CN111968715A - Drug recommendation modeling method based on medical record data and drug interaction risk - Google Patents
Drug recommendation modeling method based on medical record data and drug interaction risk Download PDFInfo
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Abstract
The invention discloses a medicine recommendation modeling method based on medical record data and medicine interaction risks, which specifically comprises the following steps: s100: heterogeneous feature extraction operation is carried out on medical record data, outpatient medical record data and TWOSIDES drug interaction data; s200: constructing a drug recommendation sequence generation model according to the extracted multi-source heterogeneous characteristics; s300: and inputting the patient medical record into the medicine recommendation sequence generation model, and calculating and outputting the recommended medication information by the model. According to the medicine recommendation modeling method based on medical history data and medicine interaction risks, medicine recommendation information is given by automatically learning and modeling from massive historical case data and medicine interaction information, accurate and effective decision basis is provided for reasonable combination of clinical medicines, the incidence rate of adverse medicine reactions is reduced, and effective guarantee is provided for medication safety of users.
Description
Technical Field
The invention relates to the field of information processing, in particular to a medicine recommendation modeling method based on medical record data and medicine interaction risks.
Background
According to investigation, the disease and symptoms are not simply in a one-to-one relationship, and the occurrence of one disease may cause multiple symptoms to occur simultaneously, so that doctors must cure patients by matching multiple medicines. Likewise, it is increasingly common for two or more chronic or acute diseases to occur simultaneously in individual patients. Therefore, how to select the most suitable treatment scheme for the disease condition of the patient becomes one of the most important factors in the hospitalization of the patient. The combination of drugs should take into account the medical condition of the patient, the effectiveness of the treatment and the potential adverse effects of the treatment. Physicians typically prescribe them based on their own experience and intuition. However, due to knowledge gaps or unexpected prejudices, these clinical decisions may often not be the best choices. With the development of big data technology, the construction of the electronic medical record system and the wide use of the electronic health record provide an excellent opportunity for improving clinical decision making by using medical care data. In this case, a computer-assisted treatment recommendation system based on electronic medical record data may provide powerful assistance. The present method attempts to provide clinical medication decisions by discovering hidden clinical knowledge from historical medical record data and utilizing it to form effective and safe medication recommendations.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a medicine recommendation modeling method based on medical history data and medicine interaction risks, which provides medicine recommendation information by automatically learning and modeling from massive historical case data and medicine interaction information, provides an accurate and effective decision basis for reasonable combination of clinical medicines, reduces the incidence rate of adverse drug reactions, and provides effective guarantee for the medication safety of users.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a drug recommendation modeling method based on medical record data and drug interaction risks comprises the following steps:
s100: heterogeneous feature extraction operation is carried out on medical record data, outpatient medical record data and TWOSIDES drug interaction data;
s200: constructing a drug recommendation sequence generation model according to the extracted multi-source heterogeneous characteristics;
s300: and inputting the patient medical record into the medicine recommendation sequence generation model, and calculating and outputting the recommended medication information by the model.
Further, step S100 specifically includes the following steps:
s110: and extracting physiological information and diagnosis information of the patient from medical record data and outpatient medical record data as the characteristics of reasonable medication recommendation, wherein the physiological information is three characteristics of sex, age and weight, and the diagnosis information is diagnosis sequence characteristics.
S120: drug interaction relationships were established from the twosid drug interaction data as another feature of rational drug recommendations.
Further, step S110 specifically includes the following steps:
s111: the gender of the patient in the historical medical record data set is distinguished by F and M, which are designated by 0 and 1, respectively, for ease of computer processing. And code conversion is performed on the medicines, and ATC4 codes are used for representing different medicines.
S112: dictionaries are separately built for all diagnoses and drugs in the data set, and integers are used to represent the diagnoses and drugs in the finally generated data set.
Further, step S120 specifically includes the following steps:
s121: for case history data and the MIMIC _ III dataset, two drugs in the drug set are arbitrarily selected, assuming the encoded value is i, j, and then their ATC4 codes are mapped into the CID classification.
S122: searching the drug interaction database for the two types of drugs by using CID codes as key words to determine whether the two types of drugs have adverse drug interaction risks, and if so, Aij1, otherwise Aij0. And sequentially traversing all the medicine pairs in the database to generate a final adjacency matrix of the medicine interaction graph.
S123: and performing one-dimensional space transformation on the adjacent matrix characteristics of the drug interaction map. A graph neural network is built with two GCN layers, and after feature aggregation, non-linear permutations such as ReLU are applied to the generated output, and the final hidden representation of each node in the graph gets information from the subsequent neighborhood through stacking of multiple layers of GCNs. And finally, accessing a full-connection network to obtain one-dimensional vector output.
Further, step S200 specifically includes the following steps:
s210: symbols are defined such that X represents a diagnosis space and Y represents a medication space. R is a set of prescription records, wherein
Is a diagnostic sequence, and
is a drug sequence. I XkI and YkEach is XkAnd YkThe length of the sequence of (c).
