JP2022139613A - Medical information processing apparatus and medical information processing system - Google Patents

Abstract

To provide a medical information processing apparatus and a medical information processing system for supporting selection of a therapy for a patient.SOLUTION: A medical information processing apparatus 1 includes an interface unit 10, a data processing unit 20, and a display 30. The interface unit 10 includes an acquisition unit 11, an input unit 12, and an output unit 13. The data processing unit 20 executes various types of processing on target patient data 2 and past patient data 3 to generate support information 4. The support information 4 includes various types of information generated with a patient characteristic specification function 21, a similar patient group extraction function 22, a similar patient group analysis function 23, a comparison information generation function 24, a similarity calculation function 25, and an output mode determination function 26. The output unit 13 outputs the support information 4 generated by the data processing unit 20 to the display 30.SELECTED DRAWING: Figure 1

Description

The embodiments disclosed in this specification and drawings relate to a medical information processing apparatus and a medical information processing system.

Along with the recent progress of aging society and improvement of prognosis due to advances in medical care, the approach to chronic diseases including lifestyle-related diseases such as cancer, low back pain, diabetes, hypertension, etc. is gaining weight rather than acute diseases. . Against this background, there are an increasing number of cases where approaches that focus only on the patient's biological factors are insufficient to treat the patient's disease. Therefore, it is important to select a treatment method by focusing not only on the patient's biological factors, but also on the patient's social and psychological factors.

In the case of chronic diseases, there are many opportunities for not only healthcare professionals but also patients to be involved in the selection of treatment methods. For example, patients may deepen their understanding of their own diseases and select treatment methods through communities (patient associations) of patients with the same or similar diseases as themselves. However, patients consider their own biological, social, and psychological factors, collect information from patients similar to themselves, and comprehensively evaluate the collected information before deciding on a treatment method. Choosing is not easy. In addition, because each patient has different abilities to use information related to health and medical care (health literacy), and the characteristics of thinking and cognition, it is difficult for each patient to understand the provided information when providing uniform information to all patients. However, it is difficult to select a treatment method based on an appropriate evaluation.

JP-A-11-353183

Norem, J. K. (2001). Defensive pessimism, optimism, and pessimism. In E. C. Chang. (Ed.), Optimism and pessimism: Implications for theory, research, and practice. Washington. DC: American Psychological Association Press. 100.

One of the problems that the embodiments disclosed in the specification and drawings attempt to solve is to assist patients in selecting treatment modalities. However, the problems to be solved by the embodiments disclosed in this specification and drawings are not limited to the above problems. A problem corresponding to each effect of each configuration shown in the embodiments described later can be positioned as another problem.

A medical information processing apparatus according to an embodiment includes an extraction unit, an analysis unit, and an output unit. The extraction unit extracts one or more second data similar to the first patient based on first data including biological factors related to the first patient and at least one of social factors and psychological factors related to the first patient. A second patient group consisting of two patients is extracted. The analysis unit analyzes second data about the treatment method selected by each of the second patients belonging to the second patient group, thereby calculating first information about the tendency of the treatment method selection of the second patient group. do. The output unit outputs the first information.

FIG. 1 is a diagram showing a configuration example of a medical information processing apparatus according to the first embodiment. FIG. 2 is a diagram showing an operation example of the medical information processing apparatus according to the first embodiment. FIG. 3 is a diagram showing an example of patient data. FIG. 4 is a diagram showing an example of the psychological characteristics of the identified target patient. FIG. 5 is a diagram showing an example of an extracted similar patient group. FIG. 6 is a diagram showing an example of analyzed treatment information of a group of similar patients. FIG. 7 is a diagram showing an example of comparison information between a target patient and similar patients. FIG. 8 is a diagram showing an example of the degree of similarity between the target patient and similar patients. FIG. 9 is a diagram showing an example of a method for determining an output mode. FIG. 10 is a diagram showing an example of displayed support information. FIG. 11 is a diagram showing an example of correspondence between positive expressions and negative expressions. FIG. 12 is a diagram showing a hardware configuration example of the medical information processing apparatus according to the first embodiment. FIG. 13 is a diagram showing a configuration example of a medical information processing system according to the second embodiment.

A medical information processing apparatus and a medical information processing system according to embodiments will be described below with reference to the drawings. In the following embodiments, it is assumed that parts with the same reference numerals perform the same operations, and overlapping descriptions will be omitted as appropriate. Also, each unit included in the medical information processing apparatus according to the embodiment can be implemented in the form of hardware or software.

(First embodiment)
FIG. 1 is a diagram showing a configuration example of a medical information processing apparatus 1 according to the first embodiment.
The medical information processing apparatus 1 is an apparatus that processes various types of medical information. The medical information processing apparatus 1 includes an interface section 10 , a data processing section 20 and a display section 30 . The interface unit 10 includes an acquisition unit 11 , an input unit 12 and an output unit 13 . The data processing unit 20 includes a patient characteristic identification function 21, a similar patient group extraction function 22, a similar patient group analysis function 23, a comparison information generation function 24, a similarity calculation function 25, and an output mode determination function 26. The medical information processing apparatus 1 may be installed in a device capable of executing various types of information processing (eg, server PC, notebook PC, tablet terminal, smart phone). An operation example of the medical information processing apparatus 1 will be described later with reference to FIG. A hardware configuration example of the medical information processing apparatus 1 will be described later with reference to FIG.

The interface unit 10 communicates various data, information, and commands between the inside and outside of the medical information processing apparatus 1 . Any standard can be used as the data communication standard. For example, HL7 (Health Level 7) can be used as the communication standard for medical character information, and DICOM (Digital Imaging and Communications in Medicine) can be used for communication about medical image information. The data communication method may be wired or wireless.

The acquisition unit 11 acquires various data from the outside of the medical information processing apparatus 1 . For example, the acquisition unit 11 collects various data (hereinafter referred to as target patient data 2) related to a patient to be processed by the medical information processing apparatus 1 (hereinafter referred to as target patient), and information regarding past medical treatment at a medical institution. Various data (hereinafter referred to as past patient data 3) regarding a certain patient (hereinafter referred to as past patient) are acquired. The target patient is also referred to as the first patient or the current patient. A patient similar to the target patient among the plurality of past patients is also called a second patient. The acquisition unit 11 also transfers the acquired data to the data processing unit 20 .

The target patient data 2 is data including biological factors, social factors, and psychological factors regarding the target patient. For example, the target patient data 2 may be data stored in a medical record of a medical institution where the target patient is currently receiving medical care or data stored in a medical record of another medical institution where medical care was received in the past. Also, the target patient data 2 may be data obtained by interviewing target patients by healthcare professionals including doctors, nurses, and case workers. Medical records include, for example, electronic medical records and nursing records. An example of the target patient data 2 will be described later with reference to FIG.

