Medical information processing apparatus, medical information processing system, medical information processing method, and medical information processing program

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

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to a medical information processing apparatus and a medical information processing system.

Background Art

[0002] With the recent progress of the aging society and the improvement of prognosis due to medical advancements, the proportion of approaches to chronic diseases, including lifestyle-related diseases such as cancer, low back pain, diabetes, and hypertension, has been increasing rather than that of acute diseases. Against this background, there are an increasing number of cases where an approach focusing only on the biological factors of patients is insufficient for treating the patients' diseases. Therefore, it is important that the treatment method be selected while paying attention not only to the biological factors of the patient but also to the social and psychological factors of the patient.

[0003] In the case of chronic diseases, not only medical staff but also patients often have opportunities to participate 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, it is not easy for patients to collect information on patients similar to themselves while considering their own biological, social, and psychological factors, and then select a treatment method after comprehensively evaluating the collected information. In addition, since the ability (health literacy) to utilize information related to health and medical care and the characteristics of thinking and cognition vary from patient to patient, it is difficult for all patients to understand the information provided to each of them, appropriately evaluate it, and then select a treatment method with a one-size-fits-all information provision.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Non-Patent Documents

[0005]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to assist in the selection of a treatment method for a patient. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problems. The problems corresponding to the respective effects of each configuration shown in the embodiments described later can also be regarded as other problems.

Means for Solving the Problems

[0007] The medical information processing apparatus according to the embodiment includes an extraction unit, an analysis unit, and an output unit. The extraction unit extracts a second patient group composed of one or more second patients 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. The analysis unit calculates first information regarding a tendency in the selection of treatment methods of the second patient group by analyzing second data regarding treatment methods selected by each of the second patients belonging to the second patient group. The output unit outputs the first information.

Brief Description of the Drawings

[0008]

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BEST MODE FOR CARRYING OUT THE INVENTION

[0009] Hereinafter, a medical information processing apparatus and a medical information processing system according to an embodiment will be described with reference to the drawings. In the following embodiments, parts denoted by the same reference numerals perform the same operations, and overlapping explanations will be omitted as appropriate. In addition, each part included in the medical information processing apparatus according to the embodiment can be implemented in the form of hardware or software.

[0010] (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 device 1 is a device that processes various types of medical information. The medical information processing device 1 includes an interface unit 10, a data processing unit 20, and a display unit 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 device 1 may be installed in various devices capable of executing various information processes (e.g., server PC, notebook PC, tablet terminal, smartphone). An operation example of the medical information processing device 1 will be described later with reference to FIG. 2. A hardware configuration example of the medical information processing device 1 will be described later with reference to FIG. 12.

[0011] The interface unit 10 communicates various types of data, information, and commands between the inside and outside of the medical information processing device 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 of medical image information. The data communication method may be either wired or wireless.

[0012] The acquisition unit 11 acquires various types of data from outside the medical information processing device 1. For example, the acquisition unit 11 acquires various types of data (hereinafter referred to as target patient data 2) regarding a patient (hereinafter referred to as the target patient) who is the subject of processing by the medical information processing device 1, and various types of data (hereinafter referred to as past patient data 3) regarding a patient (hereinafter referred to as a past patient) who has received medical treatment at a medical institution in the past. The target patient is also referred to as the first patient or the current patient. Among multiple past patients, a patient similar to the target patient is also referred to as the second patient. Further, the acquisition unit 11 transfers the acquired data to the data processing unit 20.

[0013] The target patient data 2 is data including biological factors, social factors, and psychological factors related to the target patient. For example, the target patient data 2 may be data stored in the medical records of the medical institution where the target patient is currently receiving medical treatment or in the medical records of another medical institution where the target patient received medical treatment in the past. Also, the target patient data 2 may be data obtained by healthcare professionals including doctors, nurses, and caseworkers through interviews with the target patient. The 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. 3.

[0014] Also, the target patient data 2 may be data obtained not only by healthcare professionals but also by AI (Artificial Intelligence) through interviews with the target patient. For example, the target patient data 2 may be data obtained by AI analyzing image data of the target patient's body and image data related to the target patient's medical records after acquiring the image data via a web camera. Note that the AI may acquire image data related to the target patient's medical records from a data server not only in the form of images but also in the form of data conforming to HL7. First, the AI may acquire data related to biological factors such as the gender and age of the target patient by analyzing image data related to the face of the target patient. Second, the AI may acquire data related to biological factors such as the disease name, disease stage, and subtype of the target patient by analyzing image data related to the target patient's medical records. Similarly, the AI may acquire data related to the social and psychological factors of the target patient. Of course, by mounting the above AI on the medical information processing device 1, the medical information processing device 1 may execute the above operations to acquire the target patient data 2.

[0015] Alternatively, the target patient data 2 may be data obtained by the AI analyzing the conversation data regarding the conversation between the AI and the target patient via voice or text. When the conversation data is voice data, the AI may convert the voice data into text data by applying existing speech recognition technology that uses acoustic analysis, speech models, pronunciation dictionaries, and language models to the voice data. Subsequently, the AI may obtain data regarding biological factors, social factors, and psychological factors related to the target patient from the conversation data as text data. Of course, by mounting the above AI on the medical information processing device 1, the medical information processing device 1 may execute the above operations to obtain the target patient data 2.

[0016] The past patient data 3 is data including biological factors, social factors, and psychological factors regarding past patients. For example, the past patient data 3 may be data stored in the medical records of the medical institutions where past patients received medical treatment. Also, the past patient data 3 may be data obtained by healthcare professionals including doctors, nurses, and caseworkers conducting interviews with past patients. The medical records include, for example, electronic medical records and nursing records. That is, it can be said that the past patient data 3 is the same type of data as the target patient data 2. An example of the past patient data 3 will be described later with reference to FIG. 3.

[0017] Note that the past patient data 3 is assumed to include data regarding one or more past patients. Also, the past patient data 3 is assumed to include one or more past patients having factors corresponding to the biological factors, social factors, and psychological factors of the target patient.

