicated academic information).

[0067] For example, continuing with the previous example, since Doctor XXX and Doctor YYY jointly published Paper S in 2010, the search system only needs to use the relevant citation books of "Paper S" (without duplicate academic information).

[0068] 1.3 After cleaning the academic book data, the data is standardized and the educational background impact factor is assigned to obtain the academic-related book data set. After cleaning the academic book data, the data is standardized and the academic resume impact factor is assigned to obtain the academic-related book data set.

[0069] The educational background impact factor α is determined based on the number of book repetitions. For example, continuing with the previous example, if the relevant books for "1996 University A Clinical Medicine (Undergraduate)" include Book M, and since both neurosurgeon XXX and cardiac surgeon YYY studied Book M, the educational background impact factor α for Book M will be increased. For another example, continuing with the previous example, if a physical diagnostician studied Medical Imaging (Undergraduate) at University C in 1989, and the relevant books for that doctor also include Book M, the educational background impact factor α for Book M will be increased.

[0070] The academic impact factor β is also determined based on the number of times a book is repeated. For example, continuing with the previous example, the search system needs to search for "Paper S" (without duplicate academic information) to obtain the relevant citation book "Paper T". Since neurosurgeon XXX and cardiac surgeon YYY both paid attention to Paper T, the impact value of the academic impact factor β of Paper T will be increased. For another example, continuing with the previous example, if "Paper O" published by a physical diagnosis doctor cites "Paper T", the impact value of the academic impact factor β of "Paper T" will be increased.

[0071] 1.4 After removing duplicates, the academic qualification-related book dataset and the academic qualification-related book dataset are merged to obtain a medical staff-related book dataset. In a specific embodiment, as shown in Table 1, the merged academic qualification-related book dataset can simultaneously represent the relationship between the academic qualifications, academic qualifications, and books of the medical staff of the hospital.

[0072] Furthermore, “building a case-related book dataset based on case data” includes the following steps:

[0073] 2.1 Obtain case data from a digital hospital system, and extract high-level semantic information from the case data to obtain empirical data;

[0074] The experience data includes all the experience information (medical care plans, medical care results, etc.) of the hospital in which all current and historical medical staff participated in the diagnosis and treatment of the hospital, and multiple pieces of experience information are entered for multiple cases of each patient. The digital hospital system includes but is not limited to hospital information system (HIS), telemedicine system (Tele medicine), online business processing system (OLTP), clinical information system (CIS), online analytical processing system (OLAP) Internet system (Intranet / Internet), etc. When extracting high-level semantic information, well-known feature extraction technologies are used, including but not limited to clustering algorithms (such as K-MEANS), natural language processing (NPL), machine learning (ML), etc.

[0075] For example, patient A with cervical spondylosis visited the orthopedics department of the hospital in 2001 and was treated by doctor B. His case data includes medical records, imaging test reports, biochemical test reports, case study reports, etc. The extracted high-level semantic information of patient A is {b, doctor, treatment plan (vertebral artery type cervical spondylosis; dizziness and headache, blood stasis should be treated with blood stasis removal, meridian unblocking, dampness removal and liver calming; Xuefu Zhuyu Decoction treatment); treatment result (basically normal; assessment of spinal cord function status in patients with cervical spondylosis (40-point scale); I. Upper limb function is basically normal; II. Lower limb function is basically normal; III. Sphincter function is basically normal; VI. Limb sensation is basically normal; V. Girdle sensation is basically normal)}.