The target patient data 2 is not limited to medical staff, and may be data obtained by AI (Artificial Intelligence) interviewing the target patient. For example, the target patient data 2 is data obtained by image analysis of the image data after the AI acquires image data related to the body of the target patient and medical records of the target patient via the web camera. good too. It should be noted that the AI may acquire the image data related to the medical record of the target patient from the data server not only in the image state but also in the data state conforming to HL7. First, the AI may obtain data on biological factors such as gender, age, etc. of the target patient by analyzing image data on the face of the target patient. Second, the AI may obtain data on biological factors such as disease name, disease stage, subtype, etc. of the target patient by analyzing image data on the medical record of the target patient. Similarly, the AI may acquire data regarding social and psychological factors of the target patient. Of course, the medical information processing apparatus 1 may acquire the target patient data 2 by executing the above operation by installing the above AI in the medical information processing apparatus 1 .

Alternatively, the target patient data 2 is data obtained by text-analyzing the dialogue data after obtaining the dialogue data related to the dialogue performed between the target patient via voice or text by AI. good. If the dialogue data is speech data, AI applies existing speech recognition technology using acoustic analysis, speech models, pronunciation dictionaries, and language models to the speech data to convert the speech data into text data. may be converted. Subsequently, the AI may acquire data regarding biological, social, and psychological factors regarding the target patient from the dialogue data as text data. Of course, the medical information processing apparatus 1 may acquire the target patient data 2 by executing the above operation by installing the above AI in the medical information processing apparatus 1 .

The past patient data 3 is data including biological factors, social factors, and psychological factors related to past patients. For example, the past patient data 3 may be data stored in the medical record of the medical institution where the past patient received treatment. Also, the past patient data 3 may be data obtained by medical professionals (Healthcare Professionals) including doctors, nurses, and case workers interviewing past patients. Medical records include, for example, electronic medical records and nursing records. That is, it can be said that the past patient data 3 is data similar to the target patient data 2 . An example of the past patient data 3 will be described later with reference to FIG.

In addition, the past patient data 3 shall include the data regarding one or more past patients. Also, the past patient data 3 shall include one or more past patients having factors corresponding to the target patient's biological factors, social factors, and psychological factors.

The input unit 12 receives input from a user who uses the medical information processing apparatus 1 . For example, the input unit 12 receives a designation command that designates a predetermined target as an input from the user. The input unit 12 also transfers the received input to the data processing unit 20 . Users include, for example, the subject patient, the subject patient's family members, and medical personnel involved in the subject patient's care.

The output unit 13 outputs various information generated inside the medical information processing apparatus 1 to the outside of the medical information processing apparatus 1 or other components inside the medical information processing apparatus 1 . For example, the output unit 13 outputs the support information 4 generated by the data processing unit 20 to the display unit 30 . Also, the output unit 13 outputs the support information 4 in an output mode determined by the data processing unit 20 .

The data processing section 20 performs various processes on the data transferred from the interface section 10 . For example, the data processing unit 20 generates the support information 4 by executing various processes on the target patient data 2 and the past patient data 3 . The data processing unit 20 also transfers the generated support information 4 to the output unit 13 .

The support information 4 is information for supporting the decision making of the user who uses the medical information processing apparatus 1 . The support information 4 includes various information generated by the data processing section 20 . That is, the support information 4 includes various data generated by the patient characteristic identification function 21, the similar patient group extraction function 22, the similar patient group analysis function 23, the comparison information generation function 24, the similarity calculation function 25, and the output mode determination function 26. contains information about

The patient characteristic identification function 21 identifies the characteristics of the target patient. For example, the patient characteristic identification function 21 identifies psychological characteristics of the target patient based on the target patient data 2 . The patient characteristic specifying function 21 may specify the characteristics of past patients. An example of the identified target patient's psychological characteristics will be described later with reference to FIG.

The similar patient group extraction function 22 extracts a similar patient group similar to the target patient from the past patient data 3 based on the target patient data 2 . For example, the similar patient group extraction function 22 is based on the target patient data 2 including biological factors related to the target patient and at least one of social factors and psychological factors related to the target patient, one or more similar to the target patient A similar patient group consisting of similar patients is extracted. An example of the extracted similar patient group will be described later with reference to FIG.

The similar patient group analysis function 23 calculates various analysis results by executing various analyzes on the similar patient group. For example, the similar patient group analysis function 23 analyzes data on the treatment methods selected by each similar patient belonging to the similar patient group, thereby calculating treatment information on the tendency of the similar patient group to select treatment methods. In addition, the similar patient group analysis function 23 analyzes data related to the prognosis after treatment by the treatment method selected by each similar patient belonging to the similar patient group, thereby obtaining treatment information related to the trend in the prognosis after treatment of the similar patient group. may be calculated. An example of the analyzed treatment information of the similar patient group will be described later with reference to FIG.

The comparison information generating function 24 generates various information by comparing the target patient and similar patients. For example, the comparison information generation function 24 generates the past patient data 3 including at least one of biological factors related to one or more similar patients specified by the user and social factors and psychological factors related to the specified similar patients, Comparison information for comparison with the target patient data 2 is generated. An example of comparison information between the target patient and similar patients will be described later with reference to FIG.

The similarity calculation function 25 calculates the similarity between the target patient and similar patients. For example, the similarity calculation function 25 calculates the similarity between the past patient data 3 and the target patient data 2 . An example of the degree of similarity between the target patient and similar patients will be described later with reference to FIG.

The output mode determination function 26 determines the mode when the support information 4 is output. For example, the output mode determination function 26 determines at least one of the target patient's social characteristics defined by the target patient's social factors and the target patient's psychological characteristics defined by the target patient's psychological factors. Based on this, the output mode of the support information 4 is determined. An example of the method of determining the output mode will be described later with reference to FIG.

The display unit 30 displays various information. For example, the display unit 30 displays the output support information 4 . Note that the display unit 30 may be installed outside the medical information processing apparatus 1 as a separate entity capable of communicating with the medical information processing apparatus 1 . The support information 4 displayed on the display unit 30 is confirmed by the user. Therefore, it can be said that the medical information processing apparatus 1 supports the user's decision making. An example of the displayed support information 4 will be described later with reference to FIG.

FIG. 2 is a diagram showing an operation example of the medical information processing apparatus 1 according to the first embodiment.
In step S101, the medical information processing apparatus 1 acquires patient data. Specifically, the acquiring unit 11 acquires the target patient data 2 and the past patient data 3 as patient data. For example, the acquiring unit 11 may acquire the target patient data 2 and the past patient data 3 from an electronic medical chart system in a medical institution where the medical information processing apparatus 1 is installed.

FIG. 3 is a diagram showing an example of patient data.
A patient's biological factors include various factors related to the patient's body and disease. For example, biological factors include gender, age, height, weight, degree of obesity/BMI, eyesight, hearing, blood pressure, blood sugar level, heart rate, medical history, chronic disease, and lifestyle habits (e.g., sleep duration). , smoking, drinking, and exercising). Biological factors include disease name, disease stage, subtype, diseased site, tumor size, and tumor marker as factors relating to the patient's disease. These factors define the patient's biological characteristics. In addition, since these factors have the property that they can be evaluated objectively by medical professionals, it can be said that biological factors are objective factors.

