JP5280735B2 - Peg施行患者の予後の予測装置、及びpeg施行患者の予後の予測プログラム - Google Patents
Peg施行患者の予後の予測装置、及びpeg施行患者の予後の予測プログラム Download PDFInfo
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- JP5280735B2 JP5280735B2 JP2008121326A JP2008121326A JP5280735B2 JP 5280735 B2 JP5280735 B2 JP 5280735B2 JP 2008121326 A JP2008121326 A JP 2008121326A JP 2008121326 A JP2008121326 A JP 2008121326A JP 5280735 B2 JP5280735 B2 JP 5280735B2
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- 238000004393 prognosis Methods 0.000 title claims description 55
- 238000013528 artificial neural network Methods 0.000 claims description 23
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- 206010035664 Pneumonia Diseases 0.000 claims description 13
- 201000010099 disease Diseases 0.000 claims description 9
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 9
- 230000004083 survival effect Effects 0.000 claims description 8
- 206010012289 Dementia Diseases 0.000 claims description 7
- 102000007562 Serum Albumin Human genes 0.000 claims description 5
- 108010071390 Serum Albumin Proteins 0.000 claims description 5
- 208000026106 cerebrovascular disease Diseases 0.000 claims description 5
- 230000003211 malignant effect Effects 0.000 claims description 5
- 208000015122 neurodegenerative disease Diseases 0.000 claims description 5
- 102000001554 Hemoglobins Human genes 0.000 claims description 4
- 108010054147 Hemoglobins Proteins 0.000 claims description 4
- 210000002966 serum Anatomy 0.000 claims description 4
- 102000004169 proteins and genes Human genes 0.000 claims description 3
- 108090000623 proteins and genes Proteins 0.000 claims description 3
- 238000000034 method Methods 0.000 description 15
- 230000006870 function Effects 0.000 description 11
- 238000000059 patterning Methods 0.000 description 9
- 238000011156 evaluation Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
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- 230000000694 effects Effects 0.000 description 2
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- 239000011159 matrix material Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 210000002784 stomach Anatomy 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 102000009027 Albumins Human genes 0.000 description 1
- 108010088751 Albumins Proteins 0.000 description 1
- 102000004506 Blood Proteins Human genes 0.000 description 1
- 108010017384 Blood Proteins Proteins 0.000 description 1
- 210000003815 abdominal wall Anatomy 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000006735 deficit Effects 0.000 description 1
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- 239000004973 liquid crystal related substance Substances 0.000 description 1
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- 230000000750 progressive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000001356 surgical procedure Methods 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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Description
Friedenberg F, Jensen G, Gujral N, Braitman LE, Levine GM. Serum albumin is predictive of 30-day survival after percutaneous endoscopic gastrostomy. Jpen. 1997 Mar-Apr;21(2):72-4. Sanders DS, Carter MJ, D'Silva J, James G, Bolton RP, Bardhan KD. Survival analysis in percutaneous endoscopic gastrostomy feeding: a worse outcome in patients with dementia. The American journal of gastroenterology. 2000 Jun;95(6):1472-5. Rimon E , Kagansky N , Levy S .Percutaneous endoscopic gastrostomy; evidence of different prognosis in various patient subgroups. Age Ageing. 2005 Jul;34(4):353-7.