S220: constructing a two-layer Transformer model, projecting Query, Key and Value to different spaces h times through linear transformation, and then obtaining h self-attribute matrixes through calculation. And finally, splicing the h matrixes, and multiplying the h matrixes by a weight matrix to generate a final attention matrix.
S230: based on a Transformer model, the extracted features are spliced and used as an input sequence, elements with the maximum probability in probability distribution are selected for each position through a multi-step coding and decoding process to serve as prediction results of the current position until an end symbol is generated or the set maximum sequence length is reached, and finally the generated sequence is the medication recommendation scheme of the model for the current patient.
Further, step S210 specifically includes the following steps:
s211: prescription record R { (X)1，Y1)，(X2，Y2)，…，(Xk，Yk)}
S212: diagnostic sequences
S213: pharmaceutical sequence
Further, step S220 specifically includes the following steps:
s221: when self-attention between input and output sequences is calculated, Q is equal to K and V, and the specific calculation process is as follows:
MultiHead(Q，K，V)＝Concat(head1，…，headh)W0
headi＝Attention(QWi Q，KWi K，VWi V
further, step S300 specifically includes the following steps:
s310: and reading the patient medical record as the input of the drug recommendation sequence generation model, performing model calculation, and finally outputting the recommended medication information.
Compared with the prior art, the invention has the following beneficial effects:
1. the medicine recommendation modeling method based on medical history data and medicine interaction risks can automatically learn and construct a medicine recommendation model from massive historical case data and medicine interaction information, can reduce the incidence rate of adverse drug reactions, can flexibly and reasonably provide medicine recommendation information, provides accurate and effective decision basis for reasonable combination of clinical medicines, and provides effective guarantee for medication safety of users.
The invention is further explained in detail with the accompanying drawings and the embodiments; however, the method for modeling recommendation of drugs based on medical history data and drug interaction risk is not limited to the embodiment.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In an embodiment, referring to fig. 1, a method for modeling drug recommendation based on medical record data and drug interaction risk according to the present invention includes the following steps:
a drug recommendation modeling method based on medical record data and drug interaction risks comprises the following steps:
s100: heterogeneous feature extraction operation is carried out on medical record data, outpatient medical record data and TWOSIDES drug interaction data;
s200: constructing a drug recommendation sequence generation model according to the extracted multi-source heterogeneous characteristics;
s300: and inputting the patient medical record into the medicine recommendation sequence generation model, and calculating and outputting the recommended medication information by the model.
Further, step S100 specifically includes the following steps:
s110: and extracting physiological information and diagnosis information of the patient from medical record data and outpatient medical record data as the characteristics of reasonable medication recommendation, wherein the physiological information is three characteristics of sex, age and weight, and the diagnosis information is diagnosis sequence characteristics.
S120: drug interaction relationships were established from the twosid drug interaction data as another feature of rational drug recommendations.
Further, step S110 specifically includes the following steps:
s111: the gender of the patient in the historical medical record data set is distinguished by F and M, which are designated by 0 and 1, respectively, for ease of computer processing. And code conversion is performed on the medicines, and ATC4 codes are used for representing different medicines.
S112: dictionaries are separately built for all diagnoses and drugs in the data set, and integers are used to represent the diagnoses and drugs in the finally generated data set.
Further, step S120 specifically includes the following steps:
s121: for case history data and the MIMIC _ III dataset, two drugs in the drug set are arbitrarily selected, assuming the encoded value is i, j, and then their ATC4 codes are mapped into the CID classification.
S122: searching the drug interaction database for the two types of drugs by using CID codes as key words to determine whether the two types of drugs have adverse drug interaction risks, and if so, Aij1, otherwise Aij0. And sequentially traversing all the medicine pairs in the database to generate a final adjacency matrix of the medicine interaction graph.
S123: and performing one-dimensional space transformation on the adjacent matrix characteristics of the drug interaction map. A graph neural network is built with two GCN layers, and after feature aggregation, non-linear permutations such as ReLU are applied to the generated output, and the final hidden representation of each node in the graph gets information from the subsequent neighborhood through stacking of multiple layers of GCNs. And finally, accessing a full-connection network to obtain one-dimensional vector output.
Further, step S200 specifically includes the following steps:
s210: symbols are defined such that X represents a diagnosis space and Y represents a medication space. R is a set of prescription records, wherein
Is a diagnostic sequence, and
is a drug sequence. I XkI and YkEach is XkAnd YkThe length of the sequence of (c).
S220: constructing a two-layer Transformer model, projecting Query, Key and Value to different spaces h times through linear transformation, and then obtaining h self-attribute matrixes through calculation. And finally, splicing the h matrixes, and multiplying the h matrixes by a weight matrix to generate a final attention matrix.
S230: based on a Transformer model, the extracted features are spliced and used as an input sequence, elements with the maximum probability in probability distribution are selected for each position through a multi-step coding and decoding process to serve as prediction results of the current position until an end symbol is generated or the set maximum sequence length is reached, and finally the generated sequence is the medication recommendation scheme of the model for the current patient.