Patient social factors include various factors relating to the patient's social condition. For example, social factors include marital status, presence or absence of children, family status, family structure, educational background, occupation, position, income/annual income, address, housing status, insurance coverage, and human relationships (e.g. siblings, relatives, acquaintances, friends, spouses, colleagues, superiors, subordinates). These factors define the patient's social characteristics. In addition, since these factors have the property that they can be objectively evaluated by a third party, it can be said that the social factors are objective factors.

The patient's psychological factors include various factors relating to the patient's psychological state and psychological tendency. For example, psychological factors include thoughts, emotions, stress, and happiness. These factors define the patient's psychological characteristics. Since these factors depend on subjectivity of the patient, psychological factors can be said to be subjective factors.

In this embodiment, psychological traits include personality traits and cognitive traits. Personality traits are defined by personal type classification by MBTI (Myers-Briggs Type Indicator), Jung's type theory, and the like. Personality traits include, for example, categories of "pessimistic or optimistic," "extrovert or introvert," and "thinking, feeling, sensing, or intuitive." On the other hand, the cognitive characteristics include the type of "visual type, verbal type, or visual type". Cognitive characteristics refer to the ability of humans to understand, memorize, and express information from the outside world. , and “auditory type (auditory dominant)”. More specifically, the "visual type" is a subtype of "photographic type, three-dimensional video type", the "linguistic type" is a subtype of "linguistic video type, linguistic abstract type", and the "auditory type" is a subtype of "auditory language type". It is further classified into subtypes of type, auditory & sound type. For example, “visual” people are good at processing information they see (visual information), “linguistic” people are good at processing information they read (verbal information), and “auditory” people are good at processing information they read (verbal information). ” humans are good at processing the information they hear (auditory information).

Each factor described above is defined by a plurality of "values". For example, the biological factor "gender" has "male, female" as its values. Also, for example, the biological factor “age” has arbitrary numerical values such as “30, 40, 50, . . . ” as its value. In other words, one patient characteristic (biological characteristic, social characteristic, or psychological characteristic) is defined by multiple factors, each of which is defined by multiple values. The above "value" may be a value represented by a character string or a number.

In step S102, the medical information processing apparatus 1 identifies the psychological characteristics of the target patient. Specifically, the patient characteristic identifying function 21 identifies the psychological characteristic of the target patient based on the target patient data 2 acquired in step S101. The information required at this time is input into various personality diagnostic tests or cognitive characteristic analysis tools, including, for example, the aforementioned MBTI (Myers-Briggs Type Indicator) diagnosis and DPQ (Defensive Pessimism Questionnaire). Information. In other words, existing techniques may be used to identify the psychological characteristics of the target patient. The identified psychological traits may be indicated dichotomically, for example, "pessimistic" or "optimistic," or expressed as a percentage, such as "75% pessimistic, 25% optimistic." may

If the target patient data 2 already includes data relating to the psychological characteristics of the target patient as described above, the patient characteristic specifying function 21 does not need to execute step S102. Further, the patient characteristic specifying function 21 may execute step S102 each time the target patient data 2 is acquired. This may change the psychological characteristics of the target patient at any time.

FIG. 4 is a diagram showing an example of the psychological characteristics of the identified target patient.
In FIG. 4( a ), a bar graph 210 indicates the psychological characteristics of the specified target patient. A bar graph 210 schematically shows that the subject patient's personality trait is "pessimistic type 75%, optimistic type 25%". In other words, the bar graph 210 indicates that the target patient has a stronger pessimistic mental tendency than an optimistic mental tendency. The percentage of each portion of bar graph 210 (ie, pessimistic or optimistic) may be indicated by bar 211 associated with bar graph 210 .

In FIG. 4B, the identified target patient's psychological characteristics are indicated by a pie chart 220 . Pie chart 220 schematically shows that the subject patient's personality trait is "pessimistic type 75%, optimistic type 25%". That is, pie chart 220 illustrates, in another form, the subject patient's personality traits shown in bar chart 210 . Specifically, the portion occupying one quarter of the pie chart 220 corresponds to the “optimistic type”, and the portion occupying three quarters of the pie chart 220 corresponds to the “pessimistic type”.

In FIG. 4(c), a radar chart 230 indicates the psychological characteristics of the identified target patient. The radar chart 230 indicates that the "verbal type" is the most predominant or dominant among the "visual type", the "verbal type", and the "auditory type" as the cognitive characteristics of the target patient. The radar chart 230 may be displayed not only by triangles but also by polygons corresponding to the number of displayed items. Of course, each of the subtypes of the cognitive characteristics described above (photograph type, 3D image type, language image type, language abstraction type, auditory language type, auditory & sound type) may be the item to be displayed.

The psychological characteristics of the target patient may be displayed on the display unit 30 in the forms shown in FIGS. 4(a) to 4(c). Thereby, the user using the medical information processing apparatus 1 can confirm the psychological characteristics of the target patient displayed on the display unit 30 .

In step S103, the medical information processing apparatus 1 extracts a similar patient group having characteristics similar to those of the target patient. Specifically, the similar patient group extraction function 22 extracts a similar patient group having characteristics similar to those of the target patient based on the target patient data 2 and past patient data 3 acquired in step S101. For example, the similar patient group extraction function 22 is based on the target patient data 2 including biological factors related to the target patient and at least one of social factors and psychological factors related to the target patient, one or more similar to the target patient A similar patient group consisting of similar patients is extracted from the past patient data 3 . That is, the similar patient group extraction function 22 extracts one or more similar patients from one or more past patients included in the past patient data 3 using the characteristics of the target patient included in the target patient data 2 as a key, and then extracts one or more similar patients. One or more similar patient groups are extracted by clustering the similar patients. That is, clustering of similar patients includes extracting one similar patient and making one patient into a similar patient group. In this embodiment, the medical information processing apparatus 1 uses existing non-hierarchical clustering (eg, K-means method).

For example, a patient X is assumed as an example of a target patient. The biological characteristics of patient X are "female, 42 years old, breast cancer, stage 2a, triple negative", and the social characteristics of patient X are "married, have children (age 0, 4), nuclear family, graduated from high school". , part-time employees. At this time, the medical information processing apparatus 1 extracts a similar patient group similar to the patient X from the past patient data 3 based on the patient X's biological characteristics and social characteristics.