[0018] The input unit 12 receives an input from a user who uses the medical information processing device 1. For example, the input unit 12 receives a designation command for designating a predetermined target as an input from the user. Also, the input unit 12 transfers the received input to the data processing unit 20. The user includes, for example, the target patient, the family of the target patient, and healthcare professionals involved in the treatment of the target patient.

[0019] 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 to 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 the output mode determined by the data processing unit 20.

[0020] The data processing unit 20 executes various processes on the data transferred from the interface unit 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. Also, the data processing unit 20 transfers the generated support information 4 to the output unit 13.

[0021] The support information 4 is information for supporting the decision-making of a user who uses the medical information processing apparatus 1. The support information 4 includes various information generated by the data processing unit 20. That is, the support information 4 includes various information 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.

[0022] The patient characteristic identification function 21 identifies the characteristics of the target patient. For example, the patient characteristic identification function 21 identifies the psychological characteristics of the target patient based on the target patient data 2. Note that the patient characteristic identification function 21 may identify the characteristics of past patients. An example of the identified psychological characteristics of the target patient will be described later with reference to FIG. 4.

[0023] The similar patient group extraction function 22 extracts a group of similar patients 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 extracts a group of similar patients consisting of one or more similar patients similar to the target patient based on the target patient data 2 including at least one of a biological factor related to the target patient and a social factor and a psychological factor related to the target patient. An example of the extracted group of similar patients will be described later with reference to FIG. 5.

[0024] The similar patient group analysis function 23 calculates various analysis results by performing various analyses on the similar patient group. For example, the similar patient group analysis function 23 calculates treatment information regarding the tendency in the selection of treatment methods for the similar patient group by analyzing data on the treatment methods selected by each similar patient belonging to the similar patient group. Further, the similar patient group analysis function 23 may calculate treatment information regarding the tendency in the prognosis after treatment for the similar patient group by analyzing data on the prognosis after treatment by the treatment methods selected by each similar patient belonging to the similar patient group. An example of the treatment information of the analyzed similar patient group will be described later with reference to FIG. 6.

[0025] The comparison information generation function 24 generates various information obtained by comparing the target patient and the similar patients. For example, the comparison information generation function 24 generates comparison information for comparing the past patient data 3 including biological factors regarding one or more similar patients specified by the user and at least one of the social factors and psychological factors regarding the specified similar patients with the target patient data 2. An example of the comparison information between the target patient and the similar patients will be described later with reference to FIG. 7.

[0026] The similarity calculation function 25 calculates the similarity between the target patient and the 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 similarity between the target patient and the similar patients will be described later with reference to FIG. 8.

[0027] The output mode determination function 26 determines the mode for outputting the support information 4. For example, the output mode determination function 26 determines the output mode of the support information 4 based on at least one of the social characteristics of the target patient defined by the social factors of the target patient and the psychological characteristics of the target patient defined by the psychological factors of the target patient. An example of the method for determining the output mode will be described later with reference to FIG. 9.

[0028] The display unit 30 displays various types of 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 device 1 as a separate body capable of communicating with the medical information processing device 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 device 1 supports the user's decision-making. An example of the displayed support information 4 will be described later with reference to FIG. 10.

[0029] FIG. 2 is a diagram showing an operation example of the medical information processing device 1 according to the first embodiment. In step S101, the medical information processing device 1 acquires patient data. Specifically, the acquisition unit 11 acquires the target patient data 2 and the past patient data 3 as patient data. For example, the acquisition unit 11 may acquire the target patient data 2 and the past patient data 3 from the electronic medical record system in the medical institution where the medical information processing device 1 is installed.

[0030] FIG. 3 is a diagram showing an example of patient data. The biological factors of a patient include various factors related to the patient's body and diseases. For example, the biological factors include, as factors related to the patient's body, gender, age, height, weight, obesity degree / BMI, eyesight, hearing, blood pressure, blood glucose level, heart rate, medical history, underlying diseases, and lifestyle habits (e.g., sleep duration, smoking, drinking, exercise). In addition, the biological factors include, as factors related to the patient's diseases, disease name, disease stage, subtype, affected site, tumor size, and tumor marker. These factors define the biological characteristics of the patient. Note that since these factors can be objectively evaluated by medical staff, etc., it can be said that the biological factors are objective factors.

[0031] The social factors of a patient include various factors related to the patient's social conditions. For example, social factors include marital status (married / unmarried), presence or absence of children, family form, family composition, education level, occupation, job position, income / annual income, residence, housing form, insurance coverage, and human relationships (e.g., siblings, relatives, acquaintances, friends, spouses, colleagues, superiors, subordinates). These factors define the social characteristics of the patient. Since these factors can be objectively evaluated by a third party, it can be said that social factors are objective factors.

[0032] The psychological factors of a patient include various factors related to the patient's psychological state and psychological tendencies. For example, psychological factors include thinking, emotions, stress, and happiness. These factors define the psychological characteristics of the patient. Since these factors depend on the patient's subjective feelings, it can be said that psychological factors are subjective factors.

[0033] In this embodiment, psychological characteristics shall include personality characteristics and cognitive characteristics. Personality characteristics are defined by personal type classification according to the MBTI (Myers-Briggs Type Indicator) or Jung's typology theory. Personality characteristics include, for example, categories such as "pessimistic or optimistic", "extroverted or introverted", and "thinking, feeling, sensing, or intuitive". On the other hand, cognitive characteristics include categories such as "visual, verbal, or auditory". Cognitive characteristics refer to the ability of a person to understand, remember, and express information from the external world, and are broadly classified into three types: "visual (visual dominance)", "verbal (verbal dominance)", and "auditory (auditory dominance)" according to the type of this ability. More specifically, "visual" is further classified into subtypes such as "photo type, three-dimensional video type", "verbal" is further classified into subtypes such as "verbal video type, verbal abstract type", and "auditory" is further classified into subtypes such as "auditory verbal type, auditory & sound type". For example, a "visual" person is good at processing information they see (visual information), a "verbal" person is good at processing information they read (verbal information), and an "auditory" person is good at processing information they hear (auditory information).