[0076] 2.2 Experience book data representing the knowledge structure of the medical staff's clinical experience obtained by computer search based on the deduplicated experience information of the medical staff or by matching with the medical staff-related book data set;

[0077] For example, continuing the previous example, the search system uses "vertebral artery type cervical spondylosis; dizziness with headache, partial blood stasis should be treated with blood stasis-clearing and meridian-dredging, dampness-removing and liver-calming treatment; Xuefu Zhuyu Decoction for treatment", "treatment results (basically normal; assessment of spinal cord function status of patients with cervical spondylosis (40-point method); I. Upper limb function is basically normal; II. Lower limb function is basically normal; III. Sphincter function is basically normal; VI. Limb sensation is basically normal; V. Girdle sensation is basically normal" as search text to crawl related book information;

[0078] For another example, continuing with the previous example, the matching system uses "Vertebral artery type cervical spondylosis; dizziness accompanied by headache, partial blood stasis should be treated with blood stasis, meridian removal, dampness removal and liver calming; Xuefu Zhuyu Decoction for treatment", "Treatment results (basically normal; Assessment of spinal cord function status of patients with cervical spondylosis (40-point method); I. Upper limb function is basically normal; II. Lower limb function is basically normal; III. Sphincter function is basically normal; VI. Limb sensation is basically normal; V. Girdle sensation is basically normal" as reference texts for semantic matching with the book abstract text information of the medical staff-associated book dataset.

[0079] 2.3 After data cleaning, the experience book data is standardized and assigned with work experience impact factors to obtain the case-related book data set;

[0080] The work experience influence factor γ is determined according to the number of times the book is repeated; for example, continuing the previous example, the related books of doctor b’s “basically normal” patient a’s high-level semantic information include N books, and the related books of doctor b’s “basically normal” patient c’s high-level semantic information also include N books. Since both patient a and patient c benefited from doctor b’s study of N books, the influence value of the work experience influence factor γ of these N books will be increased. For another example, continuing the previous example, the related books of doctor b’s “basically normal” patient a’s high-level semantic information include N books, and the related books in doctor d’s “basically normal” patient medical staff associated book data set also include N books. Since both patient a and patient c benefited from the hospital’s doctors (doctor b, doctor d)’s study of N books, the influence value of the work experience influence factor γ of these N books will be increased. In addition, the “abnormal” doctor’s experience information on patient diagnosis and treatment will be included in the case-irrelevant book data set.

[0081] Furthermore, “merging the medical staff-related book dataset and the case-related book dataset to obtain the private identity book list library” includes the following steps:

[0082] 3.1 Obtain the medical staff-related book dataset and case-related book dataset;

[0083] 3.2 The medical staff-associated book dataset and the case-associated book dataset are merged to obtain the private identity book list library.

[0084] In a specific embodiment, the greater the influence of the educational background influence factor α, the academic resume influence factor β, and the work experience influence factor γ on a particular book, the higher the ranking of the book in the identity book list library. However, the present invention does not limit the manner in which the educational background influence factor α, the academic resume influence factor β, and the work experience influence factor γ influence the ranking. Any technique that adjusts the ranking by introducing weights, machine learning, or other methods is within the scope of the present invention.

[0085] In the above embodiment, a hospital has a fixed number of patients and a fixed amount of information it can obtain from a digital library. Some of this information is erroneous, while some is useful. The construction of the identity book list library fully reflects the relationship between the identity (education, academic qualifications, and experience) of a hospital's medical staff and the books they read. The identity book list library of the present invention represents the hospital's medical staff knowledge graph and case knowledge graph. To optimize matching results, any known knowledge graph construction technology is within the scope of the present invention.

[0086] In some other embodiments of the present invention, a hospital digital library retrieval and resource sharing method is also involved, and the shared information also includes academic-related book datasets or their subsets, academic-related book datasets or their subsets, and case-related book datasets or their subsets.

[0087] In these embodiments, referring to Table 1, it can be seen that the academic qualification-related book dataset, the academic qualification-related book dataset, and the case-related book dataset have all had the names of medical staff and patient information removed, and their elements are all summary information of the data and do not include the full text. Therefore, hospital digital libraries can share these datasets to improve resource sharing efficiency. For example, the shared information between the hospital digital libraries of multiple affiliated hospitals of a university may include academic qualification-related book datasets, academic qualification-related book datasets, and case-related book datasets. Furthermore, as needed, only a subset of the academic qualification-related book datasets, academic qualification-related book datasets, and case-related book datasets can be shared, making resource sharing more flexible.