First, the medical information processing apparatus 1 sets a plurality of biological factors (female, 42 years old, breast cancer, stage 2a, triple negative) that define the biological characteristics of the patient X, and a plurality of factors that define the social characteristics of the patient X. A plurality of similar patients are extracted using at least one of social factors (married, with children (age 0, 4), nuclear family, high school graduate, part-time employee) as a key. For example, the medical information processing apparatus 1 uses the patient X's disease name "breast cancer" as a key to extract a plurality of similar patients with "breast cancer". Next, the medical information processing apparatus 1 clusters the extracted multiple similar patients using the patient X's biological factors and social factors as keys. For example, the medical information processing apparatus 1 clusters the extracted "breast cancer" patients using the factors "age" and "married/unmarried" of the patient X as keys. As a result, a similar patient group based on the factors "age" and "married/unmarried" of patient X is extracted from a plurality of similar patients having the same disease as patient X, ie, "breast cancer". The key used for extracting similar patients and the key used for clustering may be different.

In the above example, multiple past patients who are completely the same age as the target patient may be extracted as similar patients, or multiple past patients who are close in age to the target patient, that is, the age of the target patient may be extracted as similar patients. A plurality of past patients in an age group within a predetermined range may be extracted as similar patients. For example, in the case of patient X, multiple past patients who are the same age as "42 years old", multiple past patients whose age difference from "42 years old" is within N years (N is a natural number), or "42 years old may be extracted. Of course, a plurality of past patients may be extracted with a certain allowable range for "disease stage, tumor size, tumor marker", which are biological factors other than "age". For example, in the case of patient X, a plurality of past patients with the same disease stage as "stage 2a", or a plurality of past patients belonging to "stage 2b" and "stage 2c" whose disease stage is close to "stage 2a" are extracted. may be

In addition, the values of "tumor size" and "tumor marker", which are biological factors, are thought to change over time as the tumor progresses. The similar patient group extraction function 22 may extract a plurality of past patients who showed changes over time similar to changes over time in the values of "tumor size" and "tumor marker" in the target patient. In other words, the similar patient group extraction function 22 may extract a plurality of past patients having data similar to the data indicating changes over time in the target patient.

In clustering, a plurality of similar patients extracted using a "disease name" such as "breast cancer" or "stroke" as a key may be clustered. In addition, a plurality of similar patients extracted using "treatment information" such as "treatment method" such as "chemotherapy", "removal surgery", and "thrombolytic agent treatment" as a key may be clustered. In this embodiment, the treatment information includes various information about the treatment method selected by the past patient included in the past patient data 3 and the result of the treatment.

FIG. 5 is a diagram showing an example of an extracted similar patient group.
In FIG. 5( a ), the extracted similar patient group is shown by a scatter diagram 310 . The scatter diagram 310 is a two-dimensional scatter diagram with the parameter p1 on the horizontal axis and the parameter p2 on the vertical axis. Parameters p1 and p2 are biological, social, or psychological factors, respectively, regarding the subject patient. Specifically, parameters p1 and p2 are factors used as clustering keys. Also, the range of values for each factor is shown on the horizontal axis in the range of about 20-120, and on the vertical axis in the range of about 0-80. Further, details of the extracted similar patient group may be indicated by label 311 accompanying scatter plot 310 . A label 311 indicates that "extracted group is breast cancer patients".

In the scatterplot 310 three similar patient groups are shown. Each similar patient group has a unique patient characteristic (patient characteristic 1, patient characteristic 2, patient characteristic 3). Each plot shown in scatterplot 310 represents a similar patient. Specifically, a similar patient group with patient characteristic 1 is indicated by a cross (x), a similar patient group with patient characteristic 2 is indicated by a triangle (△), and a similar patient group with patient characteristic 3 is indicated by Indicated by a circle (○). As described above, each similar patient group may be identifiably indicated by a symbol different from each other. Not limited to this, each similar patient group may be identifiably indicated by different colors (eg, blue, green, red) even with the same symbol.

Information indicating which similar patient group the target patient belongs to among the similar patient groups may be shown together with the scatter diagram 310 . Such information may be represented, for example, by placing a plot showing the target patient on the scatter plot 310 . At this time, the symbol of the plot indicating the target patient may be the same as the symbol of the plot indicating the similar patient group to which the target patient belongs. Of course, the color of the plot showing the target patient may be identifiably shown by being different from the color of the plot showing each similar patient group.

Note that the scatter diagram 310 shown in FIG. 5( a ) may be displayed on the display unit 30 . At this time, the user using the medical information processing apparatus 1 inputs a command (designation command) for selecting a desired similar patient group from among the similar patient groups shown in the scatter diagram 310 through the input unit 12. may The similar patient group extraction function 22 enlarges the selected similar patient group and displays it on the display unit 30 according to the input command. For example, when a similar patient group having patient characteristic 3 is selected, the display unit 30 switches the display from FIG. 5(a) to FIG. 5(b).

In FIG. 5(b), a magnified view 320 in which the group of similar patients with patient characteristic 3 is magnified is shown. In expanded view 320, a plot of multiple similar patients with patient characteristic 3 is shown expanded. Additionally, details of the selected similar patient group may be indicated by label 321 associated with magnified view 320 . Label 321 indicates "selected group is patient characteristic 3".

Also, the user using the medical information processing apparatus 1 may input, via the input unit 12, a command for selecting a desired similar patient from among the multiple similar patients shown in the enlarged view 320. FIG. In addition, an area 322 showing selected similar patients may be shown superimposed on the magnified view 320 . Here, let patient A be an example of a similar patient selected in area 322 .

In step S104, the medical information processing apparatus 1 analyzes data regarding the similar patient group. Specifically, the similar patient group analysis function 23 calculates various information as analysis results by analyzing the similar patient group extracted in step S103. For example, the similar patient group analysis function 23 analyzes data on the treatment methods selected by each similar patient belonging to the similar patient group, thereby calculating treatment information on the tendency of the similar patient group to select treatment methods. In addition, the similar patient group analysis function 23 analyzes data related to the prognosis after treatment by the treatment method selected by each similar patient belonging to the similar patient group, thereby obtaining treatment information related to the trend in the prognosis after treatment of the similar patient group. may be calculated.

FIG. 6 is a diagram showing an example of analyzed treatment information of a group of similar patients.
In FIG. 6, the analysis table 400 shows the treatment information of the analyzed group of similar patients. The analysis table 400 shows the treatment information of the group (patient characteristic 3) selected in FIG. 5 and the treatment information of the patient (patient A) selected in the group. For example, as the treatment information for the selected group, "70% chose chemotherapy and 30% chose excision surgery". That is, it can be said that the similar patient group of the target patient tends to select "chemotherapy" as a treatment method compared to "removal surgery". On the other hand, as the treatment information of the selected patient, "the selected patient chose chemotherapy" is displayed. Also shown in the analysis table 400 are treatment information regarding "survival", "prognosis", "complications", and "recurrence" in the selected group or selected patient. In addition, in the analysis table 400, the treatment information of the three similar patient groups shown in FIG. 5 may be displayed at the same time. Of course, analysis table 400 may be displayed on display unit 30 .

