JP2015141201A - Quantified output system of physiological parameter - Google Patents
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本発明は生理的パラメータの定量化出力システムに関し、特に尿液の試験片からパラメータを読み取り、非線形分析の演算法則と組合わせて腎糸球体濾過率を定量化する生理的パラメータの統合システムに関する。 The present invention relates to a physiological parameter quantification output system, and more particularly to a physiological parameter integration system that reads a parameter from a test piece of urine fluid and quantifies a renal glomerular filtration rate in combination with a calculation rule of nonlinear analysis.
慢性的な非感染性疾患（Chronic noncommuicable diseases、NCDs）は公共衛生及び予防医学における重要な課題であり、経済化と国際化の進展に伴って、NCDsは国の健康に対する負担になっている。また、NCDsの予防・治療に関しては、生活形態を積極的に管理することにより効果を収めることができる。国際連合の推計によれば、現在、全世界では八億六千万人が慢性疾患を罹患しており、７５〜８５％の医療費用は慢性疾患に関係しており、その中、４０％の高齢者は慢性疾患を罹患しており、１０年後には倍増し、２０３０年には、台湾における６５才以上の高齢者人口は人口総数の２１％を占めると推定されている。 Chronic noncommuicable diseases (NCDs) are an important issue in public health and preventive medicine, and with the progress of economic and internationalization, NCDs have become a burden on national health. In addition, the prevention and treatment of NCDs can be effective by actively managing the lifestyle. According to estimates by the United Nations, 860 million people worldwide currently have chronic illnesses, and 75-85% of medical costs are associated with chronic illnesses, of which 40% Elderly people suffer from chronic illnesses, doubling in 10 years, and by 2030, the elderly population over 65 years old in Taiwan is estimated to account for 21% of the total population.
台湾は腎臓透析が盛んな国であり、腎臓透析の費用は国民健康保険の支給のうちの３０８億台湾ドルに上っている。また、台湾行政院衛生署の統計によれば、２０１０年に４１０５人の国民が「腎炎、ネフローゼ症候群及びネフロパシー」によって死亡し、上位十位の国民死亡要因の第十位であった。また、台湾国民健康局が発行した「慢性腎臓病予防・治療手帳」によれば、台湾において２５０万近くの人（全人口の１１．９％を占める）が慢性腎臓病を罹患しており、そのなか、３．５％の人しか腎臓病を罹患していることを自覚していない。さらに、台湾において６．４％近くの人が第３〜５ステージの慢性腎臓病を罹患しているが、それを自覚している者はその中の１０％も満たない。腎臓病初期の症状は通常、軽微であり、例えば、血尿、尿に泡があること（タンパク尿）、排尿量が徐々に増加または減少していること、下半身が浮腫すること、血圧が高いことなどがある。そのため、患者が軽視して早期に診断せず、病状が酷くなってから（例えば、食欲の低下、倦怠感、呼吸困難、または気持ちが悪いと感じる）、治療を求めようとした時、すでに腎臓病の後期段階となっていることはよくある。その時、医療支出が大幅に増加し、さらに、患者の体が衰弱しているために働くことができず、場合によっては家族によるフルタイムの介護が必要となり、個人及び社会のコストにつながる。 Taiwan is a country where kidney dialysis is thriving, and the cost of kidney dialysis is NT $ 30.8 billion of the national health insurance. According to statistics from the Taiwan Bureau of Health, 4105 people died of “nephritis, nephrotic syndrome and nephropathy” in 2010, the 10th most common cause of national death. In addition, according to the Chronic Kidney Disease Prevention and Treatment Notebook published by the Taiwan National Health Service, nearly 2.5 million people (11.9% of the total population) have chronic kidney disease in Taiwan. Among them, only 3.5% are aware that they have kidney disease. Moreover, nearly 6.4% of people in Taiwan suffer from stage 3-5 chronic kidney disease, but less than 10% are aware of it. Symptoms of early kidney disease are usually minor, such as hematuria, foam in the urine (proteinuria), urine output gradually increasing or decreasing, lower body edema, high blood pressure and so on. Therefore, when the patient is neglected and cannot make an early diagnosis and the condition becomes severe (eg, loss of appetite, malaise, difficulty breathing, or feeling uncomfortable), the kidney is already It is often late in the disease. At that time, medical expenditures increase significantly, and the patient's body is debilitating and cannot work, and in some cases full-time care is required by the family, leading to personal and social costs.