[0088] In some other embodiments of the present invention, which also relate to a hospital digital library search and resource sharing method, the first search result, the second search result, and the third search result are all ordered sets;

[0089] The method for fusing the first search result, the second search result and the third search result is:

[0090] Calculating the intersection of the ordered sets of the first search result and the second search result;

[0091] Calculating the intersection of the ordered sets of the first search result and the third search result;

[0092] The union of the intersection of the ordered sets of the first search result and the second search result and the intersection of the ordered sets of the first search result and the third search result is calculated.

[0093] An “ordered set of search results” refers to a data structure such as a table or array that is a combination of ordered search results (data / elements).

[0094] "Intersection" means that the elements of the intersection (search results) belong to both the first search result ordered set and the second search result ordered set, or belong to both the first search result ordered set and the third search result ordered set, and the elements of the aforementioned intersection are also ordered.

[0095] The following combination Figures 6-9 The above embodiment is further described. Figure 6 As shown, the first search result ordered set 10 is recorded as {A, B, C, D, E, F, G, H, I}, including 9 ordered search results 11, each of which includes a title 12 and an abstract 13. Figure 7 As shown, the second search result ordered set 20 is recorded as {A, B, o, p, E, F, q, r, i}, including 9 ordered search results 21, each of which includes a title 22 and an abstract 23. Figure 8 As shown, the third search result ordered set 30 is recorded as {H, B, u, p, E, w, Z, X, y}, including 9 ordered search results 31, each of which includes a title 32 and an abstract 33. Figure 9 As shown, the ordered set 40 of the first user's search results is the union of the intersection of the aforementioned two sets ({A, B, E, F}, {H, B, E}), recorded as {A, H, B, E, F}, and includes 5 ordered search results 41, each search result 41 includes a title 42 and an abstract 43.

[0096] In some other embodiments of the present invention, a hospital digital library search and resource sharing method is also provided, wherein the method further comprises the following steps:

[0097] S4: Calculate the relative complement of the second search result ordered set in the first search result ordered set to generate a second user search result ordered set. The first user search result ordered set is output before the second user search result ordered set. The second user search result ordered set is output after removing duplicate values with the first user search result ordered set.

[0098] “The relative complement of the second search result ordered set in the first search result ordered set” means that the elements of the relative complement belong to the first search result ordered set but not to the second search result ordered set.

[0099] "The first user's ordered set of retrieval results is outputted before the second user's ordered set of retrieval results" means that in the matching result sorting table presented to the user, the retrieval results (data / elements) in the first user's ordered set of retrieval results are ranked higher, while the retrieval results (data / elements) in the second user's ordered set of retrieval results are ranked lower.

[0100] "Matching result ranking table" refers to a visual form presented to the user through a human-computer interaction interface (GUI), including a single-modal matching result ranking table and a mixed-modal matching result ranking table. Among them, for traditional digital libraries, it is usually a single-modal matching result ranking table, such as a ranking table of excerpt information of digitized journals, dissertations or books. For multimedia digital libraries, it is usually a mixed-modal matching result ranking table, such as a mixed ranking of journals, papers or books with videos. The way of presenting data in the aforementioned matching result ranking table belongs to known technology.

[0101] The following combination Figures 6-11 These embodiments are further described. Figure 6 As shown, the first search result ordered set 10 is recorded as {A, B, C, D, E, F, G, H, I}, including 9 ordered search results 11, each of which includes a title 12 and an abstract 13. Figure 7 As shown, the second search result ordered set 20 is recorded as {A, B, o, p, E, F, q, r, i}, including 9 ordered search results 21, each of which includes a title 22 and an abstract 23. Figure 8 As shown, the third search result ordered set 30 is recorded as {H, B, u, p, E, w, Z, X, y}, including 9 ordered search results 31, each of which includes a title 32 and an abstract 33. Figure 9 As shown, the ordered set 40 of the first user's search results is the union of the intersection of the aforementioned two sets ({A, B, E, F}, {H, B, E}), recorded as {A, H, B, E, F}, and includes 5 ordered search results 41, each search result 41 includes a title 42 and an abstract 43.

