JP2020017094A5 - - Google Patents
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- JP2020017094A5 JP2020017094A5 JP2018139951A JP2018139951A JP2020017094A5 JP 2020017094 A5 JP2020017094 A5 JP 2020017094A5 JP 2018139951 A JP2018139951 A JP 2018139951A JP 2018139951 A JP2018139951 A JP 2018139951A JP 2020017094 A5 JP2020017094 A5 JP 2020017094A5
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- 238000004458 analytical method Methods 0.000 claims 34
- 230000000875 corresponding Effects 0.000 claims 12
- 206010022114 Injury Diseases 0.000 claims 2
- 201000010099 disease Diseases 0.000 claims 2
- 239000003814 drug Substances 0.000 claims 2
- 229940079593 drugs Drugs 0.000 claims 2
- 238000007493 shaping process Methods 0.000 claims 1
Claims (9)
前記計算機が、１つのデータに複数の項目と、当該項目の要素を含む分析対象のデータ群を受け付ける第１のステップと、
前記計算機が、分析条件の前記項目と、前記要素を受け付ける第２のステップと、
前記計算機が、前記データの各項目について特徴量を算出し、当該特徴量からベクトルを算出する第３のステップと、
前記計算機が、前記データの各項目について特徴量を算出し、当該特徴量から各データのベクトルを算出する第４のステップと、
前記計算機が、前記ベクトルに対して重み付けを行う第５のステップと、
前記計算機が、前記重みを付与したベクトルについてクラスタリングを行う第６のステップと、を含み、
前記第５のステップは、
前記分析条件に該当するデータに共通して出現する項目の要素に対して重み付けを実施し、
前記分析条件に該当するデータにのみ出現する項目の要素に対して重み付けを実施し、
前記分析条件に該当するデータに出現しない項目の要素に対して重み付けを実施することを特徴とする分析方法。 A computer with a processor and memory is an analysis method that analyzes data.
A first step in which the computer accepts a plurality of items in one data and a data group to be analyzed including elements of the items.
A second step in which the computer accepts the item of analysis conditions and the element.
A third step in which the computer calculates a feature amount for each item of the data and calculates a vector from the feature amount.
A fourth step in which the computer calculates a feature amount for each item of the data and calculates a vector of each data from the feature amount.
A fifth step in which the computer weights the vector,
The computer includes a sixth step of clustering the weighted vector.
The fifth step is
Weighting is performed on the elements of items that appear in common in the data corresponding to the above analysis conditions.
Weighting is performed on the elements of items that appear only in the data that meet the above analysis conditions.
An analysis method characterized in that weighting is performed on elements of items that do not appear in the data corresponding to the analysis conditions.
前記重み付けを実施するステップは、
分析対象のデータの数と、前記分析条件に該当するデータの数の比を重みとし、または、前記分析条件に該当しないデータの数と、前記分析条件に該当するデータの数の比を重みとすることを特徴とする分析方法。 The analysis method according to claim 1.
The step of performing the weighting is
The ratio of the number of data to be analyzed to the number of data corresponding to the analysis condition is weighted, or the ratio of the number of data not corresponding to the analysis condition to the number of data corresponding to the analysis condition is weighted. An analysis method characterized by doing.
前記データは、
患者の傷病名情報と、実施された医療行為情報と、使用された医薬品情報と、使用された特定器材情報のいずれか一つ以上を含むレセプトの情報であり、
前記分析条件は、
前記レセプトの項目に審査結果を含むことを特徴とする分析方法。 The analysis method according to claim 1 or 2.
The data is
Information on the receipt including any one or more of the patient's injury / illness name information, the medical practice information performed, the drug information used, and the specific equipment information used.
The analysis conditions are
An analysis method characterized in that the item of the receipt includes the examination result.
前記分析条件は、
前記レセプトの項目に審査結果と事由を含み、当該事由の要素は、１以上の要素を含むことを特徴とする分析方法。 The analysis method according to claim 3.
The analysis conditions are
An analysis method characterized in that the items of the receipt include the examination result and the reason, and the elements of the reason include one or more elements.
前記計算機が、前記クラスタリングの結果から、第１の軸に前記データの識別子を配置し、第２の軸に各項目を配置した２次元グラフと、前記データとクラスの関係を示す樹状図を生成する第６のステップを、さらに含むことを特徴とする分析方法。 The analysis method according to claim 2.
From the result of the clustering, the computer creates a two-dimensional graph in which the identifier of the data is arranged on the first axis and each item is arranged on the second axis, and a dendrogram showing the relationship between the data and the class. An analysis method further comprising a sixth step of generation.
前記第６のステップは、
前記分析条件に該当する前記識別子について予め設定した強調表示の書式を設定することを特徴とする分析方法。 The analysis method according to claim 5.
The sixth step is
An analysis method characterized by setting a preset highlighting format for the identifier corresponding to the analysis condition.