慢性腎臓病（Chronic Kidney Disease、CKD）の病状の進行度は、軽度〜重度の順に第１ステージ〜第５ステージに分けられ、進行度の基準は、腎糸球体濾過率（eGFR）（単位：ml/min/１．７３平方メートル）とともに、タンパク尿、血尿または映像検査により異常の腎臓形態が発見されることによって、全体的に考慮する。これにより、臨床検査では、腎糸球体濾過率を慢性腎臓病の判断の主な基準としていることが知られている。 The degree of progression of chronic kidney disease (CKD) is divided into the first to fifth stages in the order of mild to severe, and the standard of progression is the glomerular filtration rate (eGFR) (unit: (ml / min / 1.73 square meters), together with proteinuria, hematuria, or imaging, the abnormal kidney morphology is found and considered overall. As a result, it is known in clinical examination that the glomerular filtration rate is the main criterion for the determination of chronic kidney disease.
正常な腎糸球体濾過率は１２０ml/min/１．７３m2であり、年齢の増加に伴って衰退していき、平均的に４０才以降は毎年１ml/min/１．７３m2で減少する。また、腎糸球体濾過率が小さいほど腎機能が悪い。さらに、一般的には、腎糸球体濾過率の数値を評価するとき、クレアチニンの数値を使用することが必要であり、それを所定の式に代入すれば腎糸球体濾過率が求められる。現在、下記式はよく使用されている。 The normal renal glomerular filtration rate is 120 ml / min / 1.73 m 2 , which declines with increasing age, and on average it decreases at 1 ml / min / 1.73 m 2 annually after age 40. In addition, the smaller the renal glomerular filtration rate, the worse the renal function. Furthermore, generally, when evaluating the numerical value of the glomerular filtration rate, it is necessary to use the numerical value of creatinine, and if it is substituted into a predetermined formula, the renal glomerular filtration rate is obtained. Currently, the following formula is often used.
男性＝（１４０−年齢）×体重（キログラム）／（７２×血液クレアチニン濃度）。 Male = (140-age) x body weight (kg) / (72 x blood creatinine concentration).
女性は男性の計算結果にさらに０．８５を掛ける必要がある。 Women need to multiply the male calculation by an additional 0.85.
以上から分かるように、腎糸球体濾過率を計算するために、先にクレアチニン数値を取得する必要があり、クレアチニン数値は血液検査でしか得られない。また、慢性腎臓病のスクリーニングは、血液検査の侵入性による反抗感、及び小型診療所には関連検査設備がないことにより、その実行と普及が難しく、多くの慢性腎臓病の早期発見のチャンスが見逃されている。仮に簡易かつ非侵入式のシステムで腎糸球体濾過率を評価することができれば、慢性腎臓病の公共衛生予防・治療及び予防医学の実現には、臨床検査応用における大きい商機がある。 As can be seen from the above, in order to calculate the glomerular filtration rate, it is necessary to first obtain a creatinine value, and the creatinine value can be obtained only by a blood test. In addition, screening for chronic kidney disease is difficult to implement and disseminate due to rebelliousness due to the invasion of blood tests and the lack of related testing facilities in small clinics, and there is an opportunity for early detection of many chronic kidney diseases. It is overlooked. If the glomerular filtration rate can be evaluated with a simple and non-invasive system, there is a great opportunity for clinical laboratory application in the realization of public health prevention / treatment and prevention medicine for chronic kidney disease.
したがって、簡易、便利且つ採血侵入方式を必要としない腎糸球体濾過率数値を得る方法を提案することは、当業者にとって重要な課題となる。 Therefore, it is an important issue for those skilled in the art to propose a method for obtaining a glomerular filtration rate value that is simple, convenient, and does not require a blood collection and entry method.
本発明は上記背景技術の問題を鑑み、試験片情報と分析演算法則を統合して生理的パラメータに応用する定量化出力システムを提供する。これにより、ユーザは従来のパラメータ取得方式に制限されることなく、普及且つ便利な生理的パラメータ取得方式をより広く利用して、分析演算法則を統合した後、取得しようとする生理的パラメータを定量化し得る。 The present invention provides a quantification output system that integrates test piece information and analytical calculation rules and applies them to physiological parameters in view of the problems of the background art. As a result, the user is not limited to the conventional parameter acquisition method, but more widely uses the popular and convenient physiological parameter acquisition method, integrates the analytical operation law, and then quantifies the physiological parameter to be acquired. Can be
本発明の主な発明の目的は、被験者の標本が含まれる試験片を計測し、該被験者の生理的情報を取得する計測装置と、補助演算情報を受信する入力装置と、分析演算法則を利用して該生理的情報と該補助演算情報を処理し、定量化された生理的パラメータを生成する演算装置と、該定量化された生理的パラメータを出力する出力装置と、を有する生理的パラメータの定量化出力システムを構築することである。 The main object of the present invention is to use a measuring device that measures a test piece including a specimen of a subject and obtains physiological information of the subject, an input device that receives auxiliary computation information, and an analytical computation law An arithmetic device that processes the physiological information and the auxiliary calculation information and generates a quantified physiological parameter; and an output device that outputs the quantified physiological parameter. It is to build a quantification output system.