In step S105, the medical information processing apparatus 1 generates comparison information between the target patient and similar patients. Specifically, the comparison information generation function 24 generates comparison information with the target patient based on the similar patients extracted in step S103. For example, the comparison information generation function 24 generates the past patient data 3 including at least one of biological factors related to one or more similar patients specified by the user and social factors and psychological factors related to the specified similar patients, Comparison information for comparison with the target patient data 2 is generated.

FIG. 7 is a diagram showing an example of comparison information between a target patient and similar patients.
In FIG. 7 , comparison information between the target patient and similar patients is shown by a comparison table 500 . The comparison table 500 shows the characteristics of the target patient (patient X), the characteristics of the group (patient characteristics 3) selected in FIG. 5, and the characteristics of the patient (patient A) selected in the group. Specifically, the comparison table 500 compares each factor for the subject patient in the selected group and the selected patient. For example, for "gender", it can be seen that all of the target patient, the selected group, and the selected patient are "female." As for "age", it can be seen that the target patient is "42 years old", the selected group is "37-47 years old", and the selected patient is "38 years old". Of course, the comparison table 500 may be displayed on the display unit 30 as well.

In step S106, the medical information processing apparatus 1 calculates the degree of similarity between the target patient and similar patients. Specifically, the similarity calculation function 25 calculates the similarity between the target patient and the similar patients based on the similar patients extracted in step S103. For example, the similarity calculation function 25 calculates the similarity between the past patient data 3 and the target patient data 2 .

FIG. 8 is a diagram showing an example of the degree of similarity between the target patient and similar patients.
In FIG. 8, the degree of similarity between the target patient and similar patients is indicated by a degree of similarity table 600 . In the similarity table 600, each factor of the target patient (patient X) and each factor of the patient (patient A) selected in FIG. 5 are digitized. For example, "sex" is quantified based on the criteria of "male: 1, female: 1, other: 0". As for "age", a numerical value related to age is extracted and quantified. In other words, each factor may be quantified based on a unique criterion. For example, based on the similarity table 600, the similarity may be calculated by averaging the similarities of the items, or the similarity may be calculated by calculating the weighted sum of the items. Here, it is calculated that the similarity (correlation) between the characteristic of the target patient and the characteristic of the selected patient is 99.94%. Further, “very similar” may be associated with the similarity table 600 as a qualitative evaluation of the calculated similarity. Of course, the similarity table 600 may be displayed on the display unit 30 as well.

In step S107, the medical information processing apparatus 1 determines an output mode based on the psychological characteristics of the target patient. Specifically, the output mode determination function 26 determines the output mode of the data analyzed in step S104 based on the psychological characteristics of the target patient identified in step S102. The analyzed data includes treatment information in analysis table 400 . In addition, the output mode determination function 26 determines the biological characteristics of the target patient defined by the biological factors of the target patient, the social characteristics of the target patient defined by the social factors of the target patient, and the psychological characteristics of the target patient. The output mode of the analyzed data may be determined based on at least one of the psychological characteristics of the target patient defined by the factors. If the user of the medical information processing apparatus 1 is not the target patient but a family member of the target patient, the output mode determination function 26 may determine the output mode based on the psychological characteristics of the family member.

FIG. 9 is a diagram showing an example of a method for determining an output mode.
FIG. 9 is a diagram showing a detailed flow in step S107. In FIG. 9, a series of steps (steps S201-S227) following step S106 are shown.

In step S201, the output mode determination function 26 determines whether the target patient's personality trait is pessimistic or optimistic. For example, the output mode determination function 26 may determine the personality traits of the target patient by using the results of the aforementioned DPQ (Defensive Pessimism Questionnaire) conducted on the target patient in step S102. If the target patient's personality trait is "pessimistic", the output mode determining function 26 proceeds to step S212. On the other hand, if the target patient's personality trait is "optimistic", the output mode determining function 26 proceeds to step S222.

In step S212, the output mode determination function 26 adopts a positive expression as the output mode. In other words, the output mode determination function 26 employs presenting information to the target patient according to the "positive frame" in behavioral economics.

In step S213, the output mode determination function 26 determines whether the target patient's cognitive characteristic is visual, verbal, or auditory. For example, the output mode determination function 26 may determine the cognitive characteristics of the target patient by using the results of the above-described cognitive characteristics analysis tool performed on the target patient in step S102. If the cognitive characteristic of the target patient is "visual", the output mode determining function 26 proceeds to step S214. On the other hand, if the cognitive characteristic of the target patient is "verbal", the output mode determination function 26 proceeds to step S215. On the other hand, if the cognitive characteristic of the target patient is "auditory", the output mode determination function 26 proceeds to step S216.

In step S214, the output mode determination function 26 adopts visual representation as the output mode. Visual representations are images, including still images and moving images, for example. A visual representation can be said to be a form of representation that is easy for a visual person to understand.

In step S215, the output mode determination function 26 adopts the linguistic expression as the output mode. A linguistic expression is, for example, a character containing text. A linguistic expression can be said to be a form of expression that is easy for a linguistic person to understand.

In step S216, the output mode determining function 26 adopts auditory expression as the output mode. The auditory representation is, for example, speech. It can be said that the auditory expression is a form of expression that is easy for an auditory person to understand.

In step S217, the output mode determination function 26 determines the expression adopted up to this step as the output mode. For example, when the processes are executed in the order of steps S212, S213, and S214, the output mode determining function 26 determines positive expressions and visual expressions as output modes. On the other hand, when the processes are executed in the order of steps S212, S213, and S215, the output mode determining function 26 determines positive expressions and verbal expressions as output modes. On the other hand, when steps S212, S213, and S216 are executed in order, the output mode determining function 26 determines positive expressions and auditory expressions as output modes. After step S217, step S108 is executed.

In step S222, the output mode determining function 26 adopts a negative expression as the output mode. In other words, the output mode determination function 26 employs presenting information to the target patient according to the "negative frame" in behavioral economics.

In step S223, the output mode determination function 26 determines whether the target patient's cognitive characteristics are visual, verbal, or auditory. Step S223 is the same as step S213. If the cognitive characteristic of the target patient is "visual", the output mode determining function 26 proceeds to step S224. On the other hand, when the cognitive characteristic of the target patient is "verbal type", the output mode determination function 26 proceeds to step S225. On the other hand, if the target patient's cognitive characteristic is "auditory", the output mode determination function 26 proceeds to step S226.

In step S224, the output mode determination function 26 adopts visual representation as the output mode. Step S224 is similar to step S214.

In step S225, the output mode determination function 26 adopts the linguistic expression as the output mode. Step S225 is the same as step S215.

In step S226, the output mode determining function 26 adopts auditory expression as the output mode. Step S226 is the same as step S216.

In step S227, the output mode determination function 26 determines the expression adopted up to this step as the output mode. For example, when the processes are executed in the order of steps S222, S223, and S224, the output mode determination function 26 determines negative expressions and visual representations as output modes. On the other hand, when the processes are executed in the order of steps S222, S223, and S225, the output mode determination function 26 determines negative expressions and verbal expressions as output modes. On the other hand, when steps S222, S223, and S226 are executed in order, the output mode determining function 26 determines negative expressions and auditory expressions as output modes. After step S227, step S108 is executed.

