Fernando A Suarez, Age 39Miami, FL

2021005, Feb 25, 2021

Aug 20, 2019

16/545891

- Armonk NY, US
Fernando Jose Suarez Saiz - Armonk NY, US
Richard J. Stevens - Monkton VT, US
Eric L. Erpenbach - Oronoco MN, US

G06K 9/00
G06F 17/27
G10L 15/183

A computer-implemented method, system and computer program product for understanding information using a document analysis technique. An initial corpus of information is formed by identifying a document(s) that match a search criteria. The initial corpus of information is expanded with a set of documents containing statements with a semantic meaning within a threshold degree of similarity to a semantic meaning of statements contained within the document(s) used to form the initial corpus of information. Viewpoint(s) are then extracted from the expanded corpus of information using a natural language processing technique. A new set of documents is analyzed by identifying the subject, assertion and context statements. Assertions in the new set of documents that are within a threshold degree of agreement or disagreement with the extracted viewpoint are highlighted to assist the user in understanding how information aligns with a viewpoint.

2020021, Jul 9, 2020

Jan 3, 2019

16/238675

- Armonk NY, US
Fernando Jose SUAREZ SAIZ - Armonk NY, US
Eric W. WILL - Rochester MN, US
Adam CLARK - Mantorville MN, US

G06F 17/27
G06N 5/04

Techniques for cognitive analysis of documents are provided. A document corresponding to a clinical trial is received. Criteria are identified for the clinical trial based on processing the electronic document using natural language processing (NLP) techniques, where the criteria specify attributes of individuals that are eligible for the trial. Further, the criteria are analyzed using NLP techniques to determine a respective confidence level for each respective criterion, where the confidence level indicates a degree of certainty that the criterion is applicable to the trial. A knowledge graph is generated based at least in part on the clinical trial, where the confidence level assigned to each criterion is included in the knowledge graph for subsequent use. Finally, one or more therapies in the knowledge graph are selected to be recommended, based on a patient profile and the confidence level assigned to each of the criteria.

2020019, Jun 18, 2020

Dec 13, 2018

16/218692

- Armonk NY, US
Fernando Jose SUAREZ SAIZ - Armonk NY, US
Adam CLARK - Mantorville MN, US

G16H 70/40
G06N 5/02
G06K 9/00
G16H 50/70

Techniques for granular analysis of a knowledge graph are provided. A profile comprising a plurality of attributes is received, and a knowledge graph is analyzed to identify a plurality of therapies, based on the plurality of attributes. A document that is relevant to a first therapy of the plurality of therapies is identified, and a criterion stated in the document is determined. Further, an aggregate value is determined for the criterion, based on a plurality of participants associated with the document, wherein the first aggregate value represents attributes of the plurality of participants. A weight is generated for the document, based at least in part on the plurality of attributes and the aggregate value. A score is generated for the first therapy, based at least in part on the weight, and an optimal therapy is determined, from the plurality of therapies, based in part on the score.

2020018, Jun 11, 2020

Dec 10, 2018

16/215280

- Armonk NY, US
Fernando Jose SUAREZ SAIZ - Armonk NY, US
Eric W. WILL - Rochester MN, US
Adam CLARK - Mantorville MN, US

G16H 10/60
G06F 16/28
G06F 16/901
G06F 16/2457
G16H 10/20
G16H 20/00

Techniques for unbounded therapy evaluation are provided. A request to suggest a potential therapy based on a patient profile is received. A plurality of accepted therapies is determined based on the patient profile, where the plurality of accepted therapies is based on stored definitions obtained from one or more subject matter experts. Next, a plurality of therapy components is identified based on the plurality of accepted therapies. A plurality of potential therapies is then identified based on the plurality of therapy components, where none of the plurality of potential therapies are included in the plurality of accepted therapies. A score is generated for a potential therapy of the plurality of potential therapies based on analyzing a knowledge graph, where the score indicates a suitability of the potential therapy for a patient associated with the patient profile. Finally, the potential therapy is provided, along with an indication of the score.

2020018, Jun 11, 2020

Dec 7, 2018

16/212683

- Armonk NY, US
Fernando Jose Suarez Saiz - Armonk NY, US

G16H 50/20
G16H 20/00
G06N 5/02
G16H 10/60
G16H 40/20

Techniques for evaluating dynamically modified plans are provided. A selection of a treatment plan template is received, where the treatment plan template specifies a plurality of treatment stages, where each treatment stage defines a plurality of treatment options. A plurality of modifications to the treatment plan template is generated. It is determined, for each respective modification of the plurality of modifications, whether the respective modification is permissible, based on one or more predefined institutional criteria. Upon determining that a first modification of the plurality of modifications is permissible, a first treatment plan is generated based on the first modification to the treatment plan template. Further, a first predicted efficacy measure is generated for the first treatment plan based on analyzing a knowledge graph. Finally, the first treatment plan is provided, along with at least an indication of the first predicted efficacy measure.

2020017, Jun 4, 2020

Dec 4, 2018

16/208838

- Armonk NY, US
Fernando Jose Suarez Saiz - Armonk NY, US
Thomas J. Eggebraaten - Rochester NY, US
Marie Louise Setnes - Bloomington MN, US

G16H 50/20
G06N 5/02
G16H 70/20
G06F 16/901
G16H 50/70
G16H 20/00

Techniques for dynamic visualization of data are provided. A plurality of therapies is received, where each of the plurality of therapies is associated with a respective plurality of guidelines. A guideline tree is generated based on the plurality of therapies, where each leaf node in the guideline tree represents a respective therapy, and where each edge in the guideline tree represents a respective guideline. A visual depiction of the guideline tree is generated. Further, a first plurality of attributes associated with a first patient is received, and a first modified visual depiction of the guideline tree is generated based on the first plurality of attributes.

2020016, May 28, 2020

Nov 28, 2018

16/202889

- Armonk NY, US
Fernando Jose SUAREZ SAIZ - Armonk NY, US
Eric W. WILL - Rochester MN, US
Adam CLARK - Mantorville MN, US

G06N 5/02
G16H 20/00
G16H 50/70

Techniques for identifying missing evidence are provided. A plurality of documents, each comprising digitally encoded natural language text data, is received. The plurality of documents is processed to determine a plurality of pair-wise comparisons between a plurality of therapies, where each of the plurality of pair-wise comparisons indicate a relative efficacy of at least one therapy in the plurality of therapies, as compared to at least one other therapy in the plurality of therapies. A knowledge graph is generated based at least in part on aggregating the plurality of pair-wise comparisons, and the knowledge graph is analyzed to identify one or more knowledge gaps within the knowledge graph. Finally, at least an indication of the identified one or more knowledge gaps is output.

2018003, Feb 8, 2018

Aug 2, 2016

15/225880

- Armonk NY, US
Fernando J. SUAREZ SAIZ - Armonk NY, US

G06N 99/00
G06F 17/30

A system rapidly produces training cases for machine based learning by automatically creating training cases from a database of historical data. The system determines a plurality of attributes relevant to each of the training cases. The system identifies a first attribute of the plurality of attributes as an issue, and a second attribute of the plurality attributes as a response to the issue. The system identifies a plurality of cohort members from the database of historical data, where each cohort member comprises cohort member attributes that match a subset of the plurality of attributes. The system analyzes the cohort member attributes of each of the plurality of cohort members to identify the most frequent responses to the issue. The system creates the training cases where each training case comprises the issue and the most frequent responses. The system then trains a machine based learning system using the training cases.