(1) 既にPEG(経皮的内視鏡下胃瘻造設術)を施行した第1の患者に関する予測入力因子および予測出力因子を記憶する予測因子データベースと、
前記第1の患者に関する前記予測入力因子および前記予測出力因子をANN(人工ニューラルネットワーク)に適用して予後予測式を算出するためのプログラムを記憶する解析プログラム記憶部と、
前記解析プログラム記憶部に記憶された前記プログラムを参照して、前記予測因子データベースに記憶された、前記第1の患者に関する前記予測入力因子および前記予測出力因子をANNに適用して前記予後予測式を算出する処理部と、
前記処理部により算出された前記予後予測式を記憶する予後予測式記憶部と、
を備え、
前記処理部は、前記予後予測式記憶部に記憶された前記予後予測式を参照して、第2の患者に対するPEGの施行の是非を判断するための、該第2の患者に関する診断入力因子を前記予後予測式に入力し、前記診断入力因子に応じた診断出力因子を算出して出力し、
前記予測出力因子および前記診断出力因子の少なくとも一つの項目は、PEGが施行された後の嚥下性肺炎の発症の有無であり、
前記予測入力因子および前記診断入力因子の少なくとも一つの項目は、胃瘻造設前の嚥下性肺炎の有無である、こと。
(2) 上記(1)の構成のPEG施行患者の予後の予測装置であって、
前記予測出力因子および前記診断出力因子は、PEGが施行された後の生存日数を項目に含む、こと。
(3) 上記(2)の構成のPEG施行患者の予後の予測装置であって、
前記予測入力因子および前記診断入力因子は、少なくとも、年齢、性別、脳血管障害の有無、悪性疾患の有無、認知症の有無、変性疾患の有無、血清総蛋白量、血清アルブミン量、ヘモグロビン量を項目に含む、こと。
また、前述した目的を達成するために、本発明に係るPEG施行患者の予後の予測プログラムは、下記(4)を特徴としている。
(4) コンピュータに、上記(1)から(3)のいずれかの構成のPEG施行患者の予後の予測装置の各機能を実現させるためのもの。
上記(3)の構成のPEG施行患者の予後の予測装置によれば、医師がPEGの施行の是非を判断する上で重要視する因子を精度良く算出することができる。
上記(4)の構成のPEG施行患者の予後の予測プログラムによれば、患者に対するPEGの施行の是非を判断するに足る充分な予測結果を医師に通知することができる。
例えば、表2に示す行列wi,jを用いて情報が伝播する。ただし、下に示したwi,jの行列は学習で得られた一例である。
12 予測因子データベース
13 解析プログラム記憶部
14 予後予測式記憶部
15 表示部
16 処理部
Claims (4)
- 既にPEG(経皮的内視鏡下胃瘻造設術)を施行した第1の患者に関する予測入力因子および予測出力因子を記憶する予測因子データベースと、
前記第1の患者に関する前記予測入力因子および前記予測出力因子をANN(人工ニューラルネットワーク)に適用して予後予測式を算出するためのプログラムを記憶する解析プログラム記憶部と、
前記解析プログラム記憶部に記憶された前記プログラムを参照して、前記予測因子データベースに記憶された、前記第1の患者に関する前記予測入力因子および前記予測出力因子をANNに適用して前記予後予測式を算出する処理部と、
前記処理部により算出された前記予後予測式を記憶する予後予測式記憶部と、
を備え、
前記処理部は、前記予後予測式記憶部に記憶された前記予後予測式を参照して、第2の患者に対するPEGの施行の是非を判断するための、該第2の患者に関する診断入力因子を前記予後予測式に入力し、前記診断入力因子に応じた診断出力因子を算出して出力し、
前記予測出力因子および前記診断出力因子の少なくとも一つの項目は、PEGが施行された後の嚥下性肺炎の発症の有無であり、
前記予測入力因子および前記診断入力因子の少なくとも一つの項目は、胃瘻造設前の嚥下性肺炎の有無である、
PEG施行患者の予後の予測装置。 - 請求項1記載のPEG施行患者の予後の予測装置であって、
前記予測出力因子および前記診断出力因子は、PEGが施行された後の生存日数を項目に含む、
PEG施行患者の予後の予測装置。 - 請求項2記載のPEG施行患者の予後の予測装置であって、
前記予測入力因子および前記診断入力因子は、少なくとも、年齢、性別、脳血管障害の有無、悪性疾患の有無、認知症の有無、変性疾患の有無、血清総蛋白量、血清アルブミン量、ヘモグロビン量を項目に含む、
PEG施行患者の予後の予測装置。 - コンピュータに、請求項1から3のいずれか1項に記載のPEG施行患者の予後の予測装置の各機能を実現させるためのPEG施行患者の予後の予測プログラム。
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JP2008121326A JP5280735B2 (ja) | 2008-05-07 | 2008-05-07 | Peg施行患者の予後の予測装置、及びpeg施行患者の予後の予測プログラム |
PCT/JP2009/055476 WO2009136520A1 (ja) | 2008-05-07 | 2009-03-19 | Peg施行患者の予後の予測装置、peg施行患者の予後の予測方法、及びコンピュータ記録媒体 |
CA2697888A CA2697888A1 (en) | 2008-05-07 | 2009-03-19 | Device for predicting prognosis of patients who undergo peg operation, method for predicting prognosis of patients who undergo peg operation and computer readable medium |