As described above, the output mode determination function 26 determines the output mode based on the psychological characteristics of the target patient. In the above flow, the output mode determining function 26 adopts a positive expression when the target patient is "pessimistic". Since a "pessimistic" target patient tends to perceive facts pessimistically, the medical information processing apparatus 1 presents information in a positive way to prevent the target patient from becoming more pessimistic and can encourage On the other hand, the output mode determination function 26 adopts a negative expression when the target patient is "optimistic". Since "optimistic" target patients tend to perceive facts optimistically, the medical information processing apparatus 1 presents information using negative expressions to prevent the target patients from underestimating the facts. It is possible to call attention to the target patient.

Also, as mentioned above, "visual," "verbal," and "auditory" subject patients are good at processing visual, verbal, and auditory information, respectively. The medical information processing apparatus 1 presents information in an appropriate manner according to the cognitive characteristics of the target patient, thereby assisting the target patient in easily understanding the presented data.

By exchanging step S212 and step S222, the output mode determination function 26 adopts a negative expression when the subject patient's personality trait is "pessimistic", while adopting a negative expression when the subject patient's personality trait is "optimistic". ”, the affirmative expression may be adopted. In this way, it is sufficient that the expressions adopted are different for each character trait of the target patient. For example, by communicating negatively to the "pessimistic type" and positively communicating to the "optimistic type," the medical information processing apparatus 1 reduces the likelihood that the target patient will select the option presented to the target patient by the apparatus. , is also possible.

Further, in the above flow, either one of "determination of the subject patient's personality traits" in step S201 and "determination of the subject patient's cognitive traits" in step S213 or S223 may be executed. Also, the latter determination may be executed before the former determination.

In step S108, the medical information processing apparatus 1 outputs the analyzed data in the determined output mode. Specifically, the output unit 13 outputs the data analyzed in step S104 in the output mode determined in step S107. The output unit 13 also outputs the support information 4 including the analyzed data to the display unit 30 . Specifically, the support information 4 includes information about the psychological characteristics of the target patient identified in step S102, information about the similar patient group extracted in step S103, and treatment information about the similar patient group analyzed in step S104. information, the comparison information generated in step S105, and the similarity calculated in step S106.

In step S109, the medical information processing apparatus 1 displays the output support information 4. FIG. Specifically, the display unit 30 displays the support information 4 output from the output unit 13 . As described above, the displayed support information 4 is confirmed by the user who uses the medical information processing apparatus 1 .

FIG. 10 is a diagram showing an example of the displayed support information 4. As shown in FIG.
10(a)-FIG. 10(c) show that after the target patient is determined to be "pessimistic", the target patient is "visual", "verbal", or "auditory" The output mode of the support information 4 in each determined case is shown. That is, FIG. 10(a) shows an output mode by affirmative expressions and visual expressions when the processes are executed in the order of steps S212, S213, and S214 in FIG. On the other hand, FIG. 10(b) shows an output mode of affirmative expressions and linguistic expressions when the processes are executed in the order of steps S212, S213, and S215 in FIG. On the other hand, FIG. 10(c) shows an output mode of affirmative expressions and auditory expressions when the processes are executed in the order of steps S212, S213, and S216 in FIG.

FIG. 10( a ) includes a block 331 , a pictogram 332 and text 333 . Block 331 shows "pessimistic-visual" as the psychological characteristic of the target patient. A pictogram 332 visually represents text 333 corresponding to treatment information that is the result of analysis of a group of similar patients. Specifically, the pictogram 332 expresses that "90% of people will survive with chemotherapy" represented by the text 333, 9 out of 10 stick figures, and the remaining 1 stick figure. Expressed by emphasizing in different ways. Note that the text 333 positively expresses the treatment information. The text 333 corresponds to the treatment information regarding "survival rate" in the analysis table 400 shown in FIG.

FIG. 10(b) includes block 341, drawing tool 342, and text 343. FIG. A block 341 indicates "pessimistic-verbal" as the psychological characteristic of the target patient. The drawing tool 342 is a drawing function operated by the user of the medical information processing apparatus 1 , and the user can draw arbitrary graphics and characters on the screen of the display unit 30 via the drawing tool 342 . The text 343 is a linguistic representation of the treatment information. Text 343 is similar to text 333 .

FIG. 10( c ) includes block 351 , voice mark 352 and text 353 . Block 351 indicates "pessimistic-auditory" as the psychological characteristic of the target patient. A voice mark 352 indicates that the voice corresponding to the text 353 is being played. Specifically, the sound mark 352 indicates that the medical information processing apparatus 1 is reproducing the sound "90% of people will survive with chemotherapy." Of course, when the user selects the sound mark 352 via the input unit 12, the above sound may be played. Text 353 is similar to text 333 .

FIG. 10(d)-FIG. 10(f) shows that after the target patient is determined to be "optimistic", the target patient is "visual", "verbal", or "auditory" The output mode of the support information 4 in each determined case is shown. That is, FIG. 10(d) shows an output mode by negative expressions and visual expressions when the processing is executed in the order of steps S222, S223, and S224 in FIG. On the other hand, FIG. 10(e) shows an output mode using negative expressions and verbal expressions when the processes are executed in the order of steps S222, S223, and S225 in FIG. On the other hand, FIG. 10(f) shows an output mode using negative expressions and auditory expressions when the processes are executed in the order of steps S222, S223, and S226 in FIG.

FIG. 10(d) includes block 361, pictogram 362, and text 363. FIG. Block 361 indicates "optimistic-visual" as the psychological characteristic of the subject patient. A pictogram 362 visually represents a text 363 corresponding to treatment information that is the result of analysis of a group of similar patients. Specifically, the pictogram 362 represents the text 363 that ``10% of people die from chemotherapy'', and the remaining 9 stick figures represent 1 out of 10 stick figures. Expressed by emphasizing in different ways. Note that the text 363 negatively expresses the treatment information. The text 363 corresponds to the treatment information regarding "survival rate" in the analysis table 400 shown in FIG.

FIG. 10( e ) includes block 371 , drawing tool 372 and text 373 . Block 371 indicates "optimistic-verbal" as the psychological characteristic of the target patient. Drawing tool 372 is similar to drawing tool 342 . Text 373 is similar to text 363 .

FIG. 10( f ) includes block 381 , voice mark 382 and text 383 . Block 381 indicates "optimistic-auditory" as the psychological characteristic of the subject patient. A voice mark 382 indicates that the voice corresponding to the text 383 is being played. Specifically, the sound mark 382 indicates that the medical information processing apparatus 1 is reproducing the sound "10% of people die from chemotherapy." Of course, when the user selects the sound mark 382 via the input unit 12, the above sound may be played. Text 383 is similar to text 363 .