本発明に使用される試験片は非侵入方式で被験者の標本を取得することが好適である。 It is preferable that the test piece used in the present invention obtains a subject's specimen in a non-intrusive manner.
計測装置が取得する生理的情報は、ブドウ糖、プロテイン、ビリルビン、ウロビリノーゲン、クレアチニン、血液、ケトン、比重、pH、亜硝酸塩、白血球などを含むことが好適である。 The physiological information acquired by the measuring device preferably includes glucose, protein, bilirubin, urobilinogen, creatinine, blood, ketone, specific gravity, pH, nitrite, leukocytes, and the like.
補助演算情報は、被験者の年齢、性別、身長、体重および生活習慣などを含むことが好適である。 The auxiliary calculation information preferably includes the subject's age, sex, height, weight, lifestyle, and the like.
演算装置は、非線形分析方式で生理的情報と補助演算情報との間の関連性を演算することが好適である。 It is preferable that the calculation device calculates the relationship between physiological information and auxiliary calculation information by a non-linear analysis method.
計測装置と、入力装置と、演算装置と、出力装置とが一つの演算設備に統合可能であることが好適である。 It is preferable that the measurement device, the input device, the calculation device, and the output device can be integrated into one calculation facility.
上記のように、本発明が提供する試験片情報と分析演算法則を統合して生理的パラメータに応用する定量化出力システムは、以下のメリットを有する。 As described above, the quantification output system that integrates the test piece information and the analytical operation law provided by the present invention and applies them to physiological parameters has the following merits.
従来の検査方式に制限されることなく、ユーザはより便利かつ簡単な検査方式を利用し、分析演算法則と組合わせて、本来の生理的パラメータを得ることができる。 Without being limited to the conventional inspection method, the user can use the more convenient and simple inspection method and obtain the original physiological parameters in combination with the analytical calculation rule.
今までよく利用されていた線形分析方式の代わりに、非線形分析方式を利用することにより、各入力パラメータ間の関連性をより高精度に表すことができると共に、異なるグループの特徴に適応的に合わせ、実際の生理的特性と評価結果にさらに近づかせる。 By using a nonlinear analysis method instead of the linear analysis method that has been used so far, the relationship between each input parameter can be expressed with higher accuracy and adaptively adapted to the characteristics of different groups. Get closer to the actual physiological characteristics and evaluation results.
以下は特定の具体的な実施例で本発明の実施方式を説明するが、当技術分野を熟知した者は本明細書の記載内容により本発明のメリットと効果を容易に理解することができる。本発明は他の方式により実施されることもでき、すなわち、本発明に開示される範囲内から逸脱していなければ、異なる変更は可能である。 In the following, the implementation method of the present invention will be described with specific specific examples. Those skilled in the art can easily understand the merits and effects of the present invention according to the description of the present specification. The present invention may be implemented in other ways, i.e., different modifications are possible without departing from the scope disclosed in the present invention.
図１のように、本発明にかかる生理的パラメータに応用する定量化出力システムは、計測装置２と、入力装置３と、演算装置４と、出力装置５とを含む。計測装置２は、被験者の標本が含まれる試験片１を計測し、該被験者の生理的情報を取得する。入力装置３は、補助演算情報を受信する。演算装置４は、分析演算方式を利用して該生理的情報と該補助演算情報を処理し、定量化された生理的パラメータを生成する。出力装置５は、該定量化された生理的パラメータを出力する。 As shown in FIG. 1, the quantification output system applied to physiological parameters according to the present invention includes a measurement device 2, an input device 3, a calculation device 4, and an output device 5. The measuring device 2 measures the test piece 1 including the subject's specimen and acquires physiological information of the subject. The input device 3 receives auxiliary calculation information. The calculation device 4 processes the physiological information and the auxiliary calculation information using an analytical calculation method, and generates a quantified physiological parameter. The output device 5 outputs the quantified physiological parameter.
