| CPC G06N 3/08 (2013.01) [G06F 18/2132 (2023.01); G06F 18/217 (2023.01); G06F 18/40 (2023.01); G06N 3/044 (2023.01); G16H 20/00 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01)] | 20 Claims |

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1. A method comprising:
with a trained, computerized discontinuation predictor machine learning component, predicting, based on an input time series, a time when a subject will discontinue a course of medical treatment;
with a trained, computerized pattern behavior extractor machine learning component, extracting from said input time series a top k discriminatory sequences via discriminatory sub-sequence mining by comparing two or more subgroups and performing mining in search of patterns that appear unequally in the two or more subgroups, wherein said top k discriminatory sequences differentiate between first and second classes of interest to provide a hypothesis for downstream analysis of a cause of discontinuing said course of medical treatment;
with a trained, causal effect estimator computerized machine learning component, determining a reason why said subject will discontinue said course of medical treatment, based on said top k discriminatory sequences and additional data;
with a computerized user interface, providing said time when said subject will discontinue said course of medical treatment and said reason why said subject will discontinue said course of medical treatment to a responsible party to initiate an intervention; and
initiating said intervention to maintain said course of medical treatment.
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