[0102] like Figure 10As shown, the second user search result ordered set 50 is the relative complement of the second search result ordered set in the first search result ordered set, recorded as {C, D, G, H, I}, and includes 5 search results 51 arranged in order, each search result 51 includes a title 52 and an abstract 53. Figure 11 As shown, in the matching result sorting table 60, the retrieval results (data / elements) in the first user retrieval result ordered set {A, H, B, E, F} are ranked higher, while the retrieval results (data / elements) in the second user retrieval result ordered set {C, D, G, I} after removing the duplicate value {H} are ranked lower.

[0103] Based on the above embodiment, the technical effect of retaining the more targeted and available search results obtained by medical staff based on the first user search result ordered set is retained, and the user's in-depth browsing needs for the superior digital library are guaranteed based on the second user search result ordered set.

[0104] In some other embodiments of the present invention, a hospital digital library search and resource sharing method is also provided, wherein the method further comprises the following steps:

[0105] S5: Calculate the relative complement of the first search result ordered set in the second search result ordered set to generate a revised set;

[0106] S6: Based on the element relationship between the revised set and the second user search result ordered set, adjust the order of the search results in the second user search result ordered set.

[0107] “Calculating the relative complement of the first search result ordered set in the second search result ordered set” means that the elements of the relative complement belong to the second search result ordered set but do not belong to the first search result ordered set.

[0108] "The correlation between the revised set and the ordered set of search results of the second user" refers to the correlation between the elements (search results) in the two sets, specifically the correlation between the titles and abstracts. The algorithm for determining the correlation between the titles and abstracts of the two search results is a known technology. For example, the ordered set of the second user's search results includes "Epidemiology (9th Edition) published by the People's Medical Publishing House", while the revised set includes "Epidemiology (8th Edition) published by the People's Medical Publishing House". The two are only different in version and have a high degree of similarity. For another example, the ordered set of the second user's search results includes "Cell Biology (4th Edition) published by the Higher Education Press", while the search results in the revised set include nursing books such as "Introduction to Nursing (5th Edition)" and "Surgical Nursing (7th Edition)". The two have a low degree of similarity.

[0109] The following combination Figures 6-10 、 Figures 12-14 These embodiments are further described. Figure 6 As shown, the first search result ordered set 10 is recorded as {A, B, C, D, E, F, G, H, I}, including 9 ordered search results 11, each of which includes a title 12 and an abstract 13. Figure 7 As shown, the second search result ordered set 20 is recorded as {A, B, o, p, E, F, q, r, i}, including 9 ordered search results 21, each of which includes a title 22 and an abstract 23. Figure 8 As shown, the third search result ordered set 30 is recorded as {H, B, u, p, E, w, Z, X, y}, including 9 ordered search results 31, each of which includes a title 32 and an abstract 33. Figure 9 As shown, the first user search result ordered set 40 is the union of the intersection of the two sets ({A, B, E, F}, {H, B, E}), recorded as {A, H, B, E, F}, including 5 ordered search results 41, each search result 41 includes a title 42 and an abstract 43. Figure 10 As shown, the second user search result ordered set 50 is the relative complement of the second search result ordered set in the first search result ordered set, recorded as {C, D, G, H, I}, and includes 5 search results 51 arranged in order, each search result 51 includes a title 52 and an abstract 53. Figure 12 As shown, revised set 70 is the relative complement of the first ordered set of search results in the second ordered set of search results, recorded as {o, p, q, r, i}, and includes five ordered search results 71. Each search result 71 includes a title 72 and an abstract 73. In this embodiment, the total relevance score between each search result in the second user's ordered set of search results {C, D, G, H, I} and each search result in the revised set {o, p, q, r, i} is calculated. First, the total similarity score is calculated for search result C with search results o, p, q, r, and i, respectively, recorded as {Co, Cp, Cq, Cr, Ci} -> {34 points, 65 points, 28 points, 96 points, 18 points} -> C total score: 241 points. The total similarity scores for D, G, H, and I are then obtained, respectively: D total score: 414 points; G total score: 119 points; H total score: 208 points; I total score: 239 points. like Figure 13 As shown, the second user search result ordered set (corrected) 50' is formed by adjusting the order of the search results of the second user search result ordered set {C, D, G, H, I}, and is recorded as {D, C, I, H, G}. Each search result 51' includes a title 52' and an abstract 53'. Figure 14As shown, in the matching result sorting table 80, the retrieval results (data / elements) in the ordered set of retrieval results of the first user {A, H, B, E, F} are ranked higher, and the retrieval results (data / elements) in the ordered set of retrieval results of the second user (after correction) after removing the duplicate value {H} {D, C, I, G} are ranked lower.