[0034] Note that each of the factors described above is defined by a plurality of "values". For example, "gender", which is a biological factor, has "male, female" as its values. Also, for example, "age", which is a biological factor, has arbitrary numerical values such as "30, 40, 50, ···" as its values. In other words, one patient characteristic (biological characteristic, social characteristic, or psychological characteristic) is defined by a plurality of factors, and each of the plurality of factors is defined by a plurality of values. The above "values" may be values represented by character strings or numbers.

[0035] In step S102, the medical information processing device 1 identifies the psychological characteristics of the target patient. Specifically, the patient characteristic identification function 21 identifies the psychological characteristics of the target patient based on the target patient data 2 acquired in step S101. The information required at this time is, for example, information input to various personality diagnostic tests or cognitive characteristic analysis tools including the aforementioned MBTI (Myers-Briggs Type Indicator) diagnosis and DPQ (Defensive Pessimism Questionnaire). That is, the psychological characteristics of the target patient may be identified by existing techniques. The identified psychological characteristics may be shown dichotomously, for example, as "pessimistic type" or "optimistic type", or may be shown as a percentage, such as "75% pessimistic type, 25% optimistic type".

[0036] Note that if the target patient data 2 already includes data regarding the psychological characteristics of the target patient as described above, the patient characteristic identification function 21 does not necessarily have to execute step S102. Also, the patient characteristic identification function 21 may execute step S102 each time the target patient data 2 is acquired. Thereby, the psychological characteristics of the target patient may be changed at any time.

[0037] FIG. 4 is a diagram showing an example of the identified psychological characteristics of the target patient. In FIG. 4(a), the psychological characteristics of the specified target patient are shown by the bar graph 210. The bar graph 210 schematically shows that the personality characteristics of the target patient are "pessimistic type 75%, optimistic type 25%". In other words, the bar graph 210 shows that the target patient has a stronger pessimistic psychological tendency than an optimistic psychological tendency. The ratio of each part (i.e., pessimistic type or optimistic type) of the bar graph 210 may be shown by the bar 211 associated with the bar graph 210.

[0038] In FIG. 4(b), the psychological characteristics of the specified target patient are shown by the pie chart 220. The pie chart 220 schematically shows that the personality characteristics of the target patient are "pessimistic type 75%, optimistic type 25%". That is, the pie chart 220 shows the personality characteristics of the target patient shown in the bar graph 210 in another form. Specifically, the part occupying one-fourth of the pie chart 220 corresponds to the "optimistic type", and the part occupying three-fourths of the pie chart 220 corresponds to the "pessimistic type".

[0039] In FIG. 4(c), the psychological characteristics of the specified target patient are shown by the radar chart 230. The radar chart 230 shows that among the "visual type", "linguistic type", and "auditory type" as the cognitive characteristics of the target patient, the "linguistic type" is the most dominant or controlling. Note that the radar chart 230 may be displayed not only by a triangle but also by a polygon according to the number of items to be displayed. Of course, each subtype (photo type, three-dimensional video type, linguistic video type, linguistic abstract type, auditory linguistic type, auditory & sound type) of the above-described cognitive characteristics may be the item to be displayed.

[0040] Note that in each form shown in FIGS. 4(a)-4(c), the psychological characteristics of the target patient may be displayed on the display unit 30. 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.

[0041] In step S103, the medical information processing apparatus 1 extracts a group of similar patients having characteristics similar to those of the target patient. Specifically, the similar patient group extraction function 22 extracts a group of similar patients having characteristics similar to those of the target patient based on the target patient data 2 and the past patient data 3 acquired in step S101. For example, the similar patient group extraction function 22 extracts, from the past patient data 3, a group of similar patients composed of one or more similar patients similar to the target patient based on the target patient data 2 including at least one of a biological factor related to the target patient and a social factor and a psychological factor related to the target patient. 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 clusters the extracted similar patients to extract one or more groups of similar patients. That is, the clustering of similar patients also includes extracting one similar patient and regarding one patient as a group of similar patients. In the present embodiment, it is assumed that the medical information processing apparatus 1 uses an existing non-hierarchical clustering (e.g., K-means method).

[0042] For example, assume patient X as an example of the target patient. Assume that 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, with children (0 years old, 4 years old), nuclear family, high school graduate, part-time employee". At this time, the medical information processing apparatus 1 extracts a group of similar patients similar to patient X from the past patient data 3 based on the biological characteristics and social characteristics of patient X.

[0043] First, the medical information processing device 1 extracts a plurality of similar patients using at least one factor among a plurality of biological factors (female, 42 years old, breast cancer, stage 2a, triple negative) that define the biological characteristics of patient X and a plurality of social factors (married, with children (0 years old, 4 years old), nuclear family, high school graduate, part-time employee) that define the social characteristics of patient X as a key. For example, the medical information processing device 1 extracts a plurality of similar patients having "breast cancer" using the disease name "breast cancer" of patient X as a key. Next, the medical information processing device 1 clusters the extracted plurality of similar patients using the biological factors and social factors of patient X as keys. For example, the medical information processing device 1 clusters the extracted "breast cancer" patients using the factors "age" and "married / unmarried" of patient X as keys. As a result, a group of similar patients based on the factors "age" and "married / unmarried" of patient X is extracted from a plurality of similar patients having the same disease "breast cancer" as patient X. Note that the key used for extracting similar patients and the key used for clustering may be different.

[0044] In the above example, a plurality of past patients whose age is exactly the same as that of the target patient may be extracted as similar patients, or a plurality of past patients whose age is close to that of the target patient, that is, a plurality of past patients in a predetermined age range including the age of the target patient may be extracted as similar patients. For example, in the case of patient X, a plurality of past patients of the same age as "42 years old", a plurality of past patients whose age difference from "42 years old" is within N years (N is a natural number), or a plurality of past patients in the "40s" which is the same age group as "42 years old" may be extracted. Of course, for biological factors other than "age", such as "disease stage, tumor size, tumor marker", a plurality of past patients may be extracted with a certain tolerance range. 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 stages are close to "stage 2a" may be extracted.