As described above, the medical information processing apparatus 1 presents the support information 4 in an output mode according to the psychological characteristics of the target patient. Of course, the medical information processing apparatus 1 may provide all of the texts, drawings, moving images, voices, and drawing tools, and the patient may select a desired method from the provided information methods. The medical information processing apparatus 1 may use the expression correspondence table 700 shown below when selecting which of the positive expressions and the negative expressions to use based on the psychological characteristics of the target patient. .

FIG. 11 is a diagram showing an example of correspondence between positive expressions and negative expressions.
FIG. 11 shows an expression correspondence table 700 storing a plurality of pairs of positive expressions and negative expressions corresponding to the expressions. For example, the expression correspondence table 700 has "90% of people will survive" and "10% of people will die" as pair a. Text 333 and text 363 in FIG. 10 are selected based on pair a.

FIG. 12 is a diagram showing a hardware configuration example of the medical information processing apparatus 1 according to the first embodiment.
The medical information processing apparatus 1 includes a processing circuit 51 , a memory 52 , a display 53 , an input interface 54 and a communication interface 55 . Each component is communicably connected to each other via a bus, which is a common signal transmission path. Note that each configuration does not have to be realized by individual hardware. For example, at least two of each configuration may be implemented by one piece of hardware.

The processing circuit 51 controls the medical information processing apparatus 1 to execute various operations. The processing circuit 51 has processors such as a CPU (Central Processing Unit), an MPU (Micro Processing Unit), and a GPU (Graphics Processing Unit) as hardware. The processing circuit 51 implements each function corresponding to each program by executing the program developed in the memory 52 via the processor. Note that each function does not have to be realized by the processing circuit 51 consisting of a single processor. For example, each function may be implemented by a processing circuit 51 that combines multiple processors. The processing circuit 51 is an example of the data processing section 20 . The processing circuit 51 executes a patient characteristic identification function 21 , a similar patient group extraction function 22 , a similar patient group analysis function 23 , a comparison information generation function 24 , a similarity calculation function 25 and an output mode determination function 26 .

The memory 52 stores information such as data and programs used by the processing circuit 51 . The memory 52 has a semiconductor memory device such as a RAM (Random Access Memory) as hardware. The memory 52 includes magnetic disks (floppy (registered trademark) disk, hard disk), magneto-optical disk (MO), optical disks (CD, DVD, Blu-ray (registered trademark)), flash memory (USB flash memory, memory It may be a drive device that reads and writes information with an external storage device such as a card, SSD), magnetic tape, or the like. The storage area of the memory 52 may be inside the medical information processing apparatus 1 or may be in an external storage device. The memory 52 stores each function executed by the processing circuit 51 (patient characteristic identification function 21, similar patient group extraction function 22, similar patient group analysis function 23, comparison information generation function 24, similarity calculation function 25, and output mode determination function 26), target patient data 2, past patient data 3, and support information 4 may be stored.

The display 53 displays data generated by the processing circuit 51, data stored in the memory 52, and the like. As the display 53, for example, a cathode ray tube (CRT) display, a liquid crystal display (LCD: Liquid Crystal Display), a plasma display, an organic EL display (OELD: Organic Electro-Luminescence Display), and a display such as a tablet terminal are used. It is possible. The display 53 is an example of the display section 30 .

The input interface 54 receives input from the user of the medical information processing apparatus 1 , converts the received input into an electrical signal, and outputs the electrical signal to the processing circuit 51 . As the input interface 54, physical operation parts such as a mouse, keyboard, trackball, switch, button, joystick, touch pad, touch panel display, etc. can be used. The input interface 54 may be a device that receives input from an external input device that is separate from the medical information processing apparatus 1 , converts the received input into an electrical signal, and outputs the electrical signal to the processing circuit 51 . The input interface 54 is an example of the input section 12 .

The communication interface 55 communicates data with external devices. The communication interface 55 is an example of the acquisition unit 11 and the output unit 13 . The processing performed by the medical information processing apparatus 1 described above may be performed within a specific medical institution, or may be performed on the cloud. For example, the medical information processing apparatus 1 installed on the cloud performs the above-described data analysis processing and output mode determination processing for the target patient data 2 received from the target patient's terminal or medical institution via the network. etc., and the support information 4, which is the processing result, may be returned to the terminal or the medical institution. That is, the above cloud service may be realized by incorporating the medical information processing apparatus 1 into an external server.

(Modification)
The medical information processing apparatus 1 may present not only various information about similar patients similar to the target patient, but also standard treatment for the target patient. For example, the medical information processing apparatus 1 may present information on the standard treatment by referring to a clinical practice guideline describing the standard treatment for the target patient's disease. This allows the subject patient to see their standard treatment along with the treatment chosen by similar patients.

The medical information processing apparatus 1 may present the support information 4 based on the social characteristics of the target patient. For example, depending on whether or not the educational background of the target patient is equal to or greater than a threshold value as the social characteristics of the target patient, whether or not numerical values are to be graphically represented as the output mode of the support information 4, and whether or not to add reading kana to kanji are determined. At least one of these may be determined and determined. At this time, for example, the educational background is "junior high school student" includes the case where the target patient is a junior high school student, and also includes the case where the educational level is at the junior high school level even if it is not actually a junior high school student. Of course, the support information 4 may be presented by means such as moving images. As a result, the information is presented in an appropriate manner according to the educational level of the target patient, so that the target patient can easily understand the presented information.

The medical information processing apparatus 1 applies existing natural language processing using morphological analysis, syntactic analysis, semantic analysis, and contextual analysis to the dialogue data related to medical interviews between the subject patient and AI. Thus, information about characteristics of similar patients that the target patient wants to know may be obtained from the dialogue data. Furthermore, the medical information processing apparatus 1 may extract a similar patient group based on the information.

According to the first embodiment described above, the medical information processing apparatus 1 can perform treatments selected by other patients similar to the patient while considering the patient's biological, social, and psychological factors. The patient is presented with information that analyzes the data related to In addition, the medical information processing apparatus 1 presents the information to the patient in an appropriate manner in accordance with the patient's biological, social, or psychological characteristics. This allows patients to empathize and see information about how other patients with similar backgrounds have made choices in a patient-friendly format. In addition, patients can deepen their understanding of their disease. That is, the medical information processing apparatus 1 can assist the patient, the patient's family, and the medical staff to make clinical decisions that are more convincing and less regrettable than before.

In addition, clinical decision-making support technology based on a biosocial-psychological model provided by the medical information processing apparatus 1 promotes the practice of holistic medical care that comprehensively considers various factors related to patients. As a result, the medical information processing apparatus 1 can assist in solving problems that cannot be solved by biomedical models.

In addition, patients can gain wisdom about their disease by referring to the decisions of other patients in situations similar to theirs. That is, the medical information processing apparatus 1 can provide the patient and the patient's family with information and mental support for coping with the disease.