[0110] Based on the above embodiment, the technical effect of retaining the more targeted and available search results obtained by medical staff based on the first user search result ordered set is retained, and the user's in-depth search needs for the superior digital library are guaranteed based on the second user search result ordered set.

[0111] In some other embodiments of the present invention, a hospital digital library search and resource sharing method is also provided, wherein:

[0112] Splitting the first search result ordered set into multiple first search result ordered subsets;

[0113] Splitting the second search result ordered set into multiple second search result ordered subsets;

[0114] The intersection of the corresponding first search result ordered subset and the second search result ordered subset is calculated to generate a first user search result ordered subset.

[0115] "Splitting of the first search result ordered set" is based on the Set Partitioning technology. According to the number n (of search results (elements)) presented in the sorted table of matching results on each page of the user's search device, and the number m (of search results (elements) of the first search result ordered set of this search, the first search result ordered set is split into m / n rounded up (pieces). For example, if a user's search device is a desktop computer and its sorted table of matching results on each page can present 15 search results (elements), and the first search result ordered set of a certain search includes 144 search results (elements), then the first search result ordered set is split into 10 first search result ordered subsets. For another example, if a user's search device is a mobile phone and its sorted table of matching results on each page can present 9 search results (elements), and the first search result ordered set of a certain search includes 144 search results (elements), then the first search result ordered set is split into 16 first search result ordered subsets.

[0116] "Splitting of the second ordered set of search results" is also based on the Set Partitioning technology. According to the number n of search results (elements) presented in the matching result sorting table on each page of the user's search device, and the number m of search results (elements) of the second ordered set of search results in this search, the second ordered set of search results is split into m / n (rounded up).

[0117] "Calculating the intersection of the corresponding ordered subset of the first search results and the ordered subset of the second search results" means taking the intersection of an ordered subset of the first search results and an ordered subset of the second search results (1 to 1), or taking the intersection of an ordered subset of the first search results and multiple ordered subsets of the second search results (1 to many).

[0118] In the above embodiment, when the data volume of the first search result ordered set and the second search result ordered set is large, the sets are divided into subsets and then the intersection operation is performed, which greatly reduces processing time. Specifically, when intersecting an ordered subset of the first search result with multiple ordered subsets of the second search result, priority is given to the second search result ordered subset to present the elements that meet the conditions as early as possible. For example, if there are 7 ordered subsets of the first search result and 16 ordered subsets of the second search result, according to a 1-to-2 relationship, 14 elements of the second search result ordered subset can be included in the intersection, while according to a 1-to-1 relationship, only 7 elements of the second search result ordered subset can be included in the intersection. The former gives priority to the second search result ordered subset.

[0119] In some other embodiments of the present invention, a hospital digital library search and resource sharing method is also provided, wherein the method further comprises the following steps:

[0120] The relative complement of the second retrieval result ordered subset in the corresponding first retrieval result ordered subset is calculated to generate a second user retrieval result ordered subset, and the first user retrieval result ordered subset is outputted in priority to the second user retrieval result ordered subset.

