WO2024073826A1 - Procédé d'entrainement d'un modèle basé sur l'intelligence artificielle pour la prédiction de résultat clinique d'un individu et procédé de prédiction de résultat clinique d'un individu utilisant un tel modèle basé sur l'intelligence artificielle - Google Patents

Procédé d'entrainement d'un modèle basé sur l'intelligence artificielle pour la prédiction de résultat clinique d'un individu et procédé de prédiction de résultat clinique d'un individu utilisant un tel modèle basé sur l'intelligence artificielle Download PDF

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Publication number
WO2024073826A1
WO2024073826A1 PCT/BR2023/050263 BR2023050263W WO2024073826A1 WO 2024073826 A1 WO2024073826 A1 WO 2024073826A1 BR 2023050263 W BR2023050263 W BR 2023050263W WO 2024073826 A1 WO2024073826 A1 WO 2024073826A1
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WIPO (PCT)
Prior art keywords
time period
time
vital sign
individual
individuals
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PCT/BR2023/050263
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English (en)
Portuguese (pt)
Inventor
Adriano JOSÉ PEREIRA
Marcelo FRANKEN
Daniel SCALDAFERRI LAGES
Wellington APARECIDO DE OLIVEIRA
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Sociedade Beneficente Israelita Brasileira Hospital Albert Einstein
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Priority claimed from BR102022020266-4A external-priority patent/BR102022020266A2/pt
Application filed by Sociedade Beneficente Israelita Brasileira Hospital Albert Einstein filed Critical Sociedade Beneficente Israelita Brasileira Hospital Albert Einstein
Publication of WO2024073826A1 publication Critical patent/WO2024073826A1/fr

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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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • 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
    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons

Definitions

  • the present invention refers to a method of training a model based on artificial intelligence and, more specifically, to a training method capable of training a model based on artificial intelligence to predict the outcome of an individual's clinical evolution. admitted to hospital or at risk of hospitalization/readmission.
  • patient data (vital signs, mainly) are integrated from electronic medical records, adding that there may also be alert systems and automatic activation of rapid response teams based on pre-determined triggers (i.e. That is, when this patient's score reaches the trigger, the emergency team is called).
  • the clinical evolution can be, for example, clinical deterioration in patients admitted to a semi-intensive care unit, clinical deterioration in patients admitted to wards/wards or clinical deterioration at home in patients at high risk of hospitalization/reaction. ntern action.
  • the present invention achieves the above objectives through a method of training a model based on artificial intelligence for predicting an individual's clinical outcome, which comprises: - for each individual from a first set of individuals: identify an event indicative of the clinical outcome for the individual as a favorable outcome event or as an unfavorable outcome event; establishing a first predetermined time period, wherein the first predetermined time period is a time interval preceding the outcome event; establishing a second predetermined time period, wherein the second predetermined time period is a time interval preceding the time interval of the first predetermined time period and wherein the second time period comprises multiple equal sub-time periods; obtain, for each sub-period of time, multiple measurements of each vital sign from a plurality of the individual's vital signs; obtain a representative value for each vital sign within each sub-time period based on multiple measurements performed for each vital sign in that sub-time period; establishing a temporal sequence of aggregated vital sign values for each of the plurality of vital signs within the second time period, the temporal sequence comprising a
  • the representative value for Each vital sign within each sub-time period is obtained by calculating an arithmetic average of the multiple measurements taken for each vital sign in that time sub-period.
  • the method further comprises: creating a three-dimensional training matrix in which, for each individual of the first set of individuals, the temporal sequence of aggregated vital sign values for each of the plurality of vital signs within the second time period is arranged in a plane such that an x axis of the plane comprises the vital sign values for each of the plurality of vital signs, and in a y axis of the plan, the values of the subtime periods of the second time period, and, in which the plans created for each individual of the first set of individuals are stacked on a geometric axis z to create the matrix; and the use of the matrix created with training input data from the recurrent neural network.
  • the method further comprises, for each temporal sequence of values aggregates of vital signs for each of the plurality of vital signs within the second time period: an average value of the vital signs of the time sequence; a minimum value of the temporal sequence vital signs; a maximum value of the vital signs of the time sequence; a difference value between the calculated maximum and minimum values; a difference value between each of the values of the time sequence that exceeded a pre-established threshold value and that pre-established threshold value; a difference value between each of the values in the time sequence that are below a pre-established threshold value and that pre-established threshold value; use the calculated values as training input data for the classical machine learning model neural network.
  • the present invention also contemplates a method for predicting the clinical outcome of an individual that uses the artificial intelligence-based clinical deterioration predictive model trained in accordance with the model training method of the present invention, and a system for predicting clinical outcome of an individual comprising at least one vital signs monitoring device configured to perform multiple measurements of at least one vital sign of a plurality of vital signs of the individual; and at least one processing means for carrying out such prediction method.
  • the system comprises a plurality of vital sign monitoring devices, with one of the devices being configured to perform multiple measurements of a different vital sign among a plurality of the individual's vital signs.
  • Such monitoring devices may be monitoring devices installed in the place where the individual is hospitalized or located - such as a hospital unit or home care facility - or wearable monitoring devices connected to the internet (IOT / wearable devices). Internet of Things).
  • IOT internet of Things
  • Figure 1 - illustrates the training parameters of the training method of an artificial neural network according to an embodiment of the present invention
  • Figure 2 illustrates a simplified flowchart of the artificial neural network training method according to an embodiment of the present invention
  • Figure 3 illustrates a simplified flowchart of the method for predicting the outcome of an individual's clinical evolution according to an embodiment of the present invention.
  • the present invention comprises a method of training a model based on artificial intelligence to predict an individual's clinical outcome.
  • the artificial intelligence-based model is an artificial neural network.
  • clinical outcome data from a first set of individuals are used.
  • this first set of individuals comprises patients admitted to semi-intensive units, in which the clinical outcome can be classified as being a favorable outcome or an unfavorable outcome according to a defined event as the trajectory of the patient's passage through the semi-ICU room, as follows:
  • Unfavorable outcome (outcome 0): the patient was transferred from the semi-ICU room to an intensive care unit (ICU) room or died. [0027] It is important to highlight that the same patient may have multiple visits to semi-ICU rooms. In these cases, each passage is considered as an independent “individual”, inserted in the data set.
  • the first pass comprises the transfer from the semi-ICU bed (S1) to the ICU bed (U1).
  • This passage corresponds to an “individual” who has the values of his attributes (features) calculated over the hours that preceded his transfer.
  • the individual is transferred from a semi-ICU bed (S1) to an ICU bed (U1) or the condition progresses to death. This case describes a trajectory with an unfavorable clinical outcome.
  • the trajectory T2 shows exemplifies the unfavorable outcome in which the individual is transferred from a common bed (L1) to a semi-ICU bed (S1) or to an ICU bed (U1) or in which their condition progresses to death after admission to the common bed (L1).
  • trajectory T3 an unfavorable outcome is exemplified in which the individual, monitored at home, needs to be admitted for hospitalization.
  • the individual has the values of his attributes (features) calculated over the hours preceding his hospitalization.
  • monitoring can be carried out using wearable resources - “wearables” - to acquire data in real time or close to real time.
  • the T4 trajectory shows a favorable outcome, with the transfer of the individual from a semi-ICU bed (S1) to a common bed or with the individual being discharged home.
  • the T5 trajectory has a similar favorable outcome, with the individual being discharged after hospitalization.
  • trajectory T6 there is a favorable outcome that includes keeping the patient at home for a determined period.
  • training a neural network comprises a first step of collecting data from the individual for prediction (ET101).
  • a time interval preceding the outcome event can be considered as a prediction window (a first predetermined time period) and a second time interval preceding the first time period can be considered as an observation window (a second predetermined period of time), in which an individual's vital signs are observed/measured up to the prediction window.
  • the first determined period of time and the second determined period of time will be used as parameters for training the model.
  • the first predetermined time period is the “GAP” parameter and the second predetermined time period is the “LOOKBACK” parameter.
  • the sequence of vital sign measurements follows aggregation in time, considering subperiods of equal times (parameter AGG_TIME in figure 1).
  • the observation window (second predetermined period of time) comprises multiple equal sub-periods of time.
  • the training method of the present invention pre-processes the measured data in a data pre-processing step (ET102).
  • Figure 1 represents the history of patient 1's passage through a semi-ICU bed.
  • Figure 1 illustrates the trajectory of this patient and, when analyzing a second predetermined period of time of, for example, 05 hours (observation window, LOOKBACK parameter) that precedes a first predetermined period of time of, for example, 01 hour ( prediction window, GAP parameter) before the patient leaves the semi-ICU bed and is discharged from the hospital, each of the 05 hours that make up the observation window (LOOKBACK) has several measurements for the same vital sign. If n respiratory rate measurements were carried out in the last hour of the observation window (represented by dark green bars), a representative value of the values obtained through the n measurements is used to represent the respiratory rate of this patient in this hour under analysis.
  • AGG_TIME 1-hour interval
  • the training method of the present invention comprises obtain a first representative value for each vital sign within each sub-period of time from the different possible forms of aggregation performed for each vital sign in that sub-period of time (i.e., the calculation of the representative value of each vital sign in the sub-period AGG_TIME ), and establish a temporal sequence of vital sign values for each of the plurality of vital signs within the second time period (observation window, LOOKNACK parameter), with the temporal sequence comprising the representative values for each vital sign in the subperiods of time of the second time period.
  • the representative value is the arithmetic mean of the values measured for the vital sign.
  • the data is formatted according to the model based on artificial intelligence that will be used by the method, with the formatting depending on the model chosen for training in steps ET104a and ET104b.
  • the formatting depending on the model chosen for training in steps ET104a and ET104b.
  • the first time period, the second time period, and the temporal sequences of vital signs for each of all individuals in the first set of individuals are model training input data; and the events indicative of the clinical outcome identified as a favorable outcome event or as an unfavorable outcome event for each of all individuals in the first set of individuals are target output data for training the present artificial intelligence-based model.
  • Modeling for recurrent neural network (example of one of the techniques used in the invention)
  • each of the vital sign sequences of the observation windows was organized in a plane , having, on the X axis, all monitored vital signs and, on the Y axis, the values of all AGG_TIME intervals that make up the observation window (LOOKBACK).
  • the LSTM recurrent neural network architecture used has the following layers:
  • RNN layer (LSTM) - input [0049]
  • input_shape output dimension of the RNN layer - number of features (representative values derived from the values of different vital signs measured over time).
  • Dropout layer - intermediate dropout: “dropout” rate (withdrawal) of neurons from the intermediate layer (0.1 - 0.2)
  • Modeling for classic artificial neural network models (example of different techniques used by the present invention).
  • MEAN for each vital sign, the average value within the observation window was calculated