[0045] In addition, the values of the "tumor size" and "tumor marker", which are biological factors, are considered to change over time as the tumor progresses. The similar patient group extraction function 22 may extract a plurality of past patients who show a change over time similar to the change over time regarding the values of the "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 showing the change over time in the target patient.

[0046] In addition, in clustering, clustering may be performed on a plurality of similar patients extracted using the "disease name" such as "breast cancer" or "stroke" as a key. Alternatively, clustering may be performed on a plurality of similar patients extracted using "treatment information" such as "treatment method" such as "chemotherapy", "surgical operation", or "thrombolytic agent treatment" as a key. In the present embodiment, the treatment information includes the treatment method selected by the past patient included in the past patient data 3 and various information regarding the treatment result.

[0047] FIG. 5 is a diagram showing an example of the 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. The parameters p1 and p2 are biological factors, social factors, or psychological factors regarding the target patient, respectively. Specifically, the parameters p1 and p2 are the factors used as keys for clustering. In addition, the range of values for each factor is shown in the range of about 20 - 120 on the horizontal axis and in the range of about 0 - 80 on the vertical axis. Further, the details of the extracted similar patient group may be shown by a label 311 attached to the scatter diagram 310. The label 311 indicates that "the extracted group is breast cancer patients".

[0048] In scatter diagram 310, three similar patient groups are shown. Each similar patient group has its own unique patient characteristics (patient characteristic 1, patient characteristic 2, patient characteristic 3). The plots shown in scatter diagram 310 each represent a similar patient. Specifically, the similar patient group with patient characteristic 1 is indicated by a cross (×), the similar patient group with patient characteristic 2 is indicated by a triangle (△), and the similar patient group with patient characteristic 3 is indicated by a circle (○). As described above, each similar patient group may be distinguishable by different symbols. Not limited to this, each similar patient group may also be distinguishable by different colors (e.g., blue, green, red) even if they have the same symbol.

[0049] Note that information indicating to which similar patient group the target patient belongs among each similar patient group may be shown together with scatter diagram 310. This information may be expressed, for example, by placing a plot representing the target patient on scatter diagram 310. At this time, the symbol of the plot representing the target patient may be the same as the symbol of the plot representing the similar patient group to which the target patient belongs. Of course, the color of the plot representing the target patient may be distinguishable from the colors of the plots representing each similar patient group.

[0050] Note that scatter diagram 310 shown in FIG. 5(a) may be displayed on display unit 30. At this time, a user using medical information processing device 1 may input a command (designation command) for selecting a desired similar patient group among each similar patient group shown in scatter diagram 310 via input unit 12. Similar patient group extraction function 22 expands the selected similar patient group and displays it on display unit 30 according to the input command. For example, when the similar patient group with patient characteristic 3 is selected, display unit 30 switches from FIG. 5(a) to FIG. 5(b) for display.

[0051] In FIG. 5(b), an enlarged view 320 in which the similar patient group with patient characteristic 3 is enlarged is shown. In enlarged view 320, the plots of a plurality of similar patients with patient characteristic 3 are shown enlarged. Furthermore, details of the selected similar patient group may be indicated by label 321 attached to enlarged view 320. Label 321 indicates that "the selected group is patient characteristic 3".

[0052] Also, a user who uses the medical information processing apparatus 1 may input, via the input unit 12, a command for selecting a desired similar patient from among the plurality of similar patients shown in the enlarged view 320. Further, an area 322 indicating the selected similar patient may be shown superimposed on the enlarged view 320. Here, an example of the selected similar patient in the area 322 is assumed to be Patient A.

[0053] In step S104, the medical information processing apparatus 1 analyzes data regarding the group of similar patients. Specifically, the similar patient group analysis function 23 calculates various information as analysis results by analyzing the group of similar patients extracted in step S103. For example, the similar patient group analysis function 23 calculates treatment information regarding the tendency in the selection of treatment methods for the group of similar patients by analyzing data regarding the treatment methods selected by each of the similar patients belonging to the group of similar patients. Also, the similar patient group analysis function 23 may calculate treatment information regarding the tendency in the prognosis after treatment for the group of similar patients by analyzing data regarding the prognosis after treatment by the treatment methods selected by each of the similar patients belonging to the group of similar patients.

[0054] FIG. 6 is a diagram showing an example of the treatment information of the analyzed group of similar patients. In FIG. 6, the treatment information of the analyzed similar patient group is shown by analysis table 400. 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 of the selected group, it is shown that "70% of people selected chemotherapy and 30% of people selected resection surgery". That is, it can be said that the similar patient group of the target patient has a tendency to select "chemotherapy" as the treatment method compared to "resection surgery". On the other hand, as the treatment information of the selected patient, it is shown that "the selected patient selected chemotherapy". In addition, in analysis table 400, treatment information regarding "survival rate", "prognosis", "complications", and "recurrence" in the selected group or the selected patient is shown. Note that in analysis table 400, the treatment information of the three similar patient groups shown in FIG. 5 may be displayed simultaneously. Of course, analysis table 400 may be displayed on display unit 30.

[0055] In step S105, medical information processing apparatus 1 generates comparison information between the target patient and the similar patients. Specifically, comparison information generation function 24 generates comparison information with the target patient based on the similar patients extracted in step S103. For example, comparison information generation function 24 generates comparison information for comparing past patient data 3 including biological factors regarding one or more similar patients specified by the user and at least one of the social factors and psychological factors regarding the specified similar patients with target patient data 2.

[0056] FIG. 7 is a diagram showing an example of comparison information between the target patient and the similar patients. In FIG. 7, comparison information between a target patient and similar patients is shown by comparison table 500. Comparison table 500 shows the characteristics of the target patient (patient X), the characteristics of the group selected in FIG. 5 (patient characteristic 3), and the characteristics of the patient (patient A) selected in the group. Specifically, comparison table 500 compares each factor regarding the target patient with those in the selected group and the selected patient. For example, regarding "gender", it can be seen that it is "female" in all of the target patient, the selected group, and the selected patient. Also, regarding "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, comparison table 500 may be displayed on display unit 30.