Further, step S210 specifically includes the following steps:
s211: prescription record R { (X)1，Y1)，(X2，Y2)，…，(Xk，Yk)}
S212: diagnostic sequences
S213: pharmaceutical sequence
Further, step S220 specifically includes the following steps:
s221: when self-attention between input and output sequences is calculated, Q is equal to K and V, and the specific calculation process is as follows:
MultiHead(Q，K，V)＝Concat(head1，…，headh)W0
headi＝Atteation(QWi Q，KWi K，VWi V
further, step S300 specifically includes the following steps:
s310: and reading the patient medical record as the input of the drug recommendation sequence generation model, performing model calculation, and finally outputting the recommended medication information.
The above embodiments are only for further illustrating the present invention, but the present invention is not limited to the embodiments, and any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention fall within the protection scope of the technical solution of the present invention.
Claims (8)
1. A drug recommendation modeling method based on medical record data and drug interaction risks comprises the following steps:
s100: heterogeneous feature extraction operation is carried out on medical record data, outpatient medical record data and TWOSIDES drug interaction data;
s200: constructing a drug recommendation sequence generation model according to the extracted multi-source heterogeneous characteristics;
s300: and inputting the patient medical record into the medicine recommendation sequence generation model, and calculating and outputting the recommended medication information by the model.
2. The medical record data and drug interaction risk based drug recommendation modeling method of claim 1, wherein the step S100 specifically comprises the steps of:
s110: and extracting physiological information and diagnosis information of the patient from medical record data and outpatient medical record data as the characteristics of reasonable medication recommendation, wherein the physiological information is three characteristics of sex, age and weight, and the diagnosis information is diagnosis sequence characteristics.
S120: drug interaction relationships were established from the twosid drug interaction data as another feature of rational drug recommendations.
3. The medical record data and drug interaction risk based drug recommendation modeling method of claim 2, wherein the step S110 specifically comprises the steps of:
s111: the gender of the patient in the historical medical record data set is distinguished by F and M, which are designated by 0 and 1, respectively, for ease of computer processing. And code conversion is performed on the medicines, and ATC4 codes are used for representing different medicines.
S112: dictionaries are separately built for all diagnoses and drugs in the data set, and integers are used to represent the diagnoses and drugs in the finally generated data set.
4. The medical record data and drug interaction risk based drug recommendation modeling method of claim 2, wherein the step S120 specifically comprises the steps of:
s121: for case history data and the MIMIC _ III dataset, two drugs in the drug set are arbitrarily selected, assuming the encoded value is i, j, and then their ATC4 codes are mapped into the CID classification.
S122: searching the drug interaction database for the two types of drugs by using CID codes as key words to determine whether the two types of drugs have adverse drug interaction risks, and if so, Aij1, otherwise Aij0. And sequentially traversing all the medicine pairs in the database to generate a final adjacency matrix of the medicine interaction graph.
S123: and performing one-dimensional space transformation on the adjacent matrix characteristics of the drug interaction map. A graph neural network is built with two GCN layers, and after feature aggregation, non-linear permutations such as ReLU are applied to the generated output, and the final hidden representation of each node in the graph gets information from the subsequent neighborhood through stacking of multiple layers of GCNs. And finally, accessing a full-connection network to obtain one-dimensional vector output.
5. The medical record data and drug interaction risk based drug recommendation modeling method of claim 1, wherein the step S200 specifically comprises the steps of:
s210: symbols are defined such that X represents a diagnosis space and Y represents a medication space. R is a set of prescription records, wherein
Is a diagnostic sequence, and
is a drug sequence. I XkI and YkEach is XkAnd XkThe length of the sequence of (c).
S220: constructing a two-layer Transformer model, projecting Query, Key and Value to different spaces h times through linear transformation, and then obtaining h self-attribute matrixes through calculation. And finally, splicing the h matrixes, and multiplying the h matrixes by a weight matrix to generate a final attention matrix.
S230: based on a Transformer model, the extracted features are spliced and used as an input sequence, elements with the maximum probability in probability distribution are selected for each position through a multi-step coding and decoding process to serve as prediction results of the current position until an end symbol is generated or the set maximum sequence length is reached, and finally the generated sequence is the medication recommendation scheme of the model for the current patient.
6. The method according to claim 5, wherein the step S210 comprises the following steps:
s211: prescription record R { (X)1，Y1)，(X2，Y2)，…，(Xk，Yk)}
S212: diagnostic sequences
S213: pharmaceutical sequence
7. The medical record data and drug interaction risk based drug recommendation modeling method of claim 5, wherein the step S220 specifically comprises the steps of:
s221: when self-attention between input and output sequences is calculated, Q is equal to K and V, and the specific calculation process is as follows:
MultiHead(Q,K,V)＝Concat(head1,...,headh)W0
headi＝Attention(QWi Q,KWi K,VWi V
8. the medical record data and drug interaction risk based drug recommendation modeling method of claim 1, wherein the step S300 specifically comprises: and reading the patient medical record as the input of the drug recommendation sequence generation model, performing model calculation, and finally outputting the recommended medication information.
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