US 12,229,684 B2
Claim analysis with deep learning
Byung-Hak Kim, San Jose, CA (US); Hariraam Varun Ganapathi, San Francisco, CA (US); and Andrew Atwal, Foster City, CA (US)
Assigned to AKASA, Inc., South San Francisco, CA (US)
Filed by AKASA, Inc., South San Francisco, CA (US)
Filed on Dec. 8, 2023, as Appl. No. 18/534,481.
Application 18/534,481 is a continuation of application No. 17/362,820, filed on Jun. 29, 2021, granted, now 11,861,503.
Application 17/362,820 is a continuation of application No. 16/818,899, filed on Mar. 13, 2020, granted, now 11,170,448, issued on Nov. 9, 2021.
Claims priority of provisional application 62/951,934, filed on Dec. 20, 2019.
Prior Publication US 2024/0193425 A1, Jun. 13, 2024
Int. Cl. G06N 3/084 (2023.01); G06F 17/18 (2006.01); G06F 18/24 (2023.01); G06N 3/08 (2023.01); G06Q 40/08 (2012.01)
CPC G06N 3/084 (2013.01) [G06F 17/18 (2013.01); G06F 18/24 (2023.01); G06N 3/08 (2013.01); G06Q 40/08 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A method comprising: training a first portion of one or more neural networks implemented on computer, the first portion of the one or more neural networks to generate an embedding based on a first vector of claim features, the first vector comprising at least features of a claim, the embedding comprising a second vector having a lower dimensionality than the first vector, the second vector comprising at least a first sub-vector to express one or more diagnosis tokens and a second sub-vector to express one or more procedure tokens; and
training a second portion of one or more neural networks to generate a prediction of a payer's response to the claim based, at least in part, on the embedding, the prediction of the payer's response to the claim comprising at least a likelihood that the claim would be denied,
wherein training the first portion of the one or more neural networks and training the second portion of the one or more neural networks comprises determining weights for the first and second portions of the one or more neural networks based, at least in part, on a computed prediction of payer's response and a label representing a payer's response.