[0057] In step S106, medical information processing apparatus 1 calculates the similarity between the target patient and similar patients. Specifically, similarity calculation function 25 calculates the similarity between the target patient and similar patients based on the similar patients extracted in step S103. For example, similarity calculation function 25 calculates the similarity between past patient data 3 and target patient data 2.

[0058] FIG. 8 is a diagram showing an example of the similarity between a target patient and similar patients. In FIG. 8, the similarity between the target patient and similar patients is shown by similarity table 600. In similarity table 600, each factor of the target patient (patient X) and each factor of the patient (patient A) selected in FIG. 5 are each quantified. For example, regarding "gender", it is quantified based on the criterion of "male: 1, female: 1, other: 0". Regarding "age", the numerical value related to age is extracted and quantified. That is, it may be quantified based on a criterion specific to each factor. For example, based on similarity table 600, a value obtained by averaging the similarities of each item may be calculated as the similarity, or the weighted sum of each item may be calculated to obtain the similarity. Here, the similarity (correlation degree) between the characteristics of the target patient and the characteristics of the selected patient is calculated to be 99.94%. Furthermore, as a qualitative evaluation of the calculated similarity, "very similar" may be associated with similarity table 600. Of course, similarity table 600 may be displayed on display unit 30.

[0059] 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 the analysis table 400. Further, the output mode determination function 26 may determine the output mode of the analyzed data based on at least one of 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 defined by the psychological factors of the target patient. When the user of the medical information processing apparatus 1 is a family member of the target patient rather than the target patient, the output mode determination function 26 may determine the output mode based on the psychological characteristics of the family member.

[0060] 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.

[0061] In step S201, the output mode determination function 26 determines whether the personality characteristic of the target patient is pessimistic or optimistic. For example, the output mode determination function 26 may determine the personality characteristic of the target patient by using the result of the aforementioned DPQ (Defensive Pessimism Questionnaire) performed on the target patient in step S102. If the personality characteristic of the target patient is "pessimistic", the output mode determination function 26 proceeds to step S212. On the other hand, if the personality characteristic of the target patient is "optimistic", the output mode determination function 26 proceeds to step S222.

[0062] In step S212, the output mode determination function 26 adopts an affirmative expression as the output mode. In other words, the output mode determination function 26 adopts presenting information to the target patient in accordance with the "positive frame" in behavioral economics.

[0063] In step S213, the output mode determination function 26 determines whether the cognitive characteristics of the target patient are visual, linguistic, 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 aforementioned cognitive characteristics analysis tool implemented on the target patient in step S102. If the cognitive characteristics of the target patient are "visual", the output mode determination function 26 proceeds to step S214. On the other hand, if the cognitive characteristics of the target patient are "linguistic", the output mode determination function 26 proceeds to step S215. On the other hand, if the cognitive characteristics of the target patient are "auditory", the output mode determination function 26 proceeds to step S216.

[0064] In step S214, the output mode determination function 26 adopts a visual representation as the output mode. The visual representation is, for example, an image including a still image or a moving image. It can be said that the visual representation is an expression in a mode that is easy for visual-type people to understand.

[0065] In step S215, the output mode determination function 26 adopts a linguistic representation as the output mode. The linguistic representation is, for example, characters including text. It can be said that the linguistic representation is an expression in a mode that is easy for linguistic-type people to understand.

[0066] In step S216, the output mode determination function 26 adopts an auditory representation as the output mode. The auditory representation is, for example, voice. It can be said that the auditory representation is an expression in a mode that is easy for auditory-type people to understand.

[0067] 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 determination function 26 determines the positive expression and the visual expression as the output mode. On the other hand, when the processes are executed in the order of steps S212, S213, and S215, the output mode determination function 26 determines the positive expression and the language expression as the output mode. On the other hand, when the processes are executed in the order of steps S212, S213, and S216, the output mode determination function 26 determines the positive expression and the auditory expression as the output mode. After step S217, step S108 is executed.

[0068] In step S222, the output mode determination function 26 adopts a negative expression as the output mode. In other words, the output mode determination function 26 adopts presenting information to the target patient in accordance with the "negative frame" in behavioral economics.

[0069] In step S223, the output mode determination function 26 determines whether the cognitive characteristics of the target patient are visual, linguistic, or auditory. Step S223 is the same as step S213. If the cognitive characteristics of the target patient are "visual", the output mode determination function 26 proceeds to step S224. On the other hand, if the cognitive characteristics of the target patient are "linguistic", the output mode determination function 26 proceeds to step S225. On the other hand, if the cognitive characteristics of the target patient are "auditory", the output mode determination function 26 proceeds to step S226.

[0070] In step S224, the output mode determination function 26 adopts a visual expression as the output mode. Step S224 is the same as step S214.

[0071] In step S225, the output mode determination function 26 adopts a language expression as the output mode. Step S225 is the same as step S215.

[0072] In step S226, the output mode determination function 26 adopts an auditory expression as the output mode. Step S226 is the same as step S216.

[0073] In step S227, the output mode determination function 26 determines the expressions 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 a negative expression and a visual expression as the output mode. 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 a negative expression and a linguistic expression as the output mode. On the other hand, when the processes are executed in the order of steps S222, S223, and S226, the output mode determination function 26 determines a negative expression and an auditory expression as the output mode. After step S227, step S108 is executed.

[0074] 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 determination function 26 adopts an affirmative expression when the target patient is "pessimistic". Since a "pessimistic" target patient tends to perceive facts pessimistically, the medical information processing apparatus 1 can prevent the target patient from becoming more pessimistic and encourage the target patient by presenting information with an affirmative expression. On the other hand, the output mode determination function 26 adopts a negative expression when the target patient is "optimistic". Since an "optimistic" target patient tends to perceive facts optimistically, the medical information processing apparatus 1 can prevent the target patient from underestimating the facts and prompt the target patient to pay attention by presenting information with a negative expression.

