SG10201902151UA - Bayesian causal relationship network models for healthcare diagnosis and treatment based on patient data - Google Patents

Bayesian causal relationship network models for healthcare diagnosis and treatment based on patient data

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Publication number
SG10201902151UA
SG10201902151UA SG10201902151UA SG10201902151UA SG10201902151UA SG 10201902151U A SG10201902151U A SG 10201902151UA SG 10201902151U A SG10201902151U A SG 10201902151UA SG 10201902151U A SG10201902151U A SG 10201902151UA SG 10201902151U A SG10201902151U A SG 10201902151UA
Authority
SG
Singapore
Prior art keywords
causal relationship
relationship network
network
variables
data
Prior art date
Application number
SG10201902151UA
Inventor
Niven Narain
Viatcheslav Akmaev
Vijetha Vemulapalli
Original Assignee
Berg Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority to US201462049148P priority Critical
Application filed by Berg Llc filed Critical Berg Llc
Publication of SG10201902151UA publication Critical patent/SG10201902151UA/en

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Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50ICT 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
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

BAYESIAN CAUSAL RELATIONSHIP NETWORK MODELS FOR HEALTHCARE DIAGNOSIS AND TREATMENT BASED ON PATIENT DATA Systems, methods, and computer-readable medium are provided for healthcare analysis. Data corresponding to a plurality of patients is received. The data is parsed to generate normalized data for a plurality of variables, with normalized data generated for more than one variable for each patient. A causal relationship network model is generated relating the plurality of variables based on the generated normalized data using a Bayesian network algorithm. The causal relationship network model includes variables related to a plurality of medical conditions or medical drugs. In another aspect, a selection of a medical condition or drug is received. A sub- network is determined from a causal relationship network model. The sub-network includes one or more variables associated with the selected medical condition or drug. One or more predictors for the selected medical condition or drug are identified. [Figure 2]
SG10201902151UA 2014-09-11 2015-09-11 Bayesian causal relationship network models for healthcare diagnosis and treatment based on patient data SG10201902151UA (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US201462049148P true 2014-09-11 2014-09-11

Publications (1)

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SG10201902151UA true SG10201902151UA (en) 2019-04-29

Family

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Family Applications (2)

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SG10201902151UA SG10201902151UA (en) 2014-09-11 2015-09-11 Bayesian causal relationship network models for healthcare diagnosis and treatment based on patient data
SG11201701963UA SG11201701963UA (en) 2014-09-11 2015-09-11 Bayesian causal relationship network models for healthcare diagnosis and treatment based on patient data

Family Applications After (1)

Application Number Title Priority Date Filing Date
SG11201701963UA SG11201701963UA (en) 2014-09-11 2015-09-11 Bayesian causal relationship network models for healthcare diagnosis and treatment based on patient data

Country Status (12)

Country Link
US (2) US10482385B2 (en)
EP (1) EP3191975A4 (en)
JP (1) JP2017537365A (en)
KR (1) KR20170058391A (en)
CN (1) CN107111603A (en)
AU (2) AU2015314956A1 (en)
CA (1) CA2960837A1 (en)
HK (1) HK1243509A1 (en)
IL (1) IL250966D0 (en)
SG (2) SG10201902151UA (en)
WO (1) WO2016040725A1 (en)
ZA (1) ZA201701611B (en)

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Also Published As

Publication number Publication date
EP3191975A1 (en) 2017-07-19
US10482385B2 (en) 2019-11-19
HK1243509A1 (en) 2018-07-13
CN107111603A (en) 2017-08-29
IL250966D0 (en) 2017-04-30
AU2015314956A1 (en) 2017-04-06
EP3191975A4 (en) 2018-04-18
WO2016040725A1 (en) 2016-03-17
KR20170058391A (en) 2017-05-26
AU2020244596A1 (en) 2020-11-05
SG11201701963UA (en) 2017-04-27
US20160171383A1 (en) 2016-06-16
US20200143278A1 (en) 2020-05-07
ZA201701611B (en) 2019-09-25
JP2017537365A (en) 2017-12-14
CA2960837A1 (en) 2016-03-17

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