前記データは、
患者の傷病名情報と、実施された医療行為情報と、使用された医薬品情報と、使用された特定器材情報のいずれか一つ以上を含むレセプトの情報であり、
前記分析条件は、
前記レセプトの項目に審査結果を含むことを特徴とする分析方法。 The analysis method according to claim 5 or 6.
The data is
Information on the receipt including any one or more of the patient's injury / illness name information, the medical practice information performed, the drug information used, and the specific equipment information used.
The analysis conditions are
An analysis method characterized in that the item of the receipt includes the examination result.
１つのデータに複数の項目と、当該項目の要素を含む分析対象のデータ群を受け付けて、分析条件の前記項目と、前記要素を受け付けるデータ整形部と、
前記データの各項目について特徴量を算出し、当該特徴量からベクトルを算出して、前記ベクトルに対して重み付けを行う特徴量計算部と、
前記重みを付与したベクトルについてクラスタリングを行うクラスタリング部と、を有し、
前記特徴量計算部は、
前記分析条件に該当するデータに共通して出現する項目の要素に対して重み付けを実施し、前記分析条件に該当するデータにのみ出現する項目の要素に対して重み付けを実施し、 前記分析条件に該当するデータに出現しない項目の要素に対して重み付けを実施することを特徴とする分析装置。 An analyzer that has a processor and a memory and analyzes data.
A data shaping unit that accepts a plurality of items and an analysis target data group including an element of the item in one data, and accepts the item of the analysis condition and the element.
A feature amount calculation unit that calculates a feature amount for each item of the data, calculates a vector from the feature amount, and weights the vector.
It has a clustering unit for clustering the weighted vector, and has a clustering unit.
The feature amount calculation unit
Weighting is performed on the elements of items that appear in common to the data corresponding to the analysis conditions, weighting is performed on the elements of items that appear only in the data corresponding to the analysis conditions, and the analysis conditions are met. An analyzer characterized in that weighting is performed on elements of items that do not appear in the corresponding data.
１つのデータに複数の項目と、当該項目の要素を含む分析対象のデータ群を受け付ける第１のステップと、
分析条件の前記項目と、前記要素を受け付ける第２のステップと、
前記データの各項目について特徴量を算出し、当該特徴量からベクトルを算出する第３のステップと、
前記データの各項目について特徴量を算出し、当該特徴量から各データのベクトルを算出する第４のステップと、
前記ベクトルに対して重み付けを行う第５のステップと、
前記重みを付与したベクトルについてクラスタリングを行う第６のステップと、を含み、
前記第５のステップは、
前記分析条件に該当するデータに共通して出現する項目の要素に対して重み付けを実施し、前記分析条件に該当するデータにのみ出現する項目の要素に対して重み付けを実施し、前記分析条件に該当するデータに出現しない項目の要素に対して重み付けを実施することを前記計算機に実行させるためのプログラム。 A program for analyzing data on a computer that has a processor and memory.
The first step of accepting a plurality of items in one data and a data group to be analyzed including the elements of the items, and
The item of the analysis condition, the second step of accepting the element, and
The third step of calculating the feature amount for each item of the data and calculating the vector from the feature amount, and
The fourth step of calculating the feature amount for each item of the data and calculating the vector of each data from the feature amount, and
The fifth step of weighting the vector and
Including a sixth step of clustering the weighted vector.
The fifth step is
Weighting is performed on the elements of items that appear in common to the data corresponding to the analysis conditions, weighting is performed on the elements of items that appear only in the data corresponding to the analysis conditions, and the analysis conditions are met. A program for causing the computer to perform weighting on elements of items that do not appear in the corresponding data.
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| Application Number | Priority Date | Filing Date | Title |
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| JP2018139951A JP6979392B2 (en) | 2018-07-26 | 2018-07-26 | Analytical methods, analyzers and programs |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2018139951A JP6979392B2 (en) | 2018-07-26 | 2018-07-26 | Analytical methods, analyzers and programs |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| JP2020017094A JP2020017094A (en) | 2020-01-30 |
| JP2020017094A5 true JP2020017094A5 (en) | 2020-12-24 |
| JP6979392B2 JP6979392B2 (en) | 2021-12-15 |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113724883B (en) * | 2021-08-31 | 2023-09-15 | 平安科技（深圳）有限公司 | Medical expense prediction method, medical expense prediction device, storage medium and computer equipment |
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| JP5450556B2 (en) * | 2011-10-14 | 2014-03-26 | 富士フイルム株式会社 | Medical information processing apparatus and method, and program |
| JP5973821B2 (en) * | 2012-07-17 | 2016-08-23 | リーズンホワイ株式会社 | Receipt analysis apparatus and receipt analysis program |
| JP6066825B2 (en) * | 2013-05-17 | 2017-01-25 | 株式会社日立製作所 | Data analysis apparatus and health business support method |
| JP6163935B2 (en) * | 2013-07-19 | 2017-07-19 | 富士通株式会社 | Support program, support method, and support device |
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