[0075] Also, as described above, "visual-type", "linguistic-type", and "auditory-type" target patients are respectively good at processing visual information, linguistic information, and auditory information. The medical information processing apparatus 1 can assist the target patient in easily understanding the presented data by presenting information in an appropriate mode according to the cognitive characteristics of the target patient.

[0076] Incidentally, by swapping step S212 and step S222, when the personality trait of the target patient is "pessimistic", the output mode determination function 26 may adopt a negative expression, while when the personality trait of the target patient is "optimistic", a positive expression may be adopted. In this way, it suffices that the expressions adopted differ for each personality trait of the target patient. For example, by communicating negatively to a "pessimistic" patient and positively to an "optimistic" patient, the medical information processing apparatus 1 can also reduce the likelihood that the target patient will select the options presented by the apparatus to the target patient.

[0077] Also, in the above flow, only one of the "determination of the personality trait of the target patient" in step S201 and the "determination of the cognitive trait of the target patient" in step S213 or S223 may be executed. Also, the latter determination may be executed prior to the former determination.

[0078] 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. Also, the output unit 13 outputs the support information 4 including the analyzed data to the display unit 30. Specifically, the support information 4 includes information regarding the psychological traits of the target patient specified in step S102, information regarding the similar patient group extracted in step S103, treatment information regarding the similar patient group analyzed in step S104, comparison information generated in step S105, and the similarity calculated in step S106.

[0079] In step S109, the medical information processing apparatus 1 displays the output support information 4. 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.

[0080] FIG. 10 is a diagram showing an example of the displayed support information 4. Figures 10(a) - 10(c) show the output modes of the support information 4 in each case where the target patient is determined to be "pessimistic" and then further determined to be "visual", "linguistic", or "auditory". That is, Figure 10(a) shows the output mode using positive expressions and visual expressions when the processes in steps S212, S213, and S214 in Figure 9 are executed in this order. On the other hand, Figure 10(b) shows the output mode using positive expressions and linguistic expressions when the processes in steps S212, S213, and S215 in Figure 9 are executed in this order. On the other hand, Figure 10(c) shows the output mode using positive expressions and auditory expressions when the processes in steps S212, S213, and S216 in Figure 9 are executed in this order.

[0081] Figure 10(a) includes a block 331, a pictogram 332, and text 333. The block 331 indicates "pessimistic - visual" as the psychological characteristic of the target patient. The pictogram 332 visually represents the text 333 corresponding to the treatment information which is the analysis result of the similar patient group. Specifically, the pictogram 332 represents the statement "90% of people survive with chemotherapy" shown in the text 333 by emphasizing 9 out of 10 stick figures in a different manner from the remaining 1 stick figure. Note that the text 333 positively represents the treatment information. The text 333 corresponds to the treatment information regarding "survival rate" in the analysis table 400 shown in Figure 6.

[0082] Figure 10(b) includes a block 341, a drawing tool 342, and text 343. The block 341 indicates "pessimistic - linguistic" 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 device 1, and the user can draw arbitrary figures and characters on the screen of the display unit 30 via the drawing tool 342. The text 343 linguistically represents the treatment information. The text 343 is the same as the text 333.

[0083] Figure 10(c) includes block 351, voice mark 352, and text 353. Block 351 indicates "pessimistic - auditory type" as the psychological characteristic of the target patient. Voice mark 352 indicates that the voice corresponding to text 353 is being played. Specifically, voice mark 352 indicates that the voice "90% of people survive with chemotherapy" is being played by the medical information processing device 1. Of course, when the user selects voice mark 352 via input unit 12, the above - mentioned voice may be played. Text 353 is the same as text 333.

[0084] Figures 10(d) - 10(f) show the output modes of support information 4 in each case where the target patient is determined to be "optimistic" and then determined to be "visual", "linguistic", or "auditory". That is, Figure 10(d) shows the output mode by negative expression and visual expression when the processes in steps S222, S223, and S224 in Figure 9 are executed in sequence. On the other hand, Figure 10(e) shows the output mode by negative expression and linguistic expression when the processes in steps S222, S223, and S225 in Figure 9 are executed in sequence. On the other hand, Figure 10(f) shows the output mode by negative expression and auditory expression when the processes in steps S222, S223, and S226 in Figure 9 are executed in sequence.

[0085] Figure 10(d) includes block 361, pictogram 362, and text 363. Block 361 indicates "optimistic - visual type" as the psychological characteristic of the target patient. Pictogram 362 visually represents text 363 corresponding to the treatment information which is the analysis result of the similar patient group. Specifically, pictogram 362 represents the statement "10% of people die with chemotherapy" shown in text 363 by emphasizing one stick figure out of ten in a different manner from the remaining nine stick figures. Note that text 363 represents the treatment information negatively. Text 363 corresponds to the treatment information regarding "survival rate" in analysis table 400 shown in Figure 6.

[0086] Figure 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 the same as drawing tool 342. Text 373 is the same as text 363.

[0087] Figure 10(f) includes block 381, voice mark 382, and text 383. Block 381 indicates "optimistic - auditory" as the psychological characteristic of the target patient. Voice mark 382 indicates that the voice corresponding to text 383 is being played. Specifically, voice mark 382 indicates that the voice "10% of people die during chemotherapy" is being played by medical information processing device 1. Of course, when the user selects voice mark 382 via input unit 12, the above - mentioned voice may be played. Text 383 is the same as text 363.

[0088] As described above, medical information processing device 1 presents support information 4 in an output mode according to the psychological characteristics of the target patient. Of course, medical information processing device 1 provides all of text, figures, videos, voices, and drawing tools, and the target patient may select the desired method among the methods of the provided information. Note that when medical information processing device 1 selects whether to use positive expressions or negative expressions based on the psychological characteristics of the target patient, it may use expression correspondence table 700 shown below.

[0089] Figure 11 is a diagram showing an example of the correspondence between positive expressions and negative expressions. In Figure 11, expression correspondence table 700 that stores a plurality of pairs of positive expressions and the negative expressions corresponding to the expressions is shown. For example, expression correspondence table 700 has "90% of people survive" and "10% of people die" as pair a. Text 333 and text 363 in Figure 10 are selected based on pair a.