[0121] The difference between the above embodiment and the previous embodiment of "the relative complement of the second search result ordered set within the first search result ordered set" is that the relative complement in the previous embodiment considers all elements that belong to the first search result ordered set but not the second search result ordered set. In contrast, the relative complement in this embodiment only considers elements that belong to the first search result ordered set but not the second search result ordered set on each page of the ranked matching results table, which can reduce the amount of computation when the data volume is large.

[0122] In some other embodiments of the present invention, a hospital digital library search and resource sharing method is also provided, wherein the method further comprises the following steps:

[0123] Calculating the relative complement of the first ordered subset of the search results in the second ordered subset of the search results to generate a modified subset;

[0124] Based on the relevance of the revised subset to a plurality of second user retrieval result ordered subsets including the corresponding second user retrieval result ordered subsets, the order of the retrieval results in the second user retrieval result ordered subsets is adjusted.

[0125] The difference between this embodiment and the previous embodiment, which is based on the element relationship between the revised set and the second user search result ordered set, is that the relative complement of the previous embodiment considers all elements that belong to the second search result ordered set but not to the first search result ordered set. However, the relative complement of this embodiment only considers elements in each page of the sorted table of matching results, which can reduce the amount of computation when the data volume is large.

[0126] In some other embodiments of the present invention, a hospital digital library retrieval and resource sharing method is also involved, which constructs a case-independent book data set based on the case data, and deletes the search results in the ordered set of the second user's search results that are identical to the case-independent book data set.

[0127] "Constructing a case-independent book dataset based on case data" means constructing a dataset based on negative case data (such as medical accident cases) and the book data that matches it.

[0128] In the above embodiment, the probability of medical staff obtaining wrong information is reduced. Figure 15 As shown, if the associated book of a negative case is "Guidelines for Diagnosis, Treatment and Rehabilitation of Cervical Spondylosis"

[0129] (2018), then in the ordered set of search results of the second user, the search results containing "Guidelines for the Diagnosis, Treatment and Rehabilitation of Cervical Spondylosis" (2018) are deleted, but "Guidelines for the Diagnosis, Treatment and Rehabilitation of Cervical Spondylosis" 2016, "Guidelines for the Diagnosis, Treatment and Rehabilitation of Cervical Spondylosis" 2017, "Guidelines for the Diagnosis, Treatment and Rehabilitation of Cervical Spondylosis" 2019, and "Guidelines for the Diagnosis, Treatment and Rehabilitation of Cervical Spondylosis" 2022 are still retained.

[0130] Some other embodiments of the present invention also involve a hospital digital library retrieval and resource sharing method, wherein the correlation between the revised set and the ordered set of search results of the second user is recorded during each search, and the high-frequency and low-correlation search results in the revised set are used to generate an ordered set of candidate books for the hospital library.

[0131] In the above embodiment, "high-frequency, low-relevance search results in the revised set" refer to search results (elements) that appear multiple times in the second user's ordered set of search results, but each time have a low relevance to the revised set. These search results correspond to books that are of interest to the hospital's medical staff but are not included in the corresponding database of the superior digital library. Therefore, the ordered set of candidate books for the hospital library can serve as a reference for the hospital to expand the scope of the hospital digital library in the future.

[0132] The embodiments and functional operations of the subject matter described in this specification may be implemented in digital electronic circuitry, tangibly implemented computer software or firmware, computer hardware, including the structures disclosed in this specification and their structural equivalents, or a combination of more than one of the foregoing. The embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on one or more tangible, non-transitory program carriers, for execution by or control of the operation of a data processing device. A computer program (also referred to or described as a program, software, software application, module, software module, script, or code) may be written in any programming language, including compiled or interpreted languages, declarative or procedural languages, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program may be stored as part of a file that stores other programs or data, such as one or more scripts: in a markup language document; in a single file dedicated to the associated program; or in multiple coordinated files, such as files storing one or more modules, subroutines, or code portions. A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