  • AMP for each vital sign, the amplitude (difference between the maximum and minimum value) was calculated within the observation window;
  • LOW_SUM for each vital sign, the differences between the values that were below the lower limit of normality and this limit throughout the entire period were added. the observation window.
  • embodiments of the invention could use different aggregation criteria, such as area under/over curves, above or below previously defined thresholds, such as normality values (or calibrated with higher or lower thresholds, for greater sensitivity/ specificity).
  • the hyperparameters of the classic models are obtained after several validation cycles (each cycle being a specific iteration conducted by GridSearchCV or Hyperopt). After each cycle, the chosen performance metric is used to check the quality of the model. At the end of all validation cycles that use the same validation metric, the hyperparameter configuration with the highest validation metric average value is selected.
  • model training is performed with data from a first set of individuals and validation is performed with data from a second set of individuals.
  • This second set, as well as the first set of individuals (used for training), is defined in a configuration file where a parameter determines the start and end date of hospitalizations of the individuals who will be considered for model testing.
  • data from this second set of individuals are submitted to the model and the predicted results of each patient's outcomes are compared with the actual results, generating the model metrics.
  • SMOTE the “not majority” option is used as a strategy for “resampling” with the aim of not removing “individuals” from the majority class and, in order to artificially generate “individuals” from the minority class , the 4, 5 or 6 closest “individuals” (“k_neighbors”).
  • the 'not majority' parameter indicates that synthetic samples (individuals) will be created only for the minority class, in the exemplary case, individuals with unfavorable outcomes.
  • the 'k_neighbors' parameter indicates how many real samples (individuals with the closest characteristics) will be used to create synthetic samples.
  • the values of monitored vital signs used for training and testing the prediction model were collected from patients admitted to the semi-intensive unit, whose admission date (entry into the unit) is within an uninterrupted period of 17 months.
  • data from 5,441 different patients were used, with the number of visits to the semi-intensive unit being 7,955, since the same patient may pass through this unit more than once during their stay in the hospital or during other hospitalizations.
  • Each of these visits to the semi-intensive unit (even if it is the same patient) together with the vital signs collected throughout the passage period corresponds to a sample provided for training and testing the prediction model.
  • Each patient's vital signs data goes through a preparation process based on observation windows (parameters AGG_TIME, LOOKBACK and GAP configured in the process). This way, different sets of data can be generated from the same patients and sent for training and testing, with the aim of choosing the model with the best performance. Changing these parameters may impact the number of samples for training and testing, as one pass may contain insufficient data (vital sign measurements).
  • the six vital signs were collected using Dr ⁇ ger multiparametric monitors (https://www.draeger.com/pt- br_br/Products/lnfinity-Delta-Series) and sent to a database.
  • Table V presents the values of the metrics obtained when applying, to the set of tests generated in a first moment of training, the models with the aggregation of vital signs every 60 minutes that reached the highest levels of area under the ROC curve (0.75 or 0.74).
  • Table V Values of metrics obtained when applying the models with the aggregation of vital signs every 60 minutes.
  • Table VI presents the values of the metrics obtained when applying, in the set of tests generated in a first moment of training, the models with the aggregation of vital signs every 30 minutes that reached the highest level of area under the curve ROC (0.74).
  • Table VII presents the values of the metrics obtained when applying, in the set of tests generated in a first moment of training, the models with the aggregation of vital signs every 10 minutes that reached the highest level of area under the curve ROC (0.73).
  • Table VII Values of metrics obtained when applying the models with the aggregation of vital signs every 10 minutes.
  • Table VIII presents the values of the metrics obtained when applying, to the set of tests generated in the first moment of training, the models with the aggregation of vital signs every 2 minutes. These models are listed in descending order of area values under the ROC curve and models with values below 0.65 for this metric were discarded.
  • Table IX presents the values of the metrics obtained when applying, in the set of tests generated in a second moment of a new round of training and testing, the models with the aggregation of vital signs every 60 minutes that reached the levels highest areas under the ROC curve in the first round of testing.
  • the models trained according to the method of the present invention are applied in a method for predicting an individual's clinical outcome.
  • the individual has their data monitored in real time using measuring devices located at the place of hospitalization, in the case of a hospital unit, at the individual's home, in the case of home care, or even through wearable monitoring devices.
  • the prediction method starts with data collection (step EP101). Data collection is based on measurements taken during real-time monitoring. Such measurements are processed and formatted so that they can serve as input data to the trained model as described previously (steps EP102 and EP103), and then the prediction of a favorable or unfavorable outcome is carried out based on the trained model (step EP104).