[0090] Figure 12 is a diagram showing an example of the hardware configuration of medical information processing device 1 according to the first embodiment. The medical information processing device 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 component does not necessarily need to be realized by individual hardware. For example, at least two of the components may be realized by one piece of hardware.

[0091] The processing circuit 51 executes various operations by controlling the medical information processing device 1. 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 realizes each function corresponding to each program by executing the program developed in the memory 52 via the processor. Note that each function does not necessarily need to be realized by the processing circuit 51 consisting of a single processor. For example, each function may be realized by the processing circuit 51 combining a plurality of processors. The processing circuit 51 is an example of the data processing unit 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.

[0092] The memory 52 stores information such as data and programs used by the processing circuit 51. The memory 52 has a semiconductor memory element such as a RAM (Random Access Memory) as hardware. Note that the memory 52 may be a drive device that reads and writes information to and from an external storage device such as a magnetic disk (floppy (registered trademark) disk, hard disk), magneto-optical disk (MO), optical disk (CD, DVD, Blu-ray (registered trademark)), flash memory (USB flash memory, memory card, SSD), or magnetic tape. Note that the storage area of the memory 52 may be inside the medical information processing apparatus 1 or in an external storage device. The memory 52 may store each program corresponding to each function (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) executed by the processing circuit 51, target patient data 2, past patient data 3, and support information 4.

[0093] 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), a plasma display, an organic EL display (OELD), or a display such as a tablet terminal can be used. The display 53 is an example of the display unit 30.

[0094] The input interface 54 receives an input from a user who uses the medical information processing device 1, converts the received input into an electrical signal, and outputs it to the processing circuit 51. As the input interface 54, physical operation components such as a mouse, keyboard, trackball, switch, button, joystick, touch pad, touch panel display, etc. can be used. Note that the input interface 54 may be a device that receives an input from an external input device that is separate from the medical information processing device 1, converts the received input into an electrical signal, and outputs it to the processing circuit 51. The input interface 54 is an example of the input unit 12.

[0095] The communication interface 55 communicates data with an external device. The communication interface 55 is an example of the acquisition unit 11 and the output unit 13. Note that the processing performed by the medical information processing device 1 described above may be executed within a specific medical institution or may be executed on the cloud. For example, the medical information processing device 1 installed on the cloud may perform information processing such as the above-described data analysis processing and determination processing of the output mode on the target patient data 2 received from the terminal or medical institution of the target patient via the network, and return the support information 4, which is the result of the processing, to the terminal or medical institution. That is, the above cloud service may be realized by incorporating the medical information processing device 1 into an external server.

[0096] (Modification example) Note that the medical information processing device 1 may present not only various information on similar patients similar to the target patient, but also the standard treatment for the target patient. For example, the medical information processing device 1 may refer to a clinical practice guideline in which the standard treatment for the disease of the target patient is described, and present information on the standard treatment. Thereby, the target patient can confirm the standard treatment method for himself / herself together with the treatment method selected by the similar patient.

[0097] Note that the medical information processing apparatus 1 may present support information 4 based on the social characteristics of the target patient. For example, at least one of whether to graphically represent a numerical value and whether to attach reading kana to Chinese characters as the output mode of the support information 4 may be determined and decided according to whether the educational background of the target patient is equal to or higher than a threshold value as the social characteristic of the target patient. At this time, for example, when the educational background is "junior high school student", it 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 the target patient is not actually a junior high school student. Of course, the support information 4 may be presented by means such as video. Thereby, since the information is presented in an appropriate manner according to the educational level of the target patient, the target patient can easily understand the presented information.

[0098] Note that the medical information processing apparatus 1 may apply existing natural language processing using morphological analysis, syntactic analysis, semantic analysis, and context analysis to the dialogue data regarding the interview conducted between the target patient and the AI, so as to obtain information regarding the characteristics of similar patients that the target patient wants to know from the dialogue data. Consequently, the medical information processing apparatus 1 may extract a group of similar patients based on the information.

[0099] According to the first embodiment described above, the medical information processing apparatus 1 analyzes information on data related to treatments selected by other patients similar to the patient while considering the biological factors, social factors, and psychological factors of the patient, and presents the information to the patient. In addition, the medical information processing apparatus 1 presents the information to the patient in an appropriate manner in accordance with the biological characteristics, social characteristics, or psychological characteristics of the patient. Thereby, the patient can refer to information regarding what choices other patients with a background similar to his or her own have made in a form that is easy for the patient to understand with empathy. In addition, the patient can deepen his or her understanding of his or her own disease. That is, the medical information processing apparatus 1 can assist the patient, the patient's family, and medical staff in making clinical decisions that are more convincing and have less regret than before.

[0100] In addition, the clinical decision-making support technology based on the biopsychosocial model provided by the medical information processing apparatus 1 promotes the practice of holistic medicine that comprehensively considers various factors related to patients. As a result, the medical information processing apparatus 1 can support problem-solving for problems that cannot be solved by the biomedical model.

[0101] Furthermore, patients can acquire wisdom regarding the diseases they suffer from by referring to the decision-making of other patients in situations similar to the situations they are in. That is, the medical information processing apparatus 1 can provide information and mental support for patients and their families to face diseases.

[0102] (Second Embodiment) FIG. 13 is a diagram showing a configuration example of the medical information processing system 100 according to the second embodiment. The medical information processing system 100 is a system that processes various information related to medical care. 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 the present embodiment, the medical information processing apparatus 1 is communicably connected to the patient terminal 5, the past patient DB 6, and the server 7. Also, the patient terminal 5 is communicably connected to the server 7.

[0103] The patient terminal 5 is a terminal operated by a target patient. In the present embodiment, the target patient transmits target patient data 2 to the medical information processing apparatus 1 via the patient terminal 5. Note that 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 information processes (e.g., a notebook PC, a tablet terminal, a smartphone). It is desirable that the patient terminal 5 be portable.