Claims

1. A hospital library search and resource sharing method, wherein the hospital library communicates with at least one shared digital library, characterized in that: The method comprises: Obtaining a first search result from the shared digital library according to the search content information and obtaining a third search result from a shared identity book list library of the shared digital library; the shared identity book list library is constructed based on shared information of an associated hospital library associated with the shared digital library, the shared information including the user search result; Obtaining a second search result from a private identity book list library of the hospital library according to the search content information and the search identity information, wherein the private identity book list library is constructed based on medical staff-associated book data and case data; the medical staff-associated book data includes academic book data and academic book data; Merging the first search result, the second search result and the third search result to obtain the user search result and sharing it to the shared digital library; The shared digital library is a superior digital library and several hospital libraries at the same level. Several hospital libraries at the same level form the associated hospital library. The first search result is obtained from the superior digital library, and the third search result is obtained from the hospital libraries at the same level.

2. The hospital library search and resource sharing method according to claim 1, characterized in that: The shared digital library is a superior digital library, and the hospital library at the same level as the hospital library is associated with the shared digital library directly or through the superior digital library of the hospital library at the same level.

3. The hospital library search and resource sharing method according to any one of claims 1 to 2, characterized in that: The method for constructing the private identity book list library based on the medical staff associated book data and the case data comprises the following steps: Constructing a medical staff-related book data set based on the medical staff-related book data; Constructing a case-related book dataset based on the case data; The medical staff-associated book dataset and the case-associated book dataset are merged to obtain the private identity book list library.

4. The hospital library search and resource sharing method according to claim 3, characterized in that: Constructing a medical staff-related book dataset based on medical staff-related book data includes the following steps: Obtain the hospital's educational and academic data; Based on the deduplicated academic information of the medical personnel, computer search or manual sorting is used to obtain academic book data representing the knowledge structure of the medical personnel during their study period; based on the deduplicated academic information of the medical personnel, computer search or manual sorting is used to obtain academic book data representing the knowledge structure of the medical personnel's academic research; After cleaning the academic book data, the data was standardized and the educational background impact factor was added to obtain the academic-related book data set. After cleaning the academic book data, the data was standardized and the academic resume impact factor was added to obtain the academic-related book data set. The academic qualification-related book dataset and the academic-related book dataset are combined after deduplication to obtain the medical staff-related book dataset.

5. The hospital library search and resource sharing method according to claim 4, characterized in that: Constructing a case-related book dataset based on case data includes the following steps: Acquiring case data from a digital hospital system, and extracting high-level semantic information from the case data to obtain empirical data; Experience book data representing the knowledge structure of the medical staff's clinical experience obtained by computer search based on the medical staff's deduplicated experience information or by matching with the medical staff's associated book data set; After data cleaning, the experience book data are standardized and given a work experience impact factor to obtain the case-related book data set.

6. The hospital library search and resource sharing method according to claim 5, characterized in that: The steps of fusing the medical staff-related book dataset and the case-related book dataset to obtain the private identity book list library include: Obtain the medical staff-related book dataset and case-related book dataset; The medical staff-related book dataset and the case-related book dataset are merged to obtain the identity book list library.

7. The hospital library search and resource sharing method according to claim 6, characterized in that: The shared information also includes a data set of academic-related books or a subset thereof, a data set of academic-related books or a subset thereof, and a data set of case-related books or a subset thereof.

8. The hospital library search and resource sharing method according to claim 1, wherein: The first search result, the second search result, and the third search result are all ordered sets; the method for fusing the first search result, the second search result, and the third search result is: Calculating the intersection of the ordered sets of the first search result and the second search result; Calculating the intersection of the ordered sets of the first search result and the third search result; The union of the intersection of the ordered sets of the first search result and the second search result and the intersection of the ordered sets of the first search result and the third search result is calculated.

9. A hospital library search and resource sharing system, characterized in that: The system includes at least one processor; and a memory storing instructions, which, when executed by the at least one processor, implement the steps of the method according to any one of claims 1 to 8.

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