慢性腎臓病の応用を例として、従来では採血でクレアチニンを取得して、所定の式を使用して腎糸球体濾過率を得る。一方、図１のように、本発明は、よく使用される尿液試験片を検査ツールとし、被験者から採取した尿液サンプル標本を利用して、針を挿入して採血するという他の過程を必要とせずに、尿液試験片を利用して計測装置２と組合わせて、尿液中のブドウ糖、プロテイン、ビリルビン、ウロビリノーゲン、クレアチニン、血液、ケトン、比重、pH値、亜硝酸塩、白血球などの生理的情報を読み取り、演算装置４の入力パラメータとする。 Taking an application of chronic kidney disease as an example, conventionally, creatinine is obtained by blood collection, and a renal glomerular filtration rate is obtained using a predetermined formula. On the other hand, as shown in FIG. 1, the present invention uses another commonly used urine fluid test piece as an inspection tool, and uses the urine fluid sample collected from the subject to insert a needle and collect blood. Without using it, in combination with the measuring device 2 using a urine specimen, glucose, protein, bilirubin, urobilinogen, creatinine, blood, ketone, specific gravity, pH value, nitrite, leukocytes, etc. Physiological information is read and used as an input parameter of the arithmetic device 4.
補助演算情報は、例えば、被験者の年齢、性別、身長、体重、および生活習慣などの情報である。 The auxiliary calculation information is information such as the age, sex, height, weight, and lifestyle of the subject, for example.
演算装置４は、非線形分析方式を採用しており、図３に示すニューラルネットワーク分析方式により、計測装置２に読み出された生理的情報及び補助演算情報を利用して入力パラメータ（I1〜Im）とし、まず被験標的の腎糸球体濾過数値は既知である条件において、ニューラルネットワークの反復学習方式により、ニューラルネットワーク隠蔽層の被験標的に対する各重み（W1〜Wn）数値を算出した後、盲サンプルの関連パラメータを入力して、下記計算式により定量化された腎糸球体濾過率数値（O1）を得る。 Arithmetic unit 4 employs a non-linear analysis method, the neural network analysis method shown in FIG. 3, by using the physiological information and the auxiliary arithmetic information read in the measurement device 2 input parameters (I 1 ~I m ), and first calculate the weight (W 1 -W n ) values for the test target of the neural network concealment layer by the iterative learning method of the neural network under the condition that the glomerular filtration rate of the test target is known The relevant parameters of the blind sample are input, and the glomerular filtration rate value (O 1 ) quantified by the following formula is obtained.
数１中、ｍは選択された入力パラメータの数であり、ｎは隠蔽層の数である。 In Equation 1, m is the number of selected input parameters, and n is the number of concealment layers.
なお、図２は本発明の他の実施例のブロック図であり、計測装置２と、入力装置３と、演算装置４と、出力装置５とが一つの演算設備Ａに統合されている。 FIG. 2 is a block diagram of another embodiment of the present invention, in which the measuring device 2, the input device 3, the computing device 4, and the output device 5 are integrated into one computing facility A.
本発明のパフォーマンスを検証するために、台湾国立台湾大学医学院附設医院にて人体試験を申請して２９９件のデータを収集し、そのうちの２３３件のデータをニューラルネットワークの訓練学習に使用し、これにより各入力パラメータ間の重み値を算出して、さらに６７件のデータを盲サンプルとし、本方式で計算して得られる腎糸球体濾過率の正確率及びパフォーマンスを調べた。ニューラルネットワークパラメータの設定では、隠蔽層の層数は１層であり、全部で３０個の重み値が使用され、学習率（Learning Rate）は０．１であり、反復学習の際に毎回２３２件本のデータからランダムで１００件のデータを選択し、停止条件は二乗平均平方根誤差（root-mean-square error）の値が０．５未満の場合または学習回数が１０００万回を越えても収束しない場合である。 In order to verify the performance of the present invention, a human body examination was applied for at the National Taiwan University Medical School attached to Taiwan, and 299 data were collected, of which 233 data were used for neural network training. The weight value between each input parameter was calculated by this, and 67 data were made into a blind sample, and the accuracy rate and performance of the glomerular filtration rate obtained by this method were investigated. In the setting of the neural network parameters, the number of concealment layers is one, a total of 30 weight values are used, the learning rate is 0.1, and 232 cases each time during iterative learning. 100 data are randomly selected from the book data, and the stop condition converges even when the root-mean-square error value is less than 0.5 or the number of learning exceeds 10 million. This is the case.
下表は、連続１０回実行し計算して得られた正確率及び平均値と標準偏差を示しており、本方式を利用し定量化して得た腎糸球体濾過率の数値の正確率は平均で８３．５％に達し、かつ再現性を有していることが分かる。 The table below shows the accuracy rate, average value, and standard deviation obtained by performing 10 consecutive runs, and the accuracy rate of the numerical value of renal glomerular filtration rate obtained by quantification using this method is the average. It can be seen that it reaches 83.5% and has reproducibility.