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Abstract

La présente invention concerne un procédé d'entraînement d'un modèle prédictif de détérioration clinique basé sur l'intelligence artificielle, initialement à partir d'un réseau neuronal artificiel, pour la prédiction de résultat clinique d'un individu et consistant, pour chaque individu d'un premier groupe d'individus, à identifier un événement indicatif du résultat d'évolution clinique pour l'individu tel qu'un événement de résultat favorable ou tel qu'un événement de résultat défavorable ; à obtenir, pour des sous-périodes de temps déterminées, des calibrages multiples de chaque signal vital parmi une pluralité de signaux vitaux de l'individu ; à obtenir une valeur représentative pour chaque signal vital dans chaque sous-période de temps sur la base des calibrages multiples effectués pour chaque signal vital dans la sous-période de temps ; à établir une séquence temporelle de valeurs agrégées de signaux vitaux pour chaque signal de la pluralité de signaux vitaux ; et à répéter les étapes précédentes pour tous les individus du premier groupe d'individus. La présente invention est caractérisée en ce que les séquences temporelles de valeurs agrégées de signaux vitaux pour chaque individu du premier groupe d'individus sont données à l'entrée de l'entrainement du modèle prédictif de détérioration clinique basé sur l'intelligence artificielle et en ce que les événements indicatifs du résultat de l'évolution clinique identifiés tel qu'un événement de résultat favorable ou tel qu'un événement de résultat défavorable pour chaque individu du premièr groupe d'individus sont donnés à la sortie cible de l'entrainement du réseau neuronal artificiel.
PCT/BR2023/050263 2022-10-06 2023-08-16 Procédé d'entrainement d'un modèle basé sur l'intelligence artificielle pour la prédiction de résultat clinique d'un individu et procédé de prédiction de résultat clinique d'un individu utilisant un tel modèle basé sur l'intelligence artificielle WO2024073826A1 (fr)

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BR102022020266-4A BR102022020266A2 (pt) 2022-10-06 Método de treinamento de um modelo baseado em inteligência artificial para predição de desfecho clínico de um indivíduo e método de predição de desfecho clínico de um indivíduo que utiliza tal modelo baseado em inteligência artificial
BR1020220202664 2022-10-06

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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130262357A1 (en) * 2011-10-28 2013-10-03 Rubendran Amarasingham Clinical predictive and monitoring system and method
US10923233B1 (en) * 2018-06-13 2021-02-16 Clarify Health Solutions, Inc. Computer network architecture with machine learning and artificial intelligence and dynamic patient guidance
US20220059223A1 (en) * 2020-08-24 2022-02-24 University-Industry Cooperation Group Of Kyung Hee University Evolving symptom-disease prediction system for smart healthcare decision support system
CN114242259A (zh) * 2020-09-07 2022-03-25 奇美医疗财团法人奇美医院 高龄流感病情预测系统、程序产品及其建立与使用方法
US20220122732A1 (en) * 2020-10-16 2022-04-21 Alpha Global IT Solutions System and method for contactless monitoring and early prediction of a person
US11322234B2 (en) * 2019-07-25 2022-05-03 International Business Machines Corporation Automated content avoidance based on medical conditions
WO2022115356A1 (fr) * 2020-11-26 2022-06-02 F. Hoffmann-La Roche Ag Techniques destinées à générer des issues prédictives se rapportant à l'amyotrophie spinale à l'aide d'une intelligence artificielle

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130262357A1 (en) * 2011-10-28 2013-10-03 Rubendran Amarasingham Clinical predictive and monitoring system and method
US10923233B1 (en) * 2018-06-13 2021-02-16 Clarify Health Solutions, Inc. Computer network architecture with machine learning and artificial intelligence and dynamic patient guidance
US11322234B2 (en) * 2019-07-25 2022-05-03 International Business Machines Corporation Automated content avoidance based on medical conditions
US20220059223A1 (en) * 2020-08-24 2022-02-24 University-Industry Cooperation Group Of Kyung Hee University Evolving symptom-disease prediction system for smart healthcare decision support system
CN114242259A (zh) * 2020-09-07 2022-03-25 奇美医疗财团法人奇美医院 高龄流感病情预测系统、程序产品及其建立与使用方法
US20220122732A1 (en) * 2020-10-16 2022-04-21 Alpha Global IT Solutions System and method for contactless monitoring and early prediction of a person
WO2022115356A1 (fr) * 2020-11-26 2022-06-02 F. Hoffmann-La Roche Ag Techniques destinées à générer des issues prédictives se rapportant à l'amyotrophie spinale à l'aide d'une intelligence artificielle

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