(Second embodiment)
FIG. 13 is a diagram showing a configuration example of a medical information processing system 100 according to the second embodiment.
The medical information processing system 100 is a system that processes various medical information. The medical information processing system 100 includes a medical information processing apparatus 1 , a patient terminal 5 , a past patient DB 6 and a server 7 . In this embodiment, the medical information processing apparatus 1 is communicably connected to the patient terminal 5, the past patient DB 6, and the server 7. FIG. Also, the patient terminal 5 is communicably connected to the server 7 .

The patient terminal 5 is a terminal operated by a target patient. In this embodiment, a target patient transmits target patient data 2 to the medical information processing apparatus 1 via the patient terminal 5 . Instead of the patient terminal 5, the target patient data 2 may be transmitted from an in-hospital terminal installed in the medical institution to which the target patient belongs. The patient terminal 5 may be any device capable of executing various types of information processing (eg, notebook PC, tablet terminal, smart phone). The patient terminal 5 is desirably portable.

The medical information processing apparatus 1 serves as the functional core of the medical information processing system 100 . The medical information processing apparatus 1 according to the second embodiment is the same as the medical information processing apparatus 1 according to the first embodiment, so description thereof will be omitted. In this embodiment, the medical information processing apparatus 1 accesses the past patient DB 6 and acquires the past patient data 3 when the target patient data 2 is acquired from the patient terminal 5 . The medical information processing apparatus 1 performs the same processing as in the first embodiment based on the target patient data 2 and past patient data 3 and outputs support information 4 to the server 7 .

Past patient DB 6 stores past patient data 3 . The past patient DB 6 may be databases installed in various medical institutions and research institutions. A plurality of past patient DBs 6 may be provided. In addition, the past patient DB 6 may be installed in the server 7 .

The server 7 stores the support information 4 transmitted from the medical information processing apparatus 1 . The server 7 has a storage area for storing the support information 4 . Moreover, the server 7 transmits the support information 4 to the patient terminal 5 at arbitrary timing. For example, the server 7 may transmit the support information 4 to the patient terminal 5 at the timing of access from the patient terminal 5 .

According to the second embodiment described above, in the medical information processing system 100, the target patient data 2 transmitted from the patient terminal 5 and the past patient data 3 acquired from the past patient DB 6 are processed as medical information processing. The device 1 does it professionally. In the medical information processing system 100 , the server 7 stores the support information 4 that is the result of information processing, and the server 7 transmits the support information 4 to the patient terminal 5 . As a result, the medical information processing system 100 can provide the support information 4 to the patient terminal 5 located at any location. Note that the medical information processing apparatus 1 may store the support information 4 and transmit it to the patient terminal 5 .

According to at least one embodiment described above, it is possible to assist a patient in selecting a treatment method.

While several embodiments have been described, these embodiments are provided by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, changes, and combinations of embodiments can be made without departing from the scope of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, as well as the scope of the invention described in the claims and equivalents thereof.

1 medical information processing apparatus 2 target patient data 3 past patient data 4 support information 5 patient terminal 6 past patient DB
7 server 10 interface unit 11 acquisition unit 12 input unit 13 output unit 20 data processing unit 21 patient characteristic identification function 22 similar patient group extraction function 23 similar patient group analysis function 24 comparison information generation function 25 similarity calculation function 26 output mode determination function 30 display unit 51 processing circuit 52 memory 53 display 54 input interface 55 communication interface 100 medical information processing system 210 bar graph 211 bar 220 pie graph 230 radar chart 310 scatter diagram 311, 321 label 320 enlarged diagram 322 area 331, 341, 351, 361 , 371, 381 Blocks 332, 362 Pictograms 333, 343, 353, 363, 373, 383 Texts 342, 372 Drawing tools 352, 382 Voice marks 400 Analysis table 500 Comparison table 600 Similarity table 700 Expression correspondence table

Claims (11)

one or more second patients similar to the first patient based on first data including biological factors relating to the first patient and at least one of social factors and psychological factors relating to the first patient. an extraction unit for extracting a second patient group;
an analysis unit that calculates first information about trends in the selection of treatment methods for the second patient group by analyzing second data about the treatment methods selected by each of the second patients belonging to the second patient group; ,
an output unit that outputs the first information;
A medical information processing device comprising: an input unit that receives a specification from a user;
third data including one or more biological factors related to the second patient specified by the user and at least one of social factors and psychological factors related to the specified second patient, as the first data; a generator that generates second information for comparison;
further comprising
The output unit outputs the second information,
The medical information processing apparatus according to claim 1. a calculation unit that calculates the similarity between the third data and the first data;
further comprising
The output unit outputs the similarity,
The medical information processing apparatus according to claim 2. The analysis unit analyzes the fourth data regarding the prognosis after treatment by the treatment method selected by each of the second patients belonging to the second patient group, thereby determining the trend in the prognosis after treatment of the second patient group. Calculate the third information about
The output unit outputs the third information,
The medical information processing apparatus according to any one of claims 1 to 3. Based on at least one of the first patient's social characteristics defined by the first patient's social factors and the first patient's psychological characteristics defined by the first patient's psychological factors a determination unit that determines an output mode of the first information;
further comprising
wherein the output unit outputs the first information in the determined output mode;
The medical information processing apparatus according to any one of claims 1 to 4. The determination unit determines whether the cognitive characteristic of the first patient is a visual type, a verbal type, or an auditory type as the psychological characteristic of the first patient. determine by adopting at least one of text and voice;
The medical information processing apparatus according to claim 5. The determination unit determines whether the first patient's personality trait is a pessimistic type or an optimistic type as the psychological trait of the first patient, and determines whether the output mode of the first information is a positive expression or a negative expression. determine by adopting one of the expressions,
The medical information processing apparatus according to claim 5 or 6. The determination unit determines whether or not to graphically represent numerical values as the output mode of the first information according to whether or not the educational background of the first patient is equal to or greater than a threshold value as the social characteristics of the first patient. determine and determine at least one of whether to add phonetic kana to
The medical information processing apparatus according to any one of claims 5 to 7. an acquisition unit for acquiring first patient data including biological, social, and psychological factors relating to the first patient;
an identifying unit that identifies psychological characteristics of the first patient based on the first patient data;
The medical information processing apparatus according to any one of claims 1 to 8, further comprising: a display unit that displays the output first information;
The medical information processing apparatus according to any one of claims 1 to 9, further comprising: A medical information processing system comprising a terminal, a medical information processing apparatus, and a server,
a first transmission unit configured to transmit first data including biological factors relating to a first patient and at least one of social factors and psychological factors relating to the first patient to the medical information processing apparatus; ,
and
The medical information processing apparatus includes an extraction unit that extracts a second patient group consisting of one or more second patients similar to the first patient based on the first data;
an analysis unit that calculates first information about trends in the selection of treatment methods for the second patient group by analyzing second data about the treatment methods selected by each of the second patients belonging to the second patient group; ,
an output unit that outputs the first information to the server;
and
The server includes a storage unit that stores the first information;
a second transmission unit that transmits the first information to the terminal;
A medical information processing system comprising:

Images