図４はランダムで５回実行した結果から得られたROC（Receiver Operator Characteristic）グラフであり、本発明における特異度が８８％である時に（円形破線に囲まれた箇所）、対応する感度が８４％に達している。 FIG. 4 is a ROC (Receiver Operator Characteristic) graph obtained from the result of five executions at random. When the specificity in the present invention is 88% (a portion surrounded by a circular broken line), the corresponding sensitivity is 84. % Has been reached.
上記実施例は本発明の原理及びその効果を例示的に説明しているのに過ぎず、本発明を限定するものではない。当技術分野を熟知した者であれば、本発明の精神及び範囲から逸脱していない限り、上記実施例を変更することができる。したがって、本発明の保護を請求する範囲は、特許請求の範囲のとおりである。 The above embodiments are merely illustrative of the principle of the present invention and the effects thereof, and are not intended to limit the present invention. Those skilled in the art can modify the above embodiments without departing from the spirit and scope of the present invention. Therefore, the scope of claims for protection of the present invention is as set forth in the claims.
１ 試験片
２ 計測装置
３ 入力装置
４ 演算装置
５ 出力装置
６ 演算設備
DESCRIPTION OF SYMBOLS 1 Test piece 2 Measuring device 3 Input device 4 Arithmetic device 5 Output device 6 Arithmetic equipment
Claims (7)
補助演算情報を受信する入力装置と、
分析演算法則を利用して前記生理的情報と前記補助演算情報を処理し、定量化された生理的パラメータを生成する演算装置と、
前記定量化された生理的パラメータを出力する出力装置と、を備える生理的パラメータの定量化出力システム。 A measuring device for measuring a test piece including a specimen of a subject and acquiring physiological information of the subject;
An input device for receiving auxiliary calculation information;
An arithmetic device that processes the physiological information and the auxiliary calculation information using an analytical calculation rule to generate a quantified physiological parameter;
An output device for outputting the quantified physiological parameter; and a physiological parameter quantification output system.
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| TW (1) | TW201530139A (en) |
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|---|---|---|---|---|
| JPH01169356A (en) * | 1987-12-25 | 1989-07-04 | Hitachi Ltd | Method and apparatus for inspecting kidney function |
| JPH05176932A (en) * | 1991-12-27 | 1993-07-20 | Toa Medical Electronics Co Ltd | Diagnostic method for cerebral infarction |
| JPH09257790A (en) * | 1996-01-19 | 1997-10-03 | Nakano Vinegar Co Ltd | Diagnosis supporting method |
| JP2001272392A (en) * | 2000-03-23 | 2001-10-05 | Japan Science & Technology Corp | Method for measuring physiological function and testing food |
| JP2002318855A (en) * | 2000-04-17 | 2002-10-31 | Nec Corp | Method and system for providing health management service for person at home |
| JP2007292718A (en) * | 2006-03-28 | 2007-11-08 | Eiken Chem Co Ltd | Multi-item test paper and method for detecting test material in specimen |
-
2014
- 2014-01-29 TW TW103103316A patent/TW201530139A/en unknown
-
2015
- 2015-01-28 JP JP2015014427A patent/JP6012781B2/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH01169356A (en) * | 1987-12-25 | 1989-07-04 | Hitachi Ltd | Method and apparatus for inspecting kidney function |
| JPH05176932A (en) * | 1991-12-27 | 1993-07-20 | Toa Medical Electronics Co Ltd | Diagnostic method for cerebral infarction |
| JPH09257790A (en) * | 1996-01-19 | 1997-10-03 | Nakano Vinegar Co Ltd | Diagnosis supporting method |
| JP2001272392A (en) * | 2000-03-23 | 2001-10-05 | Japan Science & Technology Corp | Method for measuring physiological function and testing food |
| JP2002318855A (en) * | 2000-04-17 | 2002-10-31 | Nec Corp | Method and system for providing health management service for person at home |
| JP2007292718A (en) * | 2006-03-28 | 2007-11-08 | Eiken Chem Co Ltd | Multi-item test paper and method for detecting test material in specimen |
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| Title |
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| JPN6015049817; '日本腎臓学会腎機能（ＧＦＲ）・尿蛋白測定委員会報告書' 日本腎臓学会誌 Ｖｏｌ．４３ Ｎｏ．１, 20010704, ｐ．１-１９ * |
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| TW201530139A (en) | 2015-08-01 |
| JP6012781B2 (en) | 2016-10-25 |
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