[0104] The medical information processing device 1 functions as the functional center of the medical information processing system 100. Since the medical information processing device 1 in the second embodiment is the same as the medical information processing device 1 in the first embodiment, its description is omitted. In this embodiment, when the medical information processing device 1 obtains the target patient data 2 from the patient terminal 5, it accesses the past patient DB 6 and obtains the past patient data 3. The medical information processing device 1 performs the same processing as in the first embodiment based on the target patient data 2 and the past patient data 3, and outputs the support information 4 to the server 7.

[0105] The past patient DB 6 stores the past patient data 3. The past patient DB 6 may be a database installed in various medical institutions and research institutions. There may be a plurality of past patient DB 6s. Note that the past patient DB 6 may be installed in the server 7.

[0106] The server 7 stores the support information 4 transmitted from the medical information processing device 1. The server 7 has a storage area for storing the support information 4. Also, the server 7 transmits the support information 4 to the patient terminal 5 at an arbitrary timing. For example, the server 7 may transmit the support information 4 to the patient terminal 5 at the timing when it is accessed from the patient terminal 5.

[0107] According to the second embodiment described above, in the medical information processing system 100, the medical information processing device 1 specifically performs information processing on the target patient data 2 transmitted from the patient terminal 5 and the past patient data 3 obtained from the past patient DB 6. Also, in the medical information processing system 100, the server 7 stores the support information 4 that is the result of the information processing, and the server 7 transmits the support information 4 to the patient terminal 5. Thereby, the medical information processing system 100 can provide the support information 4 to the patient terminal 5 existing at an arbitrary location. Note that the medical information processing device 1 may store the support information 4 and transmit it to the patient terminal 5.

[0108] According to at least one of the embodiments described above, it is possible to support the selection of a patient's treatment method.

[0109] Although several embodiments have been described, these embodiments are presented 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 gist of the invention. These embodiments and their modifications are included in the scope and gist of the invention, as well as in the invention described in the claims and the equivalent scope thereof.

Explanation of Reference Numerals

[0110] 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 chart 230 Radar chart 310 Scatter diagram 311, 321 Label 320 Enlarged view 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

1. An extraction unit that extracts a second patient group consisting of one or more second patients similar to the first patient based on first data including a biological factor related to the first patient and at least one of a social factor and a psychological factor related to the first patient; An analysis unit that calculates a tendency in the selection of treatment methods for the second patient group by analyzing the treatment methods selected by each of the second patients belonging to the second patient group; A determination unit that determines an output mode of first information regarding the tendency based on at least one of a social characteristic of the first patient defined by the social factor of the first patient and a psychological characteristic of the first patient defined by the psychological factor of the first patient; An output unit that outputs the first information in the determined output mode; A medical information processing apparatus comprising:

2. An input unit that receives a designation from a user; A generation unit that generates second information for comparing third data including a biological factor related to one or more of the second patients designated by the user and at least one of a social factor and a psychological factor related to the designated second patient with the first data; and The output unit outputs the second information. The medical information processing apparatus according to claim 1.

3. Further comprising a calculation unit that calculates a similarity between the third data and the first data; The output unit outputs the similarity. The medical information processing apparatus according to claim 2.

4. The analysis unit calculates third information regarding a tendency in the prognosis after treatment for the second patient group by analyzing fourth data regarding the prognosis after treatment by the treatment methods selected by each of the second patients belonging to the second patient group; The output unit outputs the third information. The medical information processing apparatus according to any one of claims 1 to 3.

5. The determination unit determines by adopting at least one of an image, text, and voice as an output mode of the first information according to whether the cognitive characteristic of the first patient is a visual type, a language type, or an auditory type as the psychological characteristic of the first patient. The medical information processing apparatus according to claim 1.

6. The determination unit determines by adopting either an affirmative expression or a negative expression as an output mode of the first information according to whether the personality characteristic of the first patient is a pessimistic type or an optimistic type as the psychological characteristic of the first patient. The medical information processing apparatus according to any one of claims 1 to 5.

7. The determination unit determines by determining at least one of whether to graphically represent a numerical value and whether to attach reading kana to Chinese characters as an output mode of the first information according to whether the educational background of the first patient is equal to or higher than a threshold value as the social characteristic of the first patient. The medical information processing apparatus according to any one of claims 1 to 6.

8. An acquisition unit that acquires first patient data including biological factors, social factors, and psychological factors related to the first patient; And a specifying unit that specifies the 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 7.

9. Further comprising a display unit that displays the first information. The medical information processing apparatus according to any one of claims 1 to 8.

10. A medical information processing system including a terminal, a medical information processing apparatus, and a server, The terminal includes a first transmission unit that transmits first data including biological factors related to a first patient and at least one of social factors and psychological factors related to the first patient to the medical information processing apparatus. The medical information processing device 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 a tendency in the selection of treatment methods for the second patient group by analyzing the treatment methods selected by each of the second patients belonging to the second patient group, a determination unit that determines an output mode of first information regarding the tendency based on at least one of the social characteristics of the first patient defined by the social factors of the first patient and the psychological characteristics of the first patient defined by the psychological factors of the first patient, and an output unit that outputs the first information to the server in the determined output mode. The server includes a storage unit that stores the first information, and a second transmission unit that transmits the first information to the terminal. A medical information processing system.

11. A computer extracts a second patient group consisting of one or more second patients 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, calculates a tendency in the selection of treatment methods for the second patient group by analyzing the treatment methods selected by each of the second patients belonging to the second patient group, determines an output mode of first information regarding the tendency based on at least one of the social characteristics of the first patient defined by the social factors of the first patient and the psychological characteristics of the first patient defined by the psychological factors of the first patient, and outputs the first information in the determined output mode. A medical information processing method.

12. On a computer an extraction function that extracts a second patient group consisting of one or more second patients 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, By analyzing the treatment methods selected by each of the second patients belonging to the second patient group, an analysis function for calculating a tendency in the selection of treatment methods for the second patient group; Based on at least one of the social characteristics of the first patient defined by the social factors of the first patient and the psychological characteristics of the first patient defined by the psychological factors of the first patient, a determination function for determining an output mode of the first information regarding the tendency; An output function for outputting the first information in the determined output mode; A medical information processing program for realizing the above.

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