US12/677,932 US20100228099A1 (en) | 2008-05-07 | 2009-03-19 | Device for predicting prognosis of patients who undergo peg operation, method for predicting prognosis of patients who undergo peg operation and computer readable medium |
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JP2008121326A JP5280735B2 (ja) | 2008-05-07 | 2008-05-07 | Peg施行患者の予後の予測装置、及びpeg施行患者の予後の予測プログラム |
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JP (1) | JP5280735B2 (ja) |
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JP5484309B2 (ja) * | 2010-12-20 | 2014-05-07 | 紀文 日比 | Capを施行した潰瘍性大腸炎患者の予後予測方法 |
CN106202986A (zh) * | 2016-09-28 | 2016-12-07 | 湖南老码信息科技有限责任公司 | 一种基于增量式神经网络模型的扁桃体炎预测方法和预测系统 |
CN106339607A (zh) * | 2016-09-28 | 2017-01-18 | 湖南老码信息科技有限责任公司 | 一种基于增量式神经网络模型的风湿预测方法和预测系统 |
CN106339606A (zh) * | 2016-09-28 | 2017-01-18 | 湖南老码信息科技有限责任公司 | 一种基于增量式神经网络模型的酒精肝预测方法和预测系统 |
CN106339605A (zh) * | 2016-09-28 | 2017-01-18 | 湖南老码信息科技有限责任公司 | 一种基于增量式神经网络模型的结肠炎预测方法和预测系统 |
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ES2152548T3 (es) * | 1995-07-25 | 2001-02-01 | Horus Therapeutics Inc | Metodos asistidos por ordenador para diagnosticar enfermedades. |
US5860917A (en) * | 1997-01-15 | 1999-01-19 | Chiron Corporation | Method and apparatus for predicting therapeutic outcomes |
US6581038B1 (en) * | 1999-03-15 | 2003-06-17 | Nexcura, Inc. | Automated profiler system for providing medical information to patients |
US20030101076A1 (en) * | 2001-10-02 | 2003-05-29 | Zaleski John R. | System for supporting clinical decision making through the modeling of acquired patient medical information |
JP2003122845A (ja) * | 2001-10-09 | 2003-04-25 | Shinkichi Himeno | 医療情報の検索システム及びそのシステムを実行するためのプログラム |
US20050119534A1 (en) * | 2003-10-23 | 2005-06-02 | Pfizer, Inc. | Method for predicting the onset or change of a medical condition |
US10460080B2 (en) * | 2005-09-08 | 2019-10-29 | Gearbox, Llc | Accessing predictive data |
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- 2008-05-07 JP JP2008121326A patent/JP5280735B2/ja active Active
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2009
- 2009-03-19 US US12/677,932 patent/US20100228099A1/en not_active Abandoned
- 2009-03-19 WO PCT/JP2009/055476 patent/WO2009136520A1/ja active Application Filing
- 2009-03-19 CA CA2697888A patent/CA2697888A1/en not_active Abandoned
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CA2697888A1 (en) | 2009-11-12 |
WO2009136520A1 (ja) | 2009-11-12 |
JP2009268680A (ja) | 2009-11-19 |
US20100228099A1 (en) | 2010-09-09 |
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