Communication generation using sparse indicators and sensor data
Genetic-variant data is obtained that corresponds to one or more variants associated with a client. Each of the one or more variants corresponds to an instance of one or more bases positioned at one or more first positions in a first genetic sequence differ from corresponding one or more bases positioned in a reference genetic sequence. The first genetic sequence is a genetic sequence of the client. Sensor data is obtained that provides an indication of one or more characteristics of a current or past environment of the client. The genetic-variant data and the sensor data is processed to generate a disease-risk metric corresponding to a predicted risk of the client developing a particular disease. A communication is generated that is indicative of the disease-risk metric. The communication is transmitted to a remote device.
1 . A computer-implemented method comprising:
accessing genetic-variant data corresponding to one or more variants associated with a client, wherein each of the one or more variants corresponds to an instance of one or more bases positioned at one or more first positions in a first genetic sequence differ from corresponding one or more bases positioned in a reference genetic sequence, wherein the first genetic sequence is a genetic sequence of the client;
predicting a reliability of the genetic-variant data by transforming an input data set using an artificial intelligence technique, wherein the input data set indicates a laboratory, device, or a collection time period associated with the genetic-variant data;
obtaining other data that include at least sensor data;
processing, based on the reliability of the genetic-variant data, the genetic-variant data and the other data to generate a disease-risk metric corresponding to a predicted risk of the client developing a particular disease;
generating a communication indicative of the disease-risk metric; and
availing the communication to a remote device.
2 . The computer-implemented method of claim 1 , wherein the input data set indicates a laboratory, and wherein the artificial intelligence technique is used to predict the reliability based on the laboratory.
3 . The computer-implemented method of claim 1 , wherein the input data set indicates an equipment device, and wherein the artificial intelligence technique is used to predict the reliability based on the equipment device.
4 . The computer-implemented method of claim 1 , wherein the input data set indicates a time or time period, and wherein the artificial intelligence technique is used to predict the reliability based on the time or time period.
5 . The computer-implemented method of claim 1 , further comprising:
identifying a normalization and/or conversion factor for the genetic-variant data using a clustering technique wherein the processing performed to generate the disease-risk metric is further based on the normalization and/or conversion factor.
6 . The computer-implemented method of claim 1 , wherein the reliability is predicted using the collection time period and by performing a time-series analysis associated with a data-collection component associated with the genetic-variant data.
7 . The computer-implemented method of claim 1 , wherein the artificial intelligence technique uses a transformation and/or dimensionality reduction technique that includes principal component analysis, independent component analysis, or canonical correspondence analysis.
8 . The computer-implemented method as recited in claim 1 , wherein the sensor data includes data from an accelerometer and provides an indication of past physical activity of the client.
9 . A system for generating communications based on variant information and sensor data, comprising:
one or more hardware processors; and
a non-transitory computer readable storage medium in data communication with the one or more hardware processors, the non-transitory computer readable storage medium comprising instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations including:
obtain, at the one or more hardware processors, genetic-variant data corresponding to one or more variants associated with a client, wherein each of the one or more variants corresponds to an instance of one or more bases positioned at one or more first positions in a first genetic sequence differ from corresponding one or more bases positioned in a reference genetic sequence, wherein the first genetic sequence is a genetic sequence of the client;
predict a reliability of the genetic-variant data by performing a comparison of the genetic-variant data based on different labs, different devices, or different time periods associated with data collection;
obtain other data that include at least sensor data providing an indication of one or more characteristics of the client, current quality of the client, behavior of the client, or one or more characteristics of a current or past environment of the client;
process, based on the reliability of the genetic-variant data, the genetic-variant data and the other data to generate a disease-risk metric corresponding to a predicted risk of the client developing a particular disease;
generate a communication indicative of the disease-risk metric; and
availing the communication to a remote device.
10 . The system of claim 9 , wherein the input data set indicates a laboratory, and wherein the artificial intelligence technique is used to predict the reliability based on the laboratory.
11 . The system of claim 9 , wherein the input data set indicates an equipment device, and wherein the artificial intelligence technique is used to predict the reliability based on the equipment device.
12 . The system of claim 9 , wherein the input data set indicates a time or time period, and wherein the artificial intelligence technique is used to predict the reliability based on the time or time period.
13 . A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform a set of operations including:
accessing genetic-variant data corresponding to one or more variants associated with a client, wherein each of the one or more variants corresponds to an instance of one or more bases positioned at one or more first positions in a first genetic sequence differ from corresponding one or more bases positioned in a reference genetic sequence, wherein the first genetic sequence is a genetic sequence of the client;
predicting a reliability of the genetic-variant data by transforming an input data set using an artificial intelligence technique, wherein the input data set indicates a laboratory, device, or a collection time period associated with the genetic-variant data;
obtaining other data that include at least sensor data;
processing, based on the reliability of the genetic-variant data, the genetic-variant data and the other data to generate a disease-risk metric corresponding to a predicted risk of the client developing a particular disease;
generating a communication indicative of the disease-risk metric; and
availing the communication to a remote device.
14 . The computer-program product of claim 13 , wherein the input data set indicates a laboratory, and wherein the artificial intelligence technique is used to predict the reliability based on the laboratory.
15 . The computer-program product of claim 13 , wherein the input data set indicates an equipment device, and wherein the artificial intelligence technique is used to predict the reliability based on the equipment device.
16 . The computer-program product of claim 13 , wherein the input data set indicates a time or time period, and wherein the artificial intelligence technique is used to predict the reliability based on the time or time period.
17 . The computer-program product of claim 13 , wherein the set of operations further includes:
identifying a normalization and/or conversion factor for the genetic-variant data using a clustering technique wherein the processing performed to generate the disease-risk metric is further based on the normalization and/or conversion factor.
18 . The computer-program product of claim 13 , wherein the reliability is predicted using the collection time period and by performing a time-series analysis associated with a data-collection component associated with the genetic-variant data.
19 . The computer-program product of claim 13 , wherein the artificial intelligence technique uses a transformation and/or dimensionality reduction technique that includes principal component analysis, independent component analysis, or canonical correspondence analysis.
20 . The computer-program product as recited in claim 13 , wherein the sensor data includes data from an accelerometer and provides an indication